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LoneStriker/Tess-M-v1.1-4.65bpw-h6-exl2

LoneStriker

transformers

text-generation

--- license: other license_name: yi-34b license_link: https://huggingface.co/01-ai/Yi-34B/blob/main/LICENSE --- # Tess  Tess, short for Tessoro/Tessoso, is a general purpose Large Language Model series. Tess-M-v1.1 was trained on the Yi-34B-200K base. # Prompt Format: ``` SYSTEM: <ANY SYSTEM CONTEXT> USER: ASSISTANT: ```

arrkaye/asoiaf_gguf

arrkaye

transformers

--- base_model: arrkaye/asoiaf tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** arrkaye - **License:** apache-2.0 - **Finetuned from model :** arrkaye/asoiaf This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

mradermacher/MFANN-llama3.1-abliterated-SLERP-v3-i1-GGUF

mradermacher

transformers

--- base_model: netcat420/MFANN-llama3.1-abliterated-SLERP-v3 language: - en library_name: transformers license: llama3.1 quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/netcat420/MFANN-llama3.1-abliterated-SLERP-v3 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/MFANN-llama3.1-abliterated-SLERP-v3-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/MFANN-llama3.1-abliterated-SLERP-v3-i1-GGUF/resolve/main/MFANN-llama3.1-abliterated-SLERP-v3.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/MFANN-llama3.1-abliterated-SLERP-v3-i1-GGUF/resolve/main/MFANN-llama3.1-abliterated-SLERP-v3.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/MFANN-llama3.1-abliterated-SLERP-v3-i1-GGUF/resolve/main/MFANN-llama3.1-abliterated-SLERP-v3.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | | | [GGUF](https://huggingface.co/mradermacher/MFANN-llama3.1-abliterated-SLERP-v3-i1-GGUF/resolve/main/MFANN-llama3.1-abliterated-SLERP-v3.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | | | [GGUF](https://huggingface.co/mradermacher/MFANN-llama3.1-abliterated-SLERP-v3-i1-GGUF/resolve/main/MFANN-llama3.1-abliterated-SLERP-v3.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | | | [GGUF](https://huggingface.co/mradermacher/MFANN-llama3.1-abliterated-SLERP-v3-i1-GGUF/resolve/main/MFANN-llama3.1-abliterated-SLERP-v3.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | | | [GGUF](https://huggingface.co/mradermacher/MFANN-llama3.1-abliterated-SLERP-v3-i1-GGUF/resolve/main/MFANN-llama3.1-abliterated-SLERP-v3.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/MFANN-llama3.1-abliterated-SLERP-v3-i1-GGUF/resolve/main/MFANN-llama3.1-abliterated-SLERP-v3.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/MFANN-llama3.1-abliterated-SLERP-v3-i1-GGUF/resolve/main/MFANN-llama3.1-abliterated-SLERP-v3.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | | | [GGUF](https://huggingface.co/mradermacher/MFANN-llama3.1-abliterated-SLERP-v3-i1-GGUF/resolve/main/MFANN-llama3.1-abliterated-SLERP-v3.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/MFANN-llama3.1-abliterated-SLERP-v3-i1-GGUF/resolve/main/MFANN-llama3.1-abliterated-SLERP-v3.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/MFANN-llama3.1-abliterated-SLERP-v3-i1-GGUF/resolve/main/MFANN-llama3.1-abliterated-SLERP-v3.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | | | [GGUF](https://huggingface.co/mradermacher/MFANN-llama3.1-abliterated-SLERP-v3-i1-GGUF/resolve/main/MFANN-llama3.1-abliterated-SLERP-v3.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/MFANN-llama3.1-abliterated-SLERP-v3-i1-GGUF/resolve/main/MFANN-llama3.1-abliterated-SLERP-v3.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/MFANN-llama3.1-abliterated-SLERP-v3-i1-GGUF/resolve/main/MFANN-llama3.1-abliterated-SLERP-v3.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | | | [GGUF](https://huggingface.co/mradermacher/MFANN-llama3.1-abliterated-SLERP-v3-i1-GGUF/resolve/main/MFANN-llama3.1-abliterated-SLERP-v3.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 4.8 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/MFANN-llama3.1-abliterated-SLERP-v3-i1-GGUF/resolve/main/MFANN-llama3.1-abliterated-SLERP-v3.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 4.8 | fast on arm+i8mm, low quality | | [GGUF](https://huggingface.co/mradermacher/MFANN-llama3.1-abliterated-SLERP-v3-i1-GGUF/resolve/main/MFANN-llama3.1-abliterated-SLERP-v3.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 4.8 | fast on arm+sve, low quality | | [GGUF](https://huggingface.co/mradermacher/MFANN-llama3.1-abliterated-SLERP-v3-i1-GGUF/resolve/main/MFANN-llama3.1-abliterated-SLERP-v3.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/MFANN-llama3.1-abliterated-SLERP-v3-i1-GGUF/resolve/main/MFANN-llama3.1-abliterated-SLERP-v3.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/MFANN-llama3.1-abliterated-SLERP-v3-i1-GGUF/resolve/main/MFANN-llama3.1-abliterated-SLERP-v3.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/MFANN-llama3.1-abliterated-SLERP-v3-i1-GGUF/resolve/main/MFANN-llama3.1-abliterated-SLERP-v3.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | | | [GGUF](https://huggingface.co/mradermacher/MFANN-llama3.1-abliterated-SLERP-v3-i1-GGUF/resolve/main/MFANN-llama3.1-abliterated-SLERP-v3.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/MFANN-llama3.1-abliterated-SLERP-v3-i1-GGUF/resolve/main/MFANN-llama3.1-abliterated-SLERP-v3.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->

vedaantp/email-model

vedaantp

transformers

text-classification

--- library_name: transformers license: llama3.2 base_model: meta-llama/Llama-3.2-1B tags: - generated_from_trainer metrics: - accuracy model-index: - name: email-model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # email-model This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9912 - Accuracy: 0.7440 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.6084 | 1.0 | 540 | 1.3931 | 0.3155 | | 1.308 | 2.0 | 1080 | 0.8074 | 0.5893 | | 0.9603 | 3.0 | 1620 | 1.0167 | 0.6012 | | 0.9051 | 4.0 | 2160 | 0.8432 | 0.5952 | | 0.9171 | 5.0 | 2700 | 0.9912 | 0.7440 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.1+cu121 - Datasets 3.0.1 - Tokenizers 0.19.1

GrantW65/dqn-SpaceInvadersNoFrameskip-v4

GrantW65

stable-baselines3

reinforcement-learning

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 582.00 +/- 122.03 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga GrantW65 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga GrantW65 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga GrantW65 ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

andres-gv/cmi-models-2

andres-gv

bertopic

text-classification

--- pipeline_tag: text-classification library_name: bertopic ---

sail-rvc/v1voicesam

sail-rvc

transformers

audio-to-audio

--- pipeline_tag: audio-to-audio tags: - rvc - sail-rvc --- # v1voicesam ## RVC Model  This model repo was automatically generated. Date: 2023-07-14 07:44:59 Bot Name: juuxnscrap Model Type: RVC Source: https://huggingface.co/juuxn/RVCModels/ Reason: Converting into loadable format for https://github.com/chavinlo/rvc-runpod

mradermacher/NarrativeNexus_7B-i1-GGUF

mradermacher

transformers

--- base_model: jeiku/NarrativeNexus_7B language: - en library_name: transformers license: other quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/jeiku/NarrativeNexus_7B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/NarrativeNexus_7B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/NarrativeNexus_7B-i1-GGUF/resolve/main/NarrativeNexus_7B.i1-IQ1_S.gguf) | i1-IQ1_S | 1.7 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/NarrativeNexus_7B-i1-GGUF/resolve/main/NarrativeNexus_7B.i1-IQ1_M.gguf) | i1-IQ1_M | 1.9 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/NarrativeNexus_7B-i1-GGUF/resolve/main/NarrativeNexus_7B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.1 | | | [GGUF](https://huggingface.co/mradermacher/NarrativeNexus_7B-i1-GGUF/resolve/main/NarrativeNexus_7B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.3 | | | [GGUF](https://huggingface.co/mradermacher/NarrativeNexus_7B-i1-GGUF/resolve/main/NarrativeNexus_7B.i1-IQ2_S.gguf) | i1-IQ2_S | 2.4 | | | [GGUF](https://huggingface.co/mradermacher/NarrativeNexus_7B-i1-GGUF/resolve/main/NarrativeNexus_7B.i1-IQ2_M.gguf) | i1-IQ2_M | 2.6 | | | [GGUF](https://huggingface.co/mradermacher/NarrativeNexus_7B-i1-GGUF/resolve/main/NarrativeNexus_7B.i1-Q2_K.gguf) | i1-Q2_K | 2.8 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/NarrativeNexus_7B-i1-GGUF/resolve/main/NarrativeNexus_7B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 2.9 | lower quality | | [GGUF](https://huggingface.co/mradermacher/NarrativeNexus_7B-i1-GGUF/resolve/main/NarrativeNexus_7B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.1 | | | [GGUF](https://huggingface.co/mradermacher/NarrativeNexus_7B-i1-GGUF/resolve/main/NarrativeNexus_7B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.3 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/NarrativeNexus_7B-i1-GGUF/resolve/main/NarrativeNexus_7B.i1-IQ3_S.gguf) | i1-IQ3_S | 3.3 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/NarrativeNexus_7B-i1-GGUF/resolve/main/NarrativeNexus_7B.i1-IQ3_M.gguf) | i1-IQ3_M | 3.4 | | | [GGUF](https://huggingface.co/mradermacher/NarrativeNexus_7B-i1-GGUF/resolve/main/NarrativeNexus_7B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.6 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/NarrativeNexus_7B-i1-GGUF/resolve/main/NarrativeNexus_7B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 3.9 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/NarrativeNexus_7B-i1-GGUF/resolve/main/NarrativeNexus_7B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.0 | | | [GGUF](https://huggingface.co/mradermacher/NarrativeNexus_7B-i1-GGUF/resolve/main/NarrativeNexus_7B.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 4.2 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/NarrativeNexus_7B-i1-GGUF/resolve/main/NarrativeNexus_7B.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 4.2 | fast on arm+i8mm, low quality | | [GGUF](https://huggingface.co/mradermacher/NarrativeNexus_7B-i1-GGUF/resolve/main/NarrativeNexus_7B.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 4.2 | fast on arm+sve, low quality | | [GGUF](https://huggingface.co/mradermacher/NarrativeNexus_7B-i1-GGUF/resolve/main/NarrativeNexus_7B.i1-Q4_0.gguf) | i1-Q4_0 | 4.2 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/NarrativeNexus_7B-i1-GGUF/resolve/main/NarrativeNexus_7B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.2 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/NarrativeNexus_7B-i1-GGUF/resolve/main/NarrativeNexus_7B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/NarrativeNexus_7B-i1-GGUF/resolve/main/NarrativeNexus_7B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.1 | | | [GGUF](https://huggingface.co/mradermacher/NarrativeNexus_7B-i1-GGUF/resolve/main/NarrativeNexus_7B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.2 | | | [GGUF](https://huggingface.co/mradermacher/NarrativeNexus_7B-i1-GGUF/resolve/main/NarrativeNexus_7B.i1-Q6_K.gguf) | i1-Q6_K | 6.0 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->

davidschulte/ESM_xtreme_PAWS-X.fr

davidschulte

--- base_model: bert-base-multilingual-uncased datasets: - google/xtreme license: apache-2.0 tags: - embedding_space_map - BaseLM:bert-base-multilingual-uncased --- # ESM google/xtreme <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> ESM - **Developed by:** David Schulte - **Model type:** ESM - **Base Model:** bert-base-multilingual-uncased - **Intermediate Task:** google/xtreme - **ESM architecture:** linear - **Language(s) (NLP):** [More Information Needed] - **License:** Apache-2.0 license ## Training Details ### Intermediate Task - **Task ID:** google/xtreme - **Subset [optional]:** PAWS-X.fr - **Text Column:** ['sentence1', 'sentence2'] - **Label Column:** label - **Dataset Split:** train - **Sample size [optional]:** 10000 - **Sample seed [optional]:** 42 ### Training Procedure [optional] <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Language Model Training Hyperparameters [optional] - **Epochs:** 3 - **Batch size:** 32 - **Learning rate:** 2e-05 - **Weight Decay:** 0.01 - **Optimizer**: AdamW ### ESM Training Hyperparameters [optional] - **Epochs:** 10 - **Batch size:** 32 - **Learning rate:** 0.001 - **Weight Decay:** 0.01 - **Optimizer**: AdamW ### Additional trainiung details [optional] ## Model evaluation ### Evaluation of fine-tuned language model [optional] ### Evaluation of ESM [optional] MSE: ### Additional evaluation details [optional] ## What are Embedding Space Maps? <!-- This section describes the evaluation protocols and provides the results. --> Embedding Space Maps (ESMs) are neural networks that approximate the effect of fine-tuning a language model on a task. They can be used to quickly transform embeddings from a base model to approximate how a fine-tuned model would embed the the input text. ESMs can be used for intermediate task selection with the ESM-LogME workflow. ## How can I use Embedding Space Maps for Intermediate Task Selection? [](https://pypi.org/project/hf-dataset-selector) We release **hf-dataset-selector**, a Python package for intermediate task selection using Embedding Space Maps. **hf-dataset-selector** fetches ESMs for a given language model and uses it to find the best dataset for applying intermediate training to the target task. ESMs are found by their tags on the Huggingface Hub. ```python from hfselect import Dataset, compute_task_ranking # Load target dataset from the Hugging Face Hub dataset = Dataset.from_hugging_face( name="stanfordnlp/imdb", split="train", text_col="text", label_col="label", is_regression=False, num_examples=1000, seed=42 ) # Fetch ESMs and rank tasks task_ranking = compute_task_ranking( dataset=dataset, model_name="bert-base-multilingual-uncased" ) # Display top 5 recommendations print(task_ranking[:5]) ``` For more information on how to use ESMs please have a look at the [official Github repository](https://github.com/davidschulte/hf-dataset-selector). ## Citation <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> If you are using this Embedding Space Maps, please cite our [paper](https://arxiv.org/abs/2410.15148). **BibTeX:** ``` @misc{schulte2024moreparameterefficientselectionintermediate, title={Less is More: Parameter-Efficient Selection of Intermediate Tasks for Transfer Learning}, author={David Schulte and Felix Hamborg and Alan Akbik}, year={2024}, eprint={2410.15148}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2410.15148}, } ``` **APA:** ``` Schulte, D., Hamborg, F., & Akbik, A. (2024). Less is More: Parameter-Efficient Selection of Intermediate Tasks for Transfer Learning. arXiv preprint arXiv:2410.15148. ``` ## Additional Information

ORKAFILM/rutino2

ORKAFILM

diffusers

text-to-image

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: rutino2 --- # Rutino2 <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `rutino2` to trigger the image generation. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('ORKAFILM/rutino2', weight_name='lora.safetensors') image = pipeline('your prompt').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)

s3nh/zephyr-speakleash-007-pl-8192-32-16-0.05-GGUF

s3nh

transformers

text-generation

--- license: openrail pipeline_tag: text-generation library_name: transformers language: - zh - en --- ## Original model card Buy me a coffee if you like this project ;) <a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a> #### Description GGUF Format model files for [This project](https://huggingface.co/Nondzu/zephyr-speakleash-007-pl-8192-32-16-0.05). ### GGUF Specs GGUF is a format based on the existing GGJT, but makes a few changes to the format to make it more extensible and easier to use. The following features are desired: Single-file deployment: they can be easily distributed and loaded, and do not require any external files for additional information. Extensible: new features can be added to GGML-based executors/new information can be added to GGUF models without breaking compatibility with existing models. mmap compatibility: models can be loaded using mmap for fast loading and saving. Easy to use: models can be easily loaded and saved using a small amount of code, with no need for external libraries, regardless of the language used. Full information: all information needed to load a model is contained in the model file, and no additional information needs to be provided by the user. The key difference between GGJT and GGUF is the use of a key-value structure for the hyperparameters (now referred to as metadata), rather than a list of untyped values. This allows for new metadata to be added without breaking compatibility with existing models, and to annotate the model with additional information that may be useful for inference or for identifying the model. ### inference User: Tell me story about what is an quantization and what do we need to build. I have a little bit of experience with the term “quantization” from physics, but not much. When I hear it, the first thing that comes to mind is some kind of measuring instrument, like a ruler or voltmeter. What does the phrase “quantized by 1024” mean? It sounds more mathematical than physical. The term quantization comes from quantum mechanics and refers to a process whereby a continuous function is approximated by discrete values, that is, it is “discretized”. In this sense, we can say that the “quanta” are the differences between adjacent # Original model card

mmpc/microsoft-phi-2-squad2-qg

mmpc

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

mini1013/master_cate_ap0

mini1013

setfit

text-classification

--- base_model: mini1013/master_domain library_name: setfit metrics: - metric pipeline_tag: text-classification tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: '[현대백화점][비비안](RU1260) 40% 가격인하 순면 80수 기본 남성런닝 95 (주)현대백화점' - text: 부드러운 터치감 남성 실켓가공 런닝 트렁크 팬티 세트 VMV4183VMP4183N/비너스 브라운_런닝105-팬티105 롯데쇼핑(주) - text: '[리더스] 신축성 좋은 복부 코르셋 땀복 남자 바지 (15005144) 블랙_XL 신세계몰' - text: 탑텐 탑텐 공용 플란넬 라운지웨어 세트 MSC4UI3001 rva-482878f BE_L(540) 라비아세개 - text: BYC 남성용 50수 순면 민소매 그랜드 런닝 2호 백색 1매 BYI6035 95 (주)대화언더웨어 inference: true model-index: - name: SetFit with mini1013/master_domain results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: metric value: 0.8497076023391813 name: Metric --- # SetFit with mini1013/master_domain This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [mini1013/master_domain](https://huggingface.co/mini1013/master_domain) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - **Model Type:** SetFit - **Sentence Transformer body:** [mini1013/master_domain](https://huggingface.co/mini1013/master_domain) - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - **Maximum Sequence Length:** 512 tokens - **Number of Classes:** 6 classes <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown --> ### Model Sources - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels | Label | Examples | |:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 5.0 | <ul><li>'CK퍼포먼스 24 SUMMER 여름셋업 남여공용 4종 [0001]블랙 90(S) CJONSTYLE_LIVE'</li><li>'CALVIN KLEIN UNDERWEAR 여성 모던 코튼 T팬티_F3786D001 F3786D001 블랙_M 럭스펄스'</li><li>'[갭][갭] 옴므 트렁크 6종 택1 GPMTR1O30T 네이비/L(100) 패션플러스'</li></ul> | | 1.0 | <ul><li>'[와코루](신세계마산점)선염 모달 + 면 스판 스트라이프 조끼런닝 삼각 팬티 세트(WMV2378RWMP2378P) 95_100 주식회사 에스에스지닷컴'</li><li>'남자 속옷 등산 스포츠 SET 자전거 축구 스프츠 골프 백색_100 꼬북샵'</li><li>'싸이로컴팩 면모달 선염스트라이프 런닝RU1695T 네이비_100 신세계몰'</li></ul> | | 3.0 | <ul><li>'CJ [리복] 스피드윅 기모 웜에어 상하의 2종 세트 남성 최신상 택일 옵션01.RBMYIEM01_00_100 (주)씨제이이엔엠'</li><li>'아르메데스 남성용 히트기모 발열내의 터틀넥 상의 AR-25 3매 블랙_M (주)아르메데스'</li><li>'[기능성 의류 BEST] 시원한 냉감 기능은 기본! 완벽한 자외선 차단! 기능성 티셔츠/조거팬츠/등산바지/아웃도어 의류 01.TM-MZS303_M_ZZGRY 테슬라_TSLA'</li></ul> | | 0.0 | <ul><li>'남자 쿨 티셔츠 남성 냉감 나시티 기능성 반팔티 쿨링 EVE 화이트_100 에브리씽굿'</li><li>'비비안 모다아울렛 비비안 젠토프 텐셀솔리드 기본 반팔런닝 RU1239T 네이비_95 MODA아울렛'</li><li>'탑텐 TOPTEN 남성 쿨에어 크루넥 매쉬 탱크_MSD2UL1201 BK_100 가투투'</li></ul> | | 2.0 | <ul><li>'니플 나시 남자보정 속옷이너핏여유증커버남성뱃살가리개꼭지가슴압박복가리기티 남자보정나시 보급형/L/화이트 조니멀티샵'</li><li>'하라마키 배워머 더블 배워머 보온복대 남성용 HT-LunesDB-Charcoal-M BESTYOURS'</li><li>'고급 따뜻한 남자 밴딩 기모 레깅스 겨울 발열 내복 바지 보온 타이즈 블랙_2XL 사랑니'</li></ul> | | 4.0 | <ul><li>'[오르시떼](센텀시티점)남성 D123 오니리크 반소매 상하 S 신세계백화점'</li><li>'(신세계마산점)오르시떼남성 D105 브데뜨 긴소매 상하 S 신세계백화점'</li><li>'JAJU 남 라이트 밍크 플리스 파자마 세트 블루 L 리치쇼핑'</li></ul> | ## Evaluation ### Metrics | Label | Metric | |:--------|:-------| | **all** | 0.8497 | ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("mini1013/master_cate_ap0") # Run inference preds = model("[리더스] 신축성 좋은 복부 코르셋 땀복 남자 바지 (15005144) 블랙_XL 신세계몰") ``` <!-- ### Downstream Use *List how someone could finetune this model on their own dataset.* --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* --> ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:-------|:----| | Word count | 3 | 9.5967 | 24 | | Label | Training Sample Count | |:------|:----------------------| | 0.0 | 50 | | 1.0 | 50 | | 2.0 | 50 | | 3.0 | 50 | | 4.0 | 50 | | 5.0 | 50 | ### Training Hyperparameters - batch_size: (512, 512) - num_epochs: (20, 20) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 40 - body_learning_rate: (2e-05, 2e-05) - head_learning_rate: 2e-05 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results | Epoch | Step | Training Loss | Validation Loss | |:-------:|:----:|:-------------:|:---------------:| | 0.0213 | 1 | 0.4362 | - | | 1.0638 | 50 | 0.3126 | - | | 2.1277 | 100 | 0.0687 | - | | 3.1915 | 150 | 0.0294 | - | | 4.2553 | 200 | 0.0006 | - | | 5.3191 | 250 | 0.0003 | - | | 6.3830 | 300 | 0.0002 | - | | 7.4468 | 350 | 0.0002 | - | | 8.5106 | 400 | 0.0001 | - | | 9.5745 | 450 | 0.0001 | - | | 10.6383 | 500 | 0.0001 | - | | 11.7021 | 550 | 0.0001 | - | | 12.7660 | 600 | 0.0001 | - | | 13.8298 | 650 | 0.0001 | - | | 14.8936 | 700 | 0.0001 | - | | 15.9574 | 750 | 0.0001 | - | | 17.0213 | 800 | 0.0001 | - | | 18.0851 | 850 | 0.0001 | - | | 19.1489 | 900 | 0.0001 | - | ### Framework Versions - Python: 3.10.12 - SetFit: 1.1.0.dev0 - Sentence Transformers: 3.1.1 - Transformers: 4.46.1 - PyTorch: 2.4.0+cu121 - Datasets: 2.20.0 - Tokenizers: 0.20.0 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ## Glossary *Clearly define terms in order to be accessible across audiences.* --> <!-- ## Model Card Authors *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->

bveiseh/phi4-magpie-reasoning-v3-gguf

bveiseh

--- license: mit datasets: - Magpie-Align/Magpie-Reasoning-V2-250K-CoT-Deepseek-R1-Llama-70B base_model: - microsoft/phi-4 --- # Phi-4 Magpie Reasoning GGUF v3 This is a GGUF format version of the Phi-4 model fine-tuned on the Magpie dataset. This is v3. v3 was trained on 4 3090 GPUs for about 55 hours. In order to produce reasoning traces, make sure to tell the model in the system prompt to use them. Format is <think> response </think> response. You may have to try a few prompts to get it right. I use this system prompt with great success: "Think through your responses carefully, always break it down. Always think before you answer by using the thinking tags: <think> response </think> response. When you think, make sure you decompose the question and consider multiple answers always before you stop thinking. " The fine tuning on chain-of-thought outputs does indeed force the Phi-4 to increase its test time compute. I have observed it giving more clear and conscise answers to complex questions, be able to tackle math and logic questions that the base model is unable to correctly answer, and it does it with only a slight reduction in inference speed. ## Model Details - Base Model: Microsoft Phi-4 (14B parameters) - Format: GGUF (8-bit quantization) - Fine-tuning: LoRA with merged weights, 4-bit double quantization with Flash Attention v1 - Training Dataset: Magpie Reasoning Dataset - Version: 3 ## Training Data - 2,200 excellent quality examples - 3,000 good quality examples - Total training samples: 5,200 ## Evaluation Dataset - 5 very hard + excellent quality examples - 5 medium + excellent quality examples - 5 very easy + excellent quality examples ## Technical Details - LoRA Parameters: - Rank (r): 24 - Alpha: 48 - Target Modules: q_proj, k_proj, v_proj, o_proj - Dropout: 0.05 - Training Configuration: - Epochs: 5 - Learning Rate: 3e-5 - Batch Size: 1 with gradient accumulation steps of 16 - Optimizer: AdamW (Fused) - Precision: BFloat16 during training - Final Format: 8-bit quantized GGUF ## Usage with llama.cpp For CPU inference, use the following command: main -m phi4-magpie-reasoning.gguf -n 512 --repeat_penalty 1.1 --color -i -r User: ## Model Size - GGUF Format (8-bit): ~14GB - Original Model (14B parameters) ## License This model inherits the license terms from Microsoft Phi-4 and the Magpie dataset.

Radiantloom/radintloom-mistral-7b-fusion-dpo

Radiantloom

transformers

text-generation

--- library_name: transformers license: apache-2.0 --- <img src="https://huggingface.co/Radiantloom/radintloom-mistral-7b-fusion/resolve/main/Radiantloom Mistral 7B Fusion.png" alt="Radiantloom Mistral 7B Fusion" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> ## Radiantloom Mistral 7B Fusion DPO This model is a finetuned version of [Radiantloom Mistral 7B Fusion](https://huggingface.co/Radiantloom/radintloom-mistral-7b-fusion). It was finetuned using Direct Preference Optimization (DPO).

mrferr3t/77e76dfe-8180-412b-8330-bf3815c68e03

mrferr3t

peft

--- library_name: peft license: mit base_model: microsoft/Phi-3-mini-128k-instruct tags: - axolotl - generated_from_trainer model-index: - name: 77e76dfe-8180-412b-8330-bf3815c68e03 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora auto_find_batch_size: false base_model: microsoft/Phi-3-mini-128k-instruct bf16: auto chat_template: llama3 dataloader_num_workers: 12 dataset_prepared_path: null datasets: - data_files: - 4bc42649f3ff82d6_train_data.json ds_type: json format: custom path: /workspace/input_data/4bc42649f3ff82d6_train_data.json type: field_instruction: task_description field_output: raw_llm_response format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: 3 eval_max_new_tokens: 128 eval_steps: 6 eval_strategy: null flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 8 gradient_checkpointing: true group_by_length: false hub_model_id: mrferr3t/77e76dfe-8180-412b-8330-bf3815c68e03 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0004 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 6 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: micro_batch_size: 16 mlflow_experiment_name: /tmp/4bc42649f3ff82d6_train_data.json model_type: AutoModelForCausalLM num_epochs: 100 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: /workspace/hub_repo/last-checkpoint s2_attention: null sample_packing: false save_steps: 6 saves_per_epoch: 0 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: wandb_name: 48a8e43e-b4b0-4dd9-b312-f08688990a50 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 48a8e43e-b4b0-4dd9-b312-f08688990a50 warmup_steps: 100 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 77e76dfe-8180-412b-8330-bf3815c68e03 This model is a fine-tuned version of [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1862 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0004 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 128 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-------:|:----:|:---------------:| | No log | 0.0748 | 1 | 0.8170 | | 6.5456 | 0.2991 | 4 | 0.8158 | | 6.691 | 0.5981 | 8 | 0.8000 | | 6.3989 | 0.8972 | 12 | 0.7614 | | 6.0936 | 1.1963 | 16 | 0.7144 | | 5.5498 | 1.4953 | 20 | 0.6460 | | 5.0071 | 1.7944 | 24 | 0.5539 | | 4.267 | 2.0935 | 28 | 0.4592 | | 3.4615 | 2.3925 | 32 | 0.3938 | | 3.0627 | 2.6916 | 36 | 0.3494 | | 2.7272 | 2.9907 | 40 | 0.3245 | | 2.5849 | 3.2897 | 44 | 0.3048 | | 2.3831 | 3.5888 | 48 | 0.2903 | | 2.2856 | 3.8879 | 52 | 0.2777 | | 2.1933 | 4.1869 | 56 | 0.2681 | | 2.0663 | 4.5981 | 60 | 0.2595 | | 2.0344 | 4.8972 | 64 | 0.2522 | | 1.99 | 5.1963 | 68 | 0.2455 | | 1.8869 | 5.4953 | 72 | 0.2410 | | 1.7991 | 5.7944 | 76 | 0.2366 | | 1.7489 | 6.0935 | 80 | 0.2315 | | 1.6638 | 6.3925 | 84 | 0.2289 | | 1.6282 | 6.6916 | 88 | 0.2244 | | 1.5532 | 6.9907 | 92 | 0.2188 | | 1.3809 | 7.2897 | 96 | 0.2183 | | 1.4088 | 7.5888 | 100 | 0.2097 | | 1.3434 | 7.8879 | 104 | 0.2082 | | 1.225 | 8.1869 | 108 | 0.2118 | | 1.107 | 8.4860 | 112 | 0.2023 | | 1.0832 | 8.7850 | 116 | 0.1983 | | 1.0002 | 9.0841 | 120 | 0.1992 | | 0.8442 | 9.3832 | 124 | 0.2001 | | 0.8082 | 9.6822 | 128 | 0.1962 | | 0.8014 | 9.9813 | 132 | 0.1879 | | 0.6291 | 10.2804 | 136 | 0.1984 | | 0.5763 | 10.5794 | 140 | 0.1888 | | 0.5712 | 10.8785 | 144 | 0.1850 | | 0.4783 | 11.1776 | 148 | 0.1959 | | 0.4133 | 11.4766 | 152 | 0.1858 | | 0.4108 | 11.7757 | 156 | 0.1862 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

roleplaiapp/Slush-Sunfall-Rocinante-GGLD-12B-Q5_K_S-GGUF

roleplaiapp

transformers

text-generation

--- library_name: transformers pipeline_tag: text-generation tags: - 12b - 5-bit - Q5_K_S - ggld - gguf - llama-cpp - rocinante - slush - sunfall - text-generation --- # roleplaiapp/Slush-Sunfall-Rocinante-GGLD-12B-Q5_K_S-GGUF **Repo:** `roleplaiapp/Slush-Sunfall-Rocinante-GGLD-12B-Q5_K_S-GGUF` **Original Model:** `Slush-Sunfall-Rocinante-GGLD-12B` **Quantized File:** `Slush-Sunfall-Rocinante-GGLD-12B.Q5_K_S.gguf` **Quantization:** `GGUF` **Quantization Method:** `Q5_K_S` ## Overview This is a GGUF Q5_K_S quantized version of Slush-Sunfall-Rocinante-GGLD-12B ## Quantization By I often have idle GPUs while building/testing for the RP app, so I put them to use quantizing models. I hope the community finds these quantizations useful. Andrew Webby @ [RolePlai](https://roleplai.app/).

shafire/SkynetZero

shafire

transformers

text-generation

--- tags: - autotrain - text-generation-inference - text-generation - peft library_name: transformers base_model: meta-llama/Meta-Llama-3.1-8B widget: - messages: - role: user content: What is your favorite condiment? license: other --- # SkynetZero LLM - Trained with AutoTrain and Updated to GGUF Format THIS MODEL IS NOT WORKING CAN YOU FIX IT? https://huggingface.co/shafire/talktoaiQT Newer working GGUF here: **GGUF WORKING TESTED MODEL NEWER ONE SIMILAR TO THIS IS HERE https://huggingface.co/shafire/talktoaiQ **  **SkynetZero** is a quantum-powered language model trained with reflection datasets and TalkToAI custom data sets. The model went through several iterations, including a re-writing of datasets and validation phases due to errors encountered during testing and conversion into a fully functional LLM. This process helped ensure that SkynetZero can handle complex, multi-dimensional reasoning tasks with an emphasis on ethical decision-making. ### Key Highlights of SkynetZero: - **Advanced Quantum Reasoning**: The integration of quantum-inspired math systems enabled SkynetZero to tackle complex ethical dilemmas and multi-dimensional problem-solving tasks. - **Custom Re-Written Datasets**: The training involved multiple rounds of AI-assisted dataset curation, where reflection datasets were re-written for clarity, accuracy, and consistency. Additionally, TalkToAI datasets were integrated and re-processed to align with SkynetZero’s quantum reasoning framework. - **Iterative Improvement**: During testing and model conversion, the datasets were re-written and validated several times to address errors. Each iteration enhanced the model’s ethical consistency and problem-solving accuracy. SkynetZero is now available in **GGUF format**, following 8 hours of training on a large GPU server using the Hugging Face AutoTrain platform. **Made in Nottingham England by Shafaet Brady Hussain (shafaet.com)** # Usage - SkynetZero leverages open-source ideas and mathematical innovations. Further details can be found on [talktoai.org](https://talktoai.org) and [researchforum.online](https://researchforum.online). The model is licensed under the official legal guidelines for LLaMA 3.1 Meta. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype="auto" ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors="pt") output_ids = model.generate(input_ids.to("cuda")) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ``` ### Training Methodology SkynetZero was fine-tuned on the **LLaMA 3.1 8B** architecture, utilizing custom datasets that underwent AI-assisted re-writing. The training process focused on enhancing the model's ability to handle **multi-variable quantum reasoning** while ensuring ethical decision-making alignment. After identifying errors during testing and conversion to a model, the datasets were adjusted and the model iteratively improved across multiple epochs. ### Further Research and Contributions SkynetZero is part of an ongoing effort to explore **AI-human co-creation** in the development of quantum-enhanced AI models. The co-creation process with OpenAI’s **Agent Zero** provided valuable assistance in curating, editing, and validating datasets, pushing the boundaries of what large language models can achieve.

RylanSchaeffer/collapse_gemma-2-2b_hs2_replace_iter12_sftsd2

RylanSchaeffer

--- license: gemma base_model: google/gemma-2-2b tags: - trl - sft - generated_from_trainer model-index: - name: collapse_gemma-2-2b_hs2_replace_iter12_sftsd2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # collapse_gemma-2-2b_hs2_replace_iter12_sftsd2 This model is a fine-tuned version of [google/gemma-2-2b](https://huggingface.co/google/gemma-2-2b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5455 - Num Input Tokens Seen: 4776248 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 8e-06 - train_batch_size: 8 - eval_batch_size: 16 - seed: 2 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen | |:-------------:|:------:|:----:|:---------------:|:-----------------:| | No log | 0 | 0 | 1.3909 | 0 | | 1.576 | 0.0511 | 5 | 1.2789 | 245968 | | 0.8863 | 0.1022 | 10 | 1.2945 | 492152 | | 0.425 | 0.1533 | 15 | 1.5320 | 737520 | | 0.2379 | 0.2043 | 20 | 1.7713 | 982160 | | 0.0907 | 0.2554 | 25 | 1.9824 | 1224512 | | 0.0414 | 0.3065 | 30 | 2.2019 | 1470552 | | 0.0276 | 0.3576 | 35 | 2.3755 | 1714072 | | 0.0296 | 0.4087 | 40 | 2.4664 | 1966528 | | 0.0225 | 0.4598 | 45 | 2.4860 | 2212000 | | 0.0211 | 0.5109 | 50 | 2.5160 | 2456616 | | 0.022 | 0.5619 | 55 | 2.5310 | 2704328 | | 0.0214 | 0.6130 | 60 | 2.5350 | 2960528 | | 0.0205 | 0.6641 | 65 | 2.5329 | 3202432 | | 0.021 | 0.7152 | 70 | 2.5373 | 3446344 | | 0.0201 | 0.7663 | 75 | 2.5473 | 3689160 | | 0.0216 | 0.8174 | 80 | 2.5416 | 3935112 | | 0.0194 | 0.8685 | 85 | 2.5472 | 4182704 | | 0.0209 | 0.9195 | 90 | 2.5474 | 4433736 | | 0.0213 | 0.9706 | 95 | 2.5460 | 4680728 | ### Framework versions - Transformers 4.44.0 - Pytorch 2.4.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

rhplus0831/maid-yuzu-v2-mid

rhplus0831

transformers

text-generation

--- base_model: - smelborp/MixtralOrochi8x7B - ycros/BagelMIsteryTour-v2-8x7B library_name: transformers tags: - mergekit - merge --- # maid-yuzu-v2-mid This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [smelborp/MixtralOrochi8x7B](https://huggingface.co/smelborp/MixtralOrochi8x7B) * [ycros/BagelMIsteryTour-v2-8x7B](https://huggingface.co/ycros/BagelMIsteryTour-v2-8x7B) ### Configuration The following YAML configuration was used to produce this model: ```yaml base_model: model: path: smelborp/MixtralOrochi8x7B dtype: bfloat16 merge_method: slerp parameters: t: - value: 0.375 slices: - sources: - layer_range: [0, 32] model: model: path: smelborp/MixtralOrochi8x7B - layer_range: [0, 32] model: model: path: ycros/BagelMIsteryTour-v2-8x7B ```

mandeepbagga/upstage-llama-2048-infyGPT

mandeepbagga

peft

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0

cksghl1004/Space_8B_instruct_ver7

cksghl1004

transformers

--- base_model: unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** cksghl1004 - **License:** apache-2.0 - **Finetuned from model :** unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

Helsinki-NLP/opus-mt-ca-uk

Helsinki-NLP

transformers

translation

--- language: - ca - uk tags: - translation license: apache-2.0 --- ### cat-ukr * source group: Catalan * target group: Ukrainian * OPUS readme: [cat-ukr](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/cat-ukr/README.md) * model: transformer-align * source language(s): cat * target language(s): ukr * model: transformer-align * pre-processing: normalization + SentencePiece (spm4k,spm4k) * download original weights: [opus-2020-06-16.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/cat-ukr/opus-2020-06-16.zip) * test set translations: [opus-2020-06-16.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/cat-ukr/opus-2020-06-16.test.txt) * test set scores: [opus-2020-06-16.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/cat-ukr/opus-2020-06-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.cat.ukr | 28.6 | 0.503 | ### System Info: - hf_name: cat-ukr - source_languages: cat - target_languages: ukr - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/cat-ukr/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['ca', 'uk'] - src_constituents: {'cat'} - tgt_constituents: {'ukr'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm4k,spm4k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/cat-ukr/opus-2020-06-16.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/cat-ukr/opus-2020-06-16.test.txt - src_alpha3: cat - tgt_alpha3: ukr - short_pair: ca-uk - chrF2_score: 0.503 - bleu: 28.6 - brevity_penalty: 0.9670000000000001 - ref_len: 2438.0 - src_name: Catalan - tgt_name: Ukrainian - train_date: 2020-06-16 - src_alpha2: ca - tgt_alpha2: uk - prefer_old: False - long_pair: cat-ukr - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41

Vaishu1212/my-pet-dog

Vaishu1212

diffusers

text-to-image

--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### My-Pet-Dog Dreambooth model trained by Vaishu1212 following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: GoX19932gAS Sample pictures of this concept: 

Nelver28/chatbj-llama2-13b-chat-hr-finetuned-french

Nelver28

peft

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0

lambdaofgod/query-titles_dependencies-nbow-nbow-mnrl

lambdaofgod

sentence-transformers

sentence-similarity

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # lambdaofgod/query-titles_dependencies-nbow-nbow-mnrl This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 200 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('lambdaofgod/query-titles_dependencies-nbow-nbow-mnrl') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=lambdaofgod/query-titles_dependencies-nbow-nbow-mnrl) ## Full Model Architecture ``` SentenceTransformer( (0): WordEmbeddings( (emb_layer): Embedding(4395, 200) ) (1): WordWeights( (emb_layer): Embedding(4395, 1) ) (2): Pooling({'word_embedding_dimension': 200, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->

fliarbi/teams-new

fliarbi

transformers

text2text-generation

--- license: apache-2.0 base_model: google/flan-t5-base tags: - generated_from_trainer model-index: - name: teams-new results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # teams-new This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.36.2 - Pytorch 1.12.1+cu113 - Datasets 2.16.1 - Tokenizers 0.15.0

gustavomalkomes/vit-base-patch16-224-in21k

gustavomalkomes

transformers

image-classification

--- library_name: transformers license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - image-classification - vision - generated_from_trainer metrics: - accuracy model-index: - name: vit-base-patch16-224-in21k results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-in21k This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the chainyo/rvl-cdip dataset. It achieves the following results on the evaluation set: - Loss: 0.4223 - Accuracy: 0.8788 - Memory Allocated (gb): 1.49 - Max Memory Allocated (gb): 2.1 - Total Memory Available (gb): 126.62 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.45.2 - Pytorch 2.4.0a0+git12138a8 - Datasets 3.1.0 - Tokenizers 0.20.3

SEBIS/legal_t5_small_multitask_es_fr

SEBIS

transformers

text2text-generation

--- language: Spanish French tags: - translation Spanish French model datasets: - dcep europarl jrc-acquis widget: - text: "Fecha del anuncio en el Pleno" --- # legal_t5_small_multitask_es_fr model Model on translating legal text from Spanish to French. It was first released in [this repository](https://github.com/agemagician/LegalTrans). The model is parallely trained on the three parallel corpus with 42 language pair from jrc-acquis, europarl and dcep along with the unsupervised task where the model followed the task of prediction in a masked language model. ## Model description No pretraining is involved in case of legal_t5_small_multitask_es_fr model, rather the unsupervised task is added with all the translation task to realize the multitask learning scenario. ## Intended uses & limitations The model could be used for translation of legal texts from Spanish to French. ### How to use Here is how to use this model to translate legal text from Spanish to French in PyTorch: ```python from transformers import AutoTokenizer, AutoModelWithLMHead, TranslationPipeline pipeline = TranslationPipeline( model=AutoModelWithLMHead.from_pretrained("SEBIS/legal_t5_small_multitask_es_fr"), tokenizer=AutoTokenizer.from_pretrained(pretrained_model_name_or_path = "SEBIS/legal_t5_small_multitask_es_fr", do_lower_case=False, skip_special_tokens=True), device=0 ) es_text = "Fecha del anuncio en el Pleno" pipeline([es_text], max_length=512) ``` ## Training data The legal_t5_small_multitask_es_fr model (the supervised task which involved only the corresponding langauge pair and as well as unsupervised task where all of the data of all language pairs were available) model was trained on [JRC-ACQUIS](https://wt-public.emm4u.eu/Acquis/index_2.2.html), [EUROPARL](https://www.statmt.org/europarl/), and [DCEP](https://ec.europa.eu/jrc/en/language-technologies/dcep) dataset consisting of 9 Million parallel texts. ## Training procedure The model was trained on a single TPU Pod V3-8 for 250K steps in total, using sequence length 512 (batch size 4096). It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture. The optimizer used is AdaFactor with inverse square root learning rate schedule. ### Preprocessing An unigram model trained with 88M lines of text from the parallel corpus (of all possible language pairs) to get the vocabulary (with byte pair encoding), which is used with this model. ### Pretraining ## Evaluation results When the model is used for translation test dataset, achieves the following results: Test results : | Model | BLEU score | |:-----:|:-----:| | legal_t5_small_multitask_es_fr | 41.523| ### BibTeX entry and citation info > Created by [Ahmed Elnaggar/@Elnaggar_AI](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/)

jenishlezdo/test-model

jenishlezdo

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

mradermacher/Llama-3.1-SuperSwallow-70B-Instruct-v0.1-GGUF

mradermacher

transformers

--- base_model: nitky/Llama-3.1-SuperSwallow-70B-Instruct-v0.1 language: - ja - en library_name: transformers license: llama3.1 quantized_by: mradermacher tags: - mergekit - merge - chat --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> static quants of https://huggingface.co/nitky/Llama-3.1-SuperSwallow-70B-Instruct-v0.1 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama-3.1-SuperSwallow-70B-Instruct-v0.1-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-SuperSwallow-70B-Instruct-v0.1-GGUF/resolve/main/Llama-3.1-SuperSwallow-70B-Instruct-v0.1.Q2_K.gguf) | Q2_K | 26.5 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-SuperSwallow-70B-Instruct-v0.1-GGUF/resolve/main/Llama-3.1-SuperSwallow-70B-Instruct-v0.1.Q3_K_S.gguf) | Q3_K_S | 31.0 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-SuperSwallow-70B-Instruct-v0.1-GGUF/resolve/main/Llama-3.1-SuperSwallow-70B-Instruct-v0.1.Q3_K_M.gguf) | Q3_K_M | 34.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-SuperSwallow-70B-Instruct-v0.1-GGUF/resolve/main/Llama-3.1-SuperSwallow-70B-Instruct-v0.1.Q3_K_L.gguf) | Q3_K_L | 37.2 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-SuperSwallow-70B-Instruct-v0.1-GGUF/resolve/main/Llama-3.1-SuperSwallow-70B-Instruct-v0.1.IQ4_XS.gguf) | IQ4_XS | 38.4 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-SuperSwallow-70B-Instruct-v0.1-GGUF/resolve/main/Llama-3.1-SuperSwallow-70B-Instruct-v0.1.Q4_K_S.gguf) | Q4_K_S | 40.4 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-SuperSwallow-70B-Instruct-v0.1-GGUF/resolve/main/Llama-3.1-SuperSwallow-70B-Instruct-v0.1.Q4_K_M.gguf) | Q4_K_M | 42.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-SuperSwallow-70B-Instruct-v0.1-GGUF/resolve/main/Llama-3.1-SuperSwallow-70B-Instruct-v0.1.Q5_K_S.gguf) | Q5_K_S | 48.8 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-SuperSwallow-70B-Instruct-v0.1-GGUF/resolve/main/Llama-3.1-SuperSwallow-70B-Instruct-v0.1.Q5_K_M.gguf) | Q5_K_M | 50.0 | | | [PART 1](https://huggingface.co/mradermacher/Llama-3.1-SuperSwallow-70B-Instruct-v0.1-GGUF/resolve/main/Llama-3.1-SuperSwallow-70B-Instruct-v0.1.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3.1-SuperSwallow-70B-Instruct-v0.1-GGUF/resolve/main/Llama-3.1-SuperSwallow-70B-Instruct-v0.1.Q6_K.gguf.part2of2) | Q6_K | 58.0 | very good quality | | [PART 1](https://huggingface.co/mradermacher/Llama-3.1-SuperSwallow-70B-Instruct-v0.1-GGUF/resolve/main/Llama-3.1-SuperSwallow-70B-Instruct-v0.1.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3.1-SuperSwallow-70B-Instruct-v0.1-GGUF/resolve/main/Llama-3.1-SuperSwallow-70B-Instruct-v0.1.Q8_0.gguf.part2of2) | Q8_0 | 75.1 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->

featherless-ai-quants/VitalContribution-Evangelion-7B-GGUF

featherless-ai-quants

text-generation

--- base_model: VitalContribution/Evangelion-7B pipeline_tag: text-generation quantized_by: featherless-ai-quants --- # VitalContribution/Evangelion-7B GGUF Quantizations 🚀  *Optimized GGUF quantization files for enhanced model performance* > Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee. --- ## Available Quantizations 📊 | Quantization Type | File | Size | |-------------------|------|------| | IQ4_XS | [VitalContribution-Evangelion-7B-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/VitalContribution-Evangelion-7B-GGUF/blob/main/VitalContribution-Evangelion-7B-IQ4_XS.gguf) | 3761.66 MB | | Q2_K | [VitalContribution-Evangelion-7B-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/VitalContribution-Evangelion-7B-GGUF/blob/main/VitalContribution-Evangelion-7B-Q2_K.gguf) | 2593.27 MB | | Q3_K_L | [VitalContribution-Evangelion-7B-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/VitalContribution-Evangelion-7B-GGUF/blob/main/VitalContribution-Evangelion-7B-Q3_K_L.gguf) | 3644.97 MB | | Q3_K_M | [VitalContribution-Evangelion-7B-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/VitalContribution-Evangelion-7B-GGUF/blob/main/VitalContribution-Evangelion-7B-Q3_K_M.gguf) | 3355.97 MB | | Q3_K_S | [VitalContribution-Evangelion-7B-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/VitalContribution-Evangelion-7B-GGUF/blob/main/VitalContribution-Evangelion-7B-Q3_K_S.gguf) | 3017.97 MB | | Q4_K_M | [VitalContribution-Evangelion-7B-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/VitalContribution-Evangelion-7B-GGUF/blob/main/VitalContribution-Evangelion-7B-Q4_K_M.gguf) | 4166.07 MB | | Q4_K_S | [VitalContribution-Evangelion-7B-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/VitalContribution-Evangelion-7B-GGUF/blob/main/VitalContribution-Evangelion-7B-Q4_K_S.gguf) | 3948.57 MB | | Q5_K_M | [VitalContribution-Evangelion-7B-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/VitalContribution-Evangelion-7B-GGUF/blob/main/VitalContribution-Evangelion-7B-Q5_K_M.gguf) | 4893.69 MB | | Q5_K_S | [VitalContribution-Evangelion-7B-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/VitalContribution-Evangelion-7B-GGUF/blob/main/VitalContribution-Evangelion-7B-Q5_K_S.gguf) | 4766.19 MB | | Q6_K | [VitalContribution-Evangelion-7B-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/VitalContribution-Evangelion-7B-GGUF/blob/main/VitalContribution-Evangelion-7B-Q6_K.gguf) | 5666.80 MB | | Q8_0 | [VitalContribution-Evangelion-7B-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/VitalContribution-Evangelion-7B-GGUF/blob/main/VitalContribution-Evangelion-7B-Q8_0.gguf) | 7339.34 MB | --- ## ⚡ Powered by [Featherless AI](https://featherless.ai) ### Key Features - 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly - 🛠️ **Zero Infrastructure** - No server setup or maintenance required - 📚 **Vast Compatibility** - Support for 2400+ models and counting - 💎 **Affordable Pricing** - Starting at just $10/month --- **Links:** [Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)

Regain/PST-to-EML-Converter

Regain

Need to convert emails from a PST file (used by Microsoft Outlook) to individual EML files for wider compatibility? Regain PST to EML Converter can help. This software offers a user-friendly interface to browse and select PST files. You can choose specific emails or entire folders for conversion. Regain even lets you preview emails before conversion. The converter supports both ANSI and Unicode PST files, ensuring compatibility with various PST versions. It boasts fast conversion speeds, even for large PST files. A free version allows you to convert a limited number of emails. Upgrading unlocks batch conversion and additional output formats like MSG and MBOX. Key Features: Convert PST to EML and other formats Preview emails before conversion Supports large PST files Simple and user-friendly interface Free and paid versions available

Triangle104/Dumpling-Mistral-Nemo-8B-Q4_K_M-GGUF

Triangle104

transformers

--- library_name: transformers tags: - llama-cpp - gguf-my-repo base_model: nbeerbower/Dumpling-Mistral-Nemo-8B license: apache-2.0 --- # Triangle104/Dumpling-Mistral-Nemo-8B-Q4_K_M-GGUF This model was converted to GGUF format from [`nbeerbower/Dumpling-Mistral-Nemo-8B`](https://huggingface.co/nbeerbower/Dumpling-Mistral-Nemo-8B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/nbeerbower/Dumpling-Mistral-Nemo-8B) for more details on the model. --- 🧪 Experimental An attempt to recover intelligence with a quick train, results are meh Dumpling-Mistral-Nemo-8B nbeerbower/mistral-nemo-kartoffel-PRUNE3 finetuned on: -nbeerbower/GreatFirewall-DPO -nbeerbower/Schule-DPO -nbeerbower/Purpura-DPO -nbeerbower/Arkhaios-DPO -jondurbin/truthy-dpo-v0.1 -antiven0m/physical-reasoning-dpo -flammenai/Date-DPO-NoAsterisks -flammenai/Prude-Phi3-DPO -Atsunori/HelpSteer2-DPO (1,000 samples) -jondurbin/gutenberg-dpo-v0.1 -nbeerbower/gutenberg2-dpo -nbeerbower/gutenberg-moderne-dpo. Method --- QLoRA ORPO tune with 2x RTX 3090 for 2 epochs. --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/Dumpling-Mistral-Nemo-8B-Q4_K_M-GGUF --hf-file dumpling-mistral-nemo-8b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Dumpling-Mistral-Nemo-8B-Q4_K_M-GGUF --hf-file dumpling-mistral-nemo-8b-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/Dumpling-Mistral-Nemo-8B-Q4_K_M-GGUF --hf-file dumpling-mistral-nemo-8b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Dumpling-Mistral-Nemo-8B-Q4_K_M-GGUF --hf-file dumpling-mistral-nemo-8b-q4_k_m.gguf -c 2048 ```

mia-llm/pythia-160m-AGnews-roya

mia-llm

transformers

text-generation

--- base_model: EleutherAI/pythia-160m library_name: transformers model_name: pythia-160m-None.9.2 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for pythia-160m-None.9.2 This model is a fine-tuned version of [EleutherAI/pythia-160m](https://huggingface.co/EleutherAI/pythia-160m). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="RoyArkh/pythia-160m-None.9.2", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.13.0 - Transformers: 4.48.0 - Pytorch: 2.2.1+cu121 - Datasets: 3.2.0 - Tokenizers: 0.21.0 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

kanakg1/bert_a01_r8

kanakg1

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Owhslp/nous_researcher_tuning_2_6

Owhslp

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

DuJinHua/AiMed_PaperAbs

DuJinHua

transformers

text-generation

--- license: apache-2.0 --- # AiMed: Artificial Intelligence large language model for chinese Medicine 面向中文医学的人工智能大语言模型 [](LICENSE) [](requirements.txt) ## 🔬 介绍 **AiMed** 面向中文医学的人工智能大语言模型由**清华大学OpenDE团队**和**中国医学科学院医学信息研究所**（下称“医信所”）联合研发。 **AiMed** 期望实现有效处理医学知识问答、医学论文阅读、医学文献检索等任务和在医学科研中的应用。 **AiMed** 详细测试脚本请参考我们的项目：https://github.com/Du-JinHua/AiMed ## ⏩ 构建流程 **AiMed** 整个构建流程包括： - PT增量预训练 - SFT有监督微调 - AiMed_PaperAbs是AiMed系列模型中，通过从千万医学文献中提取高质量10万条摘要进行的论文摘要大模型微调版本。 - RLHF(奖励建模、强化学习训练) - DPO(直接偏好优化) ## 🌏 模型基座 | 模型名 | 模型大小 | 开源参数 | | ------------------------------------------------------- | --------------------------- |-----------------------------------------------------------------------------------------| | [Baichuan](https://github.com/baichuan-inc/baichuan-13B) | 13B | [baichuan-inc/Baichuan-13B-Chat](https://huggingface.co/baichuan-inc/Baichuan-13B-Chat) | ## 😜 推理和部署 推理所需的模型权重、源码、配置公开于https://github.com/Du-JinHua/AiMed ## ⚠️ 局限性 授权协议为 The Apache License 2.0，目前仅支持学术研究，不支持商业用途。

shrusti333/language_translation

shrusti333

keras

translation

--- datasets: - shrusti333/konkani_translation language: - en metrics: - accuracy pipeline_tag: translation library_name: keras ---

SakuraFoxKira/BY_RF-7

SakuraFoxKira

--- license: creativeml-openrail-m ---

Jongsim/Llama-3-MAAL-8B-Instruct-v0.1-GPTQ

Jongsim

transformers

text-generation

--- license: llama3 language: - en - ko --- original model <a rel="nofollow" href="https://huggingface.co/maum-ai/Llama-3-MAAL-8B-Instruct-v0.1">Llama-3-MAAL-8B-Instruct-v0.1</a> GPTQ quants of Llama-3-MAAL-8B-Instruct-v0.1 Located in the main branch - 8bit GPTQ model 원본 모델 <a rel="nofollow" href="https://huggingface.co/maum-ai/Llama-3-MAAL-8B-Instruct-v0.1">Llama-3-MAAL-8B-Instruct-v0.1</a> Llama-3-MAAL-8B-Instruct-v0.1 모델 GPTQ 양자화 메인 branch에 있는 파일 - 8bit GPTQ model

jeongseokoh/latent-attention-roberta

jeongseokoh

transformers

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

imagepipeline/Age-Slider-Young-V2-Negative

imagepipeline

text-to-image

--- license: creativeml-openrail-m tags: - imagepipeline - imagepipeline.io - text-to-image - ultra-realistic pinned: false pipeline_tag: text-to-image --- ## Age-Slider-Young-V2-Negative <img src="https://f005.backblazeb2.com/b2api/v2/b2_download_file_by_id?fileId=4_zfdf0a8ed59e8666b89b10713_f109c68537a69cb90_d20231210_m053523_c005_v0501010_t0024_u01702186523945" alt="Generated by Image Pipeline" style="border-radius: 10px;"> **This embedding model is uploaded on [imagepipeline.io](https://imagepipeline.io/)** Model details - AS-YoungV2-Neg: Place in your negative prompt at strength of 1.0 to 1.3, generally removes adults from the scene [](https://imagepipeline.io/models/Age-Slider-Young-V2-Negative?id=a2a81c57-662c-4024-9eee-7a5db05edcf0/) ## How to try this model ? You can try using it locally or send an API call to test the output quality. Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/). No payment required. Coding in `php` `javascript` `node` etc ? Checkout our documentation [](https://docs.imagepipeline.io/docs/introduction) ```python import requests import json url = "https://imagepipeline.io/sd/text2image/v1/run" payload = json.dumps({ "model_id": "sd1.5", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": false, "guidance_scale": 7.5, "multi_lingual": "no", "embeddings": "a2a81c57-662c-4024-9eee-7a5db05edcf0", "lora_models": "", "lora_weights": "" }) headers = { 'Content-Type': 'application/json', 'API-Key': 'your_api_key' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) } ``` Get more ready to use `MODELS` like this for `SD 1.5` and `SDXL` : [](https://imagepipeline.io/models) ### API Reference #### Generate Image ```http https://api.imagepipeline.io/sd/text2image/v1 ``` | Headers | Type | Description | |:----------------------| :------- |:-------------------------------------------------------------------------------------------------------------------| | `API-Key` | `str` | Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/) | | `Content-Type` | `str` | application/json - content type of the request body | | Parameter | Type | Description | | :-------- | :------- | :------------------------- | | `model_id` | `str` | Your base model, find available lists in [models page](https://imagepipeline.io/models) or upload your own| | `prompt` | `str` | Text Prompt. Check our [Prompt Guide](https://docs.imagepipeline.io/docs/SD-1.5/docs/extras/prompt-guide) for tips | | `num_inference_steps` | `int [1-50]` | Noise is removed with each step, resulting in a higher-quality image over time. Ideal value 30-50 (without LCM) | | `guidance_scale` | `float [1-20]` | Higher guidance scale prioritizes text prompt relevance but sacrifices image quality. Ideal value 7.5-12.5 | | `lora_models` | `str, array` | Pass the model_id(s) of LoRA models that can be found in models page | | `lora_weights` | `str, array` | Strength of the LoRA effect | --- license: creativeml-openrail-m tags: - imagepipeline - imagepipeline.io - text-to-image - ultra-realistic pinned: false pipeline_tag: text-to-image --- ### Feedback If you have any feedback, please reach out to us at hello@imagepipeline.io #### 🔗 Visit Website [](https://imagepipeline.io/) If you are the original author of this model, please [click here](https://airtable.com/apprTaRnJbDJ8ufOx/shr4g7o9B6fWfOlUR) to add credits

ndeclarke/wav2vec2-mms-1b-CV17.0

ndeclarke

transformers

automatic-speech-recognition

--- library_name: transformers license: cc-by-nc-4.0 base_model: facebook/mms-1b-all tags: - generated_from_trainer datasets: - common_voice_17_0 metrics: - wer - bleu model-index: - name: wav2vec2-mms-1b-CV17.0 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice_17_0 type: common_voice_17_0 metrics: - name: Wer type: wer value: 0.6538388264431321 - name: Bleu type: bleu value: 0.14202013774436864 --- # wav2vec2-mms-1b-CV17.0 This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the common_voice_17_0 dataset. Adapters for several languages were trained. ## Intended uses & limitations Speech-to-text transciption of Malayalam, Tamil, Telugu, and Yoruba. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.15 - training_steps: 2000 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0+cu121 - Datasets 3.0.0 - Tokenizers 0.19.1

begeri/a2c-PandaReachDense-v3

begeri

stable-baselines3

reinforcement-learning

--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -0.21 +/- 0.07 name: mean_reward verified: false --- # **A2C** Agent playing **PandaReachDense-v3** This is a trained model of a **A2C** agent playing **PandaReachDense-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

azxky6645/01252129-under500-filtering_NuminaMath-CoT

azxky6645

transformers

text-generation

--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

rcebot/Llama-3-8B

rcebot

text-generation

--- language: - en pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-3 license: llama3 extra_gated_prompt: >- ### META LLAMA 3 COMMUNITY LICENSE AGREEMENT Meta Llama 3 Version Release Date: April 18, 2024 "Agreement" means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. "Documentation" means the specifications, manuals and documentation accompanying Meta Llama 3 distributed by Meta at https://llama.meta.com/get-started/. "Licensee" or "you" means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf. 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Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement. 7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement. ### Meta Llama 3 Acceptable Use Policy Meta is committed to promoting safe and fair use of its tools and features, including Meta Llama 3. If you access or use Meta Llama 3, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy](https://llama.meta.com/llama3/use-policy) #### Prohibited Uses We want everyone to use Meta Llama 3 safely and responsibly. You agree you will not use, or allow others to use, Meta Llama 3 to: 1. Violate the law or others’ rights, including to: 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as: 1. Violence or terrorism 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material 3. Human trafficking, exploitation, and sexual violence 4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials. 5. Sexual solicitation 6. Any other criminal activity 2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals 3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services 4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices 5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws 6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama Materials 7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system 2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Meta Llama 3 related to the following: 1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State 2. Guns and illegal weapons (including weapon development) 3. Illegal drugs and regulated/controlled substances 4. Operation of critical infrastructure, transportation technologies, or heavy machinery 5. Self-harm or harm to others, including suicide, cutting, and eating disorders 6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual 3. Intentionally deceive or mislead others, including use of Meta Llama 3 related to the following: 1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation 2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content 3. Generating, promoting, or further distributing spam 4. Impersonating another individual without consent, authorization, or legal right 5. Representing that the use of Meta Llama 3 or outputs are human-generated 6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement 4. Fail to appropriately disclose to end users any known dangers of your AI system Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means: * Reporting issues with the model: [https://github.com/meta-llama/llama3](https://github.com/meta-llama/llama3) * Reporting risky content generated by the model: developers.facebook.com/llama_output_feedback * Reporting bugs and security concerns: facebook.com/whitehat/info * Reporting violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: LlamaUseReport@meta.com extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text geo: ip_location By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox extra_gated_description: The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit widget: - example_title: Hello messages: - role: user content: Hey my name is Julien! How are you? - example_title: Winter holidays messages: - role: system content: You are a helpful and honest assistant. Please, respond concisely and truthfully. - role: user content: Can you recommend a good destination for Winter holidays? - example_title: Programming assistant messages: - role: system content: You are a helpful and honest code and programming assistant. Please, respond concisely and truthfully. - role: user content: Write a function that computes the nth fibonacci number. inference: parameters: max_new_tokens: 300 stop: - <|end_of_text|> - <|eot_id|> --- ## Model Details Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety. **Model developers** Meta **Variations** Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants. **Input** Models input text only. **Output** Models generate text and code only. **Model Architecture** Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. <table> <tr> <td> </td> <td><strong>Training Data</strong> </td> <td><strong>Params</strong> </td> <td><strong>Context length</strong> </td> <td><strong>GQA</strong> </td> <td><strong>Token count</strong> </td> <td><strong>Knowledge cutoff</strong> </td> </tr> <tr> <td rowspan="2" >Llama 3 </td> <td rowspan="2" >A new mix of publicly available online data. </td> <td>8B </td> <td>8k </td> <td>Yes </td> <td rowspan="2" >15T+ </td> <td>March, 2023 </td> </tr> <tr> <td>70B </td> <td>8k </td> <td>Yes </td> <td>December, 2023 </td> </tr> </table> **Llama 3 family of models**. Token counts refer to pretraining data only. Both the 8 and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability. **Model Release Date** April 18, 2024. **Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. **License** A custom commercial license is available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license) Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go [here](https://github.com/meta-llama/llama-recipes). ## Intended Use **Intended Use Cases** Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. **Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English**. **Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy. ## How to use This repository contains two versions of Meta-Llama-3-8B-Instruct, for use with transformers and with the original `llama3` codebase. ### Use with transformers You can run conversational inference using the Transformers pipeline abstraction, or by leveraging the Auto classes with the `generate()` function. Let's see examples of both. #### Transformers pipeline ```python import transformers import torch model_id = "meta-llama/Meta-Llama-3-8B-Instruct" pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto", ) messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] terminators = [ pipeline.tokenizer.eos_token_id, pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>") ] outputs = pipeline( messages, max_new_tokens=256, eos_token_id=terminators, do_sample=True, temperature=0.6, top_p=0.9, ) print(outputs[0]["generated_text"][-1]) ``` #### Transformers AutoModelForCausalLM ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch model_id = "meta-llama/Meta-Llama-3-8B-Instruct" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto", ) messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] input_ids = tokenizer.apply_chat_template( messages, add_generation_prompt=True, return_tensors="pt" ).to(model.device) terminators = [ tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<|eot_id|>") ] outputs = model.generate( input_ids, max_new_tokens=256, eos_token_id=terminators, do_sample=True, temperature=0.6, top_p=0.9, ) response = outputs[0][input_ids.shape[-1]:] print(tokenizer.decode(response, skip_special_tokens=True)) ``` ### Use with `llama3` Please, follow the instructions in the [repository](https://github.com/meta-llama/llama3) To download Original checkpoints, see the example command below leveraging `huggingface-cli`: ``` huggingface-cli download meta-llama/Meta-Llama-3-8B-Instruct --include "original/*" --local-dir Meta-Llama-3-8B-Instruct ``` For Hugging Face support, we recommend using transformers or TGI, but a similar command works. ## Hardware and Software **Training Factors** We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. **Carbon Footprint Pretraining utilized a cumulative** 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program. <table> <tr> <td> </td> <td><strong>Time (GPU hours)</strong> </td> <td><strong>Power Consumption (W)</strong> </td> <td><strong>Carbon Emitted(tCO2eq)</strong> </td> </tr> <tr> <td>Llama 3 8B </td> <td>1.3M </td> <td>700 </td> <td>390 </td> </tr> <tr> <td>Llama 3 70B </td> <td>6.4M </td> <td>700 </td> <td>1900 </td> </tr> <tr> <td>Total </td> <td>7.7M </td> <td> </td> <td>2290 </td> </tr> </table> **CO2 emissions during pre-training**. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data **Overview** Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. **Data Freshness** The pretraining data has a cutoff of March 2023 for the 8B and December 2023 for the 70B models respectively. ## Benchmarks In this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see [here](https://github.com/meta-llama/llama3/blob/main/eval_methodology.md). ### Base pretrained models <table> <tr> <td><strong>Category</strong> </td> <td><strong>Benchmark</strong> </td> <td><strong>Llama 3 8B</strong> </td> <td><strong>Llama2 7B</strong> </td> <td><strong>Llama2 13B</strong> </td> <td><strong>Llama 3 70B</strong> </td> <td><strong>Llama2 70B</strong> </td> </tr> <tr> <td rowspan="6" >General </td> <td>MMLU (5-shot) </td> <td>66.6 </td> <td>45.7 </td> <td>53.8 </td> <td>79.5 </td> <td>69.7 </td> </tr> <tr> <td>AGIEval English (3-5 shot) </td> <td>45.9 </td> <td>28.8 </td> <td>38.7 </td> <td>63.0 </td> <td>54.8 </td> </tr> <tr> <td>CommonSenseQA (7-shot) </td> <td>72.6 </td> <td>57.6 </td> <td>67.6 </td> <td>83.8 </td> <td>78.7 </td> </tr> <tr> <td>Winogrande (5-shot) </td> <td>76.1 </td> <td>73.3 </td> <td>75.4 </td> <td>83.1 </td> <td>81.8 </td> </tr> <tr> <td>BIG-Bench Hard (3-shot, CoT) </td> <td>61.1 </td> <td>38.1 </td> <td>47.0 </td> <td>81.3 </td> <td>65.7 </td> </tr> <tr> <td>ARC-Challenge (25-shot) </td> <td>78.6 </td> <td>53.7 </td> <td>67.6 </td> <td>93.0 </td> <td>85.3 </td> </tr> <tr> <td>Knowledge reasoning </td> <td>TriviaQA-Wiki (5-shot) </td> <td>78.5 </td> <td>72.1 </td> <td>79.6 </td> <td>89.7 </td> <td>87.5 </td> </tr> <tr> <td rowspan="4" >Reading comprehension </td> <td>SQuAD (1-shot) </td> <td>76.4 </td> <td>72.2 </td> <td>72.1 </td> <td>85.6 </td> <td>82.6 </td> </tr> <tr> <td>QuAC (1-shot, F1) </td> <td>44.4 </td> <td>39.6 </td> <td>44.9 </td> <td>51.1 </td> <td>49.4 </td> </tr> <tr> <td>BoolQ (0-shot) </td> <td>75.7 </td> <td>65.5 </td> <td>66.9 </td> <td>79.0 </td> <td>73.1 </td> </tr> <tr> <td>DROP (3-shot, F1) </td> <td>58.4 </td> <td>37.9 </td> <td>49.8 </td> <td>79.7 </td> <td>70.2 </td> </tr> </table> ### Instruction tuned models <table> <tr> <td><strong>Benchmark</strong> </td> <td><strong>Llama 3 8B</strong> </td> <td><strong>Llama 2 7B</strong> </td> <td><strong>Llama 2 13B</strong> </td> <td><strong>Llama 3 70B</strong> </td> <td><strong>Llama 2 70B</strong> </td> </tr> <tr> <td>MMLU (5-shot) </td> <td>68.4 </td> <td>34.1 </td> <td>47.8 </td> <td>82.0 </td> <td>52.9 </td> </tr> <tr> <td>GPQA (0-shot) </td> <td>34.2 </td> <td>21.7 </td> <td>22.3 </td> <td>39.5 </td> <td>21.0 </td> </tr> <tr> <td>HumanEval (0-shot) </td> <td>62.2 </td> <td>7.9 </td> <td>14.0 </td> <td>81.7 </td> <td>25.6 </td> </tr> <tr> <td>GSM-8K (8-shot, CoT) </td> <td>79.6 </td> <td>25.7 </td> <td>77.4 </td> <td>93.0 </td> <td>57.5 </td> </tr> <tr> <td>MATH (4-shot, CoT) </td> <td>30.0 </td> <td>3.8 </td> <td>6.7 </td> <td>50.4 </td> <td>11.6 </td> </tr> </table> ### Responsibility & Safety We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community. Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications. Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience. As part of the Llama 3 release, we updated our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including [Meta Llama Guard 2](https://llama.meta.com/purple-llama/) and [Code Shield](https://llama.meta.com/purple-llama/) safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a [reference implementation](https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai) to get you started. #### Llama 3-Instruct As outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case. <span style="text-decoration:underline;">Safety</span> For our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable. <span style="text-decoration:underline;">Refusals</span> In addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2. We built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date. #### Responsible release In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision. Misuse If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy/](https://llama.meta.com/llama3/use-policy/). #### Critical risks <span style="text-decoration:underline;">CBRNE</span> (Chemical, Biological, Radiological, Nuclear, and high yield Explosives) We have conducted a two fold assessment of the safety of the model in this area: * Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks. * Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model). ### <span style="text-decoration:underline;">Cyber Security </span> We have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of [equivalent coding capability](https://huggingface.co/spaces/facebook/CyberSecEval). ### <span style="text-decoration:underline;">Child Safety</span> Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. ### Community Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community. ## Ethical Considerations and Limitations The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating [Purple Llama](https://github.com/facebookresearch/PurpleLlama) solutions into your workflows and specifically [Llama Guard](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety. Please see the Responsible Use Guide available at [http://llama.meta.com/responsible-use-guide](http://llama.meta.com/responsible-use-guide) ## Citation instructions @article{llama3modelcard, title={Llama 3 Model Card}, author={AI@Meta}, year={2024}, url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md} } ## Contributors Aaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos

Serega1200/test

Serega1200

--- license: bsd-3-clause language: - ar - be - ru - en tags: - biology - chemistry - finance - music - code - medical - text-generation-inference - merge - moe --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. 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TheBloke/WizardLM-30B-Uncensored-GGUF

TheBloke

transformers

--- license: other tags: - uncensored datasets: - ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered model_name: Wizardlm 30B Uncensored base_model: ehartford/WizardLM-30B-Uncensored inference: false model_creator: Eric Hartford model_type: llama prompt_template: '{prompt} ### Response: ' quantized_by: TheBloke --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Wizardlm 30B Uncensored - GGUF - Model creator: [Eric Hartford](https://huggingface.co/ehartford) - Original model: [Wizardlm 30B Uncensored](https://huggingface.co/ehartford/WizardLM-30B-Uncensored) <!-- description start --> ## Description This repo contains GGUF format model files for [Eric Hartford's Wizardlm 30B Uncensored](https://huggingface.co/ehartford/WizardLM-30B-Uncensored). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplate list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. <!-- README_GGUF.md-about-gguf end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/WizardLM-30B-uncensored-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/WizardLM-30B-uncensored-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/WizardLM-30B-uncensored-GGUF) * [Eric Hartford's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/ehartford/WizardLM-30B-Uncensored) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: WizardLM ``` {prompt} ### Response: ``` <!-- prompt-template end --> <!-- compatibility_gguf start --> ## Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw Refer to the Provided Files table below to see what files use which methods, and how. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-provided-files start --> ## Provided files | Name | Quant method | Bits | Size | Max RAM required | Use case | | ---- | ---- | ---- | ---- | ---- | ----- | | [WizardLM-30B-Uncensored.Q2_K.gguf](https://huggingface.co/TheBloke/WizardLM-30B-uncensored-GGUF/blob/main/WizardLM-30B-Uncensored.Q2_K.gguf) | Q2_K | 2 | 13.50 GB| 16.00 GB | smallest, significant quality loss - not recommended for most purposes | | [WizardLM-30B-Uncensored.Q3_K_S.gguf](https://huggingface.co/TheBloke/WizardLM-30B-uncensored-GGUF/blob/main/WizardLM-30B-Uncensored.Q3_K_S.gguf) | Q3_K_S | 3 | 14.06 GB| 16.56 GB | very small, high quality loss | | [WizardLM-30B-Uncensored.Q3_K_M.gguf](https://huggingface.co/TheBloke/WizardLM-30B-uncensored-GGUF/blob/main/WizardLM-30B-Uncensored.Q3_K_M.gguf) | Q3_K_M | 3 | 15.76 GB| 18.26 GB | very small, high quality loss | | [WizardLM-30B-Uncensored.Q3_K_L.gguf](https://huggingface.co/TheBloke/WizardLM-30B-uncensored-GGUF/blob/main/WizardLM-30B-Uncensored.Q3_K_L.gguf) | Q3_K_L | 3 | 17.28 GB| 19.78 GB | small, substantial quality loss | | [WizardLM-30B-Uncensored.Q4_0.gguf](https://huggingface.co/TheBloke/WizardLM-30B-uncensored-GGUF/blob/main/WizardLM-30B-Uncensored.Q4_0.gguf) | Q4_0 | 4 | 18.36 GB| 20.86 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [WizardLM-30B-Uncensored.Q4_K_S.gguf](https://huggingface.co/TheBloke/WizardLM-30B-uncensored-GGUF/blob/main/WizardLM-30B-Uncensored.Q4_K_S.gguf) | Q4_K_S | 4 | 18.44 GB| 20.94 GB | small, greater quality loss | | [WizardLM-30B-Uncensored.Q4_K_M.gguf](https://huggingface.co/TheBloke/WizardLM-30B-uncensored-GGUF/blob/main/WizardLM-30B-Uncensored.Q4_K_M.gguf) | Q4_K_M | 4 | 19.62 GB| 22.12 GB | medium, balanced quality - recommended | | [WizardLM-30B-Uncensored.Q5_0.gguf](https://huggingface.co/TheBloke/WizardLM-30B-uncensored-GGUF/blob/main/WizardLM-30B-Uncensored.Q5_0.gguf) | Q5_0 | 5 | 22.40 GB| 24.90 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [WizardLM-30B-Uncensored.Q5_K_S.gguf](https://huggingface.co/TheBloke/WizardLM-30B-uncensored-GGUF/blob/main/WizardLM-30B-Uncensored.Q5_K_S.gguf) | Q5_K_S | 5 | 22.40 GB| 24.90 GB | large, low quality loss - recommended | | [WizardLM-30B-Uncensored.Q5_K_M.gguf](https://huggingface.co/TheBloke/WizardLM-30B-uncensored-GGUF/blob/main/WizardLM-30B-Uncensored.Q5_K_M.gguf) | Q5_K_M | 5 | 23.05 GB| 25.55 GB | large, very low quality loss - recommended | | [WizardLM-30B-Uncensored.Q6_K.gguf](https://huggingface.co/TheBloke/WizardLM-30B-uncensored-GGUF/blob/main/WizardLM-30B-Uncensored.Q6_K.gguf) | Q6_K | 6 | 26.69 GB| 29.19 GB | very large, extremely low quality loss | | [WizardLM-30B-Uncensored.Q8_0.gguf](https://huggingface.co/TheBloke/WizardLM-30B-uncensored-GGUF/blob/main/WizardLM-30B-Uncensored.Q8_0.gguf) | Q8_0 | 8 | 34.57 GB| 37.07 GB | very large, extremely low quality loss - not recommended | **Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. <!-- README_GGUF.md-provided-files end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files **Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: - LM Studio - LoLLMS Web UI - Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: TheBloke/WizardLM-30B-uncensored-GGUF and below it, a specific filename to download, such as: WizardLM-30B-Uncensored.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download TheBloke/WizardLM-30B-uncensored-GGUF WizardLM-30B-Uncensored.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download TheBloke/WizardLM-30B-uncensored-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/WizardLM-30B-uncensored-GGUF WizardLM-30B-Uncensored.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 32 -m WizardLM-30B-Uncensored.Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}\n### Response:" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 2048` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. ### How to load this model in Python code, using ctransformers #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install ctransformers # Or with CUDA GPU acceleration pip install ctransformers[cuda] # Or with AMD ROCm GPU acceleration (Linux only) CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers # Or with Metal GPU acceleration for macOS systems only CT_METAL=1 pip install ctransformers --no-binary ctransformers ``` #### Simple ctransformers example code ```python from ctransformers import AutoModelForCausalLM # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = AutoModelForCausalLM.from_pretrained("TheBloke/WizardLM-30B-uncensored-GGUF", model_file="WizardLM-30B-Uncensored.Q4_K_M.gguf", model_type="llama", gpu_layers=50) print(llm("AI is going to")) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> <!-- original-model-card start --> # Original model card: Eric Hartford's Wizardlm 30B Uncensored This is WizardLM trained with a subset of the dataset - responses that contained alignment / moralizing were removed. The intent is to train a WizardLM that doesn't have alignment built-in, so that alignment (of any sort) can be added separately with for example with a RLHF LoRA. Shout out to the open source AI/ML community, and everyone who helped me out. Note: An uncensored model has no guardrails. You are responsible for anything you do with the model, just as you are responsible for anything you do with any dangerous object such as a knife, gun, lighter, or car. Publishing anything this model generates is the same as publishing it yourself. You are responsible for the content you publish, and you cannot blame the model any more than you can blame the knife, gun, lighter, or car for what you do with it. <!-- original-model-card end -->

sd-concepts-library/shitao

sd-concepts-library

--- license: mit base_model: stabilityai/stable-diffusion-2 --- ### shitao on Stable Diffusion This is the `<st>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`:    

mradermacher/openbuddy-deepseek-67b-v15.3-4k-GGUF

mradermacher

transformers

--- base_model: OpenBuddy/openbuddy-deepseek-67b-v15.3-4k language: - zh - en - fr - de - ja - ko - it - ru - fi library_name: transformers license: other license_link: https://github.com/deepseek-ai/DeepSeek-LLM/blob/548a39bdd03986297ea4e233a8b7676edd6bec3e/LICENSE-MODEL license_name: deepseek quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/OpenBuddy/openbuddy-deepseek-67b-v15.3-4k <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/openbuddy-deepseek-67b-v15.3-4k-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/openbuddy-deepseek-67b-v15.3-4k-GGUF/resolve/main/openbuddy-deepseek-67b-v15.3-4k.Q2_K.gguf) | Q2_K | 25.2 | | | [GGUF](https://huggingface.co/mradermacher/openbuddy-deepseek-67b-v15.3-4k-GGUF/resolve/main/openbuddy-deepseek-67b-v15.3-4k.Q3_K_S.gguf) | Q3_K_S | 29.4 | | | [GGUF](https://huggingface.co/mradermacher/openbuddy-deepseek-67b-v15.3-4k-GGUF/resolve/main/openbuddy-deepseek-67b-v15.3-4k.Q3_K_M.gguf) | Q3_K_M | 32.8 | lower quality | | [GGUF](https://huggingface.co/mradermacher/openbuddy-deepseek-67b-v15.3-4k-GGUF/resolve/main/openbuddy-deepseek-67b-v15.3-4k.Q3_K_L.gguf) | Q3_K_L | 35.7 | | | [GGUF](https://huggingface.co/mradermacher/openbuddy-deepseek-67b-v15.3-4k-GGUF/resolve/main/openbuddy-deepseek-67b-v15.3-4k.IQ4_XS.gguf) | IQ4_XS | 36.6 | | | [GGUF](https://huggingface.co/mradermacher/openbuddy-deepseek-67b-v15.3-4k-GGUF/resolve/main/openbuddy-deepseek-67b-v15.3-4k.Q4_K_S.gguf) | Q4_K_S | 38.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/openbuddy-deepseek-67b-v15.3-4k-GGUF/resolve/main/openbuddy-deepseek-67b-v15.3-4k.Q4_K_M.gguf) | Q4_K_M | 40.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/openbuddy-deepseek-67b-v15.3-4k-GGUF/resolve/main/openbuddy-deepseek-67b-v15.3-4k.Q5_K_S.gguf) | Q5_K_S | 46.6 | | | [GGUF](https://huggingface.co/mradermacher/openbuddy-deepseek-67b-v15.3-4k-GGUF/resolve/main/openbuddy-deepseek-67b-v15.3-4k.Q5_K_M.gguf) | Q5_K_M | 47.8 | | | [PART 1](https://huggingface.co/mradermacher/openbuddy-deepseek-67b-v15.3-4k-GGUF/resolve/main/openbuddy-deepseek-67b-v15.3-4k.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/openbuddy-deepseek-67b-v15.3-4k-GGUF/resolve/main/openbuddy-deepseek-67b-v15.3-4k.Q6_K.gguf.part2of2) | Q6_K | 55.4 | very good quality | | [PART 1](https://huggingface.co/mradermacher/openbuddy-deepseek-67b-v15.3-4k-GGUF/resolve/main/openbuddy-deepseek-67b-v15.3-4k.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/openbuddy-deepseek-67b-v15.3-4k-GGUF/resolve/main/openbuddy-deepseek-67b-v15.3-4k.Q8_0.gguf.part2of2) | Q8_0 | 71.7 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->

nat-hunt/ea76628e-e0f5-4bc8-8fd1-56508f49914a

nat-hunt

peft

--- library_name: peft license: apache-2.0 base_model: unsloth/llama-2-7b-chat tags: - axolotl - generated_from_trainer model-index: - name: ea76628e-e0f5-4bc8-8fd1-56508f49914a results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/llama-2-7b-chat bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 88a7a4ef16b7645c_train_data.json ds_type: json format: custom path: /workspace/input_data/88a7a4ef16b7645c_train_data.json type: field_instruction: utt field_output: annot_utt format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: nat-hunt/ea76628e-e0f5-4bc8-8fd1-56508f49914a hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/88a7a4ef16b7645c_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: a313e50a-ef92-4948-bd5f-6bfaeb774e2a wandb_project: Birthday-SN56-4-Gradients-On-Demand wandb_run: your_name wandb_runid: a313e50a-ef92-4948-bd5f-6bfaeb774e2a warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # ea76628e-e0f5-4bc8-8fd1-56508f49914a This model is a fine-tuned version of [unsloth/llama-2-7b-chat](https://huggingface.co/unsloth/llama-2-7b-chat) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 0.0 | 0.0000 | 1 | nan | | 0.0 | 0.0000 | 3 | nan | | 0.0 | 0.0001 | 6 | nan | | 0.0 | 0.0001 | 9 | nan | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

Best000/e3e62084-2cb3-45a1-9c69-68c5813c098a

Best000

peft

--- library_name: peft license: llama3 base_model: scb10x/llama-3-typhoon-v1.5-8b-instruct tags: - axolotl - generated_from_trainer model-index: - name: e3e62084-2cb3-45a1-9c69-68c5813c098a results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: scb10x/llama-3-typhoon-v1.5-8b-instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 40af34e5ab05396e_train_data.json ds_type: json format: custom path: /workspace/input_data/40af34e5ab05396e_train_data.json type: field_input: '' field_instruction: prompt field_output: safe_response format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: Best000/e3e62084-2cb3-45a1-9c69-68c5813c098a hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/40af34e5ab05396e_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 3cfd2269-5633-449d-b7ce-a0694eca12ec wandb_project: Birthday-SN56-15-Gradients-On-Demand wandb_run: your_name wandb_runid: 3cfd2269-5633-449d-b7ce-a0694eca12ec warmup_steps: 5 weight_decay: 0.0 xformers_attention: null ``` </details><br> # e3e62084-2cb3-45a1-9c69-68c5813c098a This model is a fine-tuned version of [scb10x/llama-3-typhoon-v1.5-8b-instruct](https://huggingface.co/scb10x/llama-3-typhoon-v1.5-8b-instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0866 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 112 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | No log | 0.0090 | 1 | 1.6880 | | 1.2215 | 0.2517 | 28 | 1.1953 | | 1.1187 | 0.5034 | 56 | 1.1266 | | 1.0951 | 0.7551 | 84 | 1.0966 | | 1.0709 | 1.0067 | 112 | 1.0866 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

thomasfm/distilbert-base-uncased-finetuned-ner-nlp

thomasfm

transformers

token-classification

--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-ner-nlp results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-ner-nlp This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0812 - Precision: 0.8835 - Recall: 0.9039 - F1: 0.8936 - Accuracy: 0.9804 ## Model description ### Essential info about tagged entities - geo: Geographical Entity - gpe: Geopolitical Entity - tim: Time Indicator ### Label description - Label 0: 'B-geo', - Label 1: 'B-gpe', - Label 2: 'B-tim', - Label 3: 'I-geo', - Label 4: 'I-gpe', - Label 5: 'I-tim', - Label 6: 'O' ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0384 | 1.0 | 1781 | 0.0671 | 0.8770 | 0.9038 | 0.8902 | 0.9799 | | 0.0295 | 2.0 | 3562 | 0.0723 | 0.8844 | 0.8989 | 0.8915 | 0.9804 | | 0.023 | 3.0 | 5343 | 0.0731 | 0.8787 | 0.9036 | 0.8910 | 0.9800 | | 0.0186 | 4.0 | 7124 | 0.0812 | 0.8835 | 0.9039 | 0.8936 | 0.9804 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.0 - Tokenizers 0.13.2

apwic/nerugm-lora-r2a0d0.05

apwic

--- language: - id license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: nerugm-lora-r2a0d0.05 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # nerugm-lora-r2a0d0.05 This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1346 - Precision: 0.7366 - Recall: 0.8629 - F1: 0.7948 - Accuracy: 0.9555 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.7885 | 1.0 | 528 | 0.4616 | 0.3182 | 0.0813 | 0.1296 | 0.8599 | | 0.3921 | 2.0 | 1056 | 0.2524 | 0.6053 | 0.6798 | 0.6404 | 0.9273 | | 0.2392 | 3.0 | 1584 | 0.1932 | 0.6500 | 0.7844 | 0.7109 | 0.9382 | | 0.1931 | 4.0 | 2112 | 0.1676 | 0.6905 | 0.8234 | 0.7511 | 0.9444 | | 0.1719 | 5.0 | 2640 | 0.1583 | 0.7056 | 0.8396 | 0.7668 | 0.9478 | | 0.1602 | 6.0 | 3168 | 0.1539 | 0.7115 | 0.8582 | 0.7780 | 0.9502 | | 0.1533 | 7.0 | 3696 | 0.1520 | 0.7031 | 0.8629 | 0.7748 | 0.9506 | | 0.1455 | 8.0 | 4224 | 0.1456 | 0.7263 | 0.8559 | 0.7858 | 0.9525 | | 0.1398 | 9.0 | 4752 | 0.1425 | 0.7301 | 0.8536 | 0.7870 | 0.9537 | | 0.1368 | 10.0 | 5280 | 0.1395 | 0.7229 | 0.8536 | 0.7828 | 0.9533 | | 0.1331 | 11.0 | 5808 | 0.1365 | 0.7360 | 0.8536 | 0.7904 | 0.9551 | | 0.1305 | 12.0 | 6336 | 0.1377 | 0.7332 | 0.8605 | 0.7918 | 0.9549 | | 0.1279 | 13.0 | 6864 | 0.1357 | 0.7415 | 0.8582 | 0.7956 | 0.9565 | | 0.1251 | 14.0 | 7392 | 0.1355 | 0.7371 | 0.8652 | 0.7960 | 0.9555 | | 0.1239 | 15.0 | 7920 | 0.1359 | 0.7366 | 0.8629 | 0.7948 | 0.9549 | | 0.1231 | 16.0 | 8448 | 0.1347 | 0.7351 | 0.8629 | 0.7939 | 0.9551 | | 0.122 | 17.0 | 8976 | 0.1353 | 0.7351 | 0.8629 | 0.7939 | 0.9555 | | 0.1205 | 18.0 | 9504 | 0.1356 | 0.7317 | 0.8605 | 0.7909 | 0.9549 | | 0.1202 | 19.0 | 10032 | 0.1347 | 0.7351 | 0.8629 | 0.7939 | 0.9551 | | 0.1204 | 20.0 | 10560 | 0.1346 | 0.7366 | 0.8629 | 0.7948 | 0.9555 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.15.2

Croolch/ppo-LunarLander-v2

Croolch

stable-baselines3

reinforcement-learning

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 279.01 +/- 14.97 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

dshin/flan-t5-ppo-user-h-batch-size-8-epoch-2

dshin

transformers

reinforcement-learning

--- license: apache-2.0 tags: - trl - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="dshin//tmp/tmpb3bkfvgo/dshin/flan-t5-ppo-user-h-batch-size-8-epoch-2") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("dshin//tmp/tmpb3bkfvgo/dshin/flan-t5-ppo-user-h-batch-size-8-epoch-2") model = AutoModelForCausalLMWithValueHead.from_pretrained("dshin//tmp/tmpb3bkfvgo/dshin/flan-t5-ppo-user-h-batch-size-8-epoch-2") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```

tensorblock/Gemmalpaca-2B-GGUF

tensorblock

transformers

--- license: other library_name: transformers datasets: - vicgalle/alpaca-gpt4 extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged-in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license license_name: gemma-terms-of-use license_link: https://ai.google.dev/gemma/terms base_model: mlabonne/Gemmalpaca-2B tags: - TensorBlock - GGUF model-index: - name: Gemmalpaca-2B results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 48.72 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Gemmalpaca-2B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 71.36 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Gemmalpaca-2B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 36.3 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Gemmalpaca-2B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 41.24 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Gemmalpaca-2B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 65.59 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Gemmalpaca-2B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 10.69 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Gemmalpaca-2B name: Open LLM Leaderboard --- <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"> Feedback and support: TensorBlock's <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a> </p> </div> </div> ## mlabonne/Gemmalpaca-2B - GGUF This repo contains GGUF format model files for [mlabonne/Gemmalpaca-2B](https://huggingface.co/mlabonne/Gemmalpaca-2B). The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4242](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d). <div style="text-align: left; margin: 20px 0;"> <a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;"> Run them on the TensorBlock client using your local machine ↗ </a> </div> ## Prompt template ``` ``` ## Model file specification | Filename | Quant type | File Size | Description | | -------- | ---------- | --------- | ----------- | | [Gemmalpaca-2B-Q2_K.gguf](https://huggingface.co/tensorblock/Gemmalpaca-2B-GGUF/blob/main/Gemmalpaca-2B-Q2_K.gguf) | Q2_K | 1.158 GB | smallest, significant quality loss - not recommended for most purposes | | [Gemmalpaca-2B-Q3_K_S.gguf](https://huggingface.co/tensorblock/Gemmalpaca-2B-GGUF/blob/main/Gemmalpaca-2B-Q3_K_S.gguf) | Q3_K_S | 1.288 GB | very small, high quality loss | | [Gemmalpaca-2B-Q3_K_M.gguf](https://huggingface.co/tensorblock/Gemmalpaca-2B-GGUF/blob/main/Gemmalpaca-2B-Q3_K_M.gguf) | Q3_K_M | 1.384 GB | very small, high quality loss | | [Gemmalpaca-2B-Q3_K_L.gguf](https://huggingface.co/tensorblock/Gemmalpaca-2B-GGUF/blob/main/Gemmalpaca-2B-Q3_K_L.gguf) | Q3_K_L | 1.466 GB | small, substantial quality loss | | [Gemmalpaca-2B-Q4_0.gguf](https://huggingface.co/tensorblock/Gemmalpaca-2B-GGUF/blob/main/Gemmalpaca-2B-Q4_0.gguf) | Q4_0 | 1.551 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [Gemmalpaca-2B-Q4_K_S.gguf](https://huggingface.co/tensorblock/Gemmalpaca-2B-GGUF/blob/main/Gemmalpaca-2B-Q4_K_S.gguf) | Q4_K_S | 1.560 GB | small, greater quality loss | | [Gemmalpaca-2B-Q4_K_M.gguf](https://huggingface.co/tensorblock/Gemmalpaca-2B-GGUF/blob/main/Gemmalpaca-2B-Q4_K_M.gguf) | Q4_K_M | 1.630 GB | medium, balanced quality - recommended | | [Gemmalpaca-2B-Q5_0.gguf](https://huggingface.co/tensorblock/Gemmalpaca-2B-GGUF/blob/main/Gemmalpaca-2B-Q5_0.gguf) | Q5_0 | 1.799 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [Gemmalpaca-2B-Q5_K_S.gguf](https://huggingface.co/tensorblock/Gemmalpaca-2B-GGUF/blob/main/Gemmalpaca-2B-Q5_K_S.gguf) | Q5_K_S | 1.799 GB | large, low quality loss - recommended | | [Gemmalpaca-2B-Q5_K_M.gguf](https://huggingface.co/tensorblock/Gemmalpaca-2B-GGUF/blob/main/Gemmalpaca-2B-Q5_K_M.gguf) | Q5_K_M | 1.840 GB | large, very low quality loss - recommended | | [Gemmalpaca-2B-Q6_K.gguf](https://huggingface.co/tensorblock/Gemmalpaca-2B-GGUF/blob/main/Gemmalpaca-2B-Q6_K.gguf) | Q6_K | 2.062 GB | very large, extremely low quality loss | | [Gemmalpaca-2B-Q8_0.gguf](https://huggingface.co/tensorblock/Gemmalpaca-2B-GGUF/blob/main/Gemmalpaca-2B-Q8_0.gguf) | Q8_0 | 2.669 GB | very large, extremely low quality loss - not recommended | ## Downloading instruction ### Command line Firstly, install Huggingface Client ```shell pip install -U "huggingface_hub[cli]" ``` Then, downoad the individual model file the a local directory ```shell huggingface-cli download tensorblock/Gemmalpaca-2B-GGUF --include "Gemmalpaca-2B-Q2_K.gguf" --local-dir MY_LOCAL_DIR ``` If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try: ```shell huggingface-cli download tensorblock/Gemmalpaca-2B-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf' ```

maghrane/speecht5_finetuned_marar

maghrane

transformers

text-to-audio

--- library_name: transformers license: mit base_model: microsoft/speecht5_tts tags: - generated_from_trainer model-index: - name: speecht5_finetuned_marar results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # speecht5_finetuned_marar This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5149 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-------:|:----:|:---------------:| | 0.7185 | 3.1873 | 100 | 0.6770 | | 0.5988 | 6.3745 | 200 | 0.5643 | | 0.5588 | 9.5618 | 300 | 0.5402 | | 0.5412 | 12.7490 | 400 | 0.5194 | | 0.5154 | 15.9363 | 500 | 0.5149 | ### Framework versions - Transformers 4.46.2 - Pytorch 2.5.1+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3

Haengbok/my-pet-cat

Haengbok

diffusers

text-to-image

--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### My-Pet-cat Dreambooth model trained by Haengbok following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: 220349106095 Sample pictures of this concept: .jpg)

Nextcloud-AI/opus-mt-sv-en

Nextcloud-AI

transformers

translation

--- pipeline_tag: translation license: apache-2.0 --- ### opus-mt-sv-en * source languages: sv * target languages: en * OPUS readme: [sv-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-02-26.zip](https://object.pouta.csc.fi/OPUS-MT-models/sv-en/opus-2020-02-26.zip) * test set translations: [opus-2020-02-26.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-en/opus-2020-02-26.test.txt) * test set scores: [opus-2020-02-26.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-en/opus-2020-02-26.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba.sv.en | 64.5 | 0.763 |

crisU8/positive_model

crisU8

transformers

text-classification

--- license: cc-by-4.0 base_model: dccuchile/tulio-chilean-spanish-bert tags: - generated_from_keras_callback model-index: - name: crisU8/positive_model results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # crisU8/positive_model This model is a fine-tuned version of [dccuchile/tulio-chilean-spanish-bert](https://huggingface.co/dccuchile/tulio-chilean-spanish-bert) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0606 - Validation Loss: 0.5070 - Epoch: 4 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 4070, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.6730 | 0.4127 | 0 | | 0.3405 | 0.3897 | 1 | | 0.1979 | 0.3586 | 2 | | 0.1007 | 0.4473 | 3 | | 0.0606 | 0.5070 | 4 | ### Framework versions - Transformers 4.35.2 - TensorFlow 2.15.0 - Datasets 2.16.1 - Tokenizers 0.15.0

hdparmar/tradfusion-v2

hdparmar

diffusers

text-to-image

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - text-to-image - diffusion-models-class --- # Example Fine-Tuned Model for Unit 2 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) Fine-tuned Stable Diffusion Model on Irish Traditional Tunes Spectrograms ## Usage ```python from diffusers import StableDiffusionPipeline pipeline = StableDiffusionPipeline.from_pretrained('hdparmar/tradfusion-v2') image = pipeline().images[0] image ```

BramVanroy/kenlm_sonar

BramVanroy

--- language: - nl tags: - kenlm license: apache-2.0 --- # KenLM (arpa) models for Dutch based on SONAR This repository contains KenLM models (n=5) for Dutch, based on the SONAR corpus - sentence-segmented (one sentence per line). Models are provided on tokens, part-of-speech, dependency labels, and lemmas, as processed with spaCy `nl_core_news_sm`: - kenlm_sonar_token.arpa[.bin]: token - kenlm_sonar_pos.arpa[.bin]: part-of-speech tag - kenlm_sonar_dep.arpa[.bin]: dependency label - kenlm_sonar_lemma.arpa[.bin]: lemma More noisy SONAR components (WRPEA, WRPED, WRUEA, WRUED, WRUEB) were excluded. Both regular `.arpa` files as well as more efficient KenLM binary files (`.arpa.bin`) are provided. You probably want to use the binary versions. ## Usage from within Python Make sure to install dependencies: ```shell pip install huggingface_hub pip install https://github.com/kpu/kenlm/archive/master.zip # If you want to use spaCy preprocessing pip install spacy python -m spacy download nl_core_news_sm ``` We can then use the Hugging Face hub software to download and cache the model file that we want, and directly use it with KenLM. ```python import kenlm from huggingface_hub import hf_hub_download model_file = hf_hub_download(repo_id="BramVanroy/kenlm_sonar", filename="kenlm_sonar_token.arpa.bin") model = kenlm.Model(model_file) text = "Ik eet graag koekjes !" # pre-tokenized model.perplexity(text) # 148.21996373689134 ``` It is recommended to use spaCy as a preprocessor to automatically use the same tagsets and tokenization as were used when creating the LMs. ```python import kenlm import spacy from huggingface_hub import hf_hub_download model_file = hf_hub_download(repo_id="BramVanroy/kenlm_sonar", filename="kenlm_sonar_pos.arpa.bin") # pos file model = kenlm.Model(model_file) nlp = spacy.load("nl_core_news_sm") text = "Ik eet graag koekjes!" pos_sequence = " ".join([token.pos_ for token in nlp(text)]) # 'PRON VERB ADV NOUN PUNCT' model.perplexity(pos_sequence) # 6.916279238079976 ``` ## Reproduction ```sh bin/lmplz -o 5 -S 75% -T ../data/tmp/ < ../data/processed_sonar_token_dedup.txt > ../data/kenlm_sonar_token.arpa ``` For class-based LMs (POS and DEP), the `--discount_fallback` was used and the parsed data was not deduplicated (but it was deduplicated on the sentence-level for token and lemma models).

touchtech/fashion-images-perspectives-vit-large-patch16-224-in21k

touchtech

transformers

image-classification

--- license: apache-2.0 base_model: google/vit-large-patch16-224-in21k tags: - image-classification - vision - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: fashion-images-perspectives-vit-large-patch16-224-in21k results: - task: name: Image Classification type: image-classification dataset: name: touchtech/fashion-images-perspectives type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9315323707498836 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # fashion-images-perspectives-vit-large-patch16-224-in21k This model is a fine-tuned version of [google/vit-large-patch16-224-in21k](https://huggingface.co/google/vit-large-patch16-224-in21k) on the touchtech/fashion-images-perspectives dataset. It achieves the following results on the evaluation set: - Loss: 0.2543 - Accuracy: 0.9315 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.4164 | 1.0 | 3042 | 0.2868 | 0.9024 | | 0.3391 | 2.0 | 6084 | 0.3055 | 0.9041 | | 0.2836 | 3.0 | 9126 | 0.3071 | 0.9180 | | 0.2292 | 4.0 | 12168 | 0.2543 | 0.9315 | | 0.1842 | 5.0 | 15210 | 0.2562 | 0.9362 | ### Framework versions - Transformers 4.33.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

qgallouedec/tqc-Swimmer-v3-999770256

qgallouedec

stable-baselines3

reinforcement-learning

--- library_name: stable-baselines3 tags: - Swimmer-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 - Swimmer-v4 model-index: - name: TQC results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Swimmer-v3 type: Swimmer-v3 metrics: - type: mean_reward value: 46.63 +/- 5.64 name: mean_reward verified: false --- # **TQC** Agent playing **Swimmer-v3** This is a trained model of a **TQC** agent playing **Swimmer-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo tqc --env Swimmer-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env Swimmer-v3 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo tqc --env Swimmer-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env Swimmer-v3 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo tqc --env Swimmer-v3 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo tqc --env Swimmer-v3 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('gamma', 0.9999), ('learning_starts', 10000), ('n_timesteps', 1000000.0), ('policy', 'MlpPolicy'), ('normalize', False)]) ```

TheRains/yt-special-batch8-base

TheRains

transformers

automatic-speech-recognition

--- license: apache-2.0 base_model: openai/whisper-base tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_9_0 metrics: - wer model-index: - name: yt-special-batch8-base results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: mozilla-foundation/common_voice_9_0 id type: mozilla-foundation/common_voice_9_0 config: id split: train args: id metrics: - name: Wer type: wer value: 11.4438961596224 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # yt-special-batch8-base This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the mozilla-foundation/common_voice_9_0 id dataset. It achieves the following results on the evaluation set: - Loss: 0.4155 - Wer: 11.4439 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 41.113 | 1.58 | 1000 | 42.9759 | 107.5628 | | 17.3442 | 3.17 | 2000 | 18.7037 | 144.1064 | | 10.8061 | 4.75 | 3000 | 7.1531 | 52.5510 | | 3.3269 | 6.34 | 4000 | 3.1035 | 47.0586 | | 0.7405 | 7.92 | 5000 | 0.4155 | 11.4439 | ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

mradermacher/deepseek-moe-16b-base-GGUF

mradermacher

transformers

--- base_model: deepseek-ai/deepseek-moe-16b-base language: - en library_name: transformers license: other license_link: https://github.com/deepseek-ai/DeepSeek-MoE/blob/main/LICENSE-MODEL license_name: deepseek quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/deepseek-ai/deepseek-moe-16b-base <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/deepseek-moe-16b-base-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/deepseek-moe-16b-base-GGUF/resolve/main/deepseek-moe-16b-base.Q2_K.gguf) | Q2_K | 6.8 | | | [GGUF](https://huggingface.co/mradermacher/deepseek-moe-16b-base-GGUF/resolve/main/deepseek-moe-16b-base.Q3_K_S.gguf) | Q3_K_S | 7.9 | | | [GGUF](https://huggingface.co/mradermacher/deepseek-moe-16b-base-GGUF/resolve/main/deepseek-moe-16b-base.Q3_K_M.gguf) | Q3_K_M | 8.6 | lower quality | | [GGUF](https://huggingface.co/mradermacher/deepseek-moe-16b-base-GGUF/resolve/main/deepseek-moe-16b-base.Q3_K_L.gguf) | Q3_K_L | 8.9 | | | [GGUF](https://huggingface.co/mradermacher/deepseek-moe-16b-base-GGUF/resolve/main/deepseek-moe-16b-base.IQ4_XS.gguf) | IQ4_XS | 9.1 | | | [GGUF](https://huggingface.co/mradermacher/deepseek-moe-16b-base-GGUF/resolve/main/deepseek-moe-16b-base.Q4_K_S.gguf) | Q4_K_S | 10.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/deepseek-moe-16b-base-GGUF/resolve/main/deepseek-moe-16b-base.Q4_K_M.gguf) | Q4_K_M | 11.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/deepseek-moe-16b-base-GGUF/resolve/main/deepseek-moe-16b-base.Q5_K_S.gguf) | Q5_K_S | 11.7 | | | [GGUF](https://huggingface.co/mradermacher/deepseek-moe-16b-base-GGUF/resolve/main/deepseek-moe-16b-base.Q5_K_M.gguf) | Q5_K_M | 12.5 | | | [GGUF](https://huggingface.co/mradermacher/deepseek-moe-16b-base-GGUF/resolve/main/deepseek-moe-16b-base.Q6_K.gguf) | Q6_K | 14.8 | very good quality | | [GGUF](https://huggingface.co/mradermacher/deepseek-moe-16b-base-GGUF/resolve/main/deepseek-moe-16b-base.Q8_0.gguf) | Q8_0 | 17.5 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->

austinmw/ppo-LunarLander-v2

austinmw

stable-baselines3

reinforcement-learning

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 251.83 +/- 22.51 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

colaibu/Game-Backup

colaibu

--- license: other license_name: smokeapi license_link: LICENSE ---

Triangle104/Llama-3.1-8B-Lexi-Uncensored-V2-Q5_K_M-GGUF

Triangle104

transformers

--- base_model: Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2 library_name: transformers license: llama3.1 tags: - llama-cpp - gguf-my-repo model-index: - name: Llama-3.1-8B-Lexi-Uncensored-V2 results: - task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: HuggingFaceH4/ifeval args: num_few_shot: 0 metrics: - type: inst_level_strict_acc and prompt_level_strict_acc value: 77.92 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: BBH args: num_few_shot: 3 metrics: - type: acc_norm value: 29.69 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: hendrycks/competition_math args: num_few_shot: 4 metrics: - type: exact_match value: 16.92 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa args: num_few_shot: 0 metrics: - type: acc_norm value: 4.36 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics: - type: acc_norm value: 7.77 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 30.9 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2 name: Open LLM Leaderboard --- # Triangle104/Llama-3.1-8B-Lexi-Uncensored-V2-Q5_K_M-GGUF This model was converted to GGUF format from [`Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2`](https://huggingface.co/Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2) for more details on the model. --- Model details: - VERSION 2 Update Notes: More compliant Smarter For best response, use this system prompt (feel free to expand upon it as you wish): Think step by step with a logical reasoning and intellectual sense before you provide any response. For more uncensored and compliant response, you can expand the system message differently, or simply enter a dot "." as system message. IMPORTANT: Upon further investigation, the Q4 seems to have refusal issues sometimes. There seems to be some of the fine-tune loss happening due to the quantization. I will look into it for V3. Until then, I suggest you run F16 or Q8 if possible. image/png GENERAL INFO: This model is based on Llama-3.1-8b-Instruct, and is governed by META LLAMA 3.1 COMMUNITY LICENSE AGREEMENT Lexi is uncensored, which makes the model compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. You are responsible for any content you create using this model. Please use it responsibly. Lexi is licensed according to Meta's Llama license. I grant permission for any use, including commercial, that falls within accordance with Meta's Llama-3.1 license. IMPORTANT: Use the same template as the official Llama 3.1 8B instruct. System tokens must be present during inference, even if you set an empty system message. If you are unsure, just add a short system message as you wish. FEEDBACK: If you find any issues or have suggestions for improvements, feel free to leave a review and I will look into it for upcoming improvements and next version. Open LLM Leaderboard Evaluation Results Detailed results can be found here Metric Value Avg. 27.93 IFEval (0-Shot) 77.92 BBH (3-Shot) 29.69 MATH Lvl 5 (4-Shot) 16.92 GPQA (0-shot) 4.36 MuSR (0-shot) 7.77 MMLU-PRO (5-shot) 30.90 --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/Llama-3.1-8B-Lexi-Uncensored-V2-Q5_K_M-GGUF --hf-file llama-3.1-8b-lexi-uncensored-v2-q5_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Llama-3.1-8B-Lexi-Uncensored-V2-Q5_K_M-GGUF --hf-file llama-3.1-8b-lexi-uncensored-v2-q5_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/Llama-3.1-8B-Lexi-Uncensored-V2-Q5_K_M-GGUF --hf-file llama-3.1-8b-lexi-uncensored-v2-q5_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Llama-3.1-8B-Lexi-Uncensored-V2-Q5_K_M-GGUF --hf-file llama-3.1-8b-lexi-uncensored-v2-q5_k_m.gguf -c 2048 ```

saideep-arikontham/twitter-roberta-base-sentiment-latest-trump-stance-1

saideep-arikontham

peft

--- library_name: peft tags: - generated_from_trainer base_model: cardiffnlp/twitter-roberta-base-sentiment-latest metrics: - accuracy - precision - recall model-index: - name: twitter-roberta-base-sentiment-latest-trump-stance-1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # twitter-roberta-base-sentiment-latest-trump-stance-1 This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1168 - Accuracy: {'accuracy': 0.6666666666666666} - Precision: {'precision': 0.5697940503432495} - Recall: {'recall': 0.7302052785923754} - F1 Score: {'f1': 0.6401028277634961} ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score | |:-------------:|:-----:|:------:|:---------------:|:----------------------:|:---------------------------------:|:-------------------:|:--------------------------:| | 0.583 | 1.0 | 3600 | 0.3772 | {'accuracy': 0.83875} | {'precision': 0.812933025404157} | {'recall': 0.88} | {'f1': 0.8451380552220888} | | 0.5621 | 2.0 | 7200 | 0.3725 | {'accuracy': 0.853125} | {'precision': 0.9407176287051482} | {'recall': 0.75375} | {'f1': 0.8369188063844553} | | 0.5813 | 3.0 | 10800 | 1.0373 | {'accuracy': 0.625625} | {'precision': 0.5719398711524696} | {'recall': 0.99875} | {'f1': 0.7273554847519345} | | 0.5317 | 4.0 | 14400 | 0.3697 | {'accuracy': 0.875625} | {'precision': 0.8917861799217731} | {'recall': 0.855} | {'f1': 0.8730057434588385} | | 0.5498 | 5.0 | 18000 | 0.4457 | {'accuracy': 0.8525} | {'precision': 0.8551637279596978} | {'recall': 0.84875} | {'f1': 0.8519447929736512} | | 0.5388 | 6.0 | 21600 | 0.4715 | {'accuracy': 0.829375} | {'precision': 0.9136577708006279} | {'recall': 0.7275} | {'f1': 0.8100208768267223} | | 0.5885 | 7.0 | 25200 | 0.3773 | {'accuracy': 0.85875} | {'precision': 0.8836898395721925} | {'recall': 0.82625} | {'f1': 0.8540051679586563} | | 0.4961 | 8.0 | 28800 | 0.3819 | {'accuracy': 0.869375} | {'precision': 0.9053497942386831} | {'recall': 0.825} | {'f1': 0.8633093525179856} | | 0.5421 | 9.0 | 32400 | 0.4011 | {'accuracy': 0.85875} | {'precision': 0.8239277652370203} | {'recall': 0.9125} | {'f1': 0.8659549228944247} | | 0.5123 | 10.0 | 36000 | 0.3404 | {'accuracy': 0.88125} | {'precision': 0.9034391534391535} | {'recall': 0.85375} | {'f1': 0.877892030848329} | | 0.5996 | 11.0 | 39600 | 0.3435 | {'accuracy': 0.880625} | {'precision': 0.8801498127340824} | {'recall': 0.88125} | {'f1': 0.8806995627732667} | | 0.4871 | 12.0 | 43200 | 0.2972 | {'accuracy': 0.890625} | {'precision': 0.9021879021879022} | {'recall': 0.87625} | {'f1': 0.8890298034242232} | | 0.5272 | 13.0 | 46800 | 0.3629 | {'accuracy': 0.874375} | {'precision': 0.9423929098966026} | {'recall': 0.7975} | {'f1': 0.8639133378469871} | | 0.5897 | 14.0 | 50400 | 0.3164 | {'accuracy': 0.88} | {'precision': 0.9075067024128687} | {'recall': 0.84625} | {'f1': 0.8758085381630013} | | 0.4963 | 15.0 | 54000 | 0.3343 | {'accuracy': 0.87625} | {'precision': 0.922752808988764} | {'recall': 0.82125} | {'f1': 0.8690476190476191} | | 0.5132 | 16.0 | 57600 | 0.5593 | {'accuracy': 0.855625} | {'precision': 0.9330289193302892} | {'recall': 0.76625} | {'f1': 0.8414550446122169} | | 0.447 | 17.0 | 61200 | 0.3651 | {'accuracy': 0.874375} | {'precision': 0.8544378698224852} | {'recall': 0.9025} | {'f1': 0.8778115501519757} | | 0.5189 | 18.0 | 64800 | 0.3919 | {'accuracy': 0.878125} | {'precision': 0.9315263908701854} | {'recall': 0.81625} | {'f1': 0.8700866089273818} | | 0.4835 | 19.0 | 68400 | 0.5706 | {'accuracy': 0.846875} | {'precision': 0.9541734860883797} | {'recall': 0.72875} | {'f1': 0.8263642806520198} | | 0.455 | 20.0 | 72000 | 0.3523 | {'accuracy': 0.881875} | {'precision': 0.8813982521847691} | {'recall': 0.8825} | {'f1': 0.8819487820112429} | | 0.4791 | 21.0 | 75600 | 0.3292 | {'accuracy': 0.884375} | {'precision': 0.8546712802768166} | {'recall': 0.92625} | {'f1': 0.8890221955608878} | | 0.512 | 22.0 | 79200 | 0.4456 | {'accuracy': 0.87} | {'precision': 0.9391691394658753} | {'recall': 0.79125} | {'f1': 0.858887381275441} | | 0.4783 | 23.0 | 82800 | 0.3283 | {'accuracy': 0.880625} | {'precision': 0.9188445667125172} | {'recall': 0.835} | {'f1': 0.8749181401440733} | | 0.4699 | 24.0 | 86400 | 0.3399 | {'accuracy': 0.885} | {'precision': 0.9074074074074074} | {'recall': 0.8575} | {'f1': 0.8817480719794345} | | 0.4485 | 25.0 | 90000 | 0.3156 | {'accuracy': 0.89} | {'precision': 0.8949367088607595} | {'recall': 0.88375} | {'f1': 0.889308176100629} | | 0.4305 | 26.0 | 93600 | 0.3105 | {'accuracy': 0.894375} | {'precision': 0.9092088197146563} | {'recall': 0.87625} | {'f1': 0.8924252068746021} | | 0.4704 | 27.0 | 97200 | 0.3528 | {'accuracy': 0.879375} | {'precision': 0.8634730538922155} | {'recall': 0.90125} | {'f1': 0.8819571865443425} | | 0.4589 | 28.0 | 100800 | 0.3534 | {'accuracy': 0.879375} | {'precision': 0.8696711327649208} | {'recall': 0.8925} | {'f1': 0.8809376927822332} | | 0.4831 | 29.0 | 104400 | 0.3315 | {'accuracy': 0.891875} | {'precision': 0.9108781127129751} | {'recall': 0.86875} | {'f1': 0.889315419065899} | | 0.4931 | 30.0 | 108000 | 0.3200 | {'accuracy': 0.891875} | {'precision': 0.9185580774365821} | {'recall': 0.86} | {'f1': 0.8883150419625565} | | 0.4286 | 31.0 | 111600 | 0.3488 | {'accuracy': 0.8825} | {'precision': 0.9180327868852459} | {'recall': 0.84} | {'f1': 0.8772845953002611} | | 0.4309 | 32.0 | 115200 | 0.3192 | {'accuracy': 0.891875} | {'precision': 0.8875154511742892} | {'recall': 0.8975} | {'f1': 0.8924798011187073} | | 0.3896 | 33.0 | 118800 | 0.3294 | {'accuracy': 0.881875} | {'precision': 0.8632580261593341} | {'recall': 0.9075} | {'f1': 0.8848263254113345} | | 0.4327 | 34.0 | 122400 | 0.3003 | {'accuracy': 0.899375} | {'precision': 0.9346938775510204} | {'recall': 0.85875} | {'f1': 0.895114006514658} | | 0.4179 | 35.0 | 126000 | 0.3189 | {'accuracy': 0.898125} | {'precision': 0.9368998628257887} | {'recall': 0.85375} | {'f1': 0.8933943754087639} | | 0.4023 | 36.0 | 129600 | 0.3284 | {'accuracy': 0.8775} | {'precision': 0.8408577878103838} | {'recall': 0.93125} | {'f1': 0.8837485172004745} | | 0.4285 | 37.0 | 133200 | 0.3221 | {'accuracy': 0.894375} | {'precision': 0.9280868385345997} | {'recall': 0.855} | {'f1': 0.8900455432661027} | | 0.3988 | 38.0 | 136800 | 0.2861 | {'accuracy': 0.896875} | {'precision': 0.8905289052890529} | {'recall': 0.905} | {'f1': 0.8977061376317421} | | 0.4034 | 39.0 | 140400 | 0.3501 | {'accuracy': 0.895625} | {'precision': 0.9438990182328191} | {'recall': 0.84125} | {'f1': 0.8896232650363516} | | 0.3743 | 40.0 | 144000 | 0.3654 | {'accuracy': 0.886875} | {'precision': 0.9176788124156545} | {'recall': 0.85} | {'f1': 0.8825438027255029} | | 0.3979 | 41.0 | 147600 | 0.3230 | {'accuracy': 0.899375} | {'precision': 0.9311740890688259} | {'recall': 0.8625} | {'f1': 0.8955223880597015} | | 0.3808 | 42.0 | 151200 | 0.2978 | {'accuracy': 0.90375} | {'precision': 0.9205729166666666} | {'recall': 0.88375} | {'f1': 0.9017857142857143} | | 0.3777 | 43.0 | 154800 | 0.2805 | {'accuracy': 0.899375} | {'precision': 0.9220607661822986} | {'recall': 0.8725} | {'f1': 0.8965960179833012} | | 0.3631 | 44.0 | 158400 | 0.2984 | {'accuracy': 0.898125} | {'precision': 0.9163398692810457} | {'recall': 0.87625} | {'f1': 0.8958466453674121} | | 0.3674 | 45.0 | 162000 | 0.2924 | {'accuracy': 0.90375} | {'precision': 0.9376693766937669} | {'recall': 0.865} | {'f1': 0.8998699609882965} | | 0.3539 | 46.0 | 165600 | 0.3158 | {'accuracy': 0.89375} | {'precision': 0.899746192893401} | {'recall': 0.88625} | {'f1': 0.8929471032745592} | | 0.3557 | 47.0 | 169200 | 0.2861 | {'accuracy': 0.9} | {'precision': 0.9145077720207254} | {'recall': 0.8825} | {'f1': 0.8982188295165394} | | 0.38 | 48.0 | 172800 | 0.2962 | {'accuracy': 0.894375} | {'precision': 0.9029374201787995} | {'recall': 0.88375} | {'f1': 0.8932406822488945} | | 0.3754 | 49.0 | 176400 | 0.2905 | {'accuracy': 0.9} | {'precision': 0.9166666666666666} | {'recall': 0.88} | {'f1': 0.8979591836734694} | | 0.3717 | 50.0 | 180000 | 0.2880 | {'accuracy': 0.89875} | {'precision': 0.9153645833333334} | {'recall': 0.87875} | {'f1': 0.8966836734693877} | ### Framework versions - PEFT 0.10.0 - Transformers 4.38.2 - Pytorch 2.2.1 - Datasets 2.18.0 - Tokenizers 0.15.2

dineshresearch/Taxi_q_v3_v1

dineshresearch

reinforcement-learning

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi_q_v3_v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="dineshresearch/Taxi_q_v3_v1", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

atsuki-yamaguchi/Llama-2-7b-hf-el-30K-align-2x2ls

atsuki-yamaguchi

transformers

text-generation

--- license: llama2 language: - el base_model: meta-llama/Llama-2-7b-hf library_name: transformers --- # Llama2 7B for Greek: 100 target vocabulary size + Align target vocabulary initialization + 2x2LS training This model is built on top of Llama2 7B adapted for Greek using 30K target language sentences sampled from CC-100. ## Model Details * **Vocabulary**: This model has an additional 100 target vocabulary. * **Target vocabulary initialization**: The target weights of the embedding and LM head were initialized using Align initialization. * **Training**: This model was additionally pre-trained on 30K target language sentences sampled from CC-100. The training was conducted with the 2x2LS strategies introduced in the paper. ## Model Description - **Language:** Greek - **License:** Llama 2 Community License Agreement - **Fine-tuned from model:** meta-llama/Llama-2-7b-hf ## Model Sources - **Repository:** https://github.com/gucci-j/lowres-cve - **Paper:** https://arxiv.org/abs/2406.11477 ## How to Get Started with the Model Use the code below to get started with the model. ```python from transformers import AutoTokenizer, AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained( "atsuki-yamaguchi/Llama-2-7b-hf-el-30K-align-2x2ls" ) tokenizer = AutoTokenizer.from_pretrained( "atsuki-yamaguchi/Llama-2-7b-hf-el-30K-align-2x2ls" ) ``` ## Citation ``` @article{yamaguchi-etal-2024-effectively, title={How Can We Effectively Expand the Vocabulary of LLMs with 0.01GB of Target Language Text?}, author={Atsuki Yamaguchi and Aline Villavicencio and Nikolaos Aletras}, year={2024}, journal={ArXiv}, year={2024}, volume={abs/2406.11477}, url={https://arxiv.org/abs/2406.11477}, } ```

Yntec/Fanatic

Yntec

diffusers

text-to-image

--- license: creativeml-openrail-m library_name: diffusers pipeline_tag: text-to-image tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers --- # Fanatic Samples and prompts:  Top left: pretty lady with tall guy together standing, cute eyes, photoreal portrait, is on top of he Closeup a of rocks on pile top of a ocean moon to the magazine. Top right: Painting, high detail, Cartoon Pretty CUTE little Girl riding a wave under clouds inside of a large jar on a table, fairy clothes, DETAILED CHIBI EYES, beautiful detailed pajamas, gorgeous detailed hair, Magazine ad, iconic, 1941, sharp focus. visible brushstrokes ​By ROSSDRAWS and artgerm and Clay Mann and leyendecker and Dave Bottom left: absurdres, adorable cute harley quinn, at night, dark alley, moon, :) red ponytail, blonde ponytail, in matte black hardsuit, military, roughed up, bat, city fog, Bottom right: pin up cute young girl. attractive dancing embodying a disco-inspired aesthetic. youthful short appearance, untidy hair. attire comprises of loose-fitting pants, a t-shirt, cropped hoodie. cap, vibrant and nostalgic vibes of groovy retro 70s style, illuminated by the radiant disco balls and neon lights of the dance floor. glimpse midriff DucHaitenFANCYxFANCY merged with the Hellmix model by Barons, Kitsch-In-Sync v2 by iamxenos, the cryptids lora by RIXYN, and artistic models with the CokeGirls lora by iamxenos. Original pages: https://civitai.com/models/101354/duchaiten-fancyxfancy https://civitai.com/models/186251/coca-cola-gil-elvgrenhaddon-sundblom-pinup-style https://civitai.com/models/142552?modelVersionId=163068 (Kitsch-In-Sync v2) https://civitai.com/models/21493/hellmix?modelVersionId=25632 https://civitai.com/models/64766/cryptids?modelVersionId=69407 (Cryptids LoRA)

qminh369/token-classification-llmlingua2-xlm-roberta-bctn-2308_chunk_10epoch

qminh369

transformers

token-classification

--- license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer model-index: - name: token-classification-llmlingua2-xlm-roberta-bctn-2308_chunk_10epoch results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # token-classification-llmlingua2-xlm-roberta-bctn-2308_chunk_10epoch This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1480 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 58 | 0.1906 | | No log | 2.0 | 116 | 0.1578 | | No log | 3.0 | 174 | 0.1530 | | No log | 4.0 | 232 | 0.1509 | | No log | 5.0 | 290 | 0.1495 | | No log | 6.0 | 348 | 0.1506 | | No log | 7.0 | 406 | 0.1489 | | No log | 8.0 | 464 | 0.1481 | | 0.1719 | 9.0 | 522 | 0.1480 | | 0.1719 | 10.0 | 580 | 0.1480 | ### Framework versions - Transformers 4.39.0.dev0 - Pytorch 2.2.1+cu118 - Datasets 2.18.0 - Tokenizers 0.15.2

hwang2006/marian-finetuned-kde4-en-to-fr

hwang2006

transformers

text2text-generation

--- license: apache-2.0 base_model: Helsinki-NLP/opus-mt-en-fr tags: - tanslation - generated_from_trainer datasets: - kde4 metrics: - bleu model-index: - name: marian-finetuned-kde4-en-to-fr results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: kde4 type: kde4 config: en-fr split: train args: en-fr metrics: - name: Bleu type: bleu value: 52.948035826652756 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # marian-finetuned-kde4-en-to-fr This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset. It achieves the following results on the evaluation set: - Loss: 0.8556 - Bleu: 52.9480 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.1 - Datasets 2.16.1 - Tokenizers 0.15.0

damgomz/ft_32_7e6_base_x12

damgomz

transformers

text-classification

--- language: en tags: - text-classification pipeline_tag: text-classification widget: - text: GEPS Techno is the pioneer of hybridization of renewable energies at sea. We imagine, design and commercialize innovative off-grid systems that aim to generate power at sea, stabilize and collect data. The success of our low power platforms WAVEPEAL enabled us to scale-up the device up to WAVEGEM, the 150-kW capacity platform. --- ## Environmental Impact (CODE CARBON DEFAULT) | Metric | Value | |--------------------------|---------------------------------| | Duration (in seconds) | [More Information Needed] | | Emissions (Co2eq in kg) | [More Information Needed] | | CPU power (W) | [NO CPU] | | GPU power (W) | [No GPU] | | RAM power (W) | [More Information Needed] | | CPU energy (kWh) | [No CPU] | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | [More Information Needed] | | Consumed energy (kWh) | [More Information Needed] | | Country name | [More Information Needed] | | Cloud provider | [No Cloud] | | Cloud region | [No Cloud] | | CPU count | [No CPU] | | CPU model | [No CPU] | | GPU count | [No GPU] | | GPU model | [No GPU] | ## Environmental Impact (for one core) | Metric | Value | |--------------------------|---------------------------------| | CPU energy (kWh) | [No CPU] | | Emissions (Co2eq in kg) | [More Information Needed] | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | albert-base-v2 | | model_name | ft_32_7e6_base_x12 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 7e-06 | | batch_size | 32 | | weight_decay | 0.0 | | warm_up_prop | 0.0 | | drop_out_prob | 0.1 | | packing_length | 100 | | train_test_split | 0.2 | | num_steps | 29328 | ## Training and Testing steps Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.698241 | 0.343033 | | 1 | 0.388068 | 0.293261 | 0.883665 | | 2 | 0.249783 | 0.291608 | 0.910089 | | 3 | 0.199442 | 0.224127 | 0.914403 | | 4 | 0.175401 | 0.235763 | 0.932895 | | 5 | 0.149710 | 0.254013 | 0.899420 | | 6 | 0.121423 | 0.259248 | 0.916229 |

drferpa/panita-base-model-llama2-7b-v3

drferpa

--- base_model: meta-llama/Llama-2-7b-hf tags: - generated_from_trainer model-index: - name: panita-base-model-llama2-7b-v3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # panita-base-model-llama2-7b-v3 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 32 - total_train_batch_size: 256 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.34.1 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.14.1

mlc-ai/WizardMath-13B-V1.0-q4f16_1-MLC

mlc-ai

mlc-llm

--- library_name: mlc-llm base_model: WizardLMTeam/WizardMath-13B-V1.0 tags: - mlc-llm - web-llm --- # WizardMath-13B-V1.0-q4f16_1-MLC This is the [WizardMath-13B-V1.0](https://huggingface.co/WizardLMTeam/WizardMath-13B-V1.0) model in MLC format `q4f16_1`. The model can be used for projects [MLC-LLM](https://github.com/mlc-ai/mlc-llm) and [WebLLM](https://github.com/mlc-ai/web-llm). ## Example Usage Here are some examples of using this model in MLC LLM. Before running the examples, please install MLC LLM by following the [installation documentation](https://llm.mlc.ai/docs/install/mlc_llm.html#install-mlc-packages). ### Chat In command line, run ```bash mlc_llm chat HF://mlc-ai/WizardMath-13B-V1.0-q4f16_1-MLC ``` ### REST Server In command line, run ```bash mlc_llm serve HF://mlc-ai/WizardMath-13B-V1.0-q4f16_1-MLC ``` ### Python API ```python from mlc_llm import MLCEngine # Create engine model = "HF://mlc-ai/WizardMath-13B-V1.0-q4f16_1-MLC" engine = MLCEngine(model) # Run chat completion in OpenAI API. for response in engine.chat.completions.create( messages=[{"role": "user", "content": "What is the meaning of life?"}], model=model, stream=True, ): for choice in response.choices: print(choice.delta.content, end="", flush=True) print("\n") engine.terminate() ``` ## Documentation For more information on MLC LLM project, please visit our [documentation](https://llm.mlc.ai/docs/) and [GitHub repo](http://github.com/mlc-ai/mlc-llm).

aliakyurek/a2c-PandaReachDense-v2

aliakyurek

stable-baselines3

reinforcement-learning

--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -1.05 +/- 0.23 name: mean_reward verified: false --- # **A2C** Agent playing **PandaReachDense-v2** This is a trained model of a **A2C** agent playing **PandaReachDense-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ``` Panda Gym environments: [arxiv.org/abs/2106.13687](https://arxiv.org/abs/2106.13687)

lesso/37db12d5-be54-4a89-ab94-e30b2eb8e997

lesso

peft

--- library_name: peft base_model: katuni4ka/tiny-random-dbrx tags: - axolotl - generated_from_trainer model-index: - name: 37db12d5-be54-4a89-ab94-e30b2eb8e997 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: katuni4ka/tiny-random-dbrx bf16: true chat_template: llama3 data_processes: 16 dataset_prepared_path: null datasets: - data_files: - 0a30047102b434d7_train_data.json ds_type: json format: custom path: /workspace/input_data/0a30047102b434d7_train_data.json type: field_instruction: query field_output: response format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null do_eval: true early_stopping_patience: 5 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 50 eval_table_size: null evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: true group_by_length: true hub_model_id: lesso/37db12d5-be54-4a89-ab94-e30b2eb8e997 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001003 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 128 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: linear max_grad_norm: 1.0 max_steps: 200 micro_batch_size: 4 mlflow_experiment_name: /tmp/G.O.D/0a30047102b434d7_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optim_args: adam_beta1: 0.9 adam_beta2: 0.95 adam_epsilon: 1e-5 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 50 saves_per_epoch: null sequence_len: 1024 strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 0be2cc61-00d1-4153-9960-910acde865cc wandb_project: new-03 wandb_run: your_name wandb_runid: 0be2cc61-00d1-4153-9960-910acde865cc warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 37db12d5-be54-4a89-ab94-e30b2eb8e997 This model is a fine-tuned version of [katuni4ka/tiny-random-dbrx](https://huggingface.co/katuni4ka/tiny-random-dbrx) on the None dataset. It achieves the following results on the evaluation set: - Loss: 11.5 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001003 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - training_steps: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 23.0 | 0.0000 | 1 | 11.5 | | 23.0 | 0.0018 | 50 | 11.5 | | 23.0 | 0.0036 | 100 | 11.5 | | 23.0 | 0.0055 | 150 | 11.5 | | 23.0 | 0.0073 | 200 | 11.5 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

mradermacher/argonium-abs-7B-4x-GGUF

mradermacher

transformers

--- base_model: Stevens/argonium-abs-7B-4x language: - en library_name: transformers quantized_by: mradermacher tags: [] --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Stevens/argonium-abs-7B-4x <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/argonium-abs-7B-4x-GGUF/resolve/main/argonium-abs-7B-4x.Q2_K.gguf) | Q2_K | 3.3 | | | [GGUF](https://huggingface.co/mradermacher/argonium-abs-7B-4x-GGUF/resolve/main/argonium-abs-7B-4x.Q3_K_S.gguf) | Q3_K_S | 3.8 | | | [GGUF](https://huggingface.co/mradermacher/argonium-abs-7B-4x-GGUF/resolve/main/argonium-abs-7B-4x.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality | | [GGUF](https://huggingface.co/mradermacher/argonium-abs-7B-4x-GGUF/resolve/main/argonium-abs-7B-4x.Q3_K_L.gguf) | Q3_K_L | 4.4 | | | [GGUF](https://huggingface.co/mradermacher/argonium-abs-7B-4x-GGUF/resolve/main/argonium-abs-7B-4x.IQ4_XS.gguf) | IQ4_XS | 4.6 | | | [GGUF](https://huggingface.co/mradermacher/argonium-abs-7B-4x-GGUF/resolve/main/argonium-abs-7B-4x.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/argonium-abs-7B-4x-GGUF/resolve/main/argonium-abs-7B-4x.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/argonium-abs-7B-4x-GGUF/resolve/main/argonium-abs-7B-4x.Q5_K_S.gguf) | Q5_K_S | 5.7 | | | [GGUF](https://huggingface.co/mradermacher/argonium-abs-7B-4x-GGUF/resolve/main/argonium-abs-7B-4x.Q5_K_M.gguf) | Q5_K_M | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/argonium-abs-7B-4x-GGUF/resolve/main/argonium-abs-7B-4x.Q6_K.gguf) | Q6_K | 6.7 | very good quality | | [GGUF](https://huggingface.co/mradermacher/argonium-abs-7B-4x-GGUF/resolve/main/argonium-abs-7B-4x.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/argonium-abs-7B-4x-GGUF/resolve/main/argonium-abs-7B-4x.f16.gguf) | f16 | 16.2 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->

m-a-p/Amber-Reproduce-29.36B

m-a-p

transformers

**Architecture & Training Configuration:** - *Base Model Configuration*: This variant is built upon the Llama2-7B configuration, ensuring a robust foundation that aligns with the latest advancements in model architecture. - *Sequence Length Adaptation*: Originally processed data for a sequence length of 2048 was detokenized and re-encoded to fit a sequence length of 4096. This step follows the preprocessing strategy of Megatron-LM, enhancing our model's capacity to understand and generate more complex sequences. - *Batch Size & Token Management*: We adopted a batch size capable of managing 4 million tokens, tailored to accommodate the increased sequence length and ensure efficient data processing. - *Integration of GQA Technologies*: To boost training efficiency, our configuration includes the integration of Gradient Quantization and Aggregation technologies. With 32 attention heads and a group size of 4, this feature significantly enhances the model's learning and processing capabilities.

asas-ai/bloom_560M_4bit_qlora_xlsum

asas-ai

--- base_model: asas-ai/bloom_560M_8bit tags: - generated_from_trainer model-index: - name: bloom_560M_4bit_qlora_xlsum results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bloom_560M_4bit_qlora_xlsum This model is a fine-tuned version of [asas-ai/bloom_560M_8bit](https://huggingface.co/asas-ai/bloom_560M_8bit) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - training_steps: 2200 ### Training results ### Framework versions - Transformers 4.34.0 - Pytorch 2.1.0+cu121 - Datasets 2.4.0 - Tokenizers 0.14.1

Helsinki-NLP/opus-mt-tl-es

Helsinki-NLP

transformers

translation

--- language: - tl - es tags: - translation license: apache-2.0 --- ### tgl-spa * source group: Tagalog * target group: Spanish * OPUS readme: [tgl-spa](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/tgl-spa/README.md) * model: transformer-align * source language(s): tgl_Latn * target language(s): spa * model: transformer-align * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/tgl-spa/opus-2020-06-17.zip) * test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/tgl-spa/opus-2020-06-17.test.txt) * test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/tgl-spa/opus-2020-06-17.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.tgl.spa | 31.6 | 0.531 | ### System Info: - hf_name: tgl-spa - source_languages: tgl - target_languages: spa - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/tgl-spa/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['tl', 'es'] - src_constituents: {'tgl_Latn'} - tgt_constituents: {'spa'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/tgl-spa/opus-2020-06-17.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/tgl-spa/opus-2020-06-17.test.txt - src_alpha3: tgl - tgt_alpha3: spa - short_pair: tl-es - chrF2_score: 0.531 - bleu: 31.6 - brevity_penalty: 0.997 - ref_len: 4327.0 - src_name: Tagalog - tgt_name: Spanish - train_date: 2020-06-17 - src_alpha2: tl - tgt_alpha2: es - prefer_old: False - long_pair: tgl-spa - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41

1231czx/7b_dpo_iter1_4e7_step200_non_mask

1231czx

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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satoshiba/test-model

satoshiba

transformers

fill-mask

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: test-model results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # test-model This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.26.0 - TensorFlow 2.9.2 - Tokenizers 0.13.2

NiiCole/timesformer-base-finetuned-k400-continual-lora-ucf101-continual-lora-ucf101

NiiCole

transformers

video-classification

--- license: cc-by-nc-4.0 base_model: NiiCole/timesformer-base-finetuned-k400-continual-lora-ucf101 tags: - generated_from_trainer metrics: - accuracy model-index: - name: timesformer-base-finetuned-k400-continual-lora-ucf101-continual-lora-ucf101 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # timesformer-base-finetuned-k400-continual-lora-ucf101-continual-lora-ucf101 This model is a fine-tuned version of [NiiCole/timesformer-base-finetuned-k400-continual-lora-ucf101](https://huggingface.co/NiiCole/timesformer-base-finetuned-k400-continual-lora-ucf101) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.8731 - Accuracy: 0.0721 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 2276 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7573 | 0.5 | 1138 | 0.6171 | 0.9349 | | 0.2567 | 1.5 | 2276 | 0.3121 | 0.9699 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.0+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3

Gnider/hack_rut5_6ep

Gnider

transformers

text2text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

jeruan3/my-awesome-text-classification

jeruan3

transformers

text-classification

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: my-awesome-text-classification results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my-awesome-text-classification This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3872 - Accuracy: 0.943 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 469 | 2.6693 | 0.7953 | | 3.9044 | 2.0 | 938 | 1.1595 | 0.8993 | | 1.773 | 3.0 | 1407 | 0.6113 | 0.9313 | | 0.7834 | 4.0 | 1876 | 0.4350 | 0.9393 | | 0.4417 | 5.0 | 2345 | 0.3872 | 0.943 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

joe32140/ModernBERT-large-msmarco

joe32140

sentence-transformers

sentence-similarity

--- base_model: answerdotai/ModernBERT-large base_model_relation: finetune datasets: - sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1 language: - en library_name: sentence-transformers metrics: - cosine_accuracy pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:11662655 - loss:CachedMultipleNegativesRankingLoss widget: - source_sentence: what county is lyndhurst, ohio in sentences: - This article is about the song written by Kenneth Gamble, Leon Huff and Cary Gilbert. For the Tina Turner song, see Don't Leave Me This Way (Tina Turner song). Don't Leave Me This Way is a song written by Kenneth Gamble, Leon Huff and Cary Gilbert. First charting as a hit for Harold Melvin & the Blue Notes featuring Teddy Pendergrass, an act on Gamble & Huff's Philadelphia International label in 1975, Don't Leave Me This Way was later a huge disco hit for Motown artist Thelma Houston in 1977. - "Lyndhurst is a city in Cuyahoga County, Ohio, United States. The population was\ \ 14,001 at the 2010 census. Lyndhurst is located in northeastern Ohio, and is\ \ a suburb of Cleveland. A small part of Lyndhurst was originally part of Mayfield\ \ Township. It used to be called Euclidville before Lyndhurst was chosen. Lyndhurst\ \ is located at 41°31â\x80²17â\x80³N 81°29â\x80²25â\x80³W / 41.52139°N 81.49028°W\ \ / 41.52139; -81.49028 (41.521352, -81.490141)." - Welcome to Trumbull County... Trumbull County, the county seat, located in Warren, Ohio, consists of a combination of both urban and rural communities situated in the northeast corner of Ohio. It is situated roughly between the Youngstown, Cleveland and Akron corridors. - source_sentence: who founded the american graphophone company sentences: - In 1886, Graham Bell and Charles Sumner Tainter founded the American Graphophone Company to distribute and sell graphophones in the US and Canada under license from the Volta Graphophone Company. In 1890, the American Graphophone Company stopped production of new phonographs due to sagging orders. - ShelfGenie How much does a ShelfGenie franchise cost? ShelfGenie has a franchise fee of up to $45,000, with a total initial investment range of $70,100 to $107,750. Local ShelfGenie franchise opportunities. ShelfGenie is looking to grow in a number of cities around the country. To find out if there's a franchise opportunity in your city, unlock more information. - "A+E Networks. The technology that made the modern music business possible came\ \ into existence in the New Jersey laboratory where Thomas Alva Edison created\ \ the first device to both record sound and play it back. He was awarded U.S.\ \ Patent No. 200,521 for his inventionâ\x80\x93the phonographâ\x80\x93on this\ \ day in 1878." - source_sentence: is housekeeping camp flooded? sentences: - 'What is the importance of housekeeping at work? A: Workplace housekeeping promotes sanitation, safety, organization and productivity. It also boosts morale. Daily housekeeping maintenance keeps the workplac... Full Answer >' - The back patio area of a cabin is partially submerged in flood water at Housekeeping Camp on Monday, Jan. 9, 2017, in Yosemite National Park. The Merced River, swollen with storm runoff, crested at 12.7 feet at 4 a.m. SILVIA FLORES sflores@fresnobee.com. - "1 Bake for 8 minutes, then rotate the pan and check the underside of the bagels.\ \ 2 If theyâ\x80\x99re getting too dark, place another pan under the baking sheet.\ \ ( 3 Doubling the pan will insulate the first baking sheet.) Bake for another\ \ 8 to 12 minutes, until the bagels are a golden brown. 4 13." - source_sentence: causes for infection in the nerve of tooth sentences: - If a cavity is causing the toothache, your dentist will fill the cavity or possibly extract the tooth, if necessary. A root canal might be needed if the cause of the toothache is determined to be an infection of the tooth's nerve. Bacteria that have worked their way into the inner aspects of the tooth cause such an infection. An antibiotic may be prescribed if there is fever or swelling of the jaw. - "According to Article III, Section 1 of the Constitution, judges and justices\ \ of the Judicial Branch serve during good behavior.. This means they are appointed\ \ for life, unles â\x80¦ s they are impeached and removed from office. + 50 others\ \ found this useful.he term length for members of the House are two years and\ \ a staggering six years for members of the Senate." - Inflamed or infected pulp (pulpitis) most often causes a toothache. To relieve the pain and prevent further complications, the tooth may be extracted (surgically removed) or saved by root canal treatment. - source_sentence: what county is hayden in sentences: - Normally, the Lead Agency is the agency with general governmental powers such as a city or a county. Agencies with limited powers or districts that provide a public service/utility such as a recreation and park district will tend to be a Responsible Agency. - According to the United States Census Bureau, the city has a total area of 9.61 square miles (24.89 km2), of which 9.60 square miles (24.86 km2) is land and 0.01 square miles (0.03 km2) is water. It lies at the southwestern end of Hayden Lake, and the elevation of the city is 2,287 feet (697 m) above sea level. Hayden is located on U.S. Route 95 at the junction of Route 41. It is also four miles (6 km) north of Interstate 90 and Coeur d'Alene. The Coeur d'Alene airport is northwest of Hayden. - Hayden is a city in Kootenai County, Idaho, United States. Located in the northern portion of the state, just north of Coeur d'Alene, its population was 13,294 at the 2010 census. model-index: - name: SentenceTransformer based on answerdotai/ModernBERT-large results: - task: type: triplet name: Triplet dataset: name: msmarco co condenser dev type: msmarco-co-condenser-dev metrics: - type: cosine_accuracy value: 0.994 name: Cosine Accuracy - task: type: retrieval dataset: name: SCIDOCS type: SCIDOCS split: test metrics: - type: ndcg@10 value: 0.15789 - task: type: retrieval dataset: name: FiQA2018 type: FiQA2018 split: test metrics: - type: ndcg@10 value: 0.33974 - task: type: retrieval dataset: name: HotpotQA type: HotpotQA split: test metrics: - type: ndcg@10 value: 0.51818 - task: type: retrieval dataset: name: ArguAna type: ArguAna split: test metrics: - type: ndcg@10 value: 0.47797 - task: type: retrieval dataset: name: NFCorpus type: NFCorpus split: test metrics: - type: ndcg@10 value: 0.28443 - task: type: retrieval dataset: name: SciFact type: SciFact split: test metrics: - type: ndcg@10 value: 0.60626 - task: type: retrieval dataset: name: TRECCOVID type: TRECCOVID split: test metrics: - type: ndcg@10 value: 0.77495 --- # SentenceTransformer based on answerdotai/ModernBERT-large This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) on the [msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1) dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. I finetune ModernBERT-base using script from offical repo [train_st.py](https://github.com/AnswerDotAI/ModernBERT/blob/main/examples/train_st.py) on a RTX 4090 GPU with the only change of setting mini-batch size of `CachedMultipleNegativesRankingLoss` to 64. Training for 1 epoch takes less than 2 hours. The mini-batch size of GradCache should not change model performnace, but the finetuned model performs better than that recorded in the paper. Training logs can be found here: https://api.wandb.ai/links/joe32140/ekuauaao. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) <!-- at revision f87846cf8be76fceb18718f0245d18c8e6571215 --> - **Maximum Sequence Length:** 8192 tokens - **Output Dimensionality:** 1024 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - [msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1) - **Language:** en <!-- - **License:** Unknown --> ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("joe32140/ModernBERT-large-msmarco") # Run inference sentences = [ 'what county is hayden in', "Hayden is a city in Kootenai County, Idaho, United States. Located in the northern portion of the state, just north of Coeur d'Alene, its population was 13,294 at the 2010 census.", "According to the United States Census Bureau, the city has a total area of 9.61 square miles (24.89 km2), of which 9.60 square miles (24.86 km2) is land and 0.01 square miles (0.03 km2) is water. It lies at the southwestern end of Hayden Lake, and the elevation of the city is 2,287 feet (697 m) above sea level. Hayden is located on U.S. Route 95 at the junction of Route 41. It is also four miles (6 km) north of Interstate 90 and Coeur d'Alene. The Coeur d'Alene airport is northwest of Hayden.", ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> ## Evaluation ### Metrics #### Triplet * Dataset: `msmarco-co-condenser-dev` * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) | Metric | Value | |:--------------------|:----------| | **cosine_accuracy** | **0.994** | #### Retrieval tasks compared to original numbers in the paper | | ModernBERT-base | ModernBERT-base (ours) | ModernBERT-large | ModernBERT-large (ours) | |:------------------|------------------|-------------------------|-------------------|--------------------------| | NFCorpus | 23.7 | 26.66 | 26.2 | 28.44 | | SciFact | 57.0 | 61.64 | 60.4 | 63.66 | | TREC-Covid | 72.1 | 71.43 | 74.1 | 77.49 | | FiQA | 28.8 | 30.73 | 33.1 | 34.35 | | ArguAna | 35.7 | 46.38 | 38.2 | 47.79 | | SciDocs | 12.5 | 13.67 | 13.8 | 15.78 | | FEVER | 59.9 | 65.7 | 62.7 | 68.2 | | Climate-FEVER | 23.6 | 22.6 | 20.5 | 22.9 | | MLDR - OOD | 27.4 | 30.58 | 34.3 | 38.99 | <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* --> ## Training Details ### Training Dataset #### msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1 * Dataset: [msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1) at [84ed2d3](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1/tree/84ed2d35626f617d890bd493b4d6db69a741e0e2) * Size: 11,662,655 training samples * Columns: <code>query</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: | | query | positive | negative | |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 4 tokens</li><li>mean: 9.26 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 79.14 tokens</li><li>max: 222 tokens</li></ul> | <ul><li>min: 24 tokens</li><li>mean: 80.09 tokens</li><li>max: 436 tokens</li></ul> | * Samples: | query | positive | negative | |:---------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>what is the meaning of menu planning</code> | <code>Menu planning is the selection of a menu for an event. Such as picking out the dinner for your wedding or even a meal at a Birthday Party. Menu planning is when you are preparing a calendar of meals and you have to sit down and decide what meat and veggies you want to serve on each certain day.</code> | <code>Menu Costs. In economics, a menu cost is the cost to a firm resulting from changing its prices. The name stems from the cost of restaurants literally printing new menus, but economists use it to refer to the costs of changing nominal prices in general.</code> | | <code>how old is brett butler</code> | <code>Brett Butler is 59 years old. To be more precise (and nerdy), the current age as of right now is 21564 days or (even more geeky) 517536 hours. That's a lot of hours!</code> | <code>Passed in: St. John's, Newfoundland and Labrador, Canada. Passed on: 16/07/2016. Published in the St. John's Telegram. Passed away suddenly at the Health Sciences Centre surrounded by his loving family, on July 16, 2016 Robert (Bobby) Joseph Butler, age 52 years. Predeceased by his special aunt Geri Murrin and uncle Mike Mchugh; grandparents Joe and Margaret Murrin and Jack and Theresa Butler.</code> | | <code>when was the last navajo treaty sign?</code> | <code>In Executive Session, Senate of the United States, July 25, 1868. Resolved, (two-thirds of the senators present concurring,) That the Senate advise and consent to the ratification of the treaty between the United States and the Navajo Indians, concluded at Fort Sumner, New Mexico, on the first day of June, 1868.</code> | <code>Share Treaty of Greenville. The Treaty of Greenville was signed August 3, 1795, between the United States, represented by Gen. Anthony Wayne, and chiefs of the Indian tribes located in the Northwest Territory, including the Wyandots, Delawares, Shawnees, Ottawas, Miamis, and others.</code> | * Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Evaluation Dataset #### msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1 * Dataset: [msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1) at [84ed2d3](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1/tree/84ed2d35626f617d890bd493b4d6db69a741e0e2) * Size: 11,662,655 evaluation samples * Columns: <code>query</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: | | query | positive | negative | |:--------|:--------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 4 tokens</li><li>mean: 9.2 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 21 tokens</li><li>mean: 80.44 tokens</li><li>max: 241 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 80.38 tokens</li><li>max: 239 tokens</li></ul> | * Samples: | query | positive | negative | |:------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>what county is holly springs nc in</code> | <code>Holly Springs, North Carolina. Holly Springs is a town in Wake County, North Carolina, United States. As of the 2010 census, the town population was 24,661, over 2½ times its population in 2000. Contents.</code> | <code>The Mt. Holly Springs Park & Resort. One of the numerous trolley routes that carried people around the county at the turn of the century was the Carlisle & Mt. Holly Railway Company. The “Holly Trolley” as it came to be known was put into service by Patricio Russo and made its first run on May 14, 1901.</code> | | <code>how long does nyquil stay in your system</code> | <code>In order to understand exactly how long Nyquil lasts, it is absolutely vital to learn about the various ingredients in the drug. One of the ingredients found in Nyquil is Doxylamine, which is an antihistamine. This specific medication has a biological half-life or 6 to 12 hours. With this in mind, it is possible for the drug to remain in the system for a period of 12 to 24 hours. It should be known that the specifics will depend on a wide variety of different factors, including your age and metabolism.</code> | <code>I confirmed that NyQuil is about 10% alcohol, a higher content than most domestic beers. When I asked about the relatively high proof, I was told that the alcohol dilutes the active ingredients. The alcohol free version is there for customers with addiction issues.. also found that in that version there is twice the amount of DXM. When I asked if I could speak to a chemist or scientist, I was told they didn't have anyone who fit that description there. It’s been eight years since I kicked NyQuil. I've been sober from alcohol for four years.</code> | | <code>what are mineral water</code> | <code>1 Mineral water – water from a mineral spring that contains various minerals, such as salts and sulfur compounds. 2 It comes from a source tapped at one or more bore holes or spring, and originates from a geologically and physically protected underground water source. Mineral water – water from a mineral spring that contains various minerals, such as salts and sulfur compounds. 2 It comes from a source tapped at one or more bore holes or spring, and originates from a geologically and physically protected underground water source.</code> | <code>Minerals for Your Body. Drinking mineral water is beneficial to health and well-being. But it is not only the amount of water you drink that is important-what the water contains is even more essential.inerals for Your Body. Drinking mineral water is beneficial to health and well-being. But it is not only the amount of water you drink that is important-what the water contains is even more essential.</code> | * Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 512 - `per_device_eval_batch_size`: 512 - `learning_rate`: 0.0001 - `num_train_epochs`: 1 - `warmup_ratio`: 0.05 - `bf16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 512 - `per_device_eval_batch_size`: 512 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 0.0001 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.05 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs <details><summary>Click to expand</summary> | Epoch | Step | Training Loss | msmarco-co-condenser-dev_cosine_accuracy | |:------:|:----:|:-------------:|:----------------------------------------:| | 0 | 0 | - | 0.599 | | 0.0041 | 10 | 6.0983 | - | | 0.0082 | 20 | 4.4588 | - | | 0.0123 | 30 | 2.2492 | - | | 0.0164 | 40 | 0.9969 | - | | 0.0205 | 50 | 0.5272 | - | | 0.0246 | 60 | 0.3982 | - | | 0.0287 | 70 | 0.3335 | - | | 0.0328 | 80 | 0.3024 | - | | 0.0369 | 90 | 0.2932 | - | | 0.0410 | 100 | 0.2695 | - | | 0.0450 | 110 | 0.2574 | - | | 0.0491 | 120 | 0.2447 | - | | 0.0532 | 130 | 0.2491 | - | | 0.0573 | 140 | 0.2318 | - | | 0.0614 | 150 | 0.2292 | - | | 0.0655 | 160 | 0.2213 | - | | 0.0696 | 170 | 0.218 | - | | 0.0737 | 180 | 0.2234 | - | | 0.0778 | 190 | 0.2066 | - | | 0.0819 | 200 | 0.1987 | - | | 0.0860 | 210 | 0.1978 | - | | 0.0901 | 220 | 0.2024 | - | | 0.0942 | 230 | 0.1959 | - | | 0.0983 | 240 | 0.1804 | - | | 0.1024 | 250 | 0.1868 | - | | 0.1065 | 260 | 0.1983 | - | | 0.1106 | 270 | 0.1641 | - | | 0.1147 | 280 | 0.1713 | - | | 0.1188 | 290 | 0.1726 | - | | 0.1229 | 300 | 0.17 | - | | 0.1269 | 310 | 0.1783 | - | | 0.1310 | 320 | 0.1742 | - | | 0.1351 | 330 | 0.1654 | - | | 0.1392 | 340 | 0.1663 | - | | 0.1433 | 350 | 0.1616 | - | | 0.1474 | 360 | 0.157 | - | | 0.1515 | 370 | 0.1574 | - | | 0.1556 | 380 | 0.1529 | - | | 0.1597 | 390 | 0.1561 | - | | 0.1638 | 400 | 0.1435 | - | | 0.1679 | 410 | 0.1555 | - | | 0.1720 | 420 | 0.1455 | - | | 0.1761 | 430 | 0.1416 | - | | 0.1802 | 440 | 0.1407 | - | | 0.1843 | 450 | 0.138 | - | | 0.1884 | 460 | 0.1387 | - | | 0.1925 | 470 | 0.1499 | - | | 0.1966 | 480 | 0.1372 | - | | 0.2007 | 490 | 0.1308 | - | | 0.2048 | 500 | 0.1367 | - | | 0.2088 | 510 | 0.1324 | - | | 0.2129 | 520 | 0.1317 | - | | 0.2170 | 530 | 0.1263 | - | | 0.2211 | 540 | 0.1209 | - | | 0.2252 | 550 | 0.1201 | - | | 0.2293 | 560 | 0.1213 | - | | 0.2334 | 570 | 0.1329 | - | | 0.2375 | 580 | 0.1207 | - | | 0.2416 | 590 | 0.1211 | - | | 0.2457 | 600 | 0.1164 | - | | 0.2498 | 610 | 0.1292 | - | | 0.2539 | 620 | 0.1223 | - | | 0.2580 | 630 | 0.1237 | - | | 0.2621 | 640 | 0.1088 | - | | 0.2662 | 650 | 0.1196 | - | | 0.2703 | 660 | 0.1209 | - | | 0.2744 | 670 | 0.1155 | - | | 0.2785 | 680 | 0.1101 | - | | 0.2826 | 690 | 0.1127 | - | | 0.2867 | 700 | 0.1082 | - | | 0.2907 | 710 | 0.1083 | - | | 0.2948 | 720 | 0.1132 | - | | 0.2989 | 730 | 0.1121 | - | | 0.3030 | 740 | 0.1146 | - | | 0.3071 | 750 | 0.1088 | - | | 0.3112 | 760 | 0.0982 | - | | 0.3153 | 770 | 0.0952 | - | | 0.3194 | 780 | 0.1034 | - | | 0.3235 | 790 | 0.1017 | - | | 0.3276 | 800 | 0.1016 | - | | 0.3317 | 810 | 0.1054 | - | | 0.3358 | 820 | 0.1003 | - | | 0.3399 | 830 | 0.0932 | - | | 0.3440 | 840 | 0.0997 | - | | 0.3481 | 850 | 0.0921 | - | | 0.3522 | 860 | 0.0958 | - | | 0.3563 | 870 | 0.0973 | - | | 0.3604 | 880 | 0.0931 | - | | 0.3645 | 890 | 0.0964 | - | | 0.3686 | 900 | 0.0982 | - | | 0.3726 | 910 | 0.0908 | - | | 0.3767 | 920 | 0.0917 | - | | 0.3808 | 930 | 0.0857 | - | | 0.3849 | 940 | 0.0925 | - | | 0.3890 | 950 | 0.0915 | - | | 0.3931 | 960 | 0.089 | - | | 0.3972 | 970 | 0.0876 | - | | 0.4013 | 980 | 0.0959 | - | | 0.4054 | 990 | 0.0879 | - | | 0.4095 | 1000 | 0.0883 | - | | 0.4136 | 1010 | 0.0824 | - | | 0.4177 | 1020 | 0.0897 | - | | 0.4218 | 1030 | 0.0954 | - | | 0.4259 | 1040 | 0.0815 | - | | 0.4300 | 1050 | 0.0806 | - | | 0.4341 | 1060 | 0.0918 | - | | 0.4382 | 1070 | 0.0851 | - | | 0.4423 | 1080 | 0.0888 | - | | 0.4464 | 1090 | 0.0863 | - | | 0.4505 | 1100 | 0.0856 | - | | 0.4545 | 1110 | 0.0809 | - | | 0.4586 | 1120 | 0.085 | - | | 0.4627 | 1130 | 0.0756 | - | | 0.4668 | 1140 | 0.0836 | - | | 0.4709 | 1150 | 0.0815 | - | | 0.4750 | 1160 | 0.084 | - | | 0.4791 | 1170 | 0.0751 | - | | 0.4832 | 1180 | 0.0794 | - | | 0.4873 | 1190 | 0.0844 | - | | 0.4914 | 1200 | 0.0835 | - | | 0.4955 | 1210 | 0.0798 | - | | 0.4996 | 1220 | 0.0825 | - | | 0.5037 | 1230 | 0.0796 | - | | 0.5078 | 1240 | 0.0758 | - | | 0.5119 | 1250 | 0.0765 | - | | 0.5160 | 1260 | 0.0806 | - | | 0.5201 | 1270 | 0.072 | - | | 0.5242 | 1280 | 0.0775 | - | | 0.5283 | 1290 | 0.076 | - | | 0.5324 | 1300 | 0.0767 | - | | 0.5364 | 1310 | 0.0782 | - | | 0.5405 | 1320 | 0.07 | - | | 0.5446 | 1330 | 0.0724 | - | | 0.5487 | 1340 | 0.0703 | - | | 0.5528 | 1350 | 0.072 | - | | 0.5569 | 1360 | 0.0763 | - | | 0.5610 | 1370 | 0.0703 | - | | 0.5651 | 1380 | 0.0688 | - | | 0.5692 | 1390 | 0.0703 | - | | 0.5733 | 1400 | 0.0659 | - | | 0.5774 | 1410 | 0.0688 | - | | 0.5815 | 1420 | 0.0713 | - | | 0.5856 | 1430 | 0.0722 | - | | 0.5897 | 1440 | 0.0682 | - | | 0.5938 | 1450 | 0.07 | - | | 0.5979 | 1460 | 0.0649 | - | | 0.6020 | 1470 | 0.0659 | - | | 0.6061 | 1480 | 0.0675 | - | | 0.6102 | 1490 | 0.0629 | - | | 0.6143 | 1500 | 0.0683 | - | | 0.6183 | 1510 | 0.0687 | - | | 0.6224 | 1520 | 0.0724 | - | | 0.6265 | 1530 | 0.0638 | - | | 0.6306 | 1540 | 0.0709 | - | | 0.6347 | 1550 | 0.064 | - | | 0.6388 | 1560 | 0.0646 | - | | 0.6429 | 1570 | 0.0673 | - | | 0.6470 | 1580 | 0.0607 | - | | 0.6511 | 1590 | 0.0671 | - | | 0.6552 | 1600 | 0.0627 | - | | 0.6593 | 1610 | 0.0644 | - | | 0.6634 | 1620 | 0.0629 | - | | 0.6675 | 1630 | 0.0656 | - | | 0.6716 | 1640 | 0.0633 | - | | 0.6757 | 1650 | 0.062 | - | | 0.6798 | 1660 | 0.0627 | - | | 0.6839 | 1670 | 0.0583 | - | | 0.6880 | 1680 | 0.0612 | - | | 0.6921 | 1690 | 0.066 | - | | 0.6962 | 1700 | 0.0645 | - | | 0.7002 | 1710 | 0.0599 | - | | 0.7043 | 1720 | 0.0552 | - | | 0.7084 | 1730 | 0.065 | - | | 0.7125 | 1740 | 0.0614 | - | | 0.7166 | 1750 | 0.0615 | - | | 0.7207 | 1760 | 0.0567 | - | | 0.7248 | 1770 | 0.0528 | - | | 0.7289 | 1780 | 0.0541 | - | | 0.7330 | 1790 | 0.0548 | - | | 0.7371 | 1800 | 0.0568 | - | | 0.7412 | 1810 | 0.053 | - | | 0.7453 | 1820 | 0.0603 | - | | 0.7494 | 1830 | 0.0594 | - | | 0.7535 | 1840 | 0.0549 | - | | 0.7576 | 1850 | 0.0601 | - | | 0.7617 | 1860 | 0.0604 | - | | 0.7658 | 1870 | 0.0524 | - | | 0.7699 | 1880 | 0.057 | - | | 0.7740 | 1890 | 0.057 | - | | 0.7781 | 1900 | 0.0551 | - | | 0.7821 | 1910 | 0.0574 | - | | 0.7862 | 1920 | 0.0555 | - | | 0.7903 | 1930 | 0.0564 | - | | 0.7944 | 1940 | 0.052 | - | | 0.7985 | 1950 | 0.054 | - | | 0.8026 | 1960 | 0.0573 | - | | 0.8067 | 1970 | 0.056 | - | | 0.8108 | 1980 | 0.0503 | - | | 0.8149 | 1990 | 0.0525 | - | | 0.8190 | 2000 | 0.0505 | - | | 0.8231 | 2010 | 0.0547 | - | | 0.8272 | 2020 | 0.0531 | - | | 0.8313 | 2030 | 0.0534 | - | | 0.8354 | 2040 | 0.0542 | - | | 0.8395 | 2050 | 0.0536 | - | | 0.8436 | 2060 | 0.0512 | - | | 0.8477 | 2070 | 0.0508 | - | | 0.8518 | 2080 | 0.0517 | - | | 0.8559 | 2090 | 0.0516 | - | | 0.8600 | 2100 | 0.0558 | - | | 0.8640 | 2110 | 0.0571 | - | | 0.8681 | 2120 | 0.0536 | - | | 0.8722 | 2130 | 0.0561 | - | | 0.8763 | 2140 | 0.0489 | - | | 0.8804 | 2150 | 0.0513 | - | | 0.8845 | 2160 | 0.0455 | - | | 0.8886 | 2170 | 0.0479 | - | | 0.8927 | 2180 | 0.0498 | - | | 0.8968 | 2190 | 0.0523 | - | | 0.9009 | 2200 | 0.0513 | - | | 0.9050 | 2210 | 0.049 | - | | 0.9091 | 2220 | 0.0504 | - | | 0.9132 | 2230 | 0.0462 | - | | 0.9173 | 2240 | 0.0469 | - | | 0.9214 | 2250 | 0.0501 | - | | 0.9255 | 2260 | 0.046 | - | | 0.9296 | 2270 | 0.0475 | - | | 0.9337 | 2280 | 0.0504 | - | | 0.9378 | 2290 | 0.0483 | - | | 0.9419 | 2300 | 0.0536 | - | | 0.9459 | 2310 | 0.0442 | - | | 0.9500 | 2320 | 0.0499 | - | | 0.9541 | 2330 | 0.0478 | - | | 0.9582 | 2340 | 0.0499 | - | | 0.9623 | 2350 | 0.048 | - | | 0.9664 | 2360 | 0.0451 | - | | 0.9705 | 2370 | 0.0501 | - | | 0.9746 | 2380 | 0.0464 | - | | 0.9787 | 2390 | 0.0451 | - | | 0.9828 | 2400 | 0.0413 | - | | 0.9869 | 2410 | 0.0478 | - | | 0.9910 | 2420 | 0.0466 | - | | 0.9951 | 2430 | 0.0515 | - | | 0.9992 | 2440 | 0.0484 | - | | 1.0 | 2442 | - | 0.994 | </details> ### Framework Versions - Python: 3.11.9 - Sentence Transformers: 3.3.0 - Transformers: 4.48.0.dev0 - PyTorch: 2.4.0 - Accelerate: 1.2.1 - Datasets: 2.21.0 - Tokenizers: 0.21.0 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### CachedMultipleNegativesRankingLoss ```bibtex @misc{gao2021scaling, title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup}, author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan}, year={2021}, eprint={2101.06983}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` <!-- ## Glossary *Clearly define terms in order to be accessible across audiences.* --> <!-- ## Model Card Authors *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->

xgmab123/r3b-1.2

xgmab123

transformers

text-generation

--- base_model: xgmab123/r3b-1.1 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** xgmab123 - **License:** apache-2.0 - **Finetuned from model :** xgmab123/r3b-1.1 This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

sail-rvc/leafyv2

sail-rvc

transformers

audio-to-audio

--- pipeline_tag: audio-to-audio tags: - rvc - sail-rvc --- # leafyv2 ## RVC Model  This model repo was automatically generated. Date: 2023-07-14 07:40:31 Bot Name: juuxnscrap Model Type: RVC Source: https://huggingface.co/juuxn/RVCModels/ Reason: Converting into loadable format for https://github.com/chavinlo/rvc-runpod

zzmez/Reinforce-model1

zzmez

reinforcement-learning

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-model1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

zelk12/MT-Merge-gemma-2-9B

zelk12

transformers

text-generation

--- library_name: transformers tags: - mergekit - merge base_model: - zelk12/MT-Merge-MMB-gemma-2-9B - zelk12/MT-Merge-GMAMUI-gemma-2-9B model-index: - name: MT-Merge-gemma-2-9B results: - task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: HuggingFaceH4/ifeval args: num_few_shot: 0 metrics: - type: inst_level_strict_acc and prompt_level_strict_acc value: 80.35 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=zelk12/MT-Merge-gemma-2-9B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: BBH args: num_few_shot: 3 metrics: - type: acc_norm value: 44.32 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=zelk12/MT-Merge-gemma-2-9B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: hendrycks/competition_math args: num_few_shot: 4 metrics: - type: exact_match value: 11.93 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=zelk12/MT-Merge-gemma-2-9B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa args: num_few_shot: 0 metrics: - type: acc_norm value: 13.09 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=zelk12/MT-Merge-gemma-2-9B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics: - type: acc_norm value: 12.1 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=zelk12/MT-Merge-gemma-2-9B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 37.35 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=zelk12/MT-Merge-gemma-2-9B name: Open LLM Leaderboard --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [zelk12/MT-Merge-MMB-gemma-2-9B](https://huggingface.co/zelk12/MT-Merge-MMB-gemma-2-9B) * [zelk12/MT-Merge-GMAMUI-gemma-2-9B](https://huggingface.co/zelk12/MT-Merge-GMAMUI-gemma-2-9B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: zelk12/MT-Merge-MMB-gemma-2-9B - model: zelk12/MT-Merge-GMAMUI-gemma-2-9B merge_method: slerp base_model: zelk12/MT-Merge-MMB-gemma-2-9B dtype: bfloat16 parameters: t: 0.5 ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_zelk12__MT-Merge-gemma-2-9B) | Metric |Value| |-------------------|----:| |Avg. |33.19| |IFEval (0-Shot) |80.35| |BBH (3-Shot) |44.32| |MATH Lvl 5 (4-Shot)|11.93| |GPQA (0-shot) |13.09| |MuSR (0-shot) |12.10| |MMLU-PRO (5-shot) |37.35|

RichardErkhov/AIGym_-_deepseek-coder-1.3b-chat-and-function-calling-gguf

RichardErkhov

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) deepseek-coder-1.3b-chat-and-function-calling - GGUF - Model creator: https://huggingface.co/AIGym/ - Original model: https://huggingface.co/AIGym/deepseek-coder-1.3b-chat-and-function-calling/ | Name | Quant method | Size | | ---- | ---- | ---- | | [deepseek-coder-1.3b-chat-and-function-calling.Q2_K.gguf](https://huggingface.co/RichardErkhov/AIGym_-_deepseek-coder-1.3b-chat-and-function-calling-gguf/blob/main/deepseek-coder-1.3b-chat-and-function-calling.Q2_K.gguf) | Q2_K | 0.52GB | | [deepseek-coder-1.3b-chat-and-function-calling.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/AIGym_-_deepseek-coder-1.3b-chat-and-function-calling-gguf/blob/main/deepseek-coder-1.3b-chat-and-function-calling.IQ3_XS.gguf) | IQ3_XS | 0.57GB | | [deepseek-coder-1.3b-chat-and-function-calling.IQ3_S.gguf](https://huggingface.co/RichardErkhov/AIGym_-_deepseek-coder-1.3b-chat-and-function-calling-gguf/blob/main/deepseek-coder-1.3b-chat-and-function-calling.IQ3_S.gguf) | IQ3_S | 0.6GB | | [deepseek-coder-1.3b-chat-and-function-calling.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/AIGym_-_deepseek-coder-1.3b-chat-and-function-calling-gguf/blob/main/deepseek-coder-1.3b-chat-and-function-calling.Q3_K_S.gguf) | Q3_K_S | 0.6GB | | [deepseek-coder-1.3b-chat-and-function-calling.IQ3_M.gguf](https://huggingface.co/RichardErkhov/AIGym_-_deepseek-coder-1.3b-chat-and-function-calling-gguf/blob/main/deepseek-coder-1.3b-chat-and-function-calling.IQ3_M.gguf) | IQ3_M | 0.63GB | | [deepseek-coder-1.3b-chat-and-function-calling.Q3_K.gguf](https://huggingface.co/RichardErkhov/AIGym_-_deepseek-coder-1.3b-chat-and-function-calling-gguf/blob/main/deepseek-coder-1.3b-chat-and-function-calling.Q3_K.gguf) | Q3_K | 0.66GB | | [deepseek-coder-1.3b-chat-and-function-calling.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/AIGym_-_deepseek-coder-1.3b-chat-and-function-calling-gguf/blob/main/deepseek-coder-1.3b-chat-and-function-calling.Q3_K_M.gguf) | Q3_K_M | 0.66GB | | [deepseek-coder-1.3b-chat-and-function-calling.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/AIGym_-_deepseek-coder-1.3b-chat-and-function-calling-gguf/blob/main/deepseek-coder-1.3b-chat-and-function-calling.Q3_K_L.gguf) | Q3_K_L | 0.69GB | | [deepseek-coder-1.3b-chat-and-function-calling.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/AIGym_-_deepseek-coder-1.3b-chat-and-function-calling-gguf/blob/main/deepseek-coder-1.3b-chat-and-function-calling.IQ4_XS.gguf) | IQ4_XS | 0.7GB | | [deepseek-coder-1.3b-chat-and-function-calling.Q4_0.gguf](https://huggingface.co/RichardErkhov/AIGym_-_deepseek-coder-1.3b-chat-and-function-calling-gguf/blob/main/deepseek-coder-1.3b-chat-and-function-calling.Q4_0.gguf) | Q4_0 | 0.72GB | | [deepseek-coder-1.3b-chat-and-function-calling.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/AIGym_-_deepseek-coder-1.3b-chat-and-function-calling-gguf/blob/main/deepseek-coder-1.3b-chat-and-function-calling.IQ4_NL.gguf) | IQ4_NL | 0.73GB | | [deepseek-coder-1.3b-chat-and-function-calling.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/AIGym_-_deepseek-coder-1.3b-chat-and-function-calling-gguf/blob/main/deepseek-coder-1.3b-chat-and-function-calling.Q4_K_S.gguf) | Q4_K_S | 0.76GB | | [deepseek-coder-1.3b-chat-and-function-calling.Q4_K.gguf](https://huggingface.co/RichardErkhov/AIGym_-_deepseek-coder-1.3b-chat-and-function-calling-gguf/blob/main/deepseek-coder-1.3b-chat-and-function-calling.Q4_K.gguf) | Q4_K | 0.81GB | | [deepseek-coder-1.3b-chat-and-function-calling.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/AIGym_-_deepseek-coder-1.3b-chat-and-function-calling-gguf/blob/main/deepseek-coder-1.3b-chat-and-function-calling.Q4_K_M.gguf) | Q4_K_M | 0.81GB | | [deepseek-coder-1.3b-chat-and-function-calling.Q4_1.gguf](https://huggingface.co/RichardErkhov/AIGym_-_deepseek-coder-1.3b-chat-and-function-calling-gguf/blob/main/deepseek-coder-1.3b-chat-and-function-calling.Q4_1.gguf) | Q4_1 | 0.8GB | | [deepseek-coder-1.3b-chat-and-function-calling.Q5_0.gguf](https://huggingface.co/RichardErkhov/AIGym_-_deepseek-coder-1.3b-chat-and-function-calling-gguf/blob/main/deepseek-coder-1.3b-chat-and-function-calling.Q5_0.gguf) | Q5_0 | 0.87GB | | [deepseek-coder-1.3b-chat-and-function-calling.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/AIGym_-_deepseek-coder-1.3b-chat-and-function-calling-gguf/blob/main/deepseek-coder-1.3b-chat-and-function-calling.Q5_K_S.gguf) | Q5_K_S | 0.89GB | | [deepseek-coder-1.3b-chat-and-function-calling.Q5_K.gguf](https://huggingface.co/RichardErkhov/AIGym_-_deepseek-coder-1.3b-chat-and-function-calling-gguf/blob/main/deepseek-coder-1.3b-chat-and-function-calling.Q5_K.gguf) | Q5_K | 0.93GB | | [deepseek-coder-1.3b-chat-and-function-calling.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/AIGym_-_deepseek-coder-1.3b-chat-and-function-calling-gguf/blob/main/deepseek-coder-1.3b-chat-and-function-calling.Q5_K_M.gguf) | Q5_K_M | 0.93GB | | [deepseek-coder-1.3b-chat-and-function-calling.Q5_1.gguf](https://huggingface.co/RichardErkhov/AIGym_-_deepseek-coder-1.3b-chat-and-function-calling-gguf/blob/main/deepseek-coder-1.3b-chat-and-function-calling.Q5_1.gguf) | Q5_1 | 0.95GB | | [deepseek-coder-1.3b-chat-and-function-calling.Q6_K.gguf](https://huggingface.co/RichardErkhov/AIGym_-_deepseek-coder-1.3b-chat-and-function-calling-gguf/blob/main/deepseek-coder-1.3b-chat-and-function-calling.Q6_K.gguf) | Q6_K | 1.09GB | | [deepseek-coder-1.3b-chat-and-function-calling.Q8_0.gguf](https://huggingface.co/RichardErkhov/AIGym_-_deepseek-coder-1.3b-chat-and-function-calling-gguf/blob/main/deepseek-coder-1.3b-chat-and-function-calling.Q8_0.gguf) | Q8_0 | 1.33GB | Original model description: --- license: apache-2.0 tags: - finetuned pipeline_tag: text-generation model-index: - name: deepseek-coder-1.3b-chat-and-function-calling results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 26.28 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AIGym/deepseek-coder-1.3b-chat-and-function-calling name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 39.27 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AIGym/deepseek-coder-1.3b-chat-and-function-calling name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 26.92 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AIGym/deepseek-coder-1.3b-chat-and-function-calling name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 43.37 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AIGym/deepseek-coder-1.3b-chat-and-function-calling name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 51.7 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AIGym/deepseek-coder-1.3b-chat-and-function-calling name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 3.41 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AIGym/deepseek-coder-1.3b-chat-and-function-calling name: Open LLM Leaderboard --- # deepseek-coder-1.3b-chat-and-function-calling It was created by starting with the deepseek-coder-1.3b and training it on the open assistant dataset then training yhat on function calling. We have attached the wandb report in pdf form to view the training run at a glance. # Reson This model was fine tuned to allow it to work with the openai syntask and will return function when apperate. # Templete Us the following templete when interacting with the fine tuned model. # Referrals Run Pod - This is who I use to train th emodels on huggingface. If you use it we both get free crdits. - <a href="https://runpod.io?ref=kilq83n1" target="_blank" style="color: #3498db; text-decoration: none; font-weight: bold;">Visit Runpod's Website!</a> Paypal - If you want to leave a tip, it is appecaheted. - <a href="https://paypal.me/OpenSourceTraining" target="_blank" style="color: #3498db; text-decoration: none; font-weight: bold;">Visit My Paypal!</a> # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_AIGym__deepseek-coder-1.3b-chat-and-function-calling) | Metric |Value| |---------------------------------|----:| |Avg. |31.82| |AI2 Reasoning Challenge (25-Shot)|26.28| |HellaSwag (10-Shot) |39.27| |MMLU (5-Shot) |26.92| |TruthfulQA (0-shot) |43.37| |Winogrande (5-shot) |51.70| |GSM8k (5-shot) | 3.41|

tringuyen-uit/MRC_ER_XLM-base_syl_ViWikiFC

tringuyen-uit

transformers

question-answering

--- license: mit base_model: FacebookAI/xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: MRC_ER_XLM-base_syl_ViWikiFC results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # MRC_ER_XLM-base_syl_ViWikiFC This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.4375 - Exact Match: 0.8010 - F1: 0.8260 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Exact Match | F1 | |:-------------:|:-----:|:-----:|:---------------:|:-----------:|:------:| | 0.5246 | 1.0 | 2093 | 1.9799 | 0.7751 | 0.8080 | | 0.4175 | 2.0 | 4186 | 1.9344 | 0.7856 | 0.8126 | | 0.3522 | 3.0 | 6279 | 2.0761 | 0.7981 | 0.8274 | | 0.2593 | 4.0 | 8372 | 2.3028 | 0.7990 | 0.8225 | | 0.1676 | 5.0 | 10465 | 2.4375 | 0.8010 | 0.8260 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.1.2 - Datasets 2.19.2 - Tokenizers 0.19.1

mnicamartins8/bert-base-uncased-with-misspellings-correction

mnicamartins8

transformers

text-classification

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: bert-base-uncased-with-misspellings-correction results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-uncased-with-misspellings-correction This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2307 - Accuracy: 0.9023 - Precision: 0.9090 - Recall: 0.9023 - F1: 0.9045 - Balanced Acc: 0.8858 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3

zachhofstad/xlm-roberta-base-finetuned-panx-de

zachhofstad

transformers

token-classification

--- library_name: transformers license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1399 - F1: 0.8620 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2556 | 1.0 | 525 | 0.1498 | 0.8286 | | 0.1305 | 2.0 | 1050 | 0.1374 | 0.8535 | | 0.0786 | 3.0 | 1575 | 0.1399 | 0.8620 | ### Framework versions - Transformers 4.46.2 - Pytorch 2.5.1+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3