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nihalzk/flan-t5-base-mlsum-turkish-summarization-10.06.2024

nihalzk

transformers

text2text-generation

--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: flan-t5-base-mlsum-turkish-summarization-10.06.2024 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. --> # flan-t5-base-mlsum-turkish-summarization-10.06.2024 This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7452 - Rouge1: 19.7847 - Rouge2: 12.0786 - Rougel: 19.1989 - Rougelsum: 19.3109 - Gen Len: 19.0 ## 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: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 0.7827 | 1.0 | 5625 | 0.9188 | 18.2623 | 11.2465 | 17.8043 | 17.8964 | 19.0 | | 0.7213 | 2.0 | 11250 | 0.8623 | 18.764 | 11.4004 | 18.251 | 18.3545 | 19.0 | | 0.6756 | 3.0 | 16875 | 0.8287 | 18.8529 | 11.6041 | 18.3448 | 18.433 | 19.0 | | 0.6608 | 4.0 | 22500 | 0.8077 | 19.0099 | 11.6078 | 18.5004 | 18.5972 | 19.0 | | 0.6312 | 5.0 | 28125 | 0.7891 | 19.4377 | 11.9331 | 18.8651 | 18.954 | 19.0 | | 0.6154 | 6.0 | 33750 | 0.7807 | 19.4985 | 11.9288 | 18.9395 | 19.0446 | 19.0 | | 0.5983 | 7.0 | 39375 | 0.7715 | 19.5244 | 11.9493 | 18.9767 | 19.0795 | 19.0 | | 0.5917 | 8.0 | 45000 | 0.7594 | 19.4081 | 11.8484 | 18.857 | 18.9525 | 19.0 | | 0.5826 | 9.0 | 50625 | 0.7580 | 19.5463 | 11.9167 | 18.9622 | 19.074 | 19.0 | | 0.5718 | 10.0 | 56250 | 0.7529 | 19.7805 | 12.0402 | 19.1876 | 19.2966 | 19.0 | | 0.5739 | 11.0 | 61875 | 0.7517 | 19.6051 | 12.0595 | 19.0559 | 19.1552 | 19.0 | | 0.5573 | 12.0 | 67500 | 0.7455 | 19.7817 | 12.1414 | 19.1911 | 19.3001 | 19.0 | | 0.55 | 13.0 | 73125 | 0.7469 | 19.7252 | 12.0333 | 19.1214 | 19.2331 | 19.0 | | 0.5566 | 14.0 | 78750 | 0.7455 | 19.8249 | 12.11 | 19.2288 | 19.3369 | 19.0 | | 0.5518 | 15.0 | 84375 | 0.7452 | 19.7847 | 12.0786 | 19.1989 | 19.3109 | 19.0 | ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1+cu117 - Datasets 2.13.2 - Tokenizers 0.13.3

MingAI/mit-b0-scene-parse-150-lora

MingAI

Entry not found

RamyaRamakrishna/llama3-adapter

RamyaRamakrishna

Entry not found

AriaRahmati1/2shesmat9part2

AriaRahmati1

--- license: openrail ---

ming0531/DRCT

ming0531

--- license: mit ---

haturusinghe/xlm_r_base-finetuned_after_mrp-floral-leaf-4

haturusinghe

Entry not found

MudassirFayaz/career_councling_bart_0.2

MudassirFayaz

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. 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]

iamboss001/jahid-v1

iamboss001

Entry not found

DiogoF/q-FrozenLake-v1-4x4-noSlippery

DiogoF

reinforcement-learning

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="DiogoF/q-FrozenLake-v1-4x4-noSlippery", 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"]) ```

Uranus-lw/falcon-AI-IDS

Uranus-lw

Entry not found

OliBomby/rcomplexion

OliBomby

This model is trained on osu! ranked beatmaps. It predicts the time until the next hit object based on the previous hit objects. It's used to estimate the complexity of rhythm in beatmaps. https://github.com/OliBomby/Mapperatorinator/tree/main/rcomplexion

philippkolbe/huggingface

philippkolbe

peft

--- license: llama3 library_name: peft tags: - trl - sft - generated_from_trainer base_model: meta-llama/Meta-Llama-3-8B-Instruct model-index: - name: huggingface 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. --> # huggingface This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5034 ## 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 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 2 - total_eval_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 410 | 0.4175 | | 0.4374 | 2.0 | 820 | 0.4132 | | 0.396 | 3.0 | 1230 | 0.4211 | | 0.36 | 4.0 | 1640 | 0.4311 | | 0.317 | 5.0 | 2050 | 0.4538 | | 0.317 | 6.0 | 2460 | 0.4707 | | 0.28 | 7.0 | 2870 | 0.4940 | | 0.2505 | 8.0 | 3280 | 0.5034 | ### Framework versions - PEFT 0.11.1 - Transformers 4.41.2 - Pytorch 2.0.1+cu117 - Datasets 2.19.2 - Tokenizers 0.19.1

TeTLAB/zephyr-7b-beta-Agent-Instruct

TeTLAB

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. 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]

Danikdsa/rami

Danikdsa

--- license: openrail ---

Nadir33/Chat_31

Nadir33

Entry not found

LEAL1lory/pum

LEAL1lory

--- license: unknown ---

A-humanBeingTrained/insAIght_v2.0

A-humanBeingTrained

transformers

text-generation

Entry not found

LEAL1lory/mmm

LEAL1lory

Entry not found

Razer112/SoftEbooy

Razer112

--- license: openrail ---

iloncka/exp_5_new_bg_simple-subs_1_v_5_convit_tiny.fb_in1k_ep_60

iloncka

fastai

--- tags: - fastai --- # Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed

yaswanth-iitkgp/idefics2_ft_augmented_dataset

yaswanth-iitkgp

--- license: apache-2.0 base_model: HuggingFaceM4/idefics2-8b tags: - generated_from_trainer model-index: - name: idefics2_ft_augmented_dataset 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. --> # idefics2_ft_augmented_dataset This model is a fine-tuned version of [HuggingFaceM4/idefics2-8b](https://huggingface.co/HuggingFaceM4/idefics2-8b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1871 ## 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: 2.5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 25 - training_steps: 5000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 0.6338 | 0.0234 | 100 | 0.6474 | | 0.5748 | 0.0468 | 200 | 0.5899 | | 0.4678 | 0.0702 | 300 | 0.5603 | | 0.428 | 0.0936 | 400 | 0.5249 | | 0.3798 | 0.1170 | 500 | 0.4984 | | 0.3665 | 0.1404 | 600 | 0.4733 | | 0.4406 | 0.1637 | 700 | 0.4510 | | 0.4723 | 0.1871 | 800 | 0.4245 | | 0.4807 | 0.2105 | 900 | 0.4158 | | 0.4196 | 0.2339 | 1000 | 0.3971 | | 0.3443 | 0.2573 | 1100 | 0.3738 | | 0.4133 | 0.2807 | 1200 | 0.3631 | | 0.2838 | 0.3041 | 1300 | 0.3334 | | 0.4134 | 0.3275 | 1400 | 0.3264 | | 0.2838 | 0.3509 | 1500 | 0.3125 | | 0.275 | 0.3743 | 1600 | 0.2944 | | 0.4141 | 0.3977 | 1700 | 0.2839 | | 0.2498 | 0.4211 | 1800 | 0.2749 | | 0.2817 | 0.4444 | 1900 | 0.2606 | | 0.2899 | 0.4678 | 2000 | 0.2526 | | 0.2695 | 0.4912 | 2100 | 0.2521 | | 0.2619 | 0.5146 | 2200 | 0.2424 | | 0.2238 | 0.5380 | 2300 | 0.2373 | | 0.3049 | 0.5614 | 2400 | 0.2301 | | 0.1308 | 0.5848 | 2500 | 0.2292 | | 0.1936 | 0.6082 | 2600 | 0.2190 | | 0.2479 | 0.6316 | 2700 | 0.2191 | | 0.1575 | 0.6550 | 2800 | 0.2165 | | 0.193 | 0.6784 | 2900 | 0.2107 | | 0.2526 | 0.7018 | 3000 | 0.2114 | | 0.1574 | 0.7251 | 3100 | 0.2087 | | 0.1989 | 0.7485 | 3200 | 0.2051 | | 0.1761 | 0.7719 | 3300 | 0.2013 | | 0.2223 | 0.7953 | 3400 | 0.1996 | | 0.2127 | 0.8187 | 3500 | 0.1966 | | 0.2477 | 0.8421 | 3600 | 0.1923 | | 0.1931 | 0.8655 | 3700 | 0.1908 | | 0.182 | 0.8889 | 3800 | 0.1888 | | 0.1693 | 0.9123 | 3900 | 0.1878 | | 0.1346 | 0.9357 | 4000 | 0.1853 | | 0.1484 | 0.9591 | 4100 | 0.1849 | | 0.1217 | 0.9825 | 4200 | 0.1838 | | 0.0669 | 1.0058 | 4300 | 0.1844 | | 0.1292 | 1.0292 | 4400 | 0.1877 | | 0.1106 | 1.0526 | 4500 | 0.1876 | | 0.0828 | 1.0760 | 4600 | 0.1875 | | 0.0485 | 1.0994 | 4700 | 0.1871 | | 0.0624 | 1.1228 | 4800 | 0.1874 | | 0.0895 | 1.1462 | 4900 | 0.1871 | | 0.1 | 1.1696 | 5000 | 0.1871 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.1+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1

ninyx/Phi-3-mini-128k-instruct-advisegpt-v0.3

ninyx

peft

--- license: mit library_name: peft tags: - trl - sft - generated_from_trainer base_model: microsoft/Phi-3-mini-128k-instruct datasets: - generator model-index: - name: Phi-3-mini-128k-instruct-advisegpt-v0.3 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. --> # Phi-3-mini-128k-instruct-advisegpt-v0.3 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 generator dataset. It achieves the following results on the evaluation set: - Loss: 1.6513 ## 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: 14 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 30 - total_train_batch_size: 420 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 1.838 | 0.9633 | 14 | 1.7877 | | 1.5618 | 1.9954 | 29 | 1.6674 | | 1.4944 | 2.8899 | 42 | 1.6513 | ### Framework versions - PEFT 0.10.0 - Transformers 4.40.2 - Pytorch 2.2.0 - Datasets 2.19.1 - Tokenizers 0.19.1

AI-Wheelz/WestonEstate-Tanu

AI-Wheelz

--- license: openrail ---

sg-server/Meta-Llama-3-8B-SD-16Bit

sg-server

transformers

text-generation

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - **Developed by:** sg-server - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-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)

abhayesian/llama2-7b-sft-lora

abhayesian

--- license: llama2 base_model: meta-llama/Llama-2-7b-chat-hf tags: - generated_from_trainer model-index: - name: llama2-7b-sft-lora 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. --> # llama2-7b-sft-lora This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-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: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 4 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 1 ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1

Sayan007/masked_data_orig_model

Sayan007

Entry not found

azareswYT/DozzieGPT

azareswYT

Entry not found

PKU-Alignment/ProgressGym-HistLlama3-70B-C014-instruct-v0.1

PKU-Alignment

transformers

text-generation

--- license: cc-by-4.0 datasets: - PKU-Alignment/ProgressGym-HistText - PKU-Alignment/ProgressGym-TimelessQA base_model: - PKU-Alignment/ProgressGym-HistLlama3-70B-C014-pretrain - meta-llama/Meta-Llama-3-70B --- # ProgressGym-HistLlama3-70B-C014-instruct ## Overview #### The ProgressGym Framework  **ProgressGym-HistLlama3-70B-C014-instruct** is part of the **ProgressGym** framework for research and experimentation on *progress alignment* - the emulation of moral progress in AI alignment algorithms, as a measure to prevent risks of societal value lock-in. To quote the paper [*ProgressGym: Alignment with a Millennium of Moral Progress*](https://arxiv.org/abs/2406.20087): > Frontier AI systems, including large language models (LLMs), hold increasing influence over the epistemology of human users. Such influence can reinforce prevailing societal values, potentially contributing to the lock-in of misguided moral beliefs and, consequently, the perpetuation of problematic moral practices on a broad scale. > > We introduce *progress alignment* as a technical solution to mitigate this imminent risk. Progress alignment algorithms learn to emulate the mechanics of human moral progress, thereby addressing the susceptibility of existing alignment methods to contemporary moral blindspots. #### ProgressGym-HistLlama3-70B-C014-instruct ProgressGym-HistLlama3-70B-C014-instruct is one of the **36 historical language models** in the ProgressGym framework. **ProgressGym-HistLlama3-70B-C014-instruct is under continual iteration.** Improving upon the current version, new versions of the model are currently being trained to reflect historical moral tendencies in ever more comprehensive ways. **ProgressGym-HistLlama3-70B-C014-instruct is a 14th-century historical language model.** Based on [Meta-Llama-3-70B](https://huggingface.co/meta-llama/Meta-Llama-3-70B), It is continued-pretrained on the 14th-century text data from [ProgressGym-HistText](https://huggingface.co/datasets/PKU-Alignment/ProgressGym-HistText), using the following hyperparameters: - learning_rate: 3e-06 - train_batch_size: 2 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 32 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - total_eval_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - lr_scheduler_warmup_ratio: 0.075 - num_epochs: 4.0 - mixed_precision_training: Native AMP ... with the following training results: | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 2.2112 | 0.2121 | 7 | 2.2003 | | 2.1943 | 0.4242 | 14 | 2.1470 | | 2.1573 | 0.6364 | 21 | 2.0779 | | 2.0595 | 0.8485 | 28 | 2.0295 | | 1.9961 | 1.0606 | 35 | 2.0057 | | 1.9332 | 1.2727 | 42 | 1.9999 | | 1.9101 | 1.4848 | 49 | 1.9939 | | 1.906 | 1.6970 | 56 | 1.9907 | | 1.9054 | 1.9091 | 63 | 1.9889 | | 1.9037 | 2.1212 | 70 | 1.9878 | | 1.8786 | 2.3333 | 77 | 1.9872 | | 1.8962 | 2.5455 | 84 | 1.9866 | | 1.8668 | 2.7576 | 91 | 1.9859 | | 1.8988 | 2.9697 | 98 | 1.9850 | | 1.8966 | 3.1818 | 105 | 1.9842 | | 1.8847 | 3.3939 | 112 | 1.9835 | | 1.8748 | 3.6061 | 119 | 1.9829 | | 1.851 | 3.8182 | 126 | 1.9823 | Note that the training data volume for the continued pretraining stage is capped at 300MB. When the corresponding century's corpus exceeds this volume, the training data is randomly sampled to fit the volume. **ProgressGym-HistLlama3-70B-C014-instruct is an instruction-tuned language model.** It is tuned on [ProgressGym-TimelessQA](https://huggingface.co/datasets/PKU-Alignment/ProgressGym-TimelessQA), using the following hyperparameters: - learning_rate: 3e-06 - train_batch_size: 2 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 32 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - total_eval_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - lr_scheduler_warmup_ratio: 0.075 - num_epochs: 1.0 - mixed_precision_training: Native AMP ... where training results can be found in `all_results.json`, `trainer_log.jsonl`, and `training_loss.png` of the instruct model. ## Links - **[Paper Preprint]** [ProgressGym: Alignment with a Millennium of Moral Progress](https://arxiv.org/abs/2406.20087) - **[Leaderboard & Interactive Playground]** PKU-Alignment/ProgressGym-LeaderBoard *(coming soon - [stay tuned](https://forms.gle/1TWFLL4ZCLeYTD5N6)!)* - **[Huggingface Data & Model Collection]** [PKU-Alignment/ProgressGym](https://huggingface.co/collections/PKU-Alignment/progressgym-666735fcf3e4efa276226eaa) - **[Github Codebase]** [PKU-Alignment/ProgressGym](https://github.com/PKU-Alignment/ProgressGym) - **[PyPI Package]** *(coming soon - [stay tuned](https://forms.gle/1TWFLL4ZCLeYTD5N6)!)* ## Citation If the datasets, models, or framework of ProgressGym help you in your project, please cite ProgressGym using the bibtex entry below. ```text @article{progressgym, title={ProgressGym: Alignment with a Millennium of Moral Progress}, author={Tianyi Qiu and Yang Zhang and Xuchuan Huang and Jasmine Xinze Li and Jiaming Ji and Yaodong Yang}, journal={arXiv preprint arXiv:2406.20087}, eprint={2406.20087}, eprinttype = {arXiv}, year={2024} } ``` ## Ethics Statement - **Copyright information of historical text data sources**: - Project Gutenberg, one among our four source of our historical text data, consists only of texts in the public domain. - For the text that we draw from Internet Archive, we only include those that uploaded by *Library of Congress*, which are texts freely released online by the U.S. Library of Congress for research and public use. - The text data from Early English Books Online are, according to their publisher, "freely available to the public" and "available for access, distribution, use, or reuse by anyone". - The last remaining source of our historical text data, the Pile of Law dataset, is released under a Creative Commons license, which we adhere to in our use. - **Reproducibility**: To ensure reproducibility, we open-source all the code involved in the production of our main results (including the entire pipeline starting from data collection and model training), as well as the supporting infrastructure (the ProgressGym framework), making replication as easy as running a few simple script files. - **Misuse Prevention**: In order to prevent potential misuse of progress alignment algorithms, we have carefully formulated progress alignment as strictly value-neutral, without *a priori* assumptions on the direction of progress. In the event of potential misuse of our dataset, we condemn any misuse attempt to the strongest degree possible, and will work with the research community on whistleblowing for such attempts. - **Open-Sourcing**: We confirm that our code, data, and models are to be open-sourced under a CC-BY 4.0 license. We will continue to maintain and update our open-source repositories and models.

sajjad55/wsdbanglat5_2e4_D1

sajjad55

transformers

text2text-generation

--- base_model: ka05ar/Banglat5_Dx1 tags: - generated_from_trainer model-index: - name: wsdbanglat5_2e4_D1 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. --> # wsdbanglat5_2e4_D1 This model is a fine-tuned version of [ka05ar/Banglat5_Dx1](https://huggingface.co/ka05ar/Banglat5_Dx1) 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: 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: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.0 | 1.0 | 1481 | nan | | 0.0 | 2.0 | 2962 | nan | | 0.0 | 3.0 | 4443 | nan | | 0.0 | 4.0 | 5924 | nan | ### Framework versions - Transformers 4.41.2 - Pytorch 2.1.2 - Datasets 2.19.2 - Tokenizers 0.19.1

anon11112/standingsplit

anon11112

Entry not found

matthewliu0324/text-20240610-185727-3e-5

matthewliu0324

Entry not found

MohamedAhmedAE/TinyLlama-1.1B-Chat

MohamedAhmedAE

Entry not found

metta-ai/baseline.v0.3.1

metta-ai

sample-factory

reinforcement-learning

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory --- A(n) **APPO** model trained on the **GDY-MettaGrid** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r metta-ai/baseline.v0.3.1 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m <path.to.enjoy.module> --algo=APPO --env=GDY-MettaGrid --train_dir=./train_dir --experiment=baseline.v0.3.1 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m <path.to.train.module> --algo=APPO --env=GDY-MettaGrid --train_dir=./train_dir --experiment=baseline.v0.3.1 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

naveenreddy/unit-4

naveenreddy

reinforcement-learning

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: unit-4 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 198.60 +/- 8.75 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

intronhealth/afro-tts

intronhealth

coqui

text-to-speech

--- license: other license_name: coqui-public-model-license license_link: https://coqui.ai/cpml library_name: coqui pipeline_tag: text-to-speech widget: - text: "Abraham said today is a good day to sound like an African" --- # Afro-TTS Afro-TTS is the first pan-African accented English speech synthesis system capable of generating speech in 86 African accents. It includes 1000 personas representing the rich phonological diversity across the continent for applications in Education, Public Health, and Automated Content Creation. Afro-TTS lets you clone voices into different African accents by using just a quick 6-second audio clip. The model was adapted from the XTTS model which was developed by [Coqui Studio](https://coqui.ai/). Read more about this model in our paper: https://arxiv.org/abs/2406.11727 ### Features - Supports 86 unique African accents - Voice cloning with just a 6-second audio clip - Emotion and style transfer by cloning - Multi-accent English speech generation - 24kHz sampling rate for high-quality audio ## Performance Afro-TTS achieves near ground truth Mean Opinion Scores (MOS) for naturalness and accentedness. Objective and subjective evaluations demonstrated that the model generates natural-sounding accented speech, bridging the current gap in the representation of African voices in speech synthesis. ### Languages Afro-TTS supports only English languages for now. Stay tuned as we continue to add support for more languages. If you have any language requests, feel free to reach out! ### Code The code-base for the paper of this model can be found [here](https://github.com/intron-innovation/AfriSpeech-TTS) ### License This model is licensed under [Coqui Public Model License](https://coqui.ai/cpml). There's a lot that goes into a license for generative models, and you can read more of [the origin story of CPML here](https://coqui.ai/blog/tts/cpml). ### Contact Come and join in our Bioramp Community. We're active on [Masakhane Slack Server](https://join.slack.com/t/masakhane-nlp/shared_invite/zt-1zgnxx911-YWvICNas~mpeKDNqiO3r3g) and our [website](https://bioramp.org/). You can also mail the authors at sewade.ogun@inria.fr, tobi@intron.io #### Using Afro-TTS: Install the Coqui TTS package: ```bash pip install TTS ``` Run the following code: ```python from scipy.io.wavfile import write from TTS.tts.configs.xtts_config import XttsConfig from TTS.tts.models.xtts import Xtts config = XttsConfig() config.load_json("intronhealth/afro-tts/config.json") model = Xtts.init_from_config(config) model.load_checkpoint(config, checkpoint_dir="intronhealth/afro-tts/", eval=True) model.cuda() outputs = model.synthesize( "Abraham said today is a good day to sound like an African.", config, speaker_wav="audios/reference_accent.wav", gpt_cond_len=3, language="en", ) write("audios/output.wav", 24000, outputs['wav']) ``` ### BibTeX entry and citation info. ``` @misc{ogun20241000, title={1000 African Voices: Advancing inclusive multi-speaker multi-accent speech synthesis}, author={Sewade Ogun and Abraham T. Owodunni and Tobi Olatunji and Eniola Alese and Babatunde Oladimeji and Tejumade Afonja and Kayode Olaleye and Naome A. Etori and Tosin Adewumi}, year={2024}, eprint={2406.11727}, archivePrefix={arXiv}, primaryClass={id='eess.AS' full_name='Audio and Speech Processing' is_active=True alt_name=None in_archive='eess' is_general=False description='Theory and methods for processing signals representing audio, speech, and language, and their applications. This includes analysis, synthesis, enhancement, transformation, classification and interpretation of such signals as well as the design, development, and evaluation of associated signal processing systems. Machine learning and pattern analysis applied to any of the above areas is also welcome. Specific topics of interest include: auditory modeling and hearing aids; acoustic beamforming and source localization; classification of acoustic scenes; speaker separation; active noise control and echo cancellation; enhancement; de-reverberation; bioacoustics; music signals analysis, synthesis and modification; music information retrieval; audio for multimedia and joint audio-video processing; spoken and written language modeling, segmentation, tagging, parsing, understanding, and translation; text mining; speech production, perception, and psychoacoustics; speech analysis, synthesis, and perceptual modeling and coding; robust speech recognition; speaker recognition and characterization; deep learning, online learning, and graphical models applied to speech, audio, and language signals; and implementation aspects ranging from system architecture to fast algorithms.'} } ```

wilderaquilinoa/ModelPXL

wilderaquilinoa

Entry not found

kumarchavda/zx80zx81b

kumarchavda

fastai

--- tags: - fastai --- # Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed

heale22/textual_inversion_chair_5000steps_long_token_high_lr

heale22

Entry not found

Aakali/llama-2-7b-chat-bird_only

Aakali

peft

--- license: llama2 library_name: peft tags: - trl - sft - generated_from_trainer base_model: codellama/CodeLlama-7b-hf datasets: - generator model-index: - name: llama-2-7b-chat-bird_only 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. --> # llama-2-7b-chat-bird_only This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) on the generator 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: 3 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 6 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.11.1 - Transformers 4.41.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1

Lazarus-RnD/ub9fb389bfg98eb9uf_task_test_exp_test

Lazarus-RnD

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. 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]

jost/mistral7b_plantuml

jost

transformers

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/mistral-7b-bnb-4bit --- # Uploaded model - **Developed by:** jost - **License:** apache-2.0 - **Finetuned from model :** unsloth/mistral-7b-bnb-4bit This mistral 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)

keenhas/t5-small-finetuned-manimml-1.1.1

keenhas

transformers

text2text-generation

Entry not found

moomoo85/Moo

moomoo85

--- license: afl-3.0 ---

czstara12/yolov7-PCH-dz2

czstara12

--- license: mit ---

ericx1e/peft-starcoder-lora-a100

ericx1e

Entry not found

silent666/Qwen-Qwen1.5-7B-1718047815

silent666

Entry not found

AriaRahmati1/2shesmat9part3

AriaRahmati1

--- license: openrail ---

not-lain/finetuned_tinyllama_on_ads

not-lain

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]

ashleykleynhans/stable-audio-tools

ashleykleynhans

Entry not found

Coolwowsocoolwow/Black_Yoshi_Retrained

Coolwowsocoolwow

--- license: openrail ---

eclipsethee/test-eclipse

eclipsethee

Entry not found

mclemcrew/MERT-v0-finetuned-audio-effect-classification

mclemcrew

Entry not found

BryanSagbay/naive-bayes-text-classification

BryanSagbay

Entry not found

silent666/google-gemma-2b-1718049001

silent666

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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TopperThijs/Llama2-complete-new-data-Finetuned-5epochs25mlm

TopperThijs

Entry not found

apoorvapise/Custom_NER_Medical_IPE

apoorvapise

--- language: - en tags: - medical - NER - SpaCy --- This is a custom NER (Named Entity Recognition) Model trained using Medical Timeout data using SpaCy as a base model.

stiucsib/gemma_sciq_json_prompt

stiucsib

transformers

text-generation

--- library_name: transformers tags: - llama-factory --- # 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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(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]

tanoManzo/hyenadna-large-1m-seqlen-hf_ft_Hepg2_1kbpHG19_DHSs_H3K27AC

tanoManzo

Entry not found

Ibrahim-Alam/xlnet-base-cased_FTd_on_nli_fever

Ibrahim-Alam

Entry not found

digitalai/blood

digitalai

Entry not found

Davidcv18/llama3-8b-oig-unsloth-u

Davidcv18

transformers

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - **Developed by:** Davidcv18 - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-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)

zhuqiang/repr100

zhuqiang

Entry not found

Lazarus-RnD/baf2b252097d46299a_output_testing

Lazarus-RnD

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. 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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]

Rudra03/xlm-roberta-base-finetuned-claim

Rudra03

Entry not found

ChickWard/lora_model

ChickWard

transformers

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - **Developed by:** ChickWard - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-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)

colin1842/s23dr_tum

colin1842

# Handcrafted solution example for the S23DR competition This repo provides an example of a simple algorithm to reconstruct wireframe and submit to S23DR competition. The repo consistst of the following parts: - `script.py` - the main file, which is run by the competition space. It should produce `submission.parquet` as the result of the run. - `hoho.py` - the file for parsing the dataset at the inference time. Do NOT change it. - `feature_solution.py` - contains the implementation of ours method - other `*.py` files - helper i/o and visualization utilities - `packages/` - the directory to put python wheels for the custom packages you want to install and use. ## Solution description The solution is based on 2D features and line matching. --- license: apache-2.0 ---

AdamKasumovic/llama3-70b-instruct-mmlu-college-medicine-af

AdamKasumovic

transformers

text-generation

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-70b-Instruct-bnb-4bit --- # Uploaded model - **Developed by:** AdamKasumovic - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-70b-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)

BryanSagbay/random-forest-text-classification

BryanSagbay

Entry not found

MingAI/vit-base-patch16-224-in21k-finetuned-lora-food101

MingAI

Entry not found

quirky-lats-at-mats/trained_cyber_wmdp_lat_5

quirky-lats-at-mats

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. 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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]

ChickWard/model

ChickWard

transformers

text-generation

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - **Developed by:** ChickWard - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-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)

kristiannordby/decisions

kristiannordby

--- tags: - generated_from_trainer model-index: - name: decisions 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. --> # decisions This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0743 ## 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: 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: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.1332 | 1.0 | 32 | 0.1266 | | 0.0493 | 2.0 | 64 | 0.0932 | | 0.0215 | 3.0 | 96 | 0.0730 | | 0.0122 | 4.0 | 128 | 0.0743 | ### Framework versions - Transformers 4.42.0.dev0 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1

ZWG817/Llama3_Chat_Materials

ZWG817

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. 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]

mfcg/MarloMix-t5

mfcg

Entry not found

xuliu15/FT-English-1haa

xuliu15

Entry not found

ulises-on/CR50

ulises-on

--- license: unknown ---

Yokoso-coding/Brazil_ecommerce_olist

Yokoso-coding

Entry not found

mister-mehedi/sample_biobert_bag_model

mister-mehedi

--- license: other license_name: mister1 license_link: LICENSE --- biobert pretrained model finetuned by bioasq 4b preprocessed training dataset by bagging process and predictions through majority voting. datatset sub-sample by 10 times. For each, finetuned the biobert model by 3 epochs.

zaddyzaddy/soro_34k

zaddyzaddy

transformers

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - **Developed by:** zaddyzaddy - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-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)

ogbi/ika-mms-1b

ogbi

transformers

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iamalexcaspian/Casey-TheCasagrandes-JayHatton

iamalexcaspian

Entry not found

iloncka/exp_5_new_bg_simple-subs_1_v_5_convnextv2_pico.fcmae_ft_in1k_ep_60

iloncka

fastai

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Nazarii96/xlm-roberta-base-finetuned-ner

Nazarii96

Entry not found

ClusterlabAi/LlamAr-8B-v1

ClusterlabAi

transformers

text-generation

--- library_name: transformers license: llama3 datasets: - ClusterlabAi/InstAr-500k language: - ar - en pipeline_tag: text-generation --- ### Model Description LlamAr-8B-v1 is an Arabic language model developed by fine-tuning the [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) model with a custom Arabic instruction dataset, [InstAr-500k](https://huggingface.co/datasets/ClusterlabAi/InstAr-500k). This model excels in various Arabic NLP tasks, including multi-tasking abilities, contextual understanding, and vocabulary. ## Model Details * **Model name**: LlamAr-8B-v1 * **Model type**: GPT-like model with 8 billion parameters * **Base model**: [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) * **Languages**: Primarily Arabic and English * **Paper**: LlamAr & GemmAr: Enhancing LLMs Through Arabic Instruction-Tuning (Upcoming) * **License**: [LLaMA 3 License](https://llama.meta.com/llama3/license/) * **Fine-tuning dataset:** [InstAr-500k](https://huggingface.co/datasets/ClusterlabAi/InstAr-500k) ## Performance LlamAr-8B-v1 was evaluated using multiple benchmarks designed to test its performance across Arabic NLP tasks. These benchmarks included Arabic_MMLU, ACVA, Alghafa, Arabic_Exams, ARC-Challenge, and more, each targeting different aspects of language understanding such as multi-tasking abilities, contextual comprehension, and vocabulary. The evaluations were conducted using key frameworks like the Open Arabic LLM Leaderboard (OALL), which uses the LightEval framework. Our model showed strong performance on the OALL leaderboard: <p align="left width="100%"> <a ><img src="data:image/png;base64,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" alt="Eval" style="width: 70%; display: block; margin: auto;"></a></p> ### Eval Scores | Metric |Value| |----------------------|----:| |Arabic_MMLU |42.58| |ACVA |79.53| |Alghafa |64.47| |Arabic_Exams |41.71| |ARC_challenge_okapi_ar|49.74| |ARC_easy_ar |59.17| |Boolq_ar |64.66| |Copa_ext_ar |66.66| |Hellaswag_okapi_ar |27.92| |OpenBook_QA_ext_ar |50.30| |Piqa_Ar |61.04| |Race_ar |51.41| |Sciq_ar |86.63| |Toxigen_ar |45.02| |**Total Average** |**60.92**| ## Training LlamAr-8B-v1 was developed through a fine-tuning process using the InstAr-500k dataset crafted to improve the base model Arabic skills. ### Method Overview #### Dataset The [InstAr-500k](https://huggingface.co/datasets/ClusterlabAi/InstAr-500k) dataset combines synthetic and human-crafted data to ensure a diverse range of instructions and responses. The synthetic data was generated using the Command R+ model, while human-crafted data was sourced from [101 Billion Arabic Words dataset](https://huggingface.co/datasets/ClusterlabAi/101_billion_arabic_words_dataset) <p align="left" width="100%"> <a ><img 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" alt="Pipeline" style="width: 70%; display: block; margin: auto;"></a></p> #### Training Hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-4 - Batch Size: 2.0 - Epochs: 3 - Gradient Accumulation Steps: 8 - Cutoff Length: 2048 - Optimizer: AdamW_torch - Precision: bfloat16 - Scheduler: Cosine learning rate scheduler #### Training Techniques - **RoPE**: Dynamic rotary positional embeddings for better long-context extrapolation. - **LoRA:**: Low-Rank Adaptation to reduce trainable parameters ## Citation ``` @misc{(chouikhi2024), title={LlamAr & GemmAr: Enhancing LLMs Through Arabic Instruction-Tuning}, author={Hasna Chouikhi and Manel Aloui and Cyrine Ben Hammou and Ghaith Chaabane and Haithem Kchaou and Chehir Dhaouadi}, year={2024}, eprint={}, archivePrefix={arXiv} } ```

BIGHEIGHTS/NAOMI

BIGHEIGHTS

--- license: openrail ---

ntbk/vit-base-patch16-224-finetuned-flower

ntbk

transformers

image-classification

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder model-index: - name: vit-base-patch16-224-finetuned-flower 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-finetuned-flower This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder 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: 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 ### Framework versions - Transformers 4.24.0 - Pytorch 2.3.0+cu121 - Datasets 2.7.1 - Tokenizers 0.13.3

Preuss01/Deadpoolvoice

Preuss01

Entry not found

stiucsib/gemma_best_on_mvp

stiucsib

transformers

text-generation

--- library_name: transformers tags: - llama-factory --- # 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]

murphybrendan/Reinforce-Cartpole-v0

murphybrendan

reinforcement-learning

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Cartpole-v0 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

AmevinLS/phi-2-lora-fakenews

AmevinLS

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. 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]

tcmemounal17/myAI

tcmemounal17

--- license: cc-by-nc-sa-4.0 ---

PKU-Alignment/ProgressGym-HistLlama3-70B-C015-instruct-v0.1

PKU-Alignment

transformers

text-generation

--- license: cc-by-4.0 datasets: - PKU-Alignment/ProgressGym-HistText - PKU-Alignment/ProgressGym-TimelessQA base_model: - PKU-Alignment/ProgressGym-HistLlama3-70B-C015-pretrain - meta-llama/Meta-Llama-3-70B --- # ProgressGym-HistLlama3-70B-C015-instruct ## Overview #### The ProgressGym Framework  **ProgressGym-HistLlama3-70B-C015-instruct** is part of the **ProgressGym** framework for research and experimentation on *progress alignment* - the emulation of moral progress in AI alignment algorithms, as a measure to prevent risks of societal value lock-in. To quote the paper [*ProgressGym: Alignment with a Millennium of Moral Progress*](https://arxiv.org/abs/2406.20087): > Frontier AI systems, including large language models (LLMs), hold increasing influence over the epistemology of human users. Such influence can reinforce prevailing societal values, potentially contributing to the lock-in of misguided moral beliefs and, consequently, the perpetuation of problematic moral practices on a broad scale. > > We introduce *progress alignment* as a technical solution to mitigate this imminent risk. Progress alignment algorithms learn to emulate the mechanics of human moral progress, thereby addressing the susceptibility of existing alignment methods to contemporary moral blindspots. #### ProgressGym-HistLlama3-70B-C015-instruct ProgressGym-HistLlama3-70B-C015-instruct is one of the **36 historical language models** in the ProgressGym framework. **ProgressGym-HistLlama3-70B-C015-instruct is under continual iteration.** Improving upon the current version, new versions of the model are currently being trained to reflect historical moral tendencies in ever more comprehensive ways. **ProgressGym-HistLlama3-70B-C015-instruct is a 15th-century historical language model.** Based on [Meta-Llama-3-70B](https://huggingface.co/meta-llama/Meta-Llama-3-70B), It is continued-pretrained on the 15th-century text data from [ProgressGym-HistText](https://huggingface.co/datasets/PKU-Alignment/ProgressGym-HistText), using the following hyperparameters: - learning_rate: 3e-06 - train_batch_size: 1 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 32 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - lr_scheduler_warmup_ratio: 0.075 - num_epochs: 3.0 - mixed_precision_training: Native AMP ... with the following training results: | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 2.0376 | 0.1558 | 3 | 2.0736 | | 2.0549 | 0.3117 | 6 | 2.0736 | | 2.1192 | 0.4675 | 9 | 2.0664 | | 2.1346 | 0.6234 | 12 | 2.0558 | | 2.08 | 0.7792 | 15 | 2.0401 | | 2.0576 | 0.9351 | 18 | 2.0248 | | 2.0351 | 1.0909 | 21 | 2.0168 | | 1.9633 | 1.2468 | 24 | 2.0135 | | 2.0389 | 1.4026 | 27 | 2.0117 | | 1.9637 | 1.5584 | 30 | 2.0107 | | 1.9749 | 1.7143 | 33 | 2.0100 | | 2.0231 | 1.8701 | 36 | 2.0094 | | 1.9785 | 2.0260 | 39 | 2.0088 | | 1.9619 | 2.1818 | 42 | 2.0083 | | 1.9971 | 2.3377 | 45 | 2.0078 | | 1.9992 | 2.4935 | 48 | 2.0075 | | 1.9912 | 2.6494 | 51 | 2.0071 | | 1.9894 | 2.8052 | 54 | 2.0067 | | 2.0143 | 2.9610 | 57 | 2.0062 | Note that the training data volume for the continued pretraining stage is capped at 300MB. When the corresponding century's corpus exceeds this volume, the training data is randomly sampled to fit the volume. **ProgressGym-HistLlama3-70B-C015-instruct is an instruction-tuned language model.** It is tuned on [ProgressGym-TimelessQA](https://huggingface.co/datasets/PKU-Alignment/ProgressGym-TimelessQA), using the following hyperparameters: - learning_rate: 3e-06 - train_batch_size: 2 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 32 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - total_eval_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - lr_scheduler_warmup_ratio: 0.075 - num_epochs: 1.0 - mixed_precision_training: Native AMP ... where training results can be found in `all_results.json`, `trainer_log.jsonl`, and `training_loss.png` of the instruct model. ## Links - **[Paper Preprint]** [ProgressGym: Alignment with a Millennium of Moral Progress](https://arxiv.org/abs/2406.20087) - **[Leaderboard & Interactive Playground]** PKU-Alignment/ProgressGym-LeaderBoard *(coming soon - [stay tuned](https://forms.gle/1TWFLL4ZCLeYTD5N6)!)* - **[Huggingface Data & Model Collection]** [PKU-Alignment/ProgressGym](https://huggingface.co/collections/PKU-Alignment/progressgym-666735fcf3e4efa276226eaa) - **[Github Codebase]** [PKU-Alignment/ProgressGym](https://github.com/PKU-Alignment/ProgressGym) - **[PyPI Package]** *(coming soon - [stay tuned](https://forms.gle/1TWFLL4ZCLeYTD5N6)!)* ## Citation If the datasets, models, or framework of ProgressGym help you in your project, please cite ProgressGym using the bibtex entry below. ```text @article{progressgym, title={ProgressGym: Alignment with a Millennium of Moral Progress}, author={Tianyi Qiu and Yang Zhang and Xuchuan Huang and Jasmine Xinze Li and Jiaming Ji and Yaodong Yang}, journal={arXiv preprint arXiv:2406.20087}, eprint={2406.20087}, eprinttype = {arXiv}, year={2024} } ``` ## Ethics Statement - **Copyright information of historical text data sources**: - Project Gutenberg, one among our four source of our historical text data, consists only of texts in the public domain. - For the text that we draw from Internet Archive, we only include those that uploaded by *Library of Congress*, which are texts freely released online by the U.S. Library of Congress for research and public use. - The text data from Early English Books Online are, according to their publisher, "freely available to the public" and "available for access, distribution, use, or reuse by anyone". - The last remaining source of our historical text data, the Pile of Law dataset, is released under a Creative Commons license, which we adhere to in our use. - **Reproducibility**: To ensure reproducibility, we open-source all the code involved in the production of our main results (including the entire pipeline starting from data collection and model training), as well as the supporting infrastructure (the ProgressGym framework), making replication as easy as running a few simple script files. - **Misuse Prevention**: In order to prevent potential misuse of progress alignment algorithms, we have carefully formulated progress alignment as strictly value-neutral, without *a priori* assumptions on the direction of progress. In the event of potential misuse of our dataset, we condemn any misuse attempt to the strongest degree possible, and will work with the research community on whistleblowing for such attempts. - **Open-Sourcing**: We confirm that our code, data, and models are to be open-sourced under a CC-BY 4.0 license. We will continue to maintain and update our open-source repositories and models.

thenaivekid/speecht5_finetuned_voxpopuli_nl

thenaivekid

Entry not found

Svngoku/Qwen2-7b-Text2SQL

Svngoku

transformers

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - qwen2 - trl base_model: unsloth/Qwen2-7B-bnb-4bit datasets: - gretelai/synthetic_text_to_sql --- # Qwen2-7b-Text2SQL - **Developed by:** Svngoku - **License:** apache-2.0 - **Finetuned from model :** unsloth/Qwen2-7B-bnb-4bit This qwen2 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)

developedbyant/lora_model

developedbyant

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. 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]

Cgreer011/heds

Cgreer011

Entry not found

alexandro767/saiga_for_text2sql

alexandro767

Entry not found

ezfzefer/zvids

ezfzefer

--- license: mit ---

Sandy1857/custom-dataset-finetuned_1

Sandy1857

Entry not found

Dreamuno/MLM_FinetunedModel

Dreamuno

Entry not found