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ptx0/terminus-xl-velocity-v2

ptx0

diffusers

--- license: openrail++ --- # Terminus XL - Gamma (v2 preview) This is an in-progress checkpoint of [the "Gamma" model](/ptx0/terminus-xl-gamma-v1) from the Terminus XL series. It's updated randomly for evaluation as progress rolls on.

shallow6414/mzi7bh3

shallow6414

transformers

text-generation

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

automerger/T3qInex12-7B

automerger

transformers

text-generation

--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - automerger base_model: - chihoonlee10/T3Q-Mistral-Orca-Math-DPO - MSL7/INEX12-7b --- # T3qInex12-7B T3qInex12-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration. * [chihoonlee10/T3Q-Mistral-Orca-Math-DPO](https://huggingface.co/chihoonlee10/T3Q-Mistral-Orca-Math-DPO) * [MSL7/INEX12-7b](https://huggingface.co/MSL7/INEX12-7b) ## 🧩 Configuration ```yaml slices: - sources: - model: chihoonlee10/T3Q-Mistral-Orca-Math-DPO layer_range: [0, 32] - model: MSL7/INEX12-7b layer_range: [0, 32] merge_method: slerp base_model: chihoonlee10/T3Q-Mistral-Orca-Math-DPO parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 random_seed: 0 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "automerger/T3qInex12-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```

hvein/melotts5235

hvein

Entry not found

liamvbetts/t5-small-finetuned-2024-03-21

liamvbetts

transformers

text2text-generation

--- base_model: liamvbetts/t5-small-finetuned-2024-03-20 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-small-finetuned-2024-03-21 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. --> # t5-small-finetuned-2024-03-21 This model is a fine-tuned version of [liamvbetts/t5-small-finetuned-2024-03-20](https://huggingface.co/liamvbetts/t5-small-finetuned-2024-03-20) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9005 - Rouge1: 32.5532 - Rouge2: 17.9983 - Rougel: 28.9441 - Rougelsum: 29.5273 - Gen Len: 18.4722 ## 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: 4e-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: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.7218 | 1.0 | 286 | 1.9005 | 32.5532 | 17.9983 | 28.9441 | 29.5273 | 18.4722 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

mradermacher/llama-65b-instruct-GGUF

mradermacher

--- exported_from: upstage/llama-65b-instruct language: - en library_name: transformers quantized_by: mradermacher tags: - upstage - llama - instruct - instruction --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/upstage/llama-65b-instruct <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [PART 1](https://huggingface.co/mradermacher/llama-65b-instruct-GGUF/resolve/main/llama-65b-instruct.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/llama-65b-instruct-GGUF/resolve/main/llama-65b-instruct.Q8_0.gguf.part2of2) | Q8_0 | 69.5 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->

URCBOY/chatboturc

URCBOY

Entry not found

miguelcidadesilva/my_awesome_model

miguelcidadesilva

Entry not found

Coolwowsocoolwow/Baldi_Laugh

Coolwowsocoolwow

--- license: openrail ---

ashishp-wiai/ClipArt_LoRA_70-2024-04-14

ashishp-wiai

Entry not found

Dracones/mixtral-8x22b-instruct-oh_exl2_4.0bpw

Dracones

transformers

text-generation

--- language: - en license: apache-2.0 datasets: - teknium/OpenHermes-2.5 base_model: mistral-community/Mixtral-8x22B-v0.1 tags: - exl2 --- # mixtral-8x22b-instruct-oh - EXL2 4.0bpw This is a 4.0bpw EXL2 quant of [fireworks-ai/mixtral-8x22b-instruct-oh](https://huggingface.co/fireworks-ai/mixtral-8x22b-instruct-oh) Details about the model can be found at the above model page. ## EXL2 Version These quants were made with exllamav2 version 0.0.18. Quants made on this version of EXL2 may not work on older versions of the exllamav2 library. If you have problems loading these models, please update Text Generation WebUI to the latest version. ## Perplexity Scoring Below are the perplexity scores for the EXL2 models. A lower score is better. _TODO_ ### Perplexity Script This was the script used for perplexity testing. ```bash #!/bin/bash # Activate the conda environment source ~/miniconda3/etc/profile.d/conda.sh conda activate exllamav2 # Set the model name and bit size MODEL_NAME="mixtral-8x22b-instruct-oh" BIT_PRECISIONS=(7.0 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.75 2.5 2.25) # Print the markdown table header echo "| Quant Level | Perplexity Score |" echo "|-------------|------------------|" for BIT_PRECISION in "${BIT_PRECISIONS[@]}" do MODEL_DIR="models/${MODEL_NAME}_exl2_${BIT_PRECISION}bpw" if [ -d "$MODEL_DIR" ]; then output=$(python test_inference.py -m "$MODEL_DIR" -gs 44,48 -ed data/wikitext/wikitext-2-v1.parquet) score=$(echo "$output" | grep -oP 'Evaluation perplexity: \K[\d.]+') echo "| $BIT_PRECISION | $score |" fi done ``` ## Quant Details This is the script used for quantization. ```bash #!/bin/bash # Activate the conda environment source ~/miniconda3/etc/profile.d/conda.sh conda activate exllamav2 # Set the model name and bit size MODEL_NAME="mixtral-8x22b-instruct-oh" # Define variables MODEL_DIR="models/$MODEL_NAME" OUTPUT_DIR="exl2_$MODEL_NAME" MEASUREMENT_FILE="measurements/$MODEL_NAME.json" # Create the measurement file if needed if [ ! -f "$MEASUREMENT_FILE" ]; then echo "Creating $MEASUREMENT_FILE" # Create directories if [ -d "$OUTPUT_DIR" ]; then rm -r "$OUTPUT_DIR" fi mkdir "$OUTPUT_DIR" python convert.py -i $MODEL_DIR -o $OUTPUT_DIR -nr -om $MEASUREMENT_FILE fi # Choose one of the below. Either create a single quant for testing or a batch of them. # BIT_PRECISIONS=(2.25) BIT_PRECISIONS=(7.0 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.75 2.5 2.25) for BIT_PRECISION in "${BIT_PRECISIONS[@]}" do CONVERTED_FOLDER="models/${MODEL_NAME}_exl2_${BIT_PRECISION}bpw" # If it doesn't already exist, make the quant if [ ! -d "$CONVERTED_FOLDER" ]; then echo "Creating $CONVERTED_FOLDER" # Create directories if [ -d "$OUTPUT_DIR" ]; then rm -r "$OUTPUT_DIR" fi mkdir "$OUTPUT_DIR" mkdir "$CONVERTED_FOLDER" # Run conversion commands python convert.py -i $MODEL_DIR -o $OUTPUT_DIR -nr -m $MEASUREMENT_FILE -b $BIT_PRECISION -cf $CONVERTED_FOLDER fi done ```

Xeolus/c4ai-command-r-v01-Q4_K_M-GGUF

Xeolus

transformers

--- language: - en - fr - de - es - it - pt - ja - ko - zh - ar license: cc-by-nc-4.0 library_name: transformers tags: - llama-cpp - gguf-my-repo --- # Xeolus/c4ai-command-r-v01-Q4_K_M-GGUF This model was converted to GGUF format from [`CohereForAI/c4ai-command-r-v01`](https://huggingface.co/CohereForAI/c4ai-command-r-v01) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/CohereForAI/c4ai-command-r-v01) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo Xeolus/c4ai-command-r-v01-Q4_K_M-GGUF --model c4ai-command-r-v01.Q4_K_M.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo Xeolus/c4ai-command-r-v01-Q4_K_M-GGUF --model c4ai-command-r-v01.Q4_K_M.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m c4ai-command-r-v01.Q4_K_M.gguf -n 128 ```

kty-bespin/test-model-public

kty-bespin

Entry not found

liamvbetts/t5-small-finetuned-2024-03-22

liamvbetts

transformers

text2text-generation

--- base_model: liamvbetts/t5-small-finetuned-2024-03-21 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-small-finetuned-2024-03-22 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. --> # t5-small-finetuned-2024-03-22 This model is a fine-tuned version of [liamvbetts/t5-small-finetuned-2024-03-21](https://huggingface.co/liamvbetts/t5-small-finetuned-2024-03-21) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6982 - Rouge1: 30.9866 - Rouge2: 16.6554 - Rougel: 27.0934 - Rougelsum: 27.6717 - Gen Len: 18.6056 ## 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: 4e-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: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.9185 | 1.0 | 284 | 1.6982 | 30.9866 | 16.6554 | 27.0934 | 27.6717 | 18.6056 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

mahiatlinux/lora_test1

mahiatlinux

transformers

Invalid username or password.

pascualeley/w266_model_3_jobbert

pascualeley

keras

--- library_name: keras --- ## 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: | Hyperparameters | Value | | :-- | :-- | | name | Adam | | weight_decay | None | | clipnorm | None | | global_clipnorm | None | | clipvalue | None | | use_ema | False | | ema_momentum | 0.99 | | ema_overwrite_frequency | None | | jit_compile | True | | is_legacy_optimizer | False | | learning_rate | 4.999999873689376e-05 | | beta_1 | 0.9 | | beta_2 | 0.999 | | epsilon | 1e-07 | | amsgrad | False | | training_precision | float32 | ## Model Plot <details> <summary>View Model Plot</summary>  </details>

iamnguyen/queen-chat

iamnguyen

Entry not found

grantlisby/Spongebob

grantlisby

--- license: openrail ---

relu-ntnu/bart-large-cnn_v1_trained_on_500

relu-ntnu

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]

grantlisby/Gary

grantlisby

--- license: openrail ---

relu-ntnu/bart-large-xsum_v1_trained_on_100

relu-ntnu

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]

mradermacher/34b-beta-i1-GGUF

mradermacher

--- exported_from: CausalLM/34b-beta language: - en library_name: transformers license: gpl-3.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> weighted/imatrix quants of https://huggingface.co/CausalLM/34b-beta <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/34b-beta-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/34b-beta-i1-GGUF/resolve/main/34b-beta.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 9.4 | | | [GGUF](https://huggingface.co/mradermacher/34b-beta-i1-GGUF/resolve/main/34b-beta.i1-IQ2_XS.gguf) | i1-IQ2_XS | 10.4 | | | [GGUF](https://huggingface.co/mradermacher/34b-beta-i1-GGUF/resolve/main/34b-beta.i1-IQ2_S.gguf) | i1-IQ2_S | 11.0 | | | [GGUF](https://huggingface.co/mradermacher/34b-beta-i1-GGUF/resolve/main/34b-beta.i1-IQ2_M.gguf) | i1-IQ2_M | 11.9 | | | [GGUF](https://huggingface.co/mradermacher/34b-beta-i1-GGUF/resolve/main/34b-beta.i1-Q2_K.gguf) | i1-Q2_K | 12.9 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/34b-beta-i1-GGUF/resolve/main/34b-beta.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 13.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/34b-beta-i1-GGUF/resolve/main/34b-beta.i1-IQ3_XS.gguf) | i1-IQ3_XS | 14.3 | | | [GGUF](https://huggingface.co/mradermacher/34b-beta-i1-GGUF/resolve/main/34b-beta.i1-Q3_K_S.gguf) | i1-Q3_K_S | 15.1 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/34b-beta-i1-GGUF/resolve/main/34b-beta.i1-IQ3_S.gguf) | i1-IQ3_S | 15.1 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/34b-beta-i1-GGUF/resolve/main/34b-beta.i1-IQ3_M.gguf) | i1-IQ3_M | 15.7 | | | [GGUF](https://huggingface.co/mradermacher/34b-beta-i1-GGUF/resolve/main/34b-beta.i1-Q3_K_M.gguf) | i1-Q3_K_M | 16.8 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/34b-beta-i1-GGUF/resolve/main/34b-beta.i1-Q3_K_L.gguf) | i1-Q3_K_L | 18.2 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/34b-beta-i1-GGUF/resolve/main/34b-beta.i1-IQ4_XS.gguf) | i1-IQ4_XS | 18.6 | | | [GGUF](https://huggingface.co/mradermacher/34b-beta-i1-GGUF/resolve/main/34b-beta.i1-Q4_0.gguf) | i1-Q4_0 | 19.6 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/34b-beta-i1-GGUF/resolve/main/34b-beta.i1-Q4_K_S.gguf) | i1-Q4_K_S | 19.7 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/34b-beta-i1-GGUF/resolve/main/34b-beta.i1-Q4_K_M.gguf) | i1-Q4_K_M | 20.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/34b-beta-i1-GGUF/resolve/main/34b-beta.i1-Q5_K_S.gguf) | i1-Q5_K_S | 23.8 | | | [GGUF](https://huggingface.co/mradermacher/34b-beta-i1-GGUF/resolve/main/34b-beta.i1-Q5_K_M.gguf) | i1-Q5_K_M | 24.4 | | | [GGUF](https://huggingface.co/mradermacher/34b-beta-i1-GGUF/resolve/main/34b-beta.i1-Q6_K.gguf) | i1-Q6_K | 28.3 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->

liamvbetts/t5-small-finetuned-2024-03-23

liamvbetts

transformers

text2text-generation

--- base_model: liamvbetts/t5-small-finetuned-2024-03-22 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-small-finetuned-2024-03-23 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. --> # t5-small-finetuned-2024-03-23 This model is a fine-tuned version of [liamvbetts/t5-small-finetuned-2024-03-22](https://huggingface.co/liamvbetts/t5-small-finetuned-2024-03-22) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9844 - Rouge1: 31.4542 - Rouge2: 16.6935 - Rougel: 26.6655 - Rougelsum: 27.3247 - Gen Len: 18.8028 ## 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: 4e-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: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 2.0832 | 1.0 | 282 | 1.9844 | 31.4542 | 16.6935 | 26.6655 | 27.3247 | 18.8028 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

imkhairulikhwan/llm-test

imkhairulikhwan

--- license: apache-2.0 ---

pthornton614/CodeLama-7b-Python-1k-platypus-v3

pthornton614

transformers

text-generation

Entry not found

Dracones/mixtral-8x22b-instruct-oh_exl2_3.5bpw

Dracones

transformers

text-generation

--- language: - en license: apache-2.0 datasets: - teknium/OpenHermes-2.5 base_model: mistral-community/Mixtral-8x22B-v0.1 tags: - exl2 --- # mixtral-8x22b-instruct-oh - EXL2 3.5bpw This is a 3.5bpw EXL2 quant of [fireworks-ai/mixtral-8x22b-instruct-oh](https://huggingface.co/fireworks-ai/mixtral-8x22b-instruct-oh) Details about the model can be found at the above model page. ## EXL2 Version These quants were made with exllamav2 version 0.0.18. Quants made on this version of EXL2 may not work on older versions of the exllamav2 library. If you have problems loading these models, please update Text Generation WebUI to the latest version. ## Perplexity Scoring Below are the perplexity scores for the EXL2 models. A lower score is better. _TODO_ ### Perplexity Script This was the script used for perplexity testing. ```bash #!/bin/bash # Activate the conda environment source ~/miniconda3/etc/profile.d/conda.sh conda activate exllamav2 # Set the model name and bit size MODEL_NAME="mixtral-8x22b-instruct-oh" BIT_PRECISIONS=(7.0 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.75 2.5 2.25) # Print the markdown table header echo "| Quant Level | Perplexity Score |" echo "|-------------|------------------|" for BIT_PRECISION in "${BIT_PRECISIONS[@]}" do MODEL_DIR="models/${MODEL_NAME}_exl2_${BIT_PRECISION}bpw" if [ -d "$MODEL_DIR" ]; then output=$(python test_inference.py -m "$MODEL_DIR" -gs 44,48 -ed data/wikitext/wikitext-2-v1.parquet) score=$(echo "$output" | grep -oP 'Evaluation perplexity: \K[\d.]+') echo "| $BIT_PRECISION | $score |" fi done ``` ## Quant Details This is the script used for quantization. ```bash #!/bin/bash # Activate the conda environment source ~/miniconda3/etc/profile.d/conda.sh conda activate exllamav2 # Set the model name and bit size MODEL_NAME="mixtral-8x22b-instruct-oh" # Define variables MODEL_DIR="models/$MODEL_NAME" OUTPUT_DIR="exl2_$MODEL_NAME" MEASUREMENT_FILE="measurements/$MODEL_NAME.json" # Create the measurement file if needed if [ ! -f "$MEASUREMENT_FILE" ]; then echo "Creating $MEASUREMENT_FILE" # Create directories if [ -d "$OUTPUT_DIR" ]; then rm -r "$OUTPUT_DIR" fi mkdir "$OUTPUT_DIR" python convert.py -i $MODEL_DIR -o $OUTPUT_DIR -nr -om $MEASUREMENT_FILE fi # Choose one of the below. Either create a single quant for testing or a batch of them. # BIT_PRECISIONS=(2.25) BIT_PRECISIONS=(7.0 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.75 2.5 2.25) for BIT_PRECISION in "${BIT_PRECISIONS[@]}" do CONVERTED_FOLDER="models/${MODEL_NAME}_exl2_${BIT_PRECISION}bpw" # If it doesn't already exist, make the quant if [ ! -d "$CONVERTED_FOLDER" ]; then echo "Creating $CONVERTED_FOLDER" # Create directories if [ -d "$OUTPUT_DIR" ]; then rm -r "$OUTPUT_DIR" fi mkdir "$OUTPUT_DIR" mkdir "$CONVERTED_FOLDER" # Run conversion commands python convert.py -i $MODEL_DIR -o $OUTPUT_DIR -nr -m $MEASUREMENT_FILE -b $BIT_PRECISION -cf $CONVERTED_FOLDER fi done ```

MohamedAhmedAE/Mistral-7b_0.2-wiki_QA-Colab_Standard

MohamedAhmedAE

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]

relu-ntnu/bart-large-xsum_v1_trained_on_500

relu-ntnu

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]

liamvbetts/t5-small-finetuned-2024-03-24

liamvbetts

transformers

text2text-generation

--- base_model: liamvbetts/t5-small-finetuned-2024-03-23 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-small-finetuned-2024-03-24 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. --> # t5-small-finetuned-2024-03-24 This model is a fine-tuned version of [liamvbetts/t5-small-finetuned-2024-03-23](https://huggingface.co/liamvbetts/t5-small-finetuned-2024-03-23) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0203 - Rouge1: 34.1604 - Rouge2: 20.5269 - Rougel: 30.0414 - Rougelsum: 30.5345 - Gen Len: 18.9452 ## 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: 4e-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: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 2.1072 | 1.0 | 288 | 2.0203 | 34.1604 | 20.5269 | 30.0414 | 30.5345 | 18.9452 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

Dracones/mixtral-8x22b-instruct-oh_exl2_3.0bpw

Dracones

transformers

text-generation

--- language: - en license: apache-2.0 datasets: - teknium/OpenHermes-2.5 base_model: mistral-community/Mixtral-8x22B-v0.1 tags: - exl2 --- # mixtral-8x22b-instruct-oh - EXL2 3.0bpw This is a 3.0bpw EXL2 quant of [fireworks-ai/mixtral-8x22b-instruct-oh](https://huggingface.co/fireworks-ai/mixtral-8x22b-instruct-oh) Details about the model can be found at the above model page. ## EXL2 Version These quants were made with exllamav2 version 0.0.18. Quants made on this version of EXL2 may not work on older versions of the exllamav2 library. If you have problems loading these models, please update Text Generation WebUI to the latest version. ## Perplexity Scoring Below are the perplexity scores for the EXL2 models. A lower score is better. _TODO_ ### Perplexity Script This was the script used for perplexity testing. ```bash #!/bin/bash # Activate the conda environment source ~/miniconda3/etc/profile.d/conda.sh conda activate exllamav2 # Set the model name and bit size MODEL_NAME="mixtral-8x22b-instruct-oh" BIT_PRECISIONS=(7.0 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.75 2.5 2.25) # Print the markdown table header echo "| Quant Level | Perplexity Score |" echo "|-------------|------------------|" for BIT_PRECISION in "${BIT_PRECISIONS[@]}" do MODEL_DIR="models/${MODEL_NAME}_exl2_${BIT_PRECISION}bpw" if [ -d "$MODEL_DIR" ]; then output=$(python test_inference.py -m "$MODEL_DIR" -gs 44,48 -ed data/wikitext/wikitext-2-v1.parquet) score=$(echo "$output" | grep -oP 'Evaluation perplexity: \K[\d.]+') echo "| $BIT_PRECISION | $score |" fi done ``` ## Quant Details This is the script used for quantization. ```bash #!/bin/bash # Activate the conda environment source ~/miniconda3/etc/profile.d/conda.sh conda activate exllamav2 # Set the model name and bit size MODEL_NAME="mixtral-8x22b-instruct-oh" # Define variables MODEL_DIR="models/$MODEL_NAME" OUTPUT_DIR="exl2_$MODEL_NAME" MEASUREMENT_FILE="measurements/$MODEL_NAME.json" # Create the measurement file if needed if [ ! -f "$MEASUREMENT_FILE" ]; then echo "Creating $MEASUREMENT_FILE" # Create directories if [ -d "$OUTPUT_DIR" ]; then rm -r "$OUTPUT_DIR" fi mkdir "$OUTPUT_DIR" python convert.py -i $MODEL_DIR -o $OUTPUT_DIR -nr -om $MEASUREMENT_FILE fi # Choose one of the below. Either create a single quant for testing or a batch of them. # BIT_PRECISIONS=(2.25) BIT_PRECISIONS=(7.0 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.75 2.5 2.25) for BIT_PRECISION in "${BIT_PRECISIONS[@]}" do CONVERTED_FOLDER="models/${MODEL_NAME}_exl2_${BIT_PRECISION}bpw" # If it doesn't already exist, make the quant if [ ! -d "$CONVERTED_FOLDER" ]; then echo "Creating $CONVERTED_FOLDER" # Create directories if [ -d "$OUTPUT_DIR" ]; then rm -r "$OUTPUT_DIR" fi mkdir "$OUTPUT_DIR" mkdir "$CONVERTED_FOLDER" # Run conversion commands python convert.py -i $MODEL_DIR -o $OUTPUT_DIR -nr -m $MEASUREMENT_FILE -b $BIT_PRECISION -cf $CONVERTED_FOLDER fi done ```

PinkPony1/RVCv2

PinkPony1

--- language: - en --- This repository contains high quality voice models of characters from MLP FIM

relu-ntnu/bart-large-xsum_v1_trained_on_50

relu-ntnu

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]

klausprietto/KlausPrietto

klausprietto

--- license: openrail ---

asddsaczxczx/asddsaczxczx

asddsaczxczx

Entry not found

ikozlov/MobileDiffusionStable

ikozlov

diffusers

text-to-image

--- license: openrail library_name: diffusers pipeline_tag: text-to-image ---

neural-commons/neural-mem-cell-32-256-0.1-v0.0.1

neural-commons

transformers

Entry not found

Dracones/mixtral-8x22b-instruct-oh_exl2_2.75bpw

Dracones

transformers

text-generation

--- language: - en license: apache-2.0 datasets: - teknium/OpenHermes-2.5 base_model: mistral-community/Mixtral-8x22B-v0.1 tags: - exl2 --- # mixtral-8x22b-instruct-oh - EXL2 2.75bpw This is a 2.75bpw EXL2 quant of [fireworks-ai/mixtral-8x22b-instruct-oh](https://huggingface.co/fireworks-ai/mixtral-8x22b-instruct-oh) Details about the model can be found at the above model page. ## EXL2 Version These quants were made with exllamav2 version 0.0.18. Quants made on this version of EXL2 may not work on older versions of the exllamav2 library. If you have problems loading these models, please update Text Generation WebUI to the latest version. ## Perplexity Scoring Below are the perplexity scores for the EXL2 models. A lower score is better. _TODO_ ### Perplexity Script This was the script used for perplexity testing. ```bash #!/bin/bash # Activate the conda environment source ~/miniconda3/etc/profile.d/conda.sh conda activate exllamav2 # Set the model name and bit size MODEL_NAME="mixtral-8x22b-instruct-oh" BIT_PRECISIONS=(7.0 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.75 2.5 2.25) # Print the markdown table header echo "| Quant Level | Perplexity Score |" echo "|-------------|------------------|" for BIT_PRECISION in "${BIT_PRECISIONS[@]}" do MODEL_DIR="models/${MODEL_NAME}_exl2_${BIT_PRECISION}bpw" if [ -d "$MODEL_DIR" ]; then output=$(python test_inference.py -m "$MODEL_DIR" -gs 44,48 -ed data/wikitext/wikitext-2-v1.parquet) score=$(echo "$output" | grep -oP 'Evaluation perplexity: \K[\d.]+') echo "| $BIT_PRECISION | $score |" fi done ``` ## Quant Details This is the script used for quantization. ```bash #!/bin/bash # Activate the conda environment source ~/miniconda3/etc/profile.d/conda.sh conda activate exllamav2 # Set the model name and bit size MODEL_NAME="mixtral-8x22b-instruct-oh" # Define variables MODEL_DIR="models/$MODEL_NAME" OUTPUT_DIR="exl2_$MODEL_NAME" MEASUREMENT_FILE="measurements/$MODEL_NAME.json" # Create the measurement file if needed if [ ! -f "$MEASUREMENT_FILE" ]; then echo "Creating $MEASUREMENT_FILE" # Create directories if [ -d "$OUTPUT_DIR" ]; then rm -r "$OUTPUT_DIR" fi mkdir "$OUTPUT_DIR" python convert.py -i $MODEL_DIR -o $OUTPUT_DIR -nr -om $MEASUREMENT_FILE fi # Choose one of the below. Either create a single quant for testing or a batch of them. # BIT_PRECISIONS=(2.25) BIT_PRECISIONS=(7.0 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.75 2.5 2.25) for BIT_PRECISION in "${BIT_PRECISIONS[@]}" do CONVERTED_FOLDER="models/${MODEL_NAME}_exl2_${BIT_PRECISION}bpw" # If it doesn't already exist, make the quant if [ ! -d "$CONVERTED_FOLDER" ]; then echo "Creating $CONVERTED_FOLDER" # Create directories if [ -d "$OUTPUT_DIR" ]; then rm -r "$OUTPUT_DIR" fi mkdir "$OUTPUT_DIR" mkdir "$CONVERTED_FOLDER" # Run conversion commands python convert.py -i $MODEL_DIR -o $OUTPUT_DIR -nr -m $MEASUREMENT_FILE -b $BIT_PRECISION -cf $CONVERTED_FOLDER fi done ```

bingbort/SOLAR-19.2B-Instruct-v1.0-Q4_K_M-GGUF

bingbort

transformers

--- language: - en license: cc library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo base_model: [] datasets: - c-s-ale/alpaca-gpt4-data - Open-Orca/OpenOrca - Intel/orca_dpo_pairs - allenai/ultrafeedback_binarized_cleaned --- # bingbort/SOLAR-19.2B-Instruct-v1.0-Q4_K_M-GGUF This model was converted to GGUF format from [`Joseph717171/SOLAR-19.2B-Instruct-v1.0`](https://huggingface.co/Joseph717171/SOLAR-19.2B-Instruct-v1.0) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Joseph717171/SOLAR-19.2B-Instruct-v1.0) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo bingbort/SOLAR-19.2B-Instruct-v1.0-Q4_K_M-GGUF --model solar-19.2b-instruct-v1.0.Q4_K_M.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo bingbort/SOLAR-19.2B-Instruct-v1.0-Q4_K_M-GGUF --model solar-19.2b-instruct-v1.0.Q4_K_M.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m solar-19.2b-instruct-v1.0.Q4_K_M.gguf -n 128 ```

shcapybara0925/masked_language_model-finetuned-github_cybersecurity_READMEs_v1

shcapybara0925

Entry not found

relu-ntnu/bart-large-xsum_v1_trained_on_250

relu-ntnu

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]

mahiatlinux/lora_test22

mahiatlinux

transformers

Invalid username or password.

relu-ntnu/bart-large-cnn_v1_trained_on_50

relu-ntnu

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]

ymoslem/whisper-small-ga2en-v2.1

ymoslem

transformers

automatic-speech-recognition

Entry not found

kyryl-opens-ml/codelama-duckdb-text-to-sql

kyryl-opens-ml

peft

--- license: llama2 library_name: peft tags: - trl - sft - generated_from_trainer base_model: codellama/CodeLlama-7b-hf datasets: - generator model-index: - name: codelama-duckdb-text-to-sql 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. --> # codelama-duckdb-text-to-sql 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: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.7.2.dev0 - Transformers 4.38.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.2

fangzhaoz/mistralv1_spectral_r8_25e5_e3

fangzhaoz

peft

--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 model-index: - name: mistralv1_spectral_r8_25e5_e3 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. --> # mistralv1_spectral_r8_25e5_e3 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2.5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.39.3 - Pytorch 2.2.2 - Datasets 2.18.0 - Tokenizers 0.15.2

Lewdiculous/Nyanade_Stunna-Maid-7B-v0.2-GGUF-IQ-Imatrix

Lewdiculous

--- inference: false tags: - gguf - quantized - roleplay - multimodal - vision - llava - sillytavern - merge - mistral - conversational --- # #Roleplay #Multimodal #Vision In this repository you can find **GGUF-IQ-Imatrix** quants for [ChaoticNeutrals/Nyanade_Stunna-Maid-7B-v0.2](https://huggingface.co/ChaoticNeutrals/Nyanade_Stunna-Maid-7B-v0.2) and you can get some basic SillyTavern presets [here](https://huggingface.co/Lewdiculous/Model-Requests/tree/main/data/presets/lewdicu-3.0.2-mistral-0.2) as needed. > [!TIP] > **Vision:** <br> > This is a **#multimodal** model that also has optional **#vision** capabilities. <br> Expand the relevant sections bellow and read the full card information if you also want to make use that functionality. > > **Quant options:** <br> > Reading bellow you can also find quant option recommendations for some common GPU VRAM capacities. **"Unhinged RP with the spice of the previous 0.420 remixes, 32k context and vision capabilities."**  # General recommendations for quant options: <details><summary> ⇲ Click here to expand/hide general common recommendations. </summary> *Assuming a context size of 8192 for simplicity and 1GB of Operating System VRAM overhead with some safety margin to avoid overflowing buffers...* <br> <br> **For 11-12GB VRAM:** <br> A GPU with **12GB** of VRAM capacity can comfortably use the **Q6_K-imat** quant option and run it at good speeds. <br> This is the same with or without using #vision capabilities. <br> <br> **For 8GB VRAM:** <br> If not using #vision, for GPUs with **8GB** of VRAM capacity the **Q5_K_M-imat** quant option will fit comfortably and should run at good speeds. <br> If **you are** also using #vision from this model opt for the **Q4_K_M-imat** quant option to avoid filling the buffers and potential slowdowns. <br><br> **For 6GB VRAM:** <br> If not using #vision, for GPUs with **6GB** of VRAM capacity the **IQ3_M-imat** quant option should fit comfortably to run at good speeds. <br> If **you are** also using #vision from this model opt for the **IQ3_XXS-imat** quant option. <br><br> </details><br> # Quantization process information: <details><summary> ⇲ Click here to expand/hide more information about this topic. </summary> ```python quantization_options = [ "IQ3_M", "IQ3_XXS", "Q4_K_M", "Q4_K_S", "IQ4_XS", "IQ4_NL", "Q5_K_M", "Q5_K_S", "Q6_K", "Q8_0" ] ``` **Steps performed:** ``` Base⇢ GGUF(F16)⇢ Imatrix-Data(F16)⇢ GGUF(Imatrix-Quants) ``` The latest of **llama.cpp** available at the time was used, with [imatrix-with-rp-ex.txt](https://huggingface.co/Lewdiculous/Nyanade_Stunna-Maid-7B-GGUF-IQ-Imatrix/blob/main/imatrix-with-rp-ex.txt) as calibration data. </details><br> # What does "Imatrix" mean? <details><summary> ⇲ Click here to expand/hide more information about this topic. </summary> It stands for **Importance Matrix**, a technique used to improve the quality of quantized models. The **Imatrix** is calculated based on calibration data, and it helps determine the importance of different model activations during the quantization process. The idea is to preserve the most important information during quantization, which can help reduce the loss of model performance, especially when the calibration data is diverse. [[1]](https://github.com/ggerganov/llama.cpp/discussions/5006) [[2]](https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-8395384) > [!NOTE] > For imatrix data generation, kalomaze's `groups_merged.txt` with additional roleplay chats was used, you can find it [here](https://huggingface.co/Lewdiculous/Model-Requests/blob/main/data/imatrix/imatrix-with-rp-ex.txt) for reference. This was just to add a bit more diversity to the data with the intended use case in mind. </details><br> # Vision/multimodal capabilities: <details><summary> ⇲ Click here to expand/hide how this would work in practice in a roleplay chat. </summary>  </details><br> <details><summary> ⇲ Click here to expand/hide how your SillyTavern Image Captions extension settings should look. </summary>  </details><br> # Required for vision functionality: > [!WARNING] > To use the multimodal capabilities of this model, such as **vision**, you also need to load the specified **mmproj** file, you can get it [here](https://huggingface.co/cjpais/llava-1.6-mistral-7b-gguf/blob/main/mmproj-model-f16.gguf) or as uploaded in the **mmproj** folder in the repository. 1: Make sure you are using the latest version of [KoboldCpp](https://github.com/LostRuins/koboldcpp). 2: Load the **mmproj file** by using the corresponding section in the interface:  2.1: For **CLI** users, you can load the **mmproj file** by adding the respective flag to your usual command: ``` --mmproj your-mmproj-file.gguf ```

Shalazary/ruBert-base-sberquad-0.001-filtered-negative

Shalazary

peft

--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: ai-forever/ruBert-base model-index: - name: ruBert-base-sberquad-0.001-filtered-negative 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. --> # ruBert-base-sberquad-0.001-filtered-negative This model is a fine-tuned version of [ai-forever/ruBert-base](https://huggingface.co/ai-forever/ruBert-base) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 5000 ### Training results ### Framework versions - PEFT 0.10.1.dev0 - Transformers 4.40.0.dev0 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

Dracones/mixtral-8x22b-instruct-oh_exl2_2.5bpw

Dracones

transformers

text-generation

--- language: - en license: apache-2.0 datasets: - teknium/OpenHermes-2.5 base_model: mistral-community/Mixtral-8x22B-v0.1 tags: - exl2 --- # mixtral-8x22b-instruct-oh - EXL2 2.5bpw This is a 2.5bpw EXL2 quant of [fireworks-ai/mixtral-8x22b-instruct-oh](https://huggingface.co/fireworks-ai/mixtral-8x22b-instruct-oh) Details about the model can be found at the above model page. ## EXL2 Version These quants were made with exllamav2 version 0.0.18. Quants made on this version of EXL2 may not work on older versions of the exllamav2 library. If you have problems loading these models, please update Text Generation WebUI to the latest version. ## Perplexity Scoring Below are the perplexity scores for the EXL2 models. A lower score is better. _TODO_ ### Perplexity Script This was the script used for perplexity testing. ```bash #!/bin/bash # Activate the conda environment source ~/miniconda3/etc/profile.d/conda.sh conda activate exllamav2 # Set the model name and bit size MODEL_NAME="mixtral-8x22b-instruct-oh" BIT_PRECISIONS=(7.0 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.75 2.5 2.25) # Print the markdown table header echo "| Quant Level | Perplexity Score |" echo "|-------------|------------------|" for BIT_PRECISION in "${BIT_PRECISIONS[@]}" do MODEL_DIR="models/${MODEL_NAME}_exl2_${BIT_PRECISION}bpw" if [ -d "$MODEL_DIR" ]; then output=$(python test_inference.py -m "$MODEL_DIR" -gs 44,48 -ed data/wikitext/wikitext-2-v1.parquet) score=$(echo "$output" | grep -oP 'Evaluation perplexity: \K[\d.]+') echo "| $BIT_PRECISION | $score |" fi done ``` ## Quant Details This is the script used for quantization. ```bash #!/bin/bash # Activate the conda environment source ~/miniconda3/etc/profile.d/conda.sh conda activate exllamav2 # Set the model name and bit size MODEL_NAME="mixtral-8x22b-instruct-oh" # Define variables MODEL_DIR="models/$MODEL_NAME" OUTPUT_DIR="exl2_$MODEL_NAME" MEASUREMENT_FILE="measurements/$MODEL_NAME.json" # Create the measurement file if needed if [ ! -f "$MEASUREMENT_FILE" ]; then echo "Creating $MEASUREMENT_FILE" # Create directories if [ -d "$OUTPUT_DIR" ]; then rm -r "$OUTPUT_DIR" fi mkdir "$OUTPUT_DIR" python convert.py -i $MODEL_DIR -o $OUTPUT_DIR -nr -om $MEASUREMENT_FILE fi # Choose one of the below. Either create a single quant for testing or a batch of them. # BIT_PRECISIONS=(2.25) BIT_PRECISIONS=(7.0 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.75 2.5 2.25) for BIT_PRECISION in "${BIT_PRECISIONS[@]}" do CONVERTED_FOLDER="models/${MODEL_NAME}_exl2_${BIT_PRECISION}bpw" # If it doesn't already exist, make the quant if [ ! -d "$CONVERTED_FOLDER" ]; then echo "Creating $CONVERTED_FOLDER" # Create directories if [ -d "$OUTPUT_DIR" ]; then rm -r "$OUTPUT_DIR" fi mkdir "$OUTPUT_DIR" mkdir "$CONVERTED_FOLDER" # Run conversion commands python convert.py -i $MODEL_DIR -o $OUTPUT_DIR -nr -m $MEASUREMENT_FILE -b $BIT_PRECISION -cf $CONVERTED_FOLDER fi done ```

GamblerOnTrain/danke20a

GamblerOnTrain

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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(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]

relu-ntnu/bart-large-cnn_v1_trained_on_250

relu-ntnu

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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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]

liamvbetts/t5-small-finetuned-2024-03-27

liamvbetts

transformers

text2text-generation

--- base_model: liamvbetts/t5-small-finetuned-2024-03-25 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-small-finetuned-2024-03-27 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. --> # t5-small-finetuned-2024-03-27 This model is a fine-tuned version of [liamvbetts/t5-small-finetuned-2024-03-25](https://huggingface.co/liamvbetts/t5-small-finetuned-2024-03-25) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5195 - Rouge1: 36.8348 - Rouge2: 24.9779 - Rougel: 33.6165 - Rougelsum: 33.7978 - Gen Len: 18.9412 ## 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: 4e-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: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.8428 | 1.0 | 268 | 1.5195 | 36.8348 | 24.9779 | 33.6165 | 33.7978 | 18.9412 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

APLunch/q-Taxi-v3

APLunch

reinforcement-learning

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.52 +/- 2.73 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="APLunch/q-Taxi-v3", 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"]) ```

feizhe/vit-base-patch16-224-in21k-pheno-run6

feizhe

Entry not found

yitzshapiro/OpenSOLAR-slerp

yitzshapiro

transformers

text-generation

--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - upstage/SOLAR-10.7B-Instruct-v1.0 - OpenBuddy/openbuddy-mistral2-7b-v20.2-32k --- # OpenSOLAR-slerp OpenSOLAR-slerp is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): These models were the CoT Leaders as of April 14th 2024, so merging them seemed like a good idea. * [upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0) * [OpenBuddy/openbuddy-mistral2-7b-v20.2-32k](https://huggingface.co/OpenBuddy/openbuddy-mistral2-7b-v20.2-32k) ## 🧩 Configuration ```yaml slices: - sources: - model: upstage/SOLAR-10.7B-Instruct-v1.0 layer_range: [0, 32] - model: OpenBuddy/openbuddy-mistral2-7b-v20.2-32k layer_range: [0, 32] merge_method: slerp base_model: upstage/SOLAR-10.7B-Instruct-v1.0 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ```

adityadwivedii/Llama-2-7b-chat-finetune

adityadwivedii

transformers

text-generation

Entry not found

lanzv/ClinicalBERTQA_97_54

lanzv

transformers

question-answering

Entry not found

liamvbetts/t5-small-finetuned-2024-03-29

liamvbetts

transformers

text2text-generation

--- base_model: liamvbetts/t5-small-finetuned-2024-03-27 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-small-finetuned-2024-03-29 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. --> # t5-small-finetuned-2024-03-29 This model is a fine-tuned version of [liamvbetts/t5-small-finetuned-2024-03-27](https://huggingface.co/liamvbetts/t5-small-finetuned-2024-03-27) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6116 - Rouge1: 40.5278 - Rouge2: 29.0669 - Rougel: 36.4774 - Rougelsum: 37.091 - Gen Len: 18.8214 ## 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: 4e-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: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.6601 | 1.0 | 333 | 1.6116 | 40.5278 | 29.0669 | 36.4774 | 37.091 | 18.8214 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

simonamdev/openai-whisper-large-v2-mt-PeftType.LORA

simonamdev

transformers

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

ashishp-wiai/ClipArt_LoRA_80-2024-04-15

ashishp-wiai

Entry not found

fangzhaoz/mistralv1_spectral_r8_25e5_e3_merged

fangzhaoz

transformers

text-generation

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

ucmp137538/pythia-160m-v0-finetuned-squad

ucmp137538

peft

--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: EleutherAI/pythia-160m-v0 model-index: - name: pythia-160m-v0-finetuned-squad 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. --> # pythia-160m-v0-finetuned-squad This model is a fine-tuned version of [EleutherAI/pythia-160m-v0](https://huggingface.co/EleutherAI/pythia-160m-v0) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6557 ## 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: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.9708 | 1.0 | 5539 | 1.6557 | ### Framework versions - PEFT 0.7.2.dev0 - Transformers 4.36.2 - Pytorch 2.2.1+cu121 - Datasets 2.16.1 - Tokenizers 0.15.2

Dracones/mixtral-8x22b-instruct-oh_exl2_2.25bpw

Dracones

transformers

text-generation

--- language: - en license: apache-2.0 datasets: - teknium/OpenHermes-2.5 base_model: mistral-community/Mixtral-8x22B-v0.1 tags: - exl2 --- # mixtral-8x22b-instruct-oh - EXL2 2.25bpw This is a 2.25bpw EXL2 quant of [fireworks-ai/mixtral-8x22b-instruct-oh](https://huggingface.co/fireworks-ai/mixtral-8x22b-instruct-oh) Details about the model can be found at the above model page. ## EXL2 Version These quants were made with exllamav2 version 0.0.18. Quants made on this version of EXL2 may not work on older versions of the exllamav2 library. If you have problems loading these models, please update Text Generation WebUI to the latest version. ## Perplexity Scoring Below are the perplexity scores for the EXL2 models. A lower score is better. _TODO_ ### Perplexity Script This was the script used for perplexity testing. ```bash #!/bin/bash # Activate the conda environment source ~/miniconda3/etc/profile.d/conda.sh conda activate exllamav2 # Set the model name and bit size MODEL_NAME="mixtral-8x22b-instruct-oh" BIT_PRECISIONS=(7.0 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.75 2.5 2.25) # Print the markdown table header echo "| Quant Level | Perplexity Score |" echo "|-------------|------------------|" for BIT_PRECISION in "${BIT_PRECISIONS[@]}" do MODEL_DIR="models/${MODEL_NAME}_exl2_${BIT_PRECISION}bpw" if [ -d "$MODEL_DIR" ]; then output=$(python test_inference.py -m "$MODEL_DIR" -gs 44,48 -ed data/wikitext/wikitext-2-v1.parquet) score=$(echo "$output" | grep -oP 'Evaluation perplexity: \K[\d.]+') echo "| $BIT_PRECISION | $score |" fi done ``` ## Quant Details This is the script used for quantization. ```bash #!/bin/bash # Activate the conda environment source ~/miniconda3/etc/profile.d/conda.sh conda activate exllamav2 # Set the model name and bit size MODEL_NAME="mixtral-8x22b-instruct-oh" # Define variables MODEL_DIR="models/$MODEL_NAME" OUTPUT_DIR="exl2_$MODEL_NAME" MEASUREMENT_FILE="measurements/$MODEL_NAME.json" # Create the measurement file if needed if [ ! -f "$MEASUREMENT_FILE" ]; then echo "Creating $MEASUREMENT_FILE" # Create directories if [ -d "$OUTPUT_DIR" ]; then rm -r "$OUTPUT_DIR" fi mkdir "$OUTPUT_DIR" python convert.py -i $MODEL_DIR -o $OUTPUT_DIR -nr -om $MEASUREMENT_FILE fi # Choose one of the below. Either create a single quant for testing or a batch of them. # BIT_PRECISIONS=(2.25) BIT_PRECISIONS=(7.0 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.75 2.5 2.25) for BIT_PRECISION in "${BIT_PRECISIONS[@]}" do CONVERTED_FOLDER="models/${MODEL_NAME}_exl2_${BIT_PRECISION}bpw" # If it doesn't already exist, make the quant if [ ! -d "$CONVERTED_FOLDER" ]; then echo "Creating $CONVERTED_FOLDER" # Create directories if [ -d "$OUTPUT_DIR" ]; then rm -r "$OUTPUT_DIR" fi mkdir "$OUTPUT_DIR" mkdir "$CONVERTED_FOLDER" # Run conversion commands python convert.py -i $MODEL_DIR -o $OUTPUT_DIR -nr -m $MEASUREMENT_FILE -b $BIT_PRECISION -cf $CONVERTED_FOLDER fi done ```

GamblerOnTrain/danke20b

GamblerOnTrain

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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saeu5407/controlnet-de-identification

saeu5407

Entry not found

Milad1b/MLM_biobert_diseases_ner_PsnoD

Milad1b

transformers

fill-mask

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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mradermacher/34b-beta2-i1-GGUF

mradermacher

--- exported_from: CausalLM/34b-beta2 language: - en library_name: transformers license: gpl-3.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> weighted/imatrix quants of https://huggingface.co/CausalLM/34b-beta2 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/34b-beta2-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/34b-beta2-i1-GGUF/resolve/main/34b-beta2.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 9.4 | | | [GGUF](https://huggingface.co/mradermacher/34b-beta2-i1-GGUF/resolve/main/34b-beta2.i1-IQ2_XS.gguf) | i1-IQ2_XS | 10.4 | | | [GGUF](https://huggingface.co/mradermacher/34b-beta2-i1-GGUF/resolve/main/34b-beta2.i1-IQ2_M.gguf) | i1-IQ2_M | 11.9 | | | [GGUF](https://huggingface.co/mradermacher/34b-beta2-i1-GGUF/resolve/main/34b-beta2.i1-Q2_K.gguf) | i1-Q2_K | 12.9 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/34b-beta2-i1-GGUF/resolve/main/34b-beta2.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 13.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/34b-beta2-i1-GGUF/resolve/main/34b-beta2.i1-IQ3_XS.gguf) | i1-IQ3_XS | 14.3 | | | [GGUF](https://huggingface.co/mradermacher/34b-beta2-i1-GGUF/resolve/main/34b-beta2.i1-Q3_K_S.gguf) | i1-Q3_K_S | 15.1 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/34b-beta2-i1-GGUF/resolve/main/34b-beta2.i1-IQ3_S.gguf) | i1-IQ3_S | 15.1 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/34b-beta2-i1-GGUF/resolve/main/34b-beta2.i1-IQ3_M.gguf) | i1-IQ3_M | 15.7 | | | [GGUF](https://huggingface.co/mradermacher/34b-beta2-i1-GGUF/resolve/main/34b-beta2.i1-Q3_K_M.gguf) | i1-Q3_K_M | 16.8 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/34b-beta2-i1-GGUF/resolve/main/34b-beta2.i1-Q3_K_L.gguf) | i1-Q3_K_L | 18.2 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/34b-beta2-i1-GGUF/resolve/main/34b-beta2.i1-IQ4_XS.gguf) | i1-IQ4_XS | 18.6 | | | [GGUF](https://huggingface.co/mradermacher/34b-beta2-i1-GGUF/resolve/main/34b-beta2.i1-Q4_0.gguf) | i1-Q4_0 | 19.6 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/34b-beta2-i1-GGUF/resolve/main/34b-beta2.i1-Q4_K_S.gguf) | i1-Q4_K_S | 19.7 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/34b-beta2-i1-GGUF/resolve/main/34b-beta2.i1-Q4_K_M.gguf) | i1-Q4_K_M | 20.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/34b-beta2-i1-GGUF/resolve/main/34b-beta2.i1-Q5_K_S.gguf) | i1-Q5_K_S | 23.8 | | | [GGUF](https://huggingface.co/mradermacher/34b-beta2-i1-GGUF/resolve/main/34b-beta2.i1-Q5_K_M.gguf) | i1-Q5_K_M | 24.4 | | | [GGUF](https://huggingface.co/mradermacher/34b-beta2-i1-GGUF/resolve/main/34b-beta2.i1-Q6_K.gguf) | i1-Q6_K | 28.3 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->

liamvbetts/t5-small-finetuned-2024-03-30

liamvbetts

transformers

text2text-generation

--- base_model: liamvbetts/t5-small-finetuned-2024-03-29 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-small-finetuned-2024-03-30 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. --> # t5-small-finetuned-2024-03-30 This model is a fine-tuned version of [liamvbetts/t5-small-finetuned-2024-03-29](https://huggingface.co/liamvbetts/t5-small-finetuned-2024-03-29) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5651 - Rouge1: 39.9156 - Rouge2: 28.5859 - Rougel: 36.8837 - Rougelsum: 36.9436 - Gen Len: 18.8778 ## 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: 4e-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: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.3546 | 1.0 | 360 | 1.5651 | 39.9156 | 28.5859 | 36.8837 | 36.9436 | 18.8778 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

GamblerOnTrain/danke20c

GamblerOnTrain

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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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]

GamblerOnTrain/danke20d

GamblerOnTrain

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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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]

mmanikanta/VIT_AI_image_detector

mmanikanta

transformers

image-classification

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: VIT_AI_image_detector 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_AI_image_detector This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0323 - Accuracy: 0.9901 ## 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 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1624 | 1.0 | 1406 | 0.0552 | 0.9799 | | 0.1376 | 2.0 | 2812 | 0.0345 | 0.9895 | | 0.086 | 3.0 | 4218 | 0.0323 | 0.9901 | ### Framework versions - Transformers 4.30.0 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.13.3

hongky/RIDNet_100k_denoiser

hongky

# RIDNet IMAGE DENOISOR  Model: https://github.com/lienghongky/RIDNet_Denoisor UI Tools: https://github.com/lienghongky/PicyShake ## FILEPATH """ Project Structure: - main.py: The main entry point of the project. - utils.py: Contains utility functions used in the project. - datasets/: Directory containing the dataset for training and testing. - models/: Directory containing the trained models. - model_checkpoints: Save model every epoch How to Run Train Test: 1. Install the required dependencies by running `pip install -r requirements.txt`. 2. Install the required dependencies by running `pip install -r requirements.txt`. 3. Prepare the dataset by placing the training and testing data in the `datasets/` directory. or run `python preprocess.py`, By runing this script the following directories will be created. ``` # datasets directory structure # datasets # ├── train # │ ├── groundtruth # │ │ ├── 0001.png # │ │ ├── 0002.png # │ │ ├── ... # │ │ └── 1000.png # │ └── input # │ ├── 0001.png # │ ├── 0002.png # │ ├── ... # │ └── 1000.png # ├── test # │ ├── groundtruth # │ │ ├── 0001.png # │ │ ├── 0002.png # │ │ ├── ... # │ │ └── 100.png # │ └── input # │ ├── 0001.png # │ ├── 0002.png # │ ├── ... # │ └── 100.png # └── validation # ├── groundtruth # │ ├── 0001.png # │ ├── 0002.png # │ ├── ... # │ └── 100.png # └── input # ├── 0001.png # ├── 0002.png # ├── ... # └── 100.png ``` 4. Run the train script by executing `python train.py`. 5. Run the test script by executing `python test.py`.

Sarojj/Gemma-2B-plcalbakVLLM16

Sarojj

transformers

text-generation

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma - trl - sft base_model: unsloth/gemma-2b-it-bnb-4bit --- # Uploaded model - **Developed by:** Sarojj - **License:** apache-2.0 - **Finetuned from model :** unsloth/gemma-2b-it-bnb-4bit This gemma 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)

harikrishnad1997/emotion_tweet_distilbert-base-uncased_2024-04-15

harikrishnad1997

transformers

text-classification

--- 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]

oneandahalfcats/11660

oneandahalfcats

Entry not found

azsxscdvfb/vetmedgpt-1B-V0.1

azsxscdvfb

transformers

text-generation

--- license: apache-2.0 pipeline_tag: text-generation tags: - finetuned inference: true widget: - messages: - role: user content: What is your favorite condiment? --- # Model Card for Mistral-7B-Instruct-v0.2 The Mistral-7B-Instruct-v0.2 Large Language Model (LLM) is an instruct fine-tuned version of the Mistral-7B-v0.2. Mistral-7B-v0.2 has the following changes compared to Mistral-7B-v0.1 - 32k context window (vs 8k context in v0.1) - Rope-theta = 1e6 - No Sliding-Window Attention For full details of this model please read our [paper](https://arxiv.org/abs/2310.06825) and [release blog post](https://mistral.ai/news/la-plateforme/). ## Instruction format In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id. E.g. ``` text = "<s>[INST] What is your favourite condiment? [/INST]" "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> " "[INST] Do you have mayonnaise recipes? [/INST]" ``` This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method: ```python from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2") tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2") messages = [ {"role": "user", "content": "What is your favourite condiment?"}, {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"}, {"role": "user", "content": "Do you have mayonnaise recipes?"} ] encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt") model_inputs = encodeds.to(device) model.to(device) generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True) decoded = tokenizer.batch_decode(generated_ids) print(decoded[0]) ``` ## Troubleshooting - If you see the following error: ``` Traceback (most recent call last): File "", line 1, in File "/transformers/models/auto/auto_factory.py", line 482, in from_pretrained config, kwargs = AutoConfig.from_pretrained( File "/transformers/models/auto/configuration_auto.py", line 1022, in from_pretrained config_class = CONFIG_MAPPING[config_dict["model_type"]] File "/transformers/models/auto/configuration_auto.py", line 723, in getitem raise KeyError(key) KeyError: 'mistral' ``` Installing transformers from source should solve the issue pip install git+https://github.com/huggingface/transformers This should not be required after transformers-v4.33.4. ## Limitations The Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs. ## The Mistral AI Team Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Louis Ternon, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.

liamvbetts/t5-small-finetuned-2024-04-01

liamvbetts

transformers

text2text-generation

--- base_model: liamvbetts/t5-small-finetuned-2024-03-30 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-small-finetuned-2024-04-01 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. --> # t5-small-finetuned-2024-04-01 This model is a fine-tuned version of [liamvbetts/t5-small-finetuned-2024-03-30](https://huggingface.co/liamvbetts/t5-small-finetuned-2024-03-30) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6678 - Rouge1: 39.0836 - Rouge2: 26.3632 - Rougel: 35.7879 - Rougelsum: 35.8539 - Gen Len: 18.8471 ## 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: 4e-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: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.507 | 1.0 | 340 | 1.6678 | 39.0836 | 26.3632 | 35.7879 | 35.8539 | 18.8471 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

uunio/ai_tabular

uunio

Entry not found

mergekit-community/mergekit-slerp-mpovshq

mergekit-community

transformers

text-generation

--- base_model: - WizardLM/WizardMath-7B-V1.1 - NousResearch/Hermes-2-Pro-Mistral-7B library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [WizardLM/WizardMath-7B-V1.1](https://huggingface.co/WizardLM/WizardMath-7B-V1.1) * [NousResearch/Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: NousResearch/Hermes-2-Pro-Mistral-7B - model: WizardLM/WizardMath-7B-V1.1 merge_method: slerp base_model: NousResearch/Hermes-2-Pro-Mistral-7B dtype: bfloat16 parameters: t: [0, 0.5, 1, 0.5, 0] # V shaped curve: Hermes for input & output, WizardMath in the middle layers ```

WesPro/MisHumHypNiche

WesPro

transformers

text-generation

Entry not found

t4coxt00t/INNU

t4coxt00t

Entry not found

relu-ntnu/bart-large-cnn_v1_trained_on_25

relu-ntnu

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]

relu-ntnu/bart-large-cnn_v1_trained_on_10

relu-ntnu

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]

aisha44/mistral_instructv3_KQL

aisha44

peft

--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer datasets: - generator base_model: mistralai/Mistral-7B-Instruct-v0.2 model-index: - name: mistral_instructv3_KQL 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. --> # mistral_instructv3_KQL This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 0.4070 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_steps: 0.03 - training_steps: 400 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.26 | 6.25 | 200 | 0.3536 | | 0.1469 | 12.5 | 400 | 0.4070 | ### Framework versions - PEFT 0.10.0 - Transformers 4.39.3 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

yuhuixu/mistral-7b-sft-beta-v0.1

yuhuixu

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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]

liamvbetts/t5-small-finetuned-2024-04-02

liamvbetts

transformers

text2text-generation

--- base_model: liamvbetts/t5-small-finetuned-2024-04-01 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-small-finetuned-2024-04-02 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. --> # t5-small-finetuned-2024-04-02 This model is a fine-tuned version of [liamvbetts/t5-small-finetuned-2024-04-01](https://huggingface.co/liamvbetts/t5-small-finetuned-2024-04-01) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5585 - Rouge1: 37.1092 - Rouge2: 25.5198 - Rougel: 34.1375 - Rougelsum: 34.3825 - Gen Len: 18.7738 ## 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: 4e-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: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.6658 | 1.0 | 335 | 1.5585 | 37.1092 | 25.5198 | 34.1375 | 34.3825 | 18.7738 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

relu-ntnu/bart-large-xsum_v1_trained_on_25

relu-ntnu

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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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]

deepapaikar/CutomGPTKatz

deepapaikar

CustomGPTKatz

mradermacher/Goku-8x22B-v0.1-i1-GGUF

mradermacher

--- base_model: v2ray/Mixtral-8x22B-v0.1 datasets: - philschmid/guanaco-sharegpt-style exported_from: MaziyarPanahi/Goku-8x22B-v0.1 language: - en library_name: transformers license: apache-2.0 model_creator: MaziyarPanahi model_name: Goku-8x22B-v0.1 quantized_by: mradermacher tags: - moe - mixtral - sharegpt - axolotl --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> weighted/imatrix quants of https://huggingface.co/MaziyarPanahi/Goku-8x22B-v0.1 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Goku-8x22B-v0.1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [PART 1](https://huggingface.co/mradermacher/Goku-8x22B-v0.1-i1-GGUF/resolve/main/Goku-8x22B-v0.1.i1-Q2_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Goku-8x22B-v0.1-i1-GGUF/resolve/main/Goku-8x22B-v0.1.i1-Q2_K.gguf.part2of2) | i1-Q2_K | 52.2 | IQ3_XXS probably better | | [PART 1](https://huggingface.co/mradermacher/Goku-8x22B-v0.1-i1-GGUF/resolve/main/Goku-8x22B-v0.1.i1-Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Goku-8x22B-v0.1-i1-GGUF/resolve/main/Goku-8x22B-v0.1.i1-Q4_K_S.gguf.part2of2) | i1-Q4_K_S | 80.6 | optimal size/speed/quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->

relu-ntnu/bart-large-xsum_v1_trained_on_10

relu-ntnu

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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Sarojj/Gemma-2B-plcabkGG16

Sarojj

transformers

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma - gguf base_model: unsloth/gemma-2b-it-bnb-4bit --- # Uploaded model - **Developed by:** Sarojj - **License:** apache-2.0 - **Finetuned from model :** unsloth/gemma-2b-it-bnb-4bit This gemma 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)

MrDorian/Danielle

MrDorian

Entry not found

phamthanhdung/merge_nsfw_rv51

phamthanhdung

diffusers

--- library_name: diffusers --- # 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 🧨 diffusers 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]

oneandahalfcats/29507

oneandahalfcats

Entry not found

LimYeri/CodeMind-Gemma-7B-QLoRA-4bit

LimYeri

transformers

text-generation

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

liamvbetts/t5-small-finetuned-2024-04-04

liamvbetts

transformers

text2text-generation

--- base_model: liamvbetts/t5-small-finetuned-2024-04-02 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-small-finetuned-2024-04-04 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. --> # t5-small-finetuned-2024-04-04 This model is a fine-tuned version of [liamvbetts/t5-small-finetuned-2024-04-02](https://huggingface.co/liamvbetts/t5-small-finetuned-2024-04-02) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6361 - Rouge1: 36.5787 - Rouge2: 23.7589 - Rougel: 33.013 - Rougelsum: 33.4725 - Gen Len: 18.9195 ## 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: 4e-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: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:------:|:---------:|:-------:| | 1.6423 | 1.0 | 346 | 1.6361 | 36.5787 | 23.7589 | 33.013 | 33.4725 | 18.9195 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

bdambrosio/dbrx-instruct-7.0bpw-h8-exl2

bdambrosio

transformers

text-generation

--- license: apache-2.0 --- like it says...

udit-k/dialogsum-FLAN-T5

udit-k

Entry not found

ahmedheakl/arsql-gemma-2b-v3

ahmedheakl

Entry not found

harikrishnad1997/emotion_tweet_albert-base-v2_2024-04-15

harikrishnad1997

transformers

text-classification

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jdeklerk10/DS-6.7B-schema_1

jdeklerk10

peft

--- license: other library_name: peft tags: - trl - sft - generated_from_trainer base_model: deepseek-ai/deepseek-coder-6.7b-instruct model-index: - name: DS-6.7B-schema_1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # DS-6.7B-schema_1 This model is a fine-tuned version of [deepseek-ai/deepseek-coder-6.7b-instruct](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1671 ## 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: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.038 | 0.19 | 50 | 0.2039 | | 0.0469 | 0.38 | 100 | 0.1783 | | 0.0531 | 0.57 | 150 | 0.1716 | | 0.0516 | 0.76 | 200 | 0.1672 | | 0.1132 | 0.95 | 250 | 0.1671 | ### Framework versions - PEFT 0.10.0 - Transformers 4.40.0.dev0 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2