Datasets:

librarian-bots

/

model_cards_with_metadata_test

like 1

Roamify/llama_3_model

Roamify

transformers

--- base_model: unsloth/llama-3-8b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** RoamifyRedefined - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

1231czx/2b_1_nll_dpo_iter2_200step

1231czx

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]

jdgallegoq96/tinyllama_instruct

jdgallegoq96

Entry not found

kanishka/smolm-autoreg-bpe-counterfactual_babylm_measure_nps_as_singular_new-seed_211-1e-3

kanishka

transformers

text-generation

--- tags: - generated_from_trainer datasets: - kanishka/counterfactual_babylm_measure_nps_as_singular_new metrics: - accuracy model-index: - name: smolm-autoreg-bpe-counterfactual_babylm_measure_nps_as_singular_new-seed_211-1e-3 results: - task: name: Causal Language Modeling type: text-generation dataset: name: kanishka/counterfactual_babylm_measure_nps_as_singular_new type: kanishka/counterfactual_babylm_measure_nps_as_singular_new metrics: - name: Accuracy type: accuracy value: 0.4093553697888651 --- <!-- 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. --> # smolm-autoreg-bpe-counterfactual_babylm_measure_nps_as_singular_new-seed_211-1e-3 This model was trained from scratch on the kanishka/counterfactual_babylm_measure_nps_as_singular_new dataset. It achieves the following results on the evaluation set: - Loss: 3.4270 - Accuracy: 0.4094 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 64 - seed: 211 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 32000 - num_epochs: 20.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:------:|:---------------:|:--------:| | 3.6072 | 1.0 | 18602 | 3.7687 | 0.3592 | | 3.3848 | 2.0 | 37204 | 3.5595 | 0.3802 | | 3.2576 | 3.0 | 55806 | 3.4654 | 0.3927 | | 3.177 | 4.0 | 74408 | 3.4207 | 0.3982 | | 3.1212 | 5.0 | 93010 | 3.4026 | 0.4006 | | 3.0724 | 6.0 | 111612 | 3.3763 | 0.4035 | | 3.0373 | 7.0 | 130214 | 3.3708 | 0.4051 | | 3.0102 | 8.0 | 148816 | 3.3649 | 0.4063 | | 2.9818 | 9.0 | 167418 | 3.3810 | 0.4072 | | 2.9526 | 10.0 | 186020 | 3.3640 | 0.4078 | | 2.9332 | 11.0 | 204622 | 3.3817 | 0.4081 | | 2.9076 | 12.0 | 223224 | 3.3767 | 0.4087 | | 2.8857 | 13.0 | 241826 | 3.3850 | 0.4089 | | 2.8653 | 14.0 | 260428 | 3.3919 | 0.4093 | | 2.8483 | 15.0 | 279030 | 3.3888 | 0.4091 | | 2.828 | 16.0 | 297632 | 3.4040 | 0.4093 | | 2.8069 | 17.0 | 316234 | 3.4020 | 0.4094 | | 2.7906 | 18.0 | 334836 | 3.4096 | 0.4096 | | 2.7701 | 19.0 | 353438 | 3.4215 | 0.4093 | | 2.7515 | 20.0 | 372040 | 3.4270 | 0.4094 | ### Framework versions - Transformers 4.38.0 - Pytorch 2.3.1+cu121 - Datasets 2.16.1 - Tokenizers 0.15.2

Ekon2002/cumshottwo

Ekon2002

Entry not found

P0x0/mergekit-model_stock-hvubjjx

P0x0

Entry not found

youssef227/llama-3-8b-Instruct-bnb-telcom

youssef227

peft

--- base_model: unsloth/llama-3-8b-Instruct-bnb-4bit library_name: peft --- # 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. --> - **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] ### Framework versions - PEFT 0.11.1

P0x0/mergekit-task_arithmetic-rdblenk

P0x0

Entry not found

Nareekk/naree

Nareekk

Entry not found

youssef227/llama-3-8b-Instruct-bnb-telcom-2

youssef227

--- license: llama3 ---

mrunalmania/palligemma-cord-test

mrunalmania

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]

jannikjw/phi2_DPO

jannikjw

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]

muyiiwaa/post_classifier

muyiiwaa

keras

--- license: mit ---

breno1996/brenio27

breno1996

--- license: openrail ---

SiMajid/xlm-roberta-base

SiMajid

transformers

text-classification

--- license: mit base_model: FacebookAI/xlm-roberta-base tags: - trl - reward-trainer - generated_from_trainer model-index: - name: xlm-roberta-base results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-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: 1.41e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 25.0 ### Training results ### Framework versions - Transformers 4.41.2 - Pytorch 2.1.2 - Datasets 2.19.2 - Tokenizers 0.19.1

nihil117/QJab_v.01

nihil117

transformers

--- base_model: unsloth/mistral-7b-v0.3-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl --- # Uploaded model - **Developed by:** nihil117 - **License:** apache-2.0 - **Finetuned from model :** unsloth/mistral-7b-v0.3-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

breno1996/brenio45

breno1996

--- license: openrail ---

sandyyuan/galaxyfactory

sandyyuan

Entry not found

fruk19/C_ASR_MID

fruk19

transformers

automatic-speech-recognition

--- language: - th license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - fruk19/C_SMALL metrics: - wer model-index: - name: South_asri results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: aicookcook type: fruk19/C_SMALL config: default split: None args: 'config: th' metrics: - name: Wer type: wer value: 3.7677461386031106 --- <!-- 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. --> # South_asri This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the aicookcook dataset. It achieves the following results on the evaluation set: - Loss: 0.0347 - Wer: 3.7677 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.0304 | 2.0 | 6000 | 0.0440 | 5.5648 | | 0.0061 | 4.0 | 12000 | 0.0358 | 4.1532 | | 0.0007 | 6.0 | 18000 | 0.0347 | 3.7677 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.0.1+cu117 - Datasets 2.20.0 - Tokenizers 0.19.1

Vyctra/Mikey

Vyctra

Entry not found

GraydientPlatformAPI/dreammode-turbo

GraydientPlatformAPI

diffusers

text-to-image

Entry not found

Isaac1992/forlora

Isaac1992

Entry not found

pushpinder08/bert-surprise

pushpinder08

--- license: apache-2.0 ---

Hemantrao/wav2vec2-large-xls-r-300m-hindi_marathi-colab-dynamic-loss

Hemantrao

transformers

automatic-speech-recognition

--- 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]

onnx-community/Florence-2-base

onnx-community

transformers.js

image-text-to-text

--- license: mit pipeline_tag: image-text-to-text tags: - vision - text-generation - text2text-generation - image-text-to-text library_name: transformers.js --- https://huggingface.co/microsoft/Florence-2-base with ONNX weights to be compatible with Transformers.js. ## Usage (Transformers.js) > [!IMPORTANT] > NOTE: Florence-2 support is experimental and requires you to install Transformers.js [v3](https://github.com/xenova/transformers.js/tree/v3) from source. If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [GitHub](https://github.com/xenova/transformers.js/tree/v3) using: ```bash npm install xenova/transformers.js#v3 ``` **Example:** Perform image captioning with `onnx-community/Florence-2-base`. ```js import { Florence2ForConditionalGeneration, AutoProcessor, AutoTokenizer, RawImage, } from '@xenova/transformers'; // Load model, processor, and tokenizer const model_id = 'onnx-community/Florence-2-base'; const model = await Florence2ForConditionalGeneration.from_pretrained(model_id, { dtype: 'fp32' }); const processor = await AutoProcessor.from_pretrained(model_id); const tokenizer = await AutoTokenizer.from_pretrained(model_id); // Load image and prepare vision inputs const url = 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg'; const image = await RawImage.fromURL(url); const vision_inputs = await processor(image); // Specify task and prepare text inputs const task = '<MORE_DETAILED_CAPTION>'; const prompts = processor.construct_prompts(task); const text_inputs = tokenizer(prompts); // Generate text const generated_ids = await model.generate({ ...text_inputs, ...vision_inputs, max_new_tokens: 100, }); // Decode generated text const generated_text = tokenizer.batch_decode(generated_ids, { skip_special_tokens: false })[0]; // Post-process the generated text const result = processor.post_process_generation(generated_text, task, image.size); console.log(result); // { '<MORE_DETAILED_CAPTION>': 'The image shows a vintage Volkswagen Beetle car parked on a cobblestone street in front of a yellow building with two wooden doors. The car is a light green color with silver rims and appears to be in good condition. The building has a sloping roof and is painted in a combination of yellow and beige colors. The sky is blue and there are trees in the background. The overall mood of the image is peaceful and serene.' } ``` We also released an online demo, which you can try yourself: https://huggingface.co/spaces/Xenova/florence2-webgpu <video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/BJj3jQXNqS_7Nt2MSb2ss.mp4"></video> --- Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).

richardkelly/Qwen-Qwen1.5-7B-1719012685

richardkelly

peft

--- library_name: peft base_model: Qwen/Qwen1.5-7B --- # 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. --> - **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] ### Framework versions - PEFT 0.11.1

richardkelly/google-gemma-2b-1719012750

richardkelly

peft

--- library_name: peft base_model: google/gemma-2b --- # 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. --> - **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] ### Framework versions - PEFT 0.11.1

rashid996958/pix2pix_exp32

rashid996958

Entry not found

Cheese619/Cheese619

Cheese619

Entry not found

ahmedesmail16/0.50-5000Train-Test-swinv2-base

ahmedesmail16

Entry not found

scottgr/scott1

scottgr

--- license: mit ---

felipesampaio2010/CaseyBurgessBRMairaParis

felipesampaio2010

Entry not found

JEFFERSONMUSIC/JKGOLDENERAV2

JEFFERSONMUSIC

--- license: apache-2.0 ---

sdadasfgdfgfdg/Gacha_Omnia

sdadasfgdfgfdg

--- license: openrail ---

RaneemElmahdi/NLP

RaneemElmahdi

text-classification

--- pipeline_tag: text-classification ---

RyanJT/quantized-tinyllama-8bit-1.1b-chat2

RyanJT

transformers

text-generation

Entry not found

Satam/tokenizer

Satam

--- license: mit ---

nasser1/kkk

nasser1

--- license: apache-2.0 ---

vdavidr/CodeLlama-13b-Instruct-hf_Fi__translations_size_104_epochs_10_2024-06-22_02-50-05_3557641

vdavidr

--- license: llama2 base_model: codellama/CodeLlama-13b-Instruct-hf tags: - generated_from_trainer metrics: - accuracy - bleu - sacrebleu - rouge model-index: - name: CodeLlama-13b-Instruct-hf_Fi__translations_size_104_epochs_10_2024-06-22_02-50-05_3557641 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. --> # CodeLlama-13b-Instruct-hf_Fi__translations_size_104_epochs_10_2024-06-22_02-50-05_3557641 This model is a fine-tuned version of [codellama/CodeLlama-13b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4179 - Accuracy: 0.035 - Chrf: 0.719 - Bleu: 0.636 - Sacrebleu: 0.6 - Rouge1: 0.678 - Rouge2: 0.497 - Rougel: 0.645 - Rougelsum: 0.675 - Meteor: 0.594 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 1 - eval_batch_size: 1 - seed: 3407 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 4 - total_eval_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 104 - training_steps: 1040 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Chrf | Bleu | Sacrebleu | Rouge1 | Rouge2 | Rougel | Rougelsum | Meteor | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----:|:-----:|:---------:|:------:|:------:|:------:|:---------:|:------:| | 0.3439 | 4.0 | 104 | 2.0524 | 0.036 | 0.581 | 0.472 | 0.5 | 0.556 | 0.309 | 0.5 | 0.55 | 0.493 | | 0.194 | 8.0 | 208 | 2.0784 | 0.036 | 0.55 | 0.435 | 0.4 | 0.519 | 0.275 | 0.479 | 0.513 | 0.466 | | 0.2001 | 12.0 | 312 | 2.1078 | 0.061 | 0.564 | 0.456 | 0.5 | 0.544 | 0.291 | 0.504 | 0.538 | 0.502 | | 0.5322 | 16.0 | 416 | 1.7052 | 0.037 | 0.64 | 0.527 | 0.5 | 0.6 | 0.366 | 0.563 | 0.598 | 0.524 | | 0.1677 | 20.0 | 520 | 1.8442 | 0.037 | 0.606 | 0.493 | 0.5 | 0.566 | 0.334 | 0.529 | 0.557 | 0.496 | | 0.1649 | 24.0 | 624 | 1.6364 | 0.037 | 0.66 | 0.558 | 0.6 | 0.627 | 0.408 | 0.593 | 0.621 | 0.535 | | 0.6799 | 28.0 | 728 | 1.5108 | 0.034 | 0.692 | 0.597 | 0.6 | 0.647 | 0.452 | 0.608 | 0.644 | 0.541 | | 0.1644 | 32.0 | 832 | 1.4941 | 0.035 | 0.709 | 0.623 | 0.6 | 0.671 | 0.478 | 0.634 | 0.667 | 0.553 | | 0.2382 | 36.0 | 936 | 1.4313 | 0.035 | 0.715 | 0.633 | 0.6 | 0.681 | 0.494 | 0.645 | 0.679 | 0.556 | | 0.1956 | 40.0 | 1040 | 1.4179 | 0.035 | 0.719 | 0.636 | 0.6 | 0.678 | 0.497 | 0.645 | 0.675 | 0.594 | ### Framework versions - Transformers 4.37.0 - Pytorch 2.2.1+cu121 - Datasets 2.20.0 - Tokenizers 0.15.2

Vyctra/Matteo

Vyctra

Entry not found

yizhujiao/sft_openassistant-guanaco

yizhujiao

Entry not found

Bertinho24/Yoon2

Bertinho24

--- license: openrail ---

BAKKALIAYOUB/testq

BAKKALIAYOUB

Entry not found

padilfm/natural-scenes-image-classification-cnn

padilfm

This is a model for scene classidication

vdavidr/CodeLlama-7b-Instruct-hf_Fi__translations_size_104_epochs_10_2024-06-22_03-11-17_3557642

vdavidr

--- license: llama2 base_model: codellama/CodeLlama-7b-Instruct-hf tags: - generated_from_trainer metrics: - accuracy - bleu - sacrebleu - rouge model-index: - name: CodeLlama-7b-Instruct-hf_Fi__translations_size_104_epochs_10_2024-06-22_03-11-17_3557642 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. --> # CodeLlama-7b-Instruct-hf_Fi__translations_size_104_epochs_10_2024-06-22_03-11-17_3557642 This model is a fine-tuned version of [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3204 - Accuracy: 0.035 - Chrf: 0.736 - Bleu: 0.656 - Sacrebleu: 0.7 - Rouge1: 0.689 - Rouge2: 0.506 - Rougel: 0.657 - Rougelsum: 0.684 - Meteor: 0.613 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 1 - eval_batch_size: 1 - seed: 3407 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 4 - total_eval_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 104 - training_steps: 1040 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Chrf | Bleu | Sacrebleu | Rouge1 | Rouge2 | Rougel | Rougelsum | Meteor | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----:|:-----:|:---------:|:------:|:------:|:------:|:---------:|:------:| | 0.2911 | 4.0 | 104 | 1.6937 | 0.042 | 0.623 | 0.509 | 0.5 | 0.586 | 0.333 | 0.536 | 0.584 | 0.513 | | 0.1827 | 8.0 | 208 | 1.9669 | 0.04 | 0.584 | 0.47 | 0.5 | 0.532 | 0.309 | 0.495 | 0.528 | 0.48 | | 0.1801 | 12.0 | 312 | 1.7206 | 0.039 | 0.621 | 0.53 | 0.5 | 0.582 | 0.353 | 0.55 | 0.579 | 0.538 | | 0.5158 | 16.0 | 416 | 1.5969 | 0.038 | 0.654 | 0.536 | 0.5 | 0.602 | 0.368 | 0.565 | 0.599 | 0.52 | | 0.1586 | 20.0 | 520 | 1.6966 | 0.037 | 0.632 | 0.522 | 0.5 | 0.576 | 0.357 | 0.544 | 0.571 | 0.513 | | 0.1584 | 24.0 | 624 | 1.5539 | 0.037 | 0.694 | 0.597 | 0.6 | 0.651 | 0.447 | 0.61 | 0.643 | 0.564 | | 0.6664 | 28.0 | 728 | 1.4467 | 0.035 | 0.701 | 0.611 | 0.6 | 0.655 | 0.452 | 0.616 | 0.651 | 0.574 | | 0.171 | 32.0 | 832 | 1.6334 | 0.033 | 0.68 | 0.568 | 0.6 | 0.633 | 0.417 | 0.59 | 0.627 | 0.526 | | 0.227 | 36.0 | 936 | 1.3442 | 0.035 | 0.727 | 0.64 | 0.6 | 0.682 | 0.491 | 0.645 | 0.679 | 0.577 | | 0.1831 | 40.0 | 1040 | 1.3204 | 0.035 | 0.736 | 0.656 | 0.7 | 0.689 | 0.506 | 0.657 | 0.684 | 0.613 | ### Framework versions - Transformers 4.37.0 - Pytorch 2.2.1+cu121 - Datasets 2.20.0 - Tokenizers 0.15.2

tannedbum/L3-Nymeria-Maid-8B-exl2

tannedbum

--- license: cc-by-nc-4.0 quantized_by: tannedbum language: - en tags: - roleplay - sillytavern - llama3 - exl2 - not-for-all-audiences ---  ## This version is solely for scientific purposes, of course. Nymeria is the balanced version, doesn't force nsfw. Nymeria-Maid has more Stheno's weights, leans more on nsfw and is more submissive. ## Available quants - [8.0 bpw](https://huggingface.co/tannedbum/L3-Nymeria-Maid-8B-exl2/tree/8.0) - [6.5 bpw](https://huggingface.co/tannedbum/L3-Nymeria-Maid-8B-exl2/tree/6.5) - [5.0 bpw](https://huggingface.co/tannedbum/L3-Nymeria-Maid-8B-exl2/tree/5.0) - [4.25 bpw](https://huggingface.co/tannedbum/L3-Nymeria-Maid-8B-exl2/tree/4.25) ## Download with git: ```shell git clone --single-branch --branch 6.5 https://huggingface.co/tannedbum/L3-Nymeria-Maid-8B-exl2 L3-Nymeria-Maid-8B-exl2-6.5 ``` ## SillyTavern ## Text Completion presets ``` temp 0.9 top_k 30 top_p 0.75 min_p 0.2 rep_pen 1.1 smooth_factor 0.25 smooth_curve 1 ``` ## Advanced Formatting [Context & Instruct preset by Virt-io](https://huggingface.co/Virt-io/SillyTavern-Presets/tree/main/Prompts/LLAMA-3/v2.0) Instruct Mode: Enabled # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). This model was merged using the slerp merge method. ### Models Merged The following models were included in the merge: * [Sao10K/L3-8B-Stheno-v3.2](https://huggingface.co/Sao10K/L3-8B-Stheno-v3.2) * [princeton-nlp/Llama-3-Instruct-8B-SimPO](https://huggingface.co/princeton-nlp/Llama-3-Instruct-8B-SimPO) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: Sao10K/L3-8B-Stheno-v3.2 layer_range: [0, 32] - model: princeton-nlp/Llama-3-Instruct-8B-SimPO layer_range: [0, 32] merge_method: slerp base_model: Sao10K/L3-8B-Stheno-v3.2 parameters: t: - filter: self_attn value: [0.2, 0.4, 0.4, 0.6] - filter: mlp value: [0.8, 0.6, 0.6, 0.4] - value: 0.4 dtype: bfloat16 ``` --- ## Original model information: ## Model: Sao10K/L3-8B-Stheno-v3.2 Stheno-v3.2-Zeta Changes compared to v3.1 <br>\- Included a mix of SFW and NSFW Storywriting Data, thanks to [Gryphe](https://huggingface.co/datasets/Gryphe/Opus-WritingPrompts) <br>\- Included More Instruct / Assistant-Style Data <br>\- Further cleaned up Roleplaying Samples from c2 Logs -> A few terrible, really bad samples escaped heavy filtering. Manual pass fixed it. <br>\- Hyperparameter tinkering for training, resulting in lower loss levels. Testing Notes - Compared to v3.1 <br>\- Handles SFW / NSFW seperately better. Not as overly excessive with NSFW now. Kinda balanced. <br>\- Better at Storywriting / Narration. <br>\- Better at Assistant-type Tasks. <br>\- Better Multi-Turn Coherency -> Reduced Issues? <br>\- Slightly less creative? A worthy tradeoff. Still creative. <br>\- Better prompt / instruction adherence. --- Want to support my work ? My Ko-fi page: https://ko-fi.com/tannedbum

Vyctra/enrico

Vyctra

Entry not found

Frixi/rbxed

Frixi

--- license: openrail ---

bendavis78/dolphin-2.7-mixtral-8x7b-3.5bpw-h6-exl2-Q4_K_M-GGUF

bendavis78

--- base_model: LoneStriker/dolphin-2.7-mixtral-8x7b-3.5bpw-h6-exl2 datasets: - ehartford/dolphin - jondurbin/airoboros-2.2.1 - ehartford/dolphin-coder - teknium/openhermes - ise-uiuc/Magicoder-OSS-Instruct-75K - ise-uiuc/Magicoder-Evol-Instruct-110K - LDJnr/Capybara language: - en license: apache-2.0 tags: - llama-cpp - gguf-my-repo --- # bendavis78/dolphin-2.7-mixtral-8x7b-3.5bpw-h6-exl2-Q4_K_M-GGUF This model was converted to GGUF format from [`LoneStriker/dolphin-2.7-mixtral-8x7b-3.5bpw-h6-exl2`](https://huggingface.co/LoneStriker/dolphin-2.7-mixtral-8x7b-3.5bpw-h6-exl2) 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/LoneStriker/dolphin-2.7-mixtral-8x7b-3.5bpw-h6-exl2) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo bendavis78/dolphin-2.7-mixtral-8x7b-3.5bpw-h6-exl2-Q4_K_M-GGUF --hf-file dolphin-2.7-mixtral-8x7b-3.5bpw-h6-exl2-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo bendavis78/dolphin-2.7-mixtral-8x7b-3.5bpw-h6-exl2-Q4_K_M-GGUF --hf-file dolphin-2.7-mixtral-8x7b-3.5bpw-h6-exl2-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo bendavis78/dolphin-2.7-mixtral-8x7b-3.5bpw-h6-exl2-Q4_K_M-GGUF --hf-file dolphin-2.7-mixtral-8x7b-3.5bpw-h6-exl2-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo bendavis78/dolphin-2.7-mixtral-8x7b-3.5bpw-h6-exl2-Q4_K_M-GGUF --hf-file dolphin-2.7-mixtral-8x7b-3.5bpw-h6-exl2-q4_k_m.gguf -c 2048 ```

vdavidr/OpenCodeInterpreter-DS-6.7B_En__translations_size_104_epochs_10_2024-06-22_03-22-06_3557996

vdavidr

--- license: apache-2.0 base_model: m-a-p/OpenCodeInterpreter-DS-6.7B tags: - generated_from_trainer metrics: - accuracy - bleu - sacrebleu - rouge model-index: - name: OpenCodeInterpreter-DS-6.7B_En__translations_size_104_epochs_10_2024-06-22_03-22-06_3557996 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. --> # OpenCodeInterpreter-DS-6.7B_En__translations_size_104_epochs_10_2024-06-22_03-22-06_3557996 This model is a fine-tuned version of [m-a-p/OpenCodeInterpreter-DS-6.7B](https://huggingface.co/m-a-p/OpenCodeInterpreter-DS-6.7B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.1148 - Accuracy: 0.042 - Chrf: 0.511 - Bleu: 0.407 - Sacrebleu: 0.4 - Rouge1: 0.516 - Rouge2: 0.259 - Rougel: 0.474 - Rougelsum: 0.508 - Meteor: 0.404 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 1 - eval_batch_size: 1 - seed: 3407 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 4 - total_eval_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 104 - training_steps: 1040 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Chrf | Bleu | Sacrebleu | Rouge1 | Rouge2 | Rougel | Rougelsum | Meteor | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----:|:-----:|:---------:|:------:|:------:|:------:|:---------:|:------:| | 0.145 | 4.0 | 104 | 1.2220 | 0.035 | 0.711 | 0.572 | 0.6 | 0.657 | 0.424 | 0.585 | 0.646 | 0.499 | | 0.1118 | 8.0 | 208 | 1.3598 | 0.034 | 0.708 | 0.563 | 0.6 | 0.66 | 0.421 | 0.586 | 0.641 | 0.519 | | 0.1119 | 12.0 | 312 | 1.4868 | 0.037 | 0.719 | 0.586 | 0.6 | 0.674 | 0.441 | 0.597 | 0.659 | 0.522 | | 0.4556 | 16.0 | 416 | 1.6801 | 0.04 | 0.704 | 0.587 | 0.6 | 0.697 | 0.468 | 0.613 | 0.682 | 0.525 | | 0.1586 | 20.0 | 520 | 2.0607 | 0.043 | 0.672 | 0.552 | 0.6 | 0.659 | 0.422 | 0.584 | 0.65 | 0.46 | | 0.2311 | 24.0 | 624 | 2.6808 | 0.039 | 0.57 | 0.449 | 0.4 | 0.574 | 0.312 | 0.504 | 0.566 | 0.42 | | 1.0024 | 28.0 | 728 | 2.7805 | 0.055 | 0.569 | 0.462 | 0.5 | 0.572 | 0.322 | 0.52 | 0.56 | 0.413 | | 0.2281 | 32.0 | 832 | 2.9914 | 0.044 | 0.546 | 0.44 | 0.4 | 0.548 | 0.296 | 0.504 | 0.542 | 0.409 | | 0.3755 | 36.0 | 936 | 3.0919 | 0.045 | 0.508 | 0.406 | 0.4 | 0.522 | 0.261 | 0.479 | 0.515 | 0.407 | | 0.2274 | 40.0 | 1040 | 3.1148 | 0.042 | 0.511 | 0.407 | 0.4 | 0.516 | 0.259 | 0.474 | 0.508 | 0.404 | ### Framework versions - Transformers 4.37.0 - Pytorch 2.2.1+cu121 - Datasets 2.20.0 - Tokenizers 0.15.2

bendavis78/dolphin-2.7-mixtral-8x7b-3.5bpw-h6-exl2-Q8_0-GGUF

bendavis78

--- base_model: LoneStriker/dolphin-2.7-mixtral-8x7b-3.5bpw-h6-exl2 datasets: - ehartford/dolphin - jondurbin/airoboros-2.2.1 - ehartford/dolphin-coder - teknium/openhermes - ise-uiuc/Magicoder-OSS-Instruct-75K - ise-uiuc/Magicoder-Evol-Instruct-110K - LDJnr/Capybara language: - en license: apache-2.0 tags: - llama-cpp - gguf-my-repo --- # bendavis78/dolphin-2.7-mixtral-8x7b-3.5bpw-h6-exl2-Q8_0-GGUF This model was converted to GGUF format from [`LoneStriker/dolphin-2.7-mixtral-8x7b-3.5bpw-h6-exl2`](https://huggingface.co/LoneStriker/dolphin-2.7-mixtral-8x7b-3.5bpw-h6-exl2) 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/LoneStriker/dolphin-2.7-mixtral-8x7b-3.5bpw-h6-exl2) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo bendavis78/dolphin-2.7-mixtral-8x7b-3.5bpw-h6-exl2-Q8_0-GGUF --hf-file dolphin-2.7-mixtral-8x7b-3.5bpw-h6-exl2-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo bendavis78/dolphin-2.7-mixtral-8x7b-3.5bpw-h6-exl2-Q8_0-GGUF --hf-file dolphin-2.7-mixtral-8x7b-3.5bpw-h6-exl2-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo bendavis78/dolphin-2.7-mixtral-8x7b-3.5bpw-h6-exl2-Q8_0-GGUF --hf-file dolphin-2.7-mixtral-8x7b-3.5bpw-h6-exl2-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo bendavis78/dolphin-2.7-mixtral-8x7b-3.5bpw-h6-exl2-Q8_0-GGUF --hf-file dolphin-2.7-mixtral-8x7b-3.5bpw-h6-exl2-q8_0.gguf -c 2048 ```

vdavidr/Artigenz-Coder-DS-6.7B_En__translations_size_104_epochs_10_2024-06-22_03-26-15_3557997

vdavidr

--- license: other base_model: Artigenz/Artigenz-Coder-DS-6.7B tags: - generated_from_trainer metrics: - accuracy - bleu - sacrebleu - rouge model-index: - name: Artigenz-Coder-DS-6.7B_En__translations_size_104_epochs_10_2024-06-22_03-26-15_3557997 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. --> # Artigenz-Coder-DS-6.7B_En__translations_size_104_epochs_10_2024-06-22_03-26-15_3557997 This model is a fine-tuned version of [Artigenz/Artigenz-Coder-DS-6.7B](https://huggingface.co/Artigenz/Artigenz-Coder-DS-6.7B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.1141 - Accuracy: 0.06 - Chrf: 0.499 - Bleu: 0.407 - Sacrebleu: 0.4 - Rouge1: 0.494 - Rouge2: 0.242 - Rougel: 0.449 - Rougelsum: 0.488 - Meteor: 0.401 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 1 - eval_batch_size: 1 - seed: 3407 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 4 - total_eval_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 104 - training_steps: 1040 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Chrf | Bleu | Sacrebleu | Rouge1 | Rouge2 | Rougel | Rougelsum | Meteor | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----:|:-----:|:---------:|:------:|:------:|:------:|:---------:|:------:| | 0.1365 | 4.0 | 104 | 1.1838 | 0.046 | 0.714 | 0.6 | 0.6 | 0.676 | 0.459 | 0.613 | 0.668 | 0.522 | | 0.1026 | 8.0 | 208 | 1.3421 | 0.045 | 0.699 | 0.569 | 0.6 | 0.66 | 0.437 | 0.601 | 0.648 | 0.482 | | 0.1001 | 12.0 | 312 | 1.3957 | 0.047 | 0.724 | 0.621 | 0.6 | 0.701 | 0.482 | 0.63 | 0.685 | 0.528 | | 0.4589 | 16.0 | 416 | 1.6948 | 0.046 | 0.702 | 0.601 | 0.6 | 0.694 | 0.473 | 0.62 | 0.681 | 0.51 | | 0.1812 | 20.0 | 520 | 2.5671 | 0.077 | 0.59 | 0.47 | 0.5 | 0.605 | 0.346 | 0.526 | 0.591 | 0.403 | | 0.1966 | 24.0 | 624 | 2.5118 | 0.066 | 0.607 | 0.502 | 0.5 | 0.607 | 0.357 | 0.544 | 0.601 | 0.428 | | 0.9528 | 28.0 | 728 | 2.7303 | 0.055 | 0.567 | 0.465 | 0.5 | 0.577 | 0.325 | 0.52 | 0.567 | 0.429 | | 0.2147 | 32.0 | 832 | 2.9680 | 0.055 | 0.529 | 0.435 | 0.4 | 0.541 | 0.285 | 0.489 | 0.533 | 0.402 | | 0.367 | 36.0 | 936 | 3.1490 | 0.067 | 0.508 | 0.417 | 0.4 | 0.516 | 0.264 | 0.469 | 0.509 | 0.392 | | 0.2157 | 40.0 | 1040 | 3.1141 | 0.06 | 0.499 | 0.407 | 0.4 | 0.494 | 0.242 | 0.449 | 0.488 | 0.401 | ### Framework versions - Transformers 4.37.0 - Pytorch 2.2.1+cu121 - Datasets 2.20.0 - Tokenizers 0.15.2

kongj/kongjij

kongj

Entry not found

sperfu/EyeDoc

sperfu

Entry not found

vdavidr/llama-7b-finnish-instruct-v0.2_En__translations_size_104_epochs_10_2024-06-22_03-40-15_3557998

vdavidr

--- license: apache-2.0 base_model: Finnish-NLP/llama-7b-finnish-instruct-v0.2 tags: - generated_from_trainer metrics: - accuracy - bleu - sacrebleu - rouge model-index: - name: llama-7b-finnish-instruct-v0.2_En__translations_size_104_epochs_10_2024-06-22_03-40-15_3557998 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # llama-7b-finnish-instruct-v0.2_En__translations_size_104_epochs_10_2024-06-22_03-40-15_3557998 This model is a fine-tuned version of [Finnish-NLP/llama-7b-finnish-instruct-v0.2](https://huggingface.co/Finnish-NLP/llama-7b-finnish-instruct-v0.2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5080 - Accuracy: 0.574 - Chrf: 0.692 - Bleu: 0.544 - Sacrebleu: 0.5 - Rouge1: 0.598 - Rouge2: 0.374 - Rougel: 0.581 - Rougelsum: 0.585 - Meteor: 0.465 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 1 - eval_batch_size: 1 - seed: 3407 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 4 - total_eval_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 104 - training_steps: 1040 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Chrf | Bleu | Sacrebleu | Rouge1 | Rouge2 | Rougel | Rougelsum | Meteor | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----:|:-----:|:---------:|:------:|:------:|:------:|:---------:|:------:| | 0.4205 | 4.0 | 104 | 1.0350 | 0.577 | 0.44 | 0.277 | 0.3 | 0.317 | 0.149 | 0.288 | 0.297 | 0.355 | | 0.2068 | 8.0 | 208 | 0.9393 | 0.578 | 0.501 | 0.338 | 0.3 | 0.391 | 0.206 | 0.357 | 0.353 | 0.362 | | 0.1289 | 12.0 | 312 | 0.8516 | 0.575 | 0.548 | 0.38 | 0.4 | 0.439 | 0.225 | 0.414 | 0.416 | 0.395 | | 0.7509 | 16.0 | 416 | 0.7754 | 0.578 | 0.578 | 0.42 | 0.4 | 0.461 | 0.259 | 0.441 | 0.44 | 0.431 | | 0.0951 | 20.0 | 520 | 0.7348 | 0.574 | 0.606 | 0.459 | 0.5 | 0.513 | 0.291 | 0.484 | 0.483 | 0.446 | | 0.1201 | 24.0 | 624 | 0.6261 | 0.577 | 0.637 | 0.484 | 0.5 | 0.541 | 0.321 | 0.516 | 0.505 | 0.437 | | 1.0287 | 28.0 | 728 | 0.5589 | 0.574 | 0.668 | 0.51 | 0.5 | 0.559 | 0.334 | 0.535 | 0.51 | 0.458 | | 0.0522 | 32.0 | 832 | 0.5623 | 0.574 | 0.659 | 0.526 | 0.5 | 0.573 | 0.349 | 0.552 | 0.541 | 0.449 | | 0.1314 | 36.0 | 936 | 0.5154 | 0.576 | 0.682 | 0.534 | 0.5 | 0.588 | 0.36 | 0.573 | 0.576 | 0.465 | | 0.0449 | 40.0 | 1040 | 0.5080 | 0.574 | 0.692 | 0.544 | 0.5 | 0.598 | 0.374 | 0.581 | 0.585 | 0.465 | ### Framework versions - Transformers 4.37.0 - Pytorch 2.2.1+cu121 - Datasets 2.20.0 - Tokenizers 0.15.2

sjunique/results_split_1

sjunique

Entry not found

Coolwowsocoolwow/Wii_Party_U_Announcer

Coolwowsocoolwow

--- license: openrail ---

aryarajput/Hii

aryarajput

Entry not found

MikuChan/JD1

MikuChan

Entry not found

C0ttontheBunny/DarkTama

C0ttontheBunny

--- license: openrail ---

yogsth0t/czme1

yogsth0t

--- license: apache-2.0 ---

yraziel/ray_william_johnson

yraziel

Entry not found

channo39mz2/whisper-small-dv

channo39mz2

Entry not found

to100mak/Llama-3-Open-Ko-8B-Instruct-to100mak

to100mak

transformers

--- base_model: beomi/Llama-3-Open-Ko-8B-Instruct-preview language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** to100mak - **License:** apache-2.0 - **Finetuned from model :** beomi/Llama-3-Open-Ko-8B-Instruct-preview This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

manbeast3b/KinoInferTry1

manbeast3b

Entry not found

lahcen001/nana

lahcen001

Entry not found

chickenrice0721/whisper-large-v3-translate-zh-v0.1-lt

chickenrice0721

transformers

automatic-speech-recognition

--- license: apache-2.0 base_model: openai/whisper-large-v3 tags: - audio - automatic-speech-recognition - translate - generated_from_trainer language: - zh metrics: - cer - wer model-index: - name: whisper-large-v3-translate-zh-v0.1-lt results: [] pipeline_tag: automatic-speech-recognition --- # whisper-large-v3-translate-zh-v0.1-lt This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3). ## Model description 3500小时 (日语音频,中文字幕) 数据微调, 翻译模式直出中文 ## Usage task='translate', language='ja' ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 4000 - dropout: 0.1 - mask_time_prob: 0.05 - mask_feature_prob: 0.2 - condition_on_previous_text_rate: 0.5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Cer | Wer | |:-------------:|:------:|:-----:|:---------------:|:------:|:------:| | 2.1282 | 0.0739 | 1000 | 2.1852 | 1.9014 | 4.4904 | | 1.8567 | 0.1478 | 2000 | 1.8366 | 1.7295 | 3.8716 | | 1.6968 | 0.2217 | 3000 | 1.2615 | 1.6279 | 2.4825 | | 1.6264 | 0.2956 | 4000 | 1.0536 | 1.5625 | 1.8101 | | 1.5687 | 0.3695 | 5000 | 1.0932 | 1.5410 | 2.1218 | | 1.531 | 0.4433 | 6000 | 1.5156 | 1.2533 | 2.3689 | | 1.4875 | 0.5172 | 7000 | 1.4697 | 0.9560 | 1.5588 | | 1.4518 | 0.5911 | 8000 | 1.4521 | 1.0170 | 1.6392 | | 1.4472 | 0.6650 | 9000 | 1.4463 | 1.0084 | 1.6420 | | 1.3991 | 0.7389 | 10000 | 1.4238 | 0.9266 | 1.6992 | | 1.4266 | 0.8128 | 11000 | 1.4141 | 0.8365 | 1.3056 | | 1.3755 | 0.8867 | 12000 | 1.4033 | 0.7904 | 1.3119 | | 1.3833 | 0.9606 | 13000 | 1.4004 | 0.8600 | 1.3333 | | 1.3224 | 1.0345 | 14000 | 1.3770 | 0.8243 | 1.4560 | | 1.3295 | 1.1084 | 15000 | 1.3770 | 0.7852 | 1.4298 | | 1.3136 | 1.1823 | 16000 | 1.3564 | 0.7176 | 1.1826 | | 1.2832 | 1.2561 | 17000 | 1.3535 | 0.6767 | 1.1781 | | 1.2917 | 1.3300 | 18000 | 1.3584 | 0.7255 | 1.1218 | | 1.27 | 1.4039 | 19000 | 1.3330 | 0.6590 | 1.1242 | | 1.2704 | 1.4778 | 20000 | 1.3379 | 0.6934 | 1.1944 | | 1.2614 | 1.5517 | 21000 | 1.3330 | 0.6949 | 1.1820 | | 1.2455 | 1.6256 | 22000 | 1.3350 | 0.6931 | 1.0892 | | 1.2475 | 1.6995 | 23000 | 1.3154 | 0.6662 | 1.1576 | | 1.2583 | 1.7734 | 24000 | 1.3164 | 0.6490 | 1.0705 | | 1.2333 | 1.8473 | 25000 | 1.3184 | 0.6833 | 1.1480 | | 1.2462 | 1.9212 | 26000 | 1.3125 | 0.6672 | 1.1612 | | 1.2279 | 1.9950 | 27000 | 1.3047 | 0.6644 | 1.2179 | | 1.1908 | 2.0689 | 28000 | 1.3047 | 0.6938 | 1.2221 | | 1.1831 | 2.1428 | 29000 | 1.2998 | 0.6316 | 1.0717 | | 1.1705 | 2.2167 | 30000 | 1.3018 | 0.6165 | 1.0958 | | 1.171 | 2.2906 | 31000 | 1.3027 | 0.6109 | 1.0868 | | 1.1567 | 2.3645 | 32000 | 1.3037 | 0.6485 | 1.1736 | | 1.1705 | 2.4384 | 33000 | 1.2969 | 0.6078 | 1.0515 | | 1.1819 | 2.5123 | 34000 | 1.2949 | 0.6158 | 1.0362 | | 1.1447 | 2.5862 | 35000 | 1.2920 | 0.6365 | 1.0558 | | 1.17 | 2.6601 | 36000 | 1.2881 | 0.6339 | 1.0868 | | 1.1495 | 2.7340 | 37000 | 1.2949 | 0.6297 | 1.0437 | | 1.1395 | 2.8078 | 38000 | 1.2900 | 0.6285 | 1.1221 | | 1.15 | 2.8817 | 39000 | 1.2891 | 0.5997 | 1.0217 | | 1.1623 | 2.9556 | 40000 | 1.2881 | 0.6085 | 1.0395 | ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1

AleDiazT/finetuning-emotion-model-5

AleDiazT

transformers

text-classification

Entry not found

howarudo/paligemma-ft

howarudo

Entry not found

SimoLM/final_model

SimoLM

transformers

--- base_model: unsloth/phi-3-medium-4k-instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl --- # Uploaded model - **Developed by:** tferdi - **License:** apache-2.0 - **Finetuned from model :** unsloth/phi-3-medium-4k-instruct-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

inflaton/Qwen2-1.5B-Instruct-bnb-4bit-MAC-merged_4bit_forced

inflaton

transformers

text-generation

--- base_model: unsloth/Qwen2-1.5B-Instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - qwen2 - trl - sft --- # Uploaded model - **Developed by:** inflaton - **License:** apache-2.0 - **Finetuned from model :** unsloth/Qwen2-1.5B-Instruct-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

magnifi/parser_user_v8-0621-epoch7-0.002_nosystemprompt

magnifi

transformers

text-generation

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit --- # Uploaded model - **Developed by:** magnifi - **License:** apache-2.0 - **Finetuned from model :** unsloth/Phi-3-mini-4k-instruct-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

BakingBeans/Archive

BakingBeans

Entry not found

sandyyuan/galaxyfactorycropped

sandyyuan

diffusers

Entry not found

darylsilva/mimimal

darylsilva

--- license: apache-2.0 ---

Hakaijuxd/Softvoice

Hakaijuxd

Entry not found

AroAITeam/Mouhu-0.1-v0.0.1

AroAITeam

text-generation

--- license: apache-2.0 language: - ja - en pipeline_tag: text-generation ---

MsgmSgmsG/llama-3-8b-rt-01

MsgmSgmsG

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]

bhaelen/example-model

bhaelen

# Example Model This is my model card readme --- license: mit ---

wahoong/llamav3-8b-unsloth-v2

wahoong

transformers

--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> Unsloth llamav3-8b stripped decompiled code to C source code ## 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]

magnifi/parser_user_v8-0621-epoch8-0.002_nosystemprompt

magnifi

transformers

text-generation

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit --- # Uploaded model - **Developed by:** magnifi - **License:** apache-2.0 - **Finetuned from model :** unsloth/Phi-3-mini-4k-instruct-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

Aptronym/LowStepLoras

Aptronym

Entry not found

VDBLOI2024/VDBLOI-AI

VDBLOI2024

Entry not found

MsgmSgmsG/llama-3-8b-rt-00

MsgmSgmsG

Entry not found

ErikGG64/Kurt_Cobain_Talking

ErikGG64

--- license: openrail ---

DFofanov78/llama-3-8b-Instruct-bnb-4bit

DFofanov78

transformers

text-generation

--- language: - en license: apache-2.0 library_name: transformers tags: - unsloth - transformers - llama - llama-3 --- # Finetune Mistral, Gemma, Llama 2-5x faster with 70% less memory via Unsloth! Directly quantized 4bit model with `bitsandbytes`. We have a Google Colab Tesla T4 notebook for Llama-3 8b here: https://colab.research.google.com/drive/135ced7oHytdxu3N2DNe1Z0kqjyYIkDXp?usp=sharing [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord%20button.png" width="200"/>](https://discord.gg/u54VK8m8tk) [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/buy%20me%20a%20coffee%20button.png" width="200"/>](https://ko-fi.com/unsloth) [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) ## ✨ Finetune for Free All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face. | Unsloth supports | Free Notebooks | Performance | Memory use | |-----------------|--------------------------------------------------------------------------------------------------------------------------|-------------|----------| | **Llama-3 8b** | [▶️ Start on Colab](https://colab.research.google.com/drive/135ced7oHytdxu3N2DNe1Z0kqjyYIkDXp?usp=sharing) | 2.4x faster | 58% less | | **Gemma 7b** | [▶️ Start on Colab](https://colab.research.google.com/drive/10NbwlsRChbma1v55m8LAPYG15uQv6HLo?usp=sharing) | 2.4x faster | 58% less | | **Mistral 7b** | [▶️ Start on Colab](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing) | 2.2x faster | 62% less | | **Llama-2 7b** | [▶️ Start on Colab](https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?usp=sharing) | 2.2x faster | 43% less | | **TinyLlama** | [▶️ Start on Colab](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing) | 3.9x faster | 74% less | | **CodeLlama 34b** A100 | [▶️ Start on Colab](https://colab.research.google.com/drive/1y7A0AxE3y8gdj4AVkl2aZX47Xu3P1wJT?usp=sharing) | 1.9x faster | 27% less | | **Mistral 7b** 1xT4 | [▶️ Start on Kaggle](https://www.kaggle.com/code/danielhanchen/kaggle-mistral-7b-unsloth-notebook) | 5x faster\* | 62% less | | **DPO - Zephyr** | [▶️ Start on Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) | 1.9x faster | 19% less | - This [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing) is useful for ShareGPT ChatML / Vicuna templates. - This [text completion notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing) is for raw text. This [DPO notebook](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) replicates Zephyr. - \* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.

fabrimath/wav2vec2-base-finetuned-ks

fabrimath

transformers

audio-classification

Entry not found

cycy233/tttt

cycy233

Entry not found

b-fujino/LUM_bfloat16

b-fujino

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]

allstrives/simple-model

allstrives

Entry not found

FurnTheFurnace/Applio-Kaggle

FurnTheFurnace

<h1 align="center"> <a href="https://applio.org" target="_blank"><img src="https://github.com/IAHispano/Applio/assets/133521603/78e975d8-b07f-47ba-ab23-5a31592f322a" alt="Applio"></a> </h1> <p align="center"> <a href="https://github.com/IAHispano/Applio/graphs/contributors"> <img alt="Contributors" src="https://img.shields.io/github/contributors/iahispano/applio?style=for-the-badge&color=FFFFFF" /> </a> <a href="https://github.com/IAHispano/Applio/releases/tag/3.2.0"> <img alt="Release" src="https://img.shields.io/github/release/iahispano/applio?style=for-the-badge&color=FFFFFF" /> </a> <a href="https://github.com/IAHispano/Applio"> <img alt="Stars" src="https://img.shields.io/github/stars/iahispano/applio?style=for-the-badge&color=FFFFFF" /> </a> <a href="https://github.com/IAHispano/Applio"> <img alt="Fork" src="https://img.shields.io/github/forks/iahispano/applio?style=for-the-badge&color=FFFFFF" /> </a> <a href="https://github.com/IAHispano/Applio/issues"> <img alt="Issues" src="https://img.shields.io/github/issues/iahispano/applio?style=for-the-badge&color=FFFFFF" /> </a> <a href="https://cdn-uploads.huggingface.co/production/uploads/652c5d34ec10d7e4810a0513/AUlwXRIMhOSfKG6al-hm0.png"> <img alt="Kaggle" src="https://camo.githubusercontent.com/74f4165a70cf43b25e5e8df17a045426ddbf698b625a7c71c0a6412daa4eb011/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4b6167676c652d3033356137643f7374796c653d666f722d7468652d6261646765266c6f676f3d6b6167676c65266c6f676f436f6c6f723d7768697465" /> </a> </p> <p align="center"> VITS-based Voice Conversion focused on simplicity, quality, and performance. Originally created by <a href="https://huggingface.co/blaise-tk">Blaise.</a> </p> <p align="center"> This is a upcoming Kaggle version of Applio made by <a href="https://discordapp.com/users/989772388508000306">Vidal</a> and <a href="https://discordapp.com/users/984567398826917918">Mantrax</a>. We kindly ask you to please not sending DMs to both of us about the link, there's no link until it's complete and ready to be public. </p>

Knowtex-ai/Oncology-Model-4bit-Llama3

Knowtex-ai

transformers

--- base_model: unsloth/llama-3-8b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** kalycodes - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

casque/Swimming_Lesson_6_v1

casque

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

bbyxinnocenz/RBN_HSR

bbyxinnocenz

Entry not found

howarudo/gemma-demo-vqa-ft

howarudo

Entry not found

bbyxinnocenz/RBN_CHVY

bbyxinnocenz

Entry not found

Rickliou/gemma-medical_qa-Finetune

Rickliou

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]

vdavidr/deepseek-coder-6.7b-instruct_En__translations_size_104_epochs_10_2024-06-22_06-06-55_3557999

vdavidr

--- license: other base_model: deepseek-ai/deepseek-coder-6.7b-instruct tags: - generated_from_trainer metrics: - accuracy - bleu - sacrebleu - rouge model-index: - name: deepseek-coder-6.7b-instruct_En__translations_size_104_epochs_10_2024-06-22_06-06-55_3557999 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. --> # deepseek-coder-6.7b-instruct_En__translations_size_104_epochs_10_2024-06-22_06-06-55_3557999 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: 3.1144 - Accuracy: 0.047 - Chrf: 0.5 - Bleu: 0.39 - Sacrebleu: 0.4 - Rouge1: 0.501 - Rouge2: 0.239 - Rougel: 0.448 - Rougelsum: 0.497 - Meteor: 0.413 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 1 - eval_batch_size: 1 - seed: 3407 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 4 - total_eval_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 104 - training_steps: 1040 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Chrf | Bleu | Sacrebleu | Rouge1 | Rouge2 | Rougel | Rougelsum | Meteor | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----:|:-----:|:---------:|:------:|:------:|:------:|:---------:|:------:| | 0.1338 | 4.0 | 104 | 1.1433 | 0.033 | 0.731 | 0.594 | 0.6 | 0.675 | 0.459 | 0.617 | 0.668 | 0.515 | | 0.1034 | 8.0 | 208 | 1.2599 | 0.03 | 0.723 | 0.594 | 0.6 | 0.687 | 0.462 | 0.611 | 0.674 | 0.495 | | 0.0974 | 12.0 | 312 | 1.3238 | 0.037 | 0.739 | 0.63 | 0.6 | 0.719 | 0.505 | 0.638 | 0.7 | 0.545 | | 0.6468 | 16.0 | 416 | 1.9216 | 0.042 | 0.664 | 0.547 | 0.5 | 0.652 | 0.409 | 0.567 | 0.64 | 0.458 | | 0.1566 | 20.0 | 520 | 2.1866 | 0.045 | 0.656 | 0.538 | 0.5 | 0.657 | 0.428 | 0.581 | 0.649 | 0.494 | | 0.2056 | 24.0 | 624 | 2.5536 | 0.042 | 0.585 | 0.468 | 0.5 | 0.586 | 0.334 | 0.527 | 0.58 | 0.441 | | 0.9677 | 28.0 | 728 | 2.8086 | 0.051 | 0.561 | 0.455 | 0.5 | 0.565 | 0.314 | 0.509 | 0.559 | 0.417 | | 0.2163 | 32.0 | 832 | 2.9769 | 0.044 | 0.532 | 0.42 | 0.4 | 0.523 | 0.26 | 0.475 | 0.517 | 0.412 | | 0.3617 | 36.0 | 936 | 3.1040 | 0.052 | 0.502 | 0.392 | 0.4 | 0.496 | 0.239 | 0.447 | 0.492 | 0.427 | | 0.2153 | 40.0 | 1040 | 3.1144 | 0.047 | 0.5 | 0.39 | 0.4 | 0.501 | 0.239 | 0.448 | 0.497 | 0.413 | ### Framework versions - Transformers 4.37.0 - Pytorch 2.2.1+cu121 - Datasets 2.20.0 - Tokenizers 0.15.2

ranggaaldosas/bart_large_cnn_with_multinews

ranggaaldosas

--- license: apache-2.0 ---

loeol/Llama-3-8b-BFI-Anonymous

loeol

transformers

text-generation

--- license: llama3 ---