Datasets:

librarian-bots

/

model_cards_with_metadata_test

like 1

cxfajar197/iqbal-fine-tuned-bert

cxfajar197

Entry not found

shuyuej/MedLLaMA3-70B-Multilingual

shuyuej

--- license: apache-2.0 ---

mmarone/quip_20200301.25-1.bf

mmarone

Entry not found

SebasCortazar/example_model

SebasCortazar

--- license: mit ---

vjnadkarni/roberta-base-peft-ag-news

vjnadkarni

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]

vjnadkarni/roberta-base-fully-fine-tuned-ag-news

vjnadkarni

transformers

text-classification

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

tjake/TinyLlama-1.1B-Chat-v1.0-Jlama-Q4

tjake

transformers

text-generation

Entry not found

Tflatval/Whisper-large-BB-NB-RUND

Tflatval

transformers

automatic-speech-recognition

Entry not found

ace-20/Ejemplo

ace-20

--- license: apache-2.0 ---

PhillipGuo/hp-lat-llama-No_PCA-epsilon1.0-pgd_layer9-rmu_layer12-wikitext-RMU-151

PhillipGuo

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]

arielmartin/test

arielmartin

--- license: unknown ---

William2357/dogfirst1

William2357

Entry not found

Tflatval/Whisper-large-NB

Tflatval

transformers

automatic-speech-recognition

Entry not found

Musix/Hector-Bonilla-Chaves-BR

Musix

Entry not found

Tflatval/Whisper-small-BB-NB

Tflatval

transformers

automatic-speech-recognition

Entry not found

Tflatval/Whisper-small_BB-RUND

Tflatval

transformers

automatic-speech-recognition

Entry not found

Pyroserenus/L3-8B-Stheno-v3.3-32K-6.9bpw-h8-exl2

Pyroserenus

transformers

text-generation

--- license: cc-by-nc-4.0 language: - en --- Trained with compute from [Backyard.ai](https://backyard.ai/) | Thanks to them and @dynafire for helping me out. --- ## Exl2 quant done of [Stheno 3.3 32k by Sao10k](https://huggingface.co/Sao10K/L3-8B-Stheno-v3.3-32K) Training Details: <br>Trained at 8K Context -> Expanded to 32K Context with PoSE training. Dataset Modifications: <br>\- Further Cleaned up Roleplaying Samples -> Quality Check <br>\- Removed Low Quality Samples from Manual Check -> Increased Baseline Quality Floor <br>\- More Creative Writing Samples -> 2x Samples <br>\- Remade and Refined Detailed Instruct Data Notes: <br>\- Training run is much less aggressive than previous Stheno versions. <br>\- This model works when tested in bf16 with the same configs as within the file. <br>\- I do not know the effects quantisation has on it. <br>\- Roleplays pretty well. Feels nice in my opinion. <br>\- It has some issues on long context understanding and reasoning. Much better vs rope scaling normally though, so that is a plus. <br>\- Reminder, this isn't a native 32K model. It has it's issues, but it's coherent and working well. Sanity Check // Needle in a Haystack Results: <br>\- This is not as complex as RULER or NIAN, but it's a basic evaluator. Some improper train examples had Haystack scores ranging from Red to Orange for most of the extended contexts.  Wandb Run:  --- Relevant Axolotl Configurations: <br>-> Taken from [winglian/Llama-3-8b-64k-PoSE](https://huggingface.co/winglian/Llama-3-8b-64k-PoSE) <br>\- I tried to find my own configs, hours of tinkering but the one he used worked best, so I stuck to it. <br>\- 2M Rope Theta had the best loss results during training compared to other values. <br>\- Leaving it at 500K rope wasn't that much worse, but 4M and 8M Theta made the grad_norm values worsen even if loss drops fast. <br>\- Mixing in Pretraining Data was a PITA. Made it a lot worse with formatting. <br>\- Pretraining / Noise made it worse at Haystack too? It wasn't all Green, Mainly Oranges. <br>\- Improper / Bad Rope Theta shows in Grad_Norm exploding to thousands. It'll drop to low values alright, but it's a scary fast drop even with gradient clipping. ``` sequence_len: 8192 use_pose: true pose_max_context_len: 32768 overrides_of_model_config: rope_theta: 2000000.0 max_position_embeddings: 32768 # peft_use_dora: true adapter: lora peft_use_rslora: true lora_model_dir: lora_r: 256 lora_alpha: 256 lora_dropout: 0.1 lora_target_linear: true lora_target_modules: - gate_proj - down_proj - up_proj - q_proj - v_proj - k_proj - o_proj warmup_steps: 80 gradient_accumulation_steps: 6 micro_batch_size: 1 num_epochs: 2 optimizer: adamw_bnb_8bit lr_scheduler: cosine_with_min_lr learning_rate: 0.00004 lr_scheduler_kwargs: min_lr: 0.000004 ```

Tflatval/Whisper-small-NB-RUND

Tflatval

transformers

automatic-speech-recognition

Entry not found

quirky-lats-at-mats/wmdp_cyber_lat_5

quirky-lats-at-mats

transformers

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

TheRealheavy/InDaClubDrums

TheRealheavy

--- license: openrail ---

William2357/personfirst

William2357

Entry not found

tsavage68/Summary_L3_200steps_1e6rate_03beta_CSFTDPO

tsavage68

transformers

text-generation

--- license: llama3 base_model: tsavage68/Summary_L3_1000steps_1e7rate_SFT2 tags: - trl - dpo - generated_from_trainer model-index: - name: Summary_L3_200steps_1e6rate_03beta_CSFTDPO 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. --> # Summary_L3_200steps_1e6rate_03beta_CSFTDPO This model is a fine-tuned version of [tsavage68/Summary_L3_1000steps_1e7rate_SFT2](https://huggingface.co/tsavage68/Summary_L3_1000steps_1e7rate_SFT2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5961 - Rewards/chosen: 0.1275 - Rewards/rejected: -2.3694 - Rewards/accuracies: 0.1400 - Rewards/margins: 2.4969 - Logps/rejected: -20.0026 - Logps/chosen: -9.1277 - Logits/rejected: -1.1104 - Logits/chosen: -1.1118 ## 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-06 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| | 0.555 | 0.2004 | 50 | 0.5962 | 0.0976 | -1.3577 | 0.1400 | 1.4553 | -17.9791 | -9.1876 | -1.0985 | -1.1002 | | 0.6585 | 0.4008 | 100 | 0.5962 | 0.1094 | -1.5231 | 0.1400 | 1.6326 | -18.3100 | -9.1639 | -1.1003 | -1.1019 | | 0.6238 | 0.6012 | 150 | 0.5961 | 0.1393 | -2.3520 | 0.1400 | 2.4913 | -19.9677 | -9.1042 | -1.1096 | -1.1111 | | 0.6065 | 0.8016 | 200 | 0.5961 | 0.1275 | -2.3694 | 0.1400 | 2.4969 | -20.0026 | -9.1277 | -1.1104 | -1.1118 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.0.0+cu117 - Datasets 2.20.0 - Tokenizers 0.19.1

gyeethaha/pout

gyeethaha

Entry not found

William2357/doggofirst

William2357

Entry not found

mjjj7/MJAgressiveVocals

mjjj7

--- license: openrail ---

chYassine/AMAN-7b-v01

chYassine

peft

--- base_model: mistralai/Mistral-7B-Instruct-v0.3 datasets: - chYassine/WebAttack-CVSSMetrics library_name: peft license: apache-2.0 --- ## Model Details **Model Description** Developed by: Yassine Chagna & Ahmed Ouidani Model type: Cyberattack Detection Language(s) (NLP): English License: apache 2.0 Finetuned from model: mistralai/Mistral-7B-Instruct-v0.3 ### Uses **Direct Use** This model can be directly used for monitoring web access logs to detect potential cyber attacks. **Downstream Use** The model can be adapted for other types of logs and cyberattack detection scenarios. **Out-of-Scope Use** This model is not designed for general-purpose NLP tasks unrelated to cyberattack detection. ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases, and limitations of the model. More information is needed for further recommendations. ### Summary ### Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: T4 GPU ### Technical Specifications [optional] **Model Architecture and Objective** The model uses the architecture of mistralai/Mistral-7B-Instruct-v0.3, fine-tuned for cyberattack detection on web access logs. **Compute Infrastructure** Hardware: Single T4 GPU Software: PEFT 0.11.2.dev0 ### Framework versions PEFT 0.11.2.dev0

cephasyeo/huggingfacemodel

cephasyeo

Entry not found

Zvin/Zvingaling

Zvin

Entry not found

rizwanaslam/model

rizwanaslam

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:** rizwanaslam - **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)

Eitezaz/Ironman

Eitezaz

--- license: apache-2.0 ---

Alsebay/TestSMP-v0.1-alt

Alsebay

transformers

text-generation

--- base_model: - MaziyarPanahi/Llama-3-8B-Instruct-v0.9 - Sao10K/L3-8B-Stheno-v3.2 library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [MaziyarPanahi/Llama-3-8B-Instruct-v0.9](https://huggingface.co/MaziyarPanahi/Llama-3-8B-Instruct-v0.9) as a base. ### 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) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - layer_range: [0, 16] model: MaziyarPanahi/Llama-3-8B-Instruct-v0.9 parameters: density: 0.4 weight: 1.0 - layer_range: [0, 16] model: Sao10K/L3-8B-Stheno-v3.2 parameters: density: 0.6 weight: 0.9 - sources: - layer_range: [16, 32] model: MaziyarPanahi/Llama-3-8B-Instruct-v0.9 parameters: density: 0.2 weight: 0.8 - layer_range: [16, 32] model: Sao10K/L3-8B-Stheno-v3.2 parameters: density: 0.8 weight: 1.0 merge_method: dare_ties base_model: MaziyarPanahi/Llama-3-8B-Instruct-v0.9 parameters: int8_mask: true dtype: bfloat16 ```

LarryAIDraw/colorburstXL_ponyV10

LarryAIDraw

--- license: creativeml-openrail-m --- https://civitai.com/models/341261/colorburst-xl?modelVersionId=384017

morgana-rodrigues/sales-bot-llama3

morgana-rodrigues

Entry not found

cminja/whisper-tiny-sr-yodas

cminja

transformers

automatic-speech-recognition

Entry not found

yong-tang/cspace

yong-tang

Entry not found

DeBiaoFan/segformer-b0-finetuned-segments-sidewalk-2

DeBiaoFan

Entry not found

KiFramesAI/SauvegardeDonnees

KiFramesAI

Entry not found

MomoD2R/LLAMA3_Lang_Vuln_Code_Detection

MomoD2R

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:** MomoD2R - **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)

Wawaworker/swrvskdx

Wawaworker

Entry not found

DBangshu/Base_gemma_e5_1_2

DBangshu

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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. 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hiroki-rad/bert_base-japanese-v3-wrime-lora-fp16

hiroki-rad

transformers

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

usuario101/zerogerman

usuario101

German DeepPhonemizer autoregressive transformer based g2p model, intended to be used in zerovox https://github.com/gooofy/zerovox based on the Zamia Speech phoneset. --- license: apache-2.0 ---

EnduGalletaa/tashicomm

EnduGalletaa

--- license: artistic-2.0 ---

mmarone/portraits-sketch-stackv2.50-50.bf

mmarone

Entry not found

Mistermango24/Toonify-PonyXL

Mistermango24

--- license: artistic-2.0 ---

karthikmit/openai-whisper-medium-LORA-EN-NQ-v2

karthikmit

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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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]

usuario101/zerohifi_gan

usuario101

pretrained VCTK hifigan models from HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis by Jungil Kong, Jaehyeon Kim, Jaekyoung Bae https://github.com/jik876/hifi-gan --- license: mit ---

dfndr11/Llama-2-7b-Intel-Basic-Finetune

dfndr11

peft

--- base_model: meta-llama/Llama-2-7b-hf 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. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure ### Framework versions - PEFT 0.6.2

nihil117/GrayI_v0.5

nihil117

transformers

text-generation

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

LoneWolfVPS/stylizedMODEL

LoneWolfVPS

Entry not found

OmarAmir2001/Visioneer-v4-4

OmarAmir2001

Entry not found

oron1208/kivolotolosulu_tuvalai

oron1208

text-to-image

--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - ja pipeline_tag: text-to-image --- <style> body { display: flex; align-items: center; justify-content: center; height: 100vh; margin: 0; font-family: Arial, sans-serif; background-color: #f4f4f9; overflow: auto; } .container { display: flex; flex-direction: column; align-items: center; justify-content: center; width: 100%; padding: 20px; } .title-container { display: flex; flex-direction: column; justify-content: center; align-items: center; padding: 1em; border-radius: 10px; } .title { font-size: 3em; font-family: 'Montserrat', sans-serif; text-align: center; font-weight: bold; } .title span { background: -webkit-linear-gradient(45deg, #D08FC0, #D2165B, #E49E1F); -webkit-background-clip: text; -webkit-text-fill-color: transparent; } .gallery { display: grid; grid-template-columns: repeat(3, 1fr); gap: 10px; } .gallery img { width: 100%; height: auto; margin-top: 0px; margin-bottom: 0px; border-radius: 10px; box-shadow: 0 4px 8px rgba(0, 0, 0, 0.2); transition: transform 0.3s; } .gallery img:hover { transform: scale(1.05); } .note { font-size: 1em; opacity: 50%; text-align: center; margin-top: 20px; color: #555; } </style> <div class="container"> <div class="title-container"> <div class="title"><span>きぼぉとぉすぅ_つゔぁい</span></div> </div> <div class="gallery"> <img src="https://huggingface.co/oron1208/kivolotolosulu_tuvalai/resolve/main/img/sample_01.png" alt="Image 1"> <img src="https://huggingface.co/oron1208/kivolotolosulu_tuvalai/resolve/main/img/sample_02.png" alt="Image 2"> <img src="https://huggingface.co/oron1208/kivolotolosulu_tuvalai/resolve/main/img/smaple_03.png" alt="Image 3"> <img src="https://huggingface.co/oron1208/kivolotolosulu_tuvalai/resolve/main/img/sample_04.png" alt="Image 4"> <img src="https://huggingface.co/oron1208/kivolotolosulu_tuvalai/resolve/main/img/sample_05.png" alt="Image 5"> <img src="https://huggingface.co/oron1208/kivolotolosulu_tuvalai/resolve/main/img/sample_06.png" alt="Image 6"> </div> <div class="note"> </div> </div> # あばぁうと　もでる ## もでるねぇーむ:きぼぉとぉすぅ_つゔぁい - このぉ、もでるわぁ。つぎのもでるぅぉー、ふぁいんちゅーにんぐしたもでるぅだよ。 1. Kivotos-XL-2.0<br>https://huggingface.co/yodayo-ai/kivotos-xl-2.0 # らいせんすLICENSE このモデルは Fair AI Public License 1.0-SD ライセンス にしたがっています。これは Stable Diffusion モデルのライセンス とも りようかのうです。 かんじんなポイントは: <p>へんしゅうのきょうゆう: もしこのモデルをへんこうしたら、そのへんこうと もとのライセンスはいっしょにしてね。</p> <p>ソースコードのていきょう: もしへんこうされたバージョンがネットワークでアクセスできるなら、ほかのひとがソースコードをてにいれるほうほう（たとえばダウンロードリンク）を ていきょうしてね。これは、そこからさくせいされたもでるぅもおなじだよ。</p> <p> はいぶんのじょうけん: どんなはいぶんもこのライセンスか、これ似たたきそくのある ほかのライセンスでやってね。</p> げんぶん - The model falls under [Fair AI Public License 1.0-SD license](https://freedevproject.org/faipl-1.0-sd/), which is compatible with [Stable Diffusion models’ license](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENSE.md). Key points: 1. Modification Sharing: If you modify the model, you must share both your changes and the original license. 2. Source Code Accessibility: If your modified version is network-accessible, provide a way (like a download link) for others to get the source code. This applies to derived models too. 3. Distribution Terms: Any distribution must be under this license or another with similar rules. ## とれーにんぐ Training | Feature | FT | |-------------------------------|----------------------------| | **Hardware** | 4090 24GB PCIe | | **Batch Size** | 12 | | **Gradient Accumulation Steps** | 1 | | **Noise Offset** | 0.035 | | **Epochs** | 10 | | **UNet Learning Rate** | 2.1e-6 | | **Text Encoder Learning Rate** | 1.5e-7 | | **Optimizer** | Adafactor | | **Scheduler** | Adafactor | | **image** | 6300image |

ymoslem/whisper-medium-ga2en-v5.3.2-19k-r

ymoslem

transformers

automatic-speech-recognition

--- language: - ga - en license: apache-2.0 base_model: openai/whisper-medium tags: - generated_from_trainer datasets: - ymoslem/IWSLT2023-GA-EN - ymoslem/FLEURS-GA-EN - ymoslem/BitesizeIrish-GA-EN - ymoslem/SpokenWords-GA-EN-MTed - ymoslem/Tatoeba-Speech-Irish - ymoslem/Wikimedia-Speech-Irish metrics: - bleu - wer model-index: - name: Whisper Medium GA-EN Speech Translation, 1 epoch, 10k steps results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, and Wikimedia type: ymoslem/IWSLT2023-GA-EN metrics: - name: Bleu type: bleu value: 31.69 - name: Wer type: wer value: 64.970733903647 --- <!-- 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. --> # Whisper Medium GA-EN Speech Translation, 1 epoch, 10k steps This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, and Wikimedia dataset. It achieves the following results on the evaluation set: - Loss: 1.5548 - Bleu: 31.69 - Chrf: 50.38 - Wer: 64.9707 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.03 - training_steps: 19000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Bleu | Chrf | Validation Loss | Wer | |:-------------:|:------:|:-----:|:-----:|:-----:|:---------------:|:--------:| | 2.4382 | 0.0109 | 100 | 3.07 | 16.85 | 2.1114 | 171.0491 | | 2.6151 | 0.0219 | 200 | 6.25 | 23.02 | 2.0207 | 126.9698 | | 2.5699 | 0.0328 | 300 | 5.71 | 24.03 | 1.8660 | 155.5606 | | 2.3084 | 0.0438 | 400 | 9.87 | 28.45 | 1.8084 | 129.0860 | | 2.3327 | 0.0547 | 500 | 12.01 | 31.92 | 1.7823 | 102.7915 | | 2.1495 | 0.0657 | 600 | 13.97 | 32.4 | 1.7238 | 98.6042 | | 2.2164 | 0.0766 | 700 | 11.21 | 33.19 | 1.6538 | 146.0153 | | 2.0071 | 0.0876 | 800 | 14.34 | 35.72 | 1.7038 | 96.9383 | | 1.8334 | 0.0985 | 900 | 16.51 | 37.23 | 1.6329 | 96.8032 | | 1.8359 | 0.1095 | 1000 | 17.87 | 35.94 | 1.6637 | 84.4665 | | 1.7703 | 0.1204 | 1100 | 19.54 | 39.02 | 1.5626 | 79.7839 | | 1.5805 | 0.1314 | 1200 | 20.19 | 40.4 | 1.5618 | 77.8028 | | 1.4545 | 0.1423 | 1300 | 13.88 | 35.53 | 1.5599 | 112.5619 | | 1.5177 | 0.1533 | 1400 | 18.79 | 40.11 | 1.4880 | 84.6916 | | 1.6335 | 0.1642 | 1500 | 16.41 | 38.64 | 1.4996 | 96.9833 | | 1.3809 | 0.1752 | 1600 | 18.3 | 40.17 | 1.4739 | 101.8910 | | 1.2694 | 0.1861 | 1700 | 22.53 | 43.15 | 1.4498 | 76.9923 | | 1.2321 | 0.1970 | 1800 | 19.92 | 42.59 | 1.4163 | 84.6015 | | 1.1969 | 0.2080 | 1900 | 21.63 | 44.92 | 1.4137 | 85.3670 | | 1.2023 | 0.2189 | 2000 | 20.42 | 41.57 | 1.3530 | 82.8906 | | 1.1676 | 0.2299 | 2100 | 22.82 | 44.23 | 1.3723 | 78.1180 | | 1.0332 | 0.2408 | 2200 | 26.73 | 44.75 | 1.3641 | 70.2386 | | 0.8589 | 0.2518 | 2300 | 26.94 | 46.89 | 1.3344 | 72.7600 | | 0.9829 | 0.2627 | 2400 | 28.15 | 47.21 | 1.3181 | 69.1130 | | 0.8228 | 0.2737 | 2500 | 26.98 | 47.41 | 1.3049 | 74.0207 | | 0.7667 | 0.2846 | 2600 | 30.0 | 49.42 | 1.2698 | 65.1058 | | 0.8749 | 0.2956 | 2700 | 27.91 | 47.67 | 1.2878 | 66.9518 | | 0.7504 | 0.3065 | 2800 | 32.03 | 50.35 | 1.2670 | 63.6650 | | 0.7069 | 0.3175 | 2900 | 30.7 | 49.53 | 1.2771 | 64.4304 | | 0.7199 | 0.3284 | 3000 | 30.21 | 48.93 | 1.2658 | 65.5561 | | 0.6207 | 0.3394 | 3100 | 30.82 | 49.11 | 1.2687 | 66.0063 | | 0.5995 | 0.3503 | 3200 | 31.99 | 50.94 | 1.2207 | 62.9446 | | 0.6294 | 0.3612 | 3300 | 31.05 | 50.85 | 1.2422 | 64.7006 | | 0.4612 | 0.3722 | 3400 | 33.1 | 51.82 | 1.2203 | 61.9090 | | 0.5138 | 0.3831 | 3500 | 32.08 | 51.86 | 1.2007 | 63.0797 | | 0.5059 | 0.3941 | 3600 | 31.8 | 51.19 | 1.2130 | 63.9352 | | 0.417 | 0.4050 | 3700 | 32.45 | 51.41 | 1.1975 | 62.2692 | | 0.2958 | 0.4160 | 3800 | 29.29 | 51.39 | 1.2046 | 62.7645 | | 0.393 | 0.4269 | 3900 | 28.95 | 51.45 | 1.1968 | 63.1697 | | 0.3858 | 0.4379 | 4000 | 29.54 | 51.58 | 1.1929 | 62.4043 | | 0.5416 | 0.4488 | 4100 | 27.29 | 43.94 | 1.3522 | 67.9424 | | 0.6644 | 0.4598 | 4200 | 23.16 | 44.45 | 1.4191 | 77.3976 | | 0.5246 | 0.4707 | 4300 | 22.26 | 44.91 | 1.4221 | 77.2625 | | 0.614 | 0.4817 | 4400 | 26.9 | 46.15 | 1.3956 | 70.4638 | | 0.5973 | 0.4926 | 4500 | 25.55 | 45.51 | 1.4152 | 76.7222 | | 0.544 | 0.5036 | 4600 | 23.54 | 47.87 | 1.4091 | 79.1085 | | 0.5975 | 0.5145 | 4700 | 21.85 | 42.69 | 1.4644 | 78.5682 | | 0.4675 | 0.5255 | 4800 | 22.93 | 43.69 | 1.4598 | 76.9023 | | 0.7959 | 0.5364 | 4900 | 24.91 | 44.98 | 1.3884 | 74.5610 | | 0.5936 | 0.5473 | 5000 | 26.91 | 44.88 | 1.4235 | 69.0680 | | 0.4631 | 0.5583 | 5100 | 25.77 | 45.81 | 1.4002 | 74.0207 | | 0.5188 | 0.5692 | 5200 | 28.37 | 45.48 | 1.4405 | 66.2765 | | 0.4675 | 0.5802 | 5300 | 21.1 | 43.11 | 1.4045 | 92.1207 | | 0.4214 | 0.5911 | 5400 | 25.62 | 44.82 | 1.4250 | 72.2197 | | 0.4592 | 0.6021 | 5500 | 27.24 | 46.44 | 1.4107 | 70.0585 | | 0.4809 | 0.6130 | 5600 | 27.93 | 47.42 | 1.3896 | 69.5182 | | 0.4364 | 0.6240 | 5700 | 25.84 | 47.47 | 1.3808 | 77.6227 | | 0.3333 | 0.6349 | 5800 | 26.46 | 47.08 | 1.4203 | 72.4899 | | 0.3345 | 0.6459 | 5900 | 23.1 | 44.6 | 1.4763 | 81.2247 | | 0.3368 | 0.6568 | 6000 | 24.55 | 45.76 | 1.4182 | 80.5493 | | 0.3061 | 0.6678 | 6100 | 23.1 | 45.97 | 1.4218 | 81.3597 | | 0.324 | 0.6787 | 6200 | 28.26 | 47.06 | 1.4453 | 67.5822 | | 0.2667 | 0.6897 | 6300 | 27.87 | 46.14 | 1.4494 | 69.0230 | | 0.2845 | 0.7006 | 6400 | 26.39 | 46.72 | 1.4448 | 71.4543 | | 0.3125 | 0.7115 | 6500 | 27.81 | 46.45 | 1.4643 | 70.0135 | | 0.264 | 0.7225 | 6600 | 26.27 | 47.75 | 1.4244 | 72.7600 | | 0.2426 | 0.7334 | 6700 | 25.84 | 46.68 | 1.4081 | 76.4070 | | 0.2174 | 0.7444 | 6800 | 30.67 | 47.92 | 1.4036 | 65.8262 | | 0.2265 | 0.7553 | 6900 | 28.11 | 49.12 | 1.4174 | 71.2292 | | 0.2016 | 0.7663 | 7000 | 30.43 | 49.47 | 1.4341 | 65.9163 | | 0.1865 | 0.7772 | 7100 | 32.05 | 49.5 | 1.3690 | 63.1697 | | 0.2148 | 0.7882 | 7200 | 32.29 | 49.91 | 1.3603 | 63.8901 | | 0.2126 | 0.7991 | 7300 | 32.07 | 49.31 | 1.4046 | 63.6650 | | 0.1594 | 0.8101 | 7400 | 29.94 | 47.48 | 1.4122 | 65.5110 | | 0.1295 | 0.8210 | 7500 | 30.14 | 49.79 | 1.4243 | 65.7812 | | 0.1378 | 0.8320 | 7600 | 31.23 | 49.42 | 1.4334 | 65.9613 | | 0.1701 | 0.8429 | 7700 | 31.04 | 49.95 | 1.4149 | 65.6461 | | 0.1102 | 0.8539 | 7800 | 31.37 | 50.2 | 1.4082 | 63.7100 | | 0.1267 | 0.8648 | 7900 | 32.86 | 50.83 | 1.3642 | 60.8285 | | 0.1384 | 0.8758 | 8000 | 33.47 | 49.61 | 1.3860 | 59.8829 | | 0.1128 | 0.8867 | 8100 | 32.78 | 50.04 | 1.3840 | 61.8190 | | 0.1197 | 0.8976 | 8200 | 33.69 | 50.94 | 1.3641 | 61.8190 | | 0.1181 | 0.9086 | 8300 | 32.0 | 49.65 | 1.3913 | 63.5299 | | 0.0866 | 0.9195 | 8400 | 30.39 | 48.48 | 1.4171 | 68.0324 | | 0.0784 | 0.9305 | 8500 | 32.27 | 49.32 | 1.3850 | 63.3949 | | 0.092 | 0.9414 | 8600 | 33.78 | 51.13 | 1.3880 | 61.2787 | | 0.0685 | 0.9524 | 8700 | 34.33 | 51.23 | 1.3876 | 61.1887 | | 0.0783 | 0.9633 | 8800 | 33.4 | 48.9 | 1.4010 | 62.5844 | | 0.0735 | 0.9743 | 8900 | 33.72 | 49.01 | 1.4035 | 61.5038 | | 0.0875 | 0.9852 | 9000 | 30.44 | 49.06 | 1.4064 | 67.5371 | | 0.0822 | 0.9962 | 9100 | 34.64 | 51.51 | 1.3803 | 60.5133 | | 0.041 | 1.0071 | 9200 | 34.66 | 52.06 | 1.3678 | 59.4327 | | 0.0351 | 1.0181 | 9300 | 33.88 | 51.16 | 1.3739 | 61.3688 | | 0.0368 | 1.0290 | 9400 | 35.2 | 51.73 | 1.3846 | 60.4232 | | 0.035 | 1.0400 | 9500 | 34.23 | 51.32 | 1.3753 | 60.8735 | | 0.0277 | 1.0509 | 9600 | 35.0 | 52.59 | 1.3788 | 60.0180 | | 0.0247 | 1.0619 | 9700 | 34.69 | 51.7 | 1.3914 | 60.2882 | | 0.0321 | 1.0728 | 9800 | 34.63 | 51.91 | 1.3804 | 60.6033 | | 0.0286 | 1.0837 | 9900 | 33.92 | 51.64 | 1.3795 | 61.8640 | | 0.0239 | 1.0947 | 10000 | 33.79 | 51.67 | 1.3818 | 61.6839 | | 0.085 | 1.1056 | 10100 | 1.5082| 26.54 | 46.14 | 70.9140 | | 0.1002 | 1.1166 | 10200 | 1.5156| 31.06 | 47.27 | 64.7006 | | 0.1144 | 1.1275 | 10300 | 1.5837| 24.93 | 44.33 | 73.3003 | | 0.1137 | 1.1385 | 10400 | 1.5372| 28.96 | 47.2 | 65.7812 | | 0.1182 | 1.1494 | 10500 | 1.5366| 30.05 | 47.09 | 65.6461 | | 0.1214 | 1.1604 | 10600 | 1.5160| 26.83 | 46.73 | 70.2386 | | 0.1413 | 1.1713 | 10700 | 1.5384| 27.92 | 47.04 | 70.3287 | | 0.1011 | 1.1823 | 10800 | 1.5791| 27.71 | 46.13 | 70.5538 | | 0.1187 | 1.1932 | 10900 | 1.6188| 22.91 | 44.41 | 81.8100 | | 0.1364 | 1.2042 | 11000 | 1.5807| 29.38 | 45.46 | 67.3570 | | 0.1158 | 1.2151 | 11100 | 1.5819| 25.33 | 44.25 | 76.0919 | | 0.1199 | 1.2261 | 11200 | 1.5727| 27.52 | 46.0 | 68.3926 | | 0.1213 | 1.2370 | 11300 | 1.5728| 26.92 | 45.92 | 69.4732 | | 0.1291 | 1.2479 | 11400 | 1.5743| 28.13 | 44.72 | 67.9874 | | 0.131 | 1.2589 | 11500 | 1.5337| 29.42 | 48.65 | 66.6367 | | 0.1279 | 1.2698 | 11600 | 1.6752| 22.78 | 43.45 | 79.2436 | | 0.116 | 1.2808 | 11700 | 1.6056| 26.91 | 45.46 | 70.4638 | | 0.1126 | 1.2917 | 11800 | 1.6341| 26.11 | 45.71 | 70.5988 | | 0.1263 | 1.3027 | 11900 | 1.6231| 28.31 | 46.34 | 69.6983 | | 0.1072 | 1.3136 | 12000 | 1.5580| 30.51 | 47.66 | 65.5110 | | 0.115 | 1.3246 | 12100 | 1.5944| 28.13 | 46.39 | 68.2575 | | 0.1014 | 1.3355 | 12200 | 1.5486| 28.75 | 47.01 | 67.9874 | | 0.1149 | 1.3465 | 12300 | 1.5973| 29.4 | 46.21 | 67.4021 | | 0.1131 | 1.3574 | 12400 | 1.5769| 29.94 | 48.17 | 65.1959 | | 0.1032 | 1.3684 | 12500 | 1.6363| 25.02 | 47.46 | 78.4331 | | 0.1103 | 1.3793 | 12600 | 1.6057| 28.2 | 45.82 | 68.7528 | | 0.109 | 1.3903 | 12700 | 1.5884| 28.0 | 45.95 | 69.1130 | | 0.0927 | 1.4012 | 12800 | 1.5881| 29.86 | 47.67 | 67.5371 | | 0.0829 | 1.4122 | 12900 | 1.5855| 29.15 | 45.76 | 67.6722 | | 0.0955 | 1.4231 | 13000 | 1.6313| 27.59 | 46.3 | 69.7884 | | 0.0874 | 1.4340 | 13100 | 1.6173| 30.52 | 45.99 | 64.5205 | | 0.0816 | 1.4450 | 13200 | 1.5864| 30.89 | 47.34 | 65.1959 | | 0.0836 | 1.4559 | 13300 | 1.6319| 30.18 | 47.3 | 65.5110 | | 0.0832 | 1.4669 | 13400 | 1.6353| 28.85 | 47.8 | 67.3570 | | 0.0622 | 1.4778 | 13500 | 1.6117| 28.91 | 46.78 | 69.0680 | | 0.0689 | 1.4888 | 13600 | 1.5919| 31.32 | 47.68 | 64.7006 | | 0.0921 | 1.4997 | 13700 | 1.6180| 30.09 | 46.8 | 66.8167 | | 0.0754 | 1.5107 | 13800 | 1.5755| 30.77 | 47.55 | 64.4755 | | 0.0844 | 1.5216 | 13900 | 1.5681| 31.12 | 48.9 | 64.9707 | | 0.0696 | 1.5326 | 14000 | 1.5481| 31.27 | 49.63 | 64.0252 | | 0.0914 | 1.5435 | 14100 | 1.5603| 29.61 | 47.28 | 65.8262 | | 0.0789 | 1.5545 | 14200 | 1.5896| 31.85 | 48.49 | 62.8546 | | 0.0572 | 1.5654 | 14300 | 1.5931| 27.55 | 46.87 | 69.1580 | | 0.0619 | 1.5764 | 14400 | 1.5988| 28.82 | 46.89 | 66.4566 | | 0.0566 | 1.5873 | 14500 | 1.5838| 29.97 | 48.6 | 66.6817 | | 0.0661 | 1.5982 | 14600 | 1.6447| 30.47 | 46.25 | 65.5561 | | 0.0607 | 1.6092 | 14700 | 1.5621| 32.31 | 48.53 | 63.1247 | | 0.0566 | 1.6201 | 14800 | 1.5838| 31.08 | 49.25 | 66.3665 | | 0.0354 | 1.6311 | 14900 | 1.5723| 30.73 | 48.58 | 66.3215 | | 0.0585 | 1.6420 | 15000 | 1.5825| 29.84 | 47.27 | 66.5466 | | 0.0542 | 1.6530 | 15100 | 1.6012| 28.7 | 47.32 | 69.8784 | | 0.0641 | 1.6639 | 15200 | 1.5662| 28.8 | 47.35 | 67.4021 | | 0.0588 | 1.6749 | 15300 | 1.5596| 30.62 | 47.51 | 65.9613 | | 0.0401 | 1.6858 | 15400 | 1.5719| 30.74 | 48.55 | 66.0513 | | 0.043 | 1.6968 | 15500 | 1.5979| 29.37 | 47.1 | 69.3381 | | 0.0384 | 1.7077 | 15600 | 1.5718| 29.56 | 48.58 | 68.7978 | | 0.0399 | 1.7187 | 15700 | 1.5873| 30.84 | 47.72 | 66.9518 | | 0.0437 | 1.7296 | 15800 | 1.5493| 31.43 | 48.06 | 65.7812 | | 0.0473 | 1.7406 | 15900 | 1.5185| 31.4 | 49.44 | 64.1153 | | 0.0477 | 1.7515 | 16000 | 1.5695| 32.89 | 49.91 | 62.0891 | | 0.0435 | 1.7625 | 16100 | 1.5550| 32.99 | 50.86 | 62.0891 | | 0.0478 | 1.7734 | 16200 | 1.5662| 31.31 | 49.58 | 63.5750 | | 0.0337 | 1.7843 | 16300 | 1.5792| 31.22 | 49.44 | 63.8901 | | 0.0387 | 1.7953 | 16400 | 1.5715| 32.2 | 49.5 | 62.3143 | | 0.0376 | 1.8062 | 16500 | 1.5783| 31.95 | 49.34 | 64.0252 | | 0.0357 | 1.8172 | 16600 | 1.5684| 30.67 | 49.15 | 65.8712 | | 0.028 | 1.8281 | 16700 | 1.5544| 29.78 | 48.23 | 67.5822 | | 0.0185 | 1.8391 | 16800 | 1.5419| 31.16 | 50.1 | 64.0252 | | 0.0408 | 1.8500 | 16900 | 1.5504| 31.25 | 49.53 | 64.6556 | | 0.0259 | 1.8610 | 17000 | 1.5501| 30.73 | 50.13 | 68.3026 | | 0.0276 | 1.8719 | 17100 | 1.5359| 29.97 | 49.96 | 69.0680 | | 0.0201 | 1.8829 | 17200 | 1.5504| 32.06 | 51.35 | 63.2598 | | 0.0268 | 1.8938 | 17300 | 1.5322| 34.47 | 51.53 | 60.0180 | | 0.023 | 1.9048 | 17400 | 1.5407| 34.65 | 52.44 | 59.5678 | | 0.0138 | 1.9157 | 17500 | 1.5653| 33.97 | 51.65 | 61.5038 | | 0.0164 | 1.9267 | 17600 | 1.5470| 32.62 | 50.82 | 64.6556 | | 0.018 | 1.9376 | 17700 | 1.5450| 33.09 | 50.06 | 61.9541 | | 0.0246 | 1.9485 | 17800 | 1.5473| 31.67 | 50.21 | 65.1058 | | 0.0196 | 1.9595 | 17900 | 1.5414| 31.1 | 50.36 | 66.9068 | | 0.0163 | 1.9704 | 18000 | 1.5453| 32.63 | 50.62 | 62.9446 | | 0.018 | 1.9814 | 18100 | 1.5361| 32.99 | 50.91 | 61.2337 | | 0.025 | 1.9923 | 18200 | 1.5394| 33.18 | 50.61 | 61.2787 | | 0.0081 | 2.0033 | 18300 | 1.5481| 33.39 | 50.63 | 61.1436 | | 0.0147 | 2.0142 | 18400 | 1.5500| 33.39 | 51.22 | 61.3688 | | 0.0121 | 2.0252 | 18500 | 1.5515| 32.35 | 50.74 | 62.0441 | | 0.0154 | 2.0361 | 18600 | 1.5524| 31.18 | 50.35 | 66.0964 | | 0.0157 | 2.0471 | 18700 | 1.5585| 31.68 | 50.44 | 65.4660 | | 0.0089 | 2.0580 | 18800 | 1.5559| 33.33 | 50.68 | 61.2337 | | 0.0112 | 2.0690 | 18900 | 1.5552| 31.64 | 50.43 | 65.1959 | | 0.0109 | 2.0799 | 19000 | 1.5548| 31.69 | 50.38 | 64.9707 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.2.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

Ariffiq99/COPA_CRAB_XLM_roberta_base_finetuned

Ariffiq99

transformers

multiple-choice

--- license: mit base_model: Ariffiq99/CRAB_xlm_roberta_base_finetuned tags: - generated_from_trainer metrics: - f1 model-index: - name: COPA_CRAB_XLM_roberta_base_finetuned 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. --> # COPA_CRAB_XLM_roberta_base_finetuned This model is a fine-tuned version of [Ariffiq99/CRAB_xlm_roberta_base_finetuned](https://huggingface.co/Ariffiq99/CRAB_xlm_roberta_base_finetuned) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6927 - F1: 0.514 ## 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 - num_epochs: 7 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:-----:| | No log | 1.0 | 250 | 0.6933 | 0.496 | | 0.7347 | 2.0 | 500 | 0.6939 | 0.52 | | 0.7347 | 3.0 | 750 | 0.6931 | 0.486 | | 0.7095 | 4.0 | 1000 | 0.6934 | 0.496 | | 0.7095 | 5.0 | 1250 | 0.6926 | 0.506 | | 0.7083 | 6.0 | 1500 | 0.6930 | 0.522 | | 0.7083 | 7.0 | 1750 | 0.6927 | 0.514 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

Ariffiq99/COPA_CRAB_Bert_Base_Uncased_Finetuned

Ariffiq99

transformers

multiple-choice

--- license: apache-2.0 base_model: Ariffiq99/CRAB_bert_base_uncased_finetuned tags: - generated_from_trainer metrics: - f1 model-index: - name: COPA_CRAB_Bert_Base_Uncased_Finetuned 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. --> # COPA_CRAB_Bert_Base_Uncased_Finetuned This model is a fine-tuned version of [Ariffiq99/CRAB_bert_base_uncased_finetuned](https://huggingface.co/Ariffiq99/CRAB_bert_base_uncased_finetuned) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6690 - F1: 0.7181 ## 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: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 63 | 0.6481 | 0.6345 | | No log | 2.0 | 126 | 0.5997 | 0.6829 | | No log | 3.0 | 189 | 0.5723 | 0.6944 | | No log | 4.0 | 252 | 0.5751 | 0.6898 | | No log | 5.0 | 315 | 0.5906 | 0.7149 | | No log | 6.0 | 378 | 0.6036 | 0.7273 | | No log | 7.0 | 441 | 0.6245 | 0.7280 | | 0.4609 | 8.0 | 504 | 0.6476 | 0.7213 | | 0.4609 | 9.0 | 567 | 0.6688 | 0.7181 | | 0.4609 | 10.0 | 630 | 0.6690 | 0.7181 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

Ariffiq99/COPA_CRAB_xlm_roberta_large_finetuned

Ariffiq99

transformers

multiple-choice

--- license: mit base_model: Ariffiq99/CRAB_xlm_roberta_large_finetuned tags: - generated_from_trainer metrics: - f1 model-index: - name: COPA_CRAB_xlm_roberta_large_finetuned 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. --> # COPA_CRAB_xlm_roberta_large_finetuned This model is a fine-tuned version of [Ariffiq99/CRAB_xlm_roberta_large_finetuned](https://huggingface.co/Ariffiq99/CRAB_xlm_roberta_large_finetuned) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6929 - F1: 0.496 ## 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: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:-----:| | No log | 1.0 | 63 | 0.6938 | 0.538 | | No log | 2.0 | 126 | 0.6944 | 0.49 | | No log | 3.0 | 189 | 0.6926 | 0.522 | | No log | 4.0 | 252 | 0.6934 | 0.492 | | No log | 5.0 | 315 | 0.6928 | 0.506 | | No log | 6.0 | 378 | 0.6945 | 0.502 | | No log | 7.0 | 441 | 0.6940 | 0.476 | | 0.7077 | 8.0 | 504 | 0.6938 | 0.528 | | 0.7077 | 9.0 | 567 | 0.6935 | 0.488 | | 0.7077 | 10.0 | 630 | 0.6929 | 0.496 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

c00kiemaster/FabioAzevedo

c00kiemaster

Entry not found

curlybox888/peft-starcoder-lora-a100

curlybox888

Entry not found

Ariffiq99/COPA_CRAB_albert_base_finetuned

Ariffiq99

transformers

multiple-choice

--- license: apache-2.0 base_model: Ariffiq99/CRAB_albert_base_finetuned tags: - generated_from_trainer metrics: - f1 model-index: - name: COPA_CRAB_albert_base_finetuned 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. --> # COPA_CRAB_albert_base_finetuned This model is a fine-tuned version of [Ariffiq99/CRAB_albert_base_finetuned](https://huggingface.co/Ariffiq99/CRAB_albert_base_finetuned) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4567 - F1: 0.674 ## 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: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:-----:| | No log | 1.0 | 63 | 0.6274 | 0.666 | | No log | 2.0 | 126 | 0.5703 | 0.69 | | No log | 3.0 | 189 | 0.6324 | 0.704 | | No log | 4.0 | 252 | 0.7201 | 0.69 | | No log | 5.0 | 315 | 1.0079 | 0.686 | | No log | 6.0 | 378 | 1.1511 | 0.678 | | No log | 7.0 | 441 | 1.2763 | 0.67 | | 0.2791 | 8.0 | 504 | 1.3775 | 0.676 | | 0.2791 | 9.0 | 567 | 1.4347 | 0.674 | | 0.2791 | 10.0 | 630 | 1.4567 | 0.674 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

Mistermango24/AutismMix-PonyXL

Mistermango24

--- license: artistic-2.0 ---

aisuko/gpt-2-1.5B-ggml

aisuko

--- license: mit --- Adapted by Aisuko

rmayormartins/speech-accent-pt-br-classifier

rmayormartins

transformers

audio-classification

--- license: ecl-2.0 datasets: - mozilla-foundation/common_voice_11_0 language: - en - pt metrics: - accuracy library_name: transformers tags: - code --- # Speech Portuguese (Brazilian) Accent Classifier 🎙️🤖🇧🇷 This project is a speech accent classifier that distinguishes between Portuguese (Brazilian) and other accents. ## Project Overview This application uses a trained model to classify speech accents into two categories: 1. Portuguese (Brazilian) 2. Other The model is based on the author's work [results] and utilizes the Portuguese portion of the Common Voice dataset (version 11.0) from Mozilla Foundation. ## Dataset The project uses the Portuguese subset of the Common Voice dataset: - Dataset: "mozilla-foundation/common_voice_11_0", "pt" Brazilian accents included in the dataset: - Português do Brasil, Região Sul do Brasil - Paulistano - Paulista, Brasileiro - Carioca - Mato Grosso - Mineiro - Interior Paulista - Gaúcho - Nordestino - And various regional mixes ## Model and Processor The project utilizes the following model and processor: - Base Model: "facebook/wav2vec2-base-960h" - Processor: Wav2Vec2Processor.from_pretrained ## Model Versions Was trained three versions of the model with different configurations: 1. **(OLD) v 1.1**: - Epochs: 3 - Training samples: 1000 - Validation samples: 200 2. **(OLD) v 1.2**: - Epochs: 10 - Training samples: 1000 - Validation samples: 500 3. **(NEW) v 1.3**: - Epochs: 20 - Training samples: 5000 - Validation samples: 1000 All models were trained using high RAM GPU on Google Colab Pro. ## Model Structure (files) Each version of the model includes the following files: results config.json | preprocessor_config.json | model.safetensors | special_tokens_map.json | tokenizer_config.json | vocab.json ## How to Use Test with recording or uploading an audio file. To test, I recommend short sentences. ## License This project is licensed under the Eclipse Public License 2.0 (ECL-2.0). ## Developer Information Developed by Ramon Mayor Martins (2024) - Email: rmayormartins@gmail.com - Homepage: https://rmayormartins.github.io/ - Twitter: @rmayormartins - GitHub: https://github.com/rmayormartins ## Acknowledgements Special thanks to Instituto Federal de Santa Catarina (Federal Institute of Santa Catarina) IFSC-São José-Brazil. ## Contact For any queries or suggestions, please contact the developer using the information provided above.

Ariffiq99/COPA_E_CARE_Bert_Base_Uncased_Finetuned

Ariffiq99

transformers

multiple-choice

--- license: apache-2.0 base_model: Ariffiq99/e_care_bert_base_uncased_finetuned tags: - generated_from_trainer metrics: - f1 model-index: - name: COPA_E_CARE_Bert_Base_Uncased_Finetuned 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. --> # COPA_E_CARE_Bert_Base_Uncased_Finetuned This model is a fine-tuned version of [Ariffiq99/e_care_bert_base_uncased_finetuned](https://huggingface.co/Ariffiq99/e_care_bert_base_uncased_finetuned) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5892 - F1: 0.7839 ## 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: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 63 | 0.5912 | 0.7258 | | No log | 2.0 | 126 | 0.5371 | 0.7702 | | No log | 3.0 | 189 | 0.5355 | 0.7627 | | No log | 4.0 | 252 | 0.5373 | 0.7747 | | No log | 5.0 | 315 | 0.5452 | 0.7832 | | No log | 6.0 | 378 | 0.5626 | 0.7822 | | No log | 7.0 | 441 | 0.5731 | 0.7815 | | 0.364 | 8.0 | 504 | 0.5825 | 0.7797 | | 0.364 | 9.0 | 567 | 0.5874 | 0.7865 | | 0.364 | 10.0 | 630 | 0.5892 | 0.7839 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

DBangshu/Base_gemma_e5_2_2

DBangshu

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]

Ariffiq99/COPA_E_CARE_xlm_roberta_large_finetuned

Ariffiq99

transformers

multiple-choice

--- license: mit base_model: Ariffiq99/e_care_xlm_roberta_large_finetuned tags: - generated_from_trainer metrics: - f1 model-index: - name: COPA_E_CARE_xlm_roberta_large_finetuned 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. --> # COPA_E_CARE_xlm_roberta_large_finetuned This model is a fine-tuned version of [Ariffiq99/e_care_xlm_roberta_large_finetuned](https://huggingface.co/Ariffiq99/e_care_xlm_roberta_large_finetuned) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6533 - F1: 0.82 ## 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: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 63 | 0.4427 | 0.796 | | No log | 2.0 | 126 | 0.3930 | 0.8140 | | No log | 3.0 | 189 | 0.3833 | 0.83 | | No log | 4.0 | 252 | 0.4824 | 0.806 | | No log | 5.0 | 315 | 0.4836 | 0.82 | | No log | 6.0 | 378 | 0.5445 | 0.8140 | | No log | 7.0 | 441 | 0.5651 | 0.816 | | 0.2666 | 8.0 | 504 | 0.5990 | 0.822 | | 0.2666 | 9.0 | 567 | 0.6457 | 0.826 | | 0.2666 | 10.0 | 630 | 0.6533 | 0.82 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

Ariffiq99/COPA_E_CARE_XLM_roberta_base_finetuned

Ariffiq99

transformers

multiple-choice

--- license: mit base_model: Ariffiq99/e_care_xlm_roberta_base_finetuned tags: - generated_from_trainer metrics: - f1 model-index: - name: COPA_E_CARE_XLM_roberta_base_finetuned 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. --> # COPA_E_CARE_XLM_roberta_base_finetuned This model is a fine-tuned version of [Ariffiq99/e_care_xlm_roberta_base_finetuned](https://huggingface.co/Ariffiq99/e_care_xlm_roberta_base_finetuned) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.7353 - F1: 0.67 ## 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 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:-----:| | No log | 1.0 | 250 | 0.9393 | 0.668 | | 0.2405 | 2.0 | 500 | 1.1072 | 0.684 | | 0.2405 | 3.0 | 750 | 1.1488 | 0.648 | | 0.3938 | 4.0 | 1000 | 0.9665 | 0.652 | | 0.3938 | 5.0 | 1250 | 1.2431 | 0.654 | | 0.4987 | 6.0 | 1500 | 1.2893 | 0.674 | | 0.4987 | 7.0 | 1750 | 1.5097 | 0.68 | | 0.3532 | 8.0 | 2000 | 1.6731 | 0.664 | | 0.3532 | 9.0 | 2250 | 1.7344 | 0.664 | | 0.3011 | 10.0 | 2500 | 1.7353 | 0.67 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

friendlyguy774/LoRA_Adapters

friendlyguy774

Entry not found

Ariffiq99/COPA_E_CARE_albert_base_finetuned

Ariffiq99

transformers

multiple-choice

--- license: apache-2.0 base_model: Ariffiq99/e_care_albert_base_finetuned tags: - generated_from_trainer metrics: - f1 model-index: - name: COPA_E_CARE_albert_base_finetuned 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. --> # COPA_E_CARE_albert_base_finetuned This model is a fine-tuned version of [Ariffiq99/e_care_albert_base_finetuned](https://huggingface.co/Ariffiq99/e_care_albert_base_finetuned) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8976 - F1: 0.732 ## 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: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 63 | 0.5300 | 0.74 | | No log | 2.0 | 126 | 0.5038 | 0.76 | | No log | 3.0 | 189 | 0.5805 | 0.75 | | No log | 4.0 | 252 | 0.5694 | 0.7700 | | No log | 5.0 | 315 | 0.6823 | 0.74 | | No log | 6.0 | 378 | 0.7699 | 0.7420 | | No log | 7.0 | 441 | 0.7680 | 0.754 | | 0.2122 | 8.0 | 504 | 0.8489 | 0.738 | | 0.2122 | 9.0 | 567 | 0.8899 | 0.7300 | | 0.2122 | 10.0 | 630 | 0.8976 | 0.732 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

manbeast3b/KinoInferTry11

manbeast3b

Entry not found

CreitinhoDublagems/Multiple_EminemEras

CreitinhoDublagems

Entry not found

Fiqking11/Fiqking

Fiqking11

--- license: apache-2.0 ---

Syldehayem/syl_finetune

Syldehayem

--- license: unknown ---

sasmita-datascientist/dummy

sasmita-datascientist

--- license: mit ---

hasininawoda/persondis

hasininawoda

diffusers

text-to-image

--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers instance_prompt: a photo of disable person widget: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - hasininawoda/persondis <Gallery /> ## Model description These are hasininawoda/persondis LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of disable person to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](hasininawoda/persondis/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]

mirobot/myfirstrepo

mirobot

--- license: afl-3.0 ---

mhMH2512/12

mhMH2512

--- license: openrail ---

abinavGanesh/JiuZhang3.0-Corpus-SFT

abinavGanesh

Entry not found

grimjim/llama-3-Nephilim-v1-8B-6.5bpw_h6_exl2

grimjim

transformers

text-generation

--- base_model: - WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0 - mlabonne/NeuralDaredevil-8B-abliterated library_name: transformers tags: - mergekit - merge license: cc-by-nc-4.0 pipeline_tag: text-generation --- # llama-3-Nephilim-v1-8B This repo contains a 6.5bpw exl2 quant of [grimjim/llama-3-Nephilim-v1-8B](https://huggingface.co/grimjim/llama-3-Nephilim-v1-8B). The originating model is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). Here we experiment with SLERP merger with the second model at very low weight (0.001) to modulate the output of the base model. The base model was assembled to achieve high MMLU while avoiding refusals, while the additional model was trained specifically (apparently as a copilot) for offensive and defensive cybersecurity. Though neither model targeted roleplay as a use case, the resulting intelligence, acuity, and text generation of the merge is of interest. The merge is aggressively creative, within bounds. Tested with temperature=1.0-1.2 and minP=0.01 along with a custom Instruct prompt geared toward reducing refusals during roleplay text generation without compromising overall model safety: [Llama 3 Instruct Direct](https://huggingface.co/debased-ai/SillyTavern-settings/tree/main/advanced_formatting/instruct_mode). Care should be taken when using this model, as it is possible that harmful outputs could be generated. Given that this model is derivative, responsible use is further mandated by the WhiteRabbitNeo Usage Restrictions Extension to the Llama-3 License. This model is further subject to CC-BY-NC-4.0 by default, meaning that commercial use is restricted, barring an alternative licensing agreement. - [GGUF quants (w/ llama-bpe pre-tokenizer)](https://huggingface.co/grimjim/llama-3-Nephilim-v1-8B-GGUF) Built with Meta Llama 3. # WhiteRabbitNeo Extension to Llama-3 Licence: Usage Restrictions ``` You agree not to use the Model or Derivatives of the Model: - In any way that violates any applicable national or international law or regulation or infringes upon the lawful rights and interests of any third party; - For military use in any way; - For the purpose of exploiting, harming or attempting to exploit or harm minors in any way; - To generate or disseminate verifiably false information and/or content with the purpose of harming others; - To generate or disseminate inappropriate content subject to applicable regulatory requirements; - To generate or disseminate personal identifiable information without due authorization or for unreasonable use; - To defame, disparage or otherwise harass others; - For fully automated decision making that adversely impacts an individual's legal rights or otherwise creates or modifies a binding, enforceable obligation; - For any use intended to or which has the effect of discriminating against or harming individuals or groups based on online or offline social behavior or known or predicted personal or personality characteristics; - To exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm; - For any use intended to or which has the effect of discriminating against individuals or groups based on legally protected characteristics or categories. ``` ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0](https://huggingface.co/WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0) * [mlabonne/NeuralDaredevil-8B-abliterated](https://huggingface.co/mlabonne/NeuralDaredevil-8B-abliterated) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: mlabonne/NeuralDaredevil-8B-abliterated layer_range: [0,32] - model: WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0 layer_range: [0,32] merge_method: slerp base_model: mlabonne/NeuralDaredevil-8B-abliterated parameters: t: - value: 0.001 dtype: bfloat16 ```

Suras2/Tchan

Suras2

--- license: apache-2.0 ---

shinieaggarwal/sematic-segmentation-model

shinieaggarwal

Entry not found

Replete-AI/Adapter_For_Replete-Coder-Qwen2-1.5b

Replete-AI

transformers

--- license: apache-2.0 base_model: Qwen/Qwen2-1.5B tags: - text-generation-inference - transformers - unsloth - qwen2 datasets: - Replete-AI/code_bagel_hermes-2.5 - Replete-AI/code_bagel - Replete-AI/OpenHermes-2.5-Uncensored - teknium/OpenHermes-2.5 - layoric/tiny-codes-alpaca - glaiveai/glaive-code-assistant-v3 - ajibawa-2023/Code-290k-ShareGPT - TIGER-Lab/MathInstruct - chargoddard/commitpack-ft-instruct-rated - iamturun/code_instructions_120k_alpaca - ise-uiuc/Magicoder-Evol-Instruct-110K - cognitivecomputations/dolphin-coder - nickrosh/Evol-Instruct-Code-80k-v1 - coseal/CodeUltraFeedback_binarized - glaiveai/glaive-function-calling-v2 - CyberNative/Code_Vulnerability_Security_DPO - jondurbin/airoboros-2.2 - camel-ai - lmsys/lmsys-chat-1m - CollectiveCognition/chats-data-2023-09-22 - CoT-Alpaca-GPT4 - WizardLM/WizardLM_evol_instruct_70k - WizardLM/WizardLM_evol_instruct_V2_196k - teknium/GPT4-LLM-Cleaned - GPTeacher - OpenGPT - meta-math/MetaMathQA - Open-Orca/SlimOrca - garage-bAInd/Open-Platypus - anon8231489123/ShareGPT_Vicuna_unfiltered - Unnatural-Instructions-GPT4 model-index: - name: Replete-Coder-llama3-8b results: - task: name: HumanEval type: text-generation dataset: type: openai_humaneval name: HumanEval metrics: - name: pass@1 type: pass@1 value: verified: false - task: name: AI2 Reasoning Challenge type: text-generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: accuracy value: name: normalized accuracy source: url: https://www.placeholderurl.com name: Open LLM Leaderboard - task: name: Text Generation type: text-generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: accuracy value: name: normalized accuracy source: url: https://www.placeholderurl.com name: Open LLM Leaderboard - task: name: Text Generation type: text-generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: accuracy value: name: accuracy source: url: https://www.placeholderurl.com name: Open LLM Leaderboard - task: name: Text Generation type: text-generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: multiple_choice_accuracy value: source: url: https://www.placeholderurl.com name: Open LLM Leaderboard - task: name: Text Generation type: text-generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: accuracy value: name: accuracy source: url: https://www.placeholderurl.com name: Open LLM Leaderboard - task: name: Text Generation type: text-generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: accuracy value: name: accuracy source: url: https://www.placeholderurl.com name: Open LLM Leaderboard --- This is the adapter (Aka Lora) For the Replete-AI/Replete-Coder-Qwen-1.5b AI model. Feel free to use it to attach to your own models and see how it performs. We'd love to hear about your models that you create with this adapter in the community posts! Link for the original model bellow: - https://huggingface.co/Replete-AI/Replete-Coder-Qwen-1.5b ___________________________________________________________________ # Original Model card # Replete-Coder-Qwen2-1.5b Finetuned by: Rombodawg ### More than just a coding model! Although Replete-Coder has amazing coding capabilities, its trained on vaste amount of non-coding data, fully cleaned and uncensored. Dont just use it for coding, use it for all your needs! We are truly trying to make the GPT killer!  Thank you to TensorDock for sponsoring Replete-Coder-llama3-8b and Replete-Coder-Qwen2-1.5b you can check out their website for cloud compute rental bellow. - https://tensordock.com __________________________________________________________________________________________________ Replete-Coder-Qwen2-1.5b is a general purpose model that is specially trained in coding in over 100 coding languages. The data used to train the model contains 25% non-code instruction data and 75% coding instruction data totaling up to 3.9 million lines, roughly 1 billion tokens, or 7.27gb of instruct data. The data used to train this model was 100% uncensored, then fully deduplicated, before training happened. The Replete-Coder models (including Replete-Coder-llama3-8b and Replete-Coder-Qwen2-1.5b) feature the following: - Advanced coding capabilities in over 100 coding languages - Advanced code translation (between languages) - Security and vulnerability prevention related coding capabilities - General purpose use - Uncensored use - Function calling - Advanced math use - Use on low end (8b) and mobile (1.5b) platforms Notice: Replete-Coder series of models are fine-tuned on a context window of 8192 tokens. Performance past this context window is not guaranteed. __________________________________________________________________________________________________ You can find the 25% non-coding instruction below: - https://huggingface.co/datasets/Replete-AI/OpenHermes-2.5-Uncensored And the 75% coding specific instruction data below: - https://huggingface.co/datasets/Replete-AI/code_bagel These two datasets were combined to create the final dataset for training, which is linked below: - https://huggingface.co/datasets/Replete-AI/code_bagel_hermes-2.5 __________________________________________________________________________________________________ ## Prompt Template: ChatML ``` <|im_start|>system {}<|im_end|> <|im_start|>user {}<|im_end|> <|im_start|>assistant {} ``` Note: The system prompt varies in training data, but the most commonly used one is: ``` Below is an instruction that describes a task, Write a response that appropriately completes the request. ``` End token: ``` <|endoftext|> ``` __________________________________________________________________________________________________ Thank you to the community for your contributions to the Replete-AI/code_bagel_hermes-2.5 dataset. Without the participation of so many members making their datasets free and open source for any to use, this amazing AI model wouldn't be possible. Extra special thanks to Teknium for the Open-Hermes-2.5 dataset and jondurbin for the bagel dataset and the naming idea for the code_bagel series of datasets. You can find both of their huggingface accounts linked below: - https://huggingface.co/teknium - https://huggingface.co/jondurbin Another special thanks to unsloth for being the main method of training for Replete-Coder. Bellow you can find their github, as well as the special Replete-Ai secret sause (Unsloth + Qlora + Galore) colab code document that was used to train this model. - https://github.com/unslothai/unsloth - https://colab.research.google.com/drive/1eXGqy5M--0yW4u0uRnmNgBka-tDk2Li0?usp=sharing __________________________________________________________________________________________________ ## Join the Replete-Ai discord! We are a great and Loving community! - https://discord.gg/ZZbnsmVnjD

DBangshu/Base_gemma_e5_3_2

DBangshu

transformers

text-generation

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

Zack157/ModelosdeVozIA

Zack157

Entry not found

SunShineFlower/Heizou_500epoch

SunShineFlower

--- license: openrail ---

luishcarvalho/llama_smart_contract_2

luishcarvalho

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]

mjjj7/mjhistoryera

mjjj7

--- license: openrail ---

SunShineFlower/BlackSwan_800epoch

SunShineFlower

--- license: openrail ---

DokiQueen/Mori-Calliope

DokiQueen

Entry not found

sirishgam001/videomae-base-finetuned-ucf101-subset

sirishgam001

transformers

video-classification

Entry not found

alphaveneno/distilbert-base-uncased-finetuned-imdb

alphaveneno

transformers

fill-mask

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-finetuned-imdb results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4891 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.6814 | 1.0 | 157 | 2.5118 | | 2.5912 | 2.0 | 314 | 2.4497 | | 2.528 | 3.0 | 471 | 2.4822 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.1+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

1xlc/korean

1xlc

Entry not found

lilrag10/lmd_8bars_tokenizer

lilrag10

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]

jiangcongtao/autotrain-phi3-mini-4k-instruct-oqkv

jiangcongtao

transformers

text-generation

--- tags: - autotrain - text-generation-inference - text-generation - peft library_name: transformers base_model: microsoft/Phi-3-mini-4k-instruct widget: - messages: - role: user content: What is your favorite condiment? license: other datasets: - dair-ai/emotion --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```

bdo1305/gemma-2b

bdo1305

Entry not found

lucasius/git-base-pokemon

lucasius

transformers

text-generation

Entry not found

copybaiter/lora

copybaiter

Entry not found

YaPyknyl/Ferryman

YaPyknyl

--- license: cc-by-nc-4.0 language: - en --- I did this cuz did not found model on AI HUB discord server 

karthikmit/openai-whisper-medium-FT-v1

karthikmit

Entry not found

NormalUserrname/adssadasddas

NormalUserrname

Entry not found

DBangshu/Base_gemma_e5_4_2

DBangshu

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]