librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

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Sorour/llama3_cls_fomc	Sorour	2024-05-19T02:20:08Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-19T02:14:34Z	--- 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]
Bibek1129/nepali-poem-generator-distilgpt2	Bibek1129	2024-05-19T02:09:20Z	131	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "ne", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-19T02:04:44Z	--- license: apache-2.0 language: ne # <-- my language widget: - text: "मेरो मन" --- Nepali poem generator finetuning on ditillpt2-nepali.
Jebadiah/lama-3-wild-stone-p2	Jebadiah	2024-05-19T02:07:51Z	6	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "custom_code", "arxiv:2311.03099", "arxiv:2306.01708", "base_model:Jebadiah/Aria-p2-sand-stone", "base_model:merge:Jebadiah/Aria-p2-sand-stone", "base_model:NeverSleep/Llama-3-Lumimaid-8B-v0.1", "base_model:merge:NeverSleep/Llama-3-Lumimaid-8B-v0.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-19T02:06:02Z	--- base_model: - NeverSleep/Llama-3-Lumimaid-8B-v0.1 - Jebadiah/Aria-p2-sand-stone 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 [Jebadiah/Aria-p2-sand-stone](https://huggingface.co/Jebadiah/Aria-p2-sand-stone) as a base. ### Models Merged The following models were included in the merge: * [NeverSleep/Llama-3-Lumimaid-8B-v0.1](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-8B-v0.1) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: NeverSleep/Llama-3-Lumimaid-8B-v0.1 parameters: density: 0.6 weight: 0.5 merge_method: dare_ties base_model: Jebadiah/Aria-p2-sand-stone parameters: normalize: false int8_mask: true dtype: float16 ```
apwic/sentiment-lora-r4a1d0.1-1	apwic	2024-05-19T02:05:43Z	0	0	null	[ "tensorboard", "generated_from_trainer", "id", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "region:us" ]	null	2024-05-19T01:32:27Z	--- language: - id license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: sentiment-lora-r4a1d0.1-1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # sentiment-lora-r4a1d0.1-1 This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3239 - Accuracy: 0.8622 - Precision: 0.8373 - Recall: 0.8250 - F1: 0.8307 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 30 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.5658 \| 1.0 \| 122 \| 0.5195 \| 0.7268 \| 0.6646 \| 0.6492 \| 0.6550 \| \| 0.5125 \| 2.0 \| 244 \| 0.5060 \| 0.7293 \| 0.6805 \| 0.6935 \| 0.6855 \| \| 0.4809 \| 3.0 \| 366 \| 0.4686 \| 0.7669 \| 0.7184 \| 0.7151 \| 0.7167 \| \| 0.4353 \| 4.0 \| 488 \| 0.4295 \| 0.7920 \| 0.7500 \| 0.7353 \| 0.7417 \| \| 0.4116 \| 5.0 \| 610 \| 0.4171 \| 0.8020 \| 0.7628 \| 0.7849 \| 0.7714 \| \| 0.3809 \| 6.0 \| 732 \| 0.3865 \| 0.8446 \| 0.8148 \| 0.8051 \| 0.8096 \| \| 0.3681 \| 7.0 \| 854 \| 0.3697 \| 0.8496 \| 0.8193 \| 0.8161 \| 0.8177 \| \| 0.3469 \| 8.0 \| 976 \| 0.3554 \| 0.8471 \| 0.8206 \| 0.8018 \| 0.8102 \| \| 0.3455 \| 9.0 \| 1098 \| 0.3494 \| 0.8496 \| 0.8211 \| 0.8111 \| 0.8158 \| \| 0.3284 \| 10.0 \| 1220 \| 0.3437 \| 0.8496 \| 0.8289 \| 0.7961 \| 0.8096 \| \| 0.3132 \| 11.0 \| 1342 \| 0.3371 \| 0.8596 \| 0.8389 \| 0.8132 \| 0.8243 \| \| 0.3042 \| 12.0 \| 1464 \| 0.3371 \| 0.8546 \| 0.8254 \| 0.8221 \| 0.8238 \| \| 0.3063 \| 13.0 \| 1586 \| 0.3317 \| 0.8596 \| 0.8406 \| 0.8107 \| 0.8233 \| \| 0.3013 \| 14.0 \| 1708 \| 0.3304 \| 0.8622 \| 0.8373 \| 0.8250 \| 0.8307 \| \| 0.2928 \| 15.0 \| 1830 \| 0.3295 \| 0.8596 \| 0.8325 \| 0.8257 \| 0.8290 \| \| 0.2864 \| 16.0 \| 1952 \| 0.3284 \| 0.8622 \| 0.8351 \| 0.8300 \| 0.8325 \| \| 0.2819 \| 17.0 \| 2074 \| 0.3254 \| 0.8596 \| 0.8347 \| 0.8207 \| 0.8272 \| \| 0.2877 \| 18.0 \| 2196 \| 0.3249 \| 0.8596 \| 0.8336 \| 0.8232 \| 0.8281 \| \| 0.2819 \| 19.0 \| 2318 \| 0.3241 \| 0.8647 \| 0.8410 \| 0.8267 \| 0.8333 \| \| 0.2803 \| 20.0 \| 2440 \| 0.3239 \| 0.8622 \| 0.8373 \| 0.8250 \| 0.8307 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.15.2
liminerity/mm4.more.star.gguf	liminerity	2024-05-19T02:04:08Z	3	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:liminerity/mm4.ascii.star", "base_model:quantized:liminerity/mm4.ascii.star", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-19T02:01:12Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf base_model: liminerity/mm4.ascii.star --- # Uploaded model - Developed by: liminerity - License: apache-2.0 - Finetuned from model : liminerity/mm4.ascii.star 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)
houbw/llama3_2	houbw	2024-05-19T01:45:17Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-19T01:44:58Z	--- library_name: transformers tags: - unsloth --- # 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]
ahmedgongi/Llama_dev3tokenizer_finale4	ahmedgongi	2024-05-19T01:35:09Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-19T01:35:07Z	--- 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]
apwic/sentiment-lora-r4a1d0.05-1	apwic	2024-05-19T01:32:10Z	0	0	null	[ "tensorboard", "generated_from_trainer", "id", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "region:us" ]	null	2024-05-19T00:59:00Z	--- language: - id license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: sentiment-lora-r4a1d0.05-1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # sentiment-lora-r4a1d0.05-1 This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3356 - Accuracy: 0.8622 - Precision: 0.8399 - Recall: 0.8200 - F1: 0.8289 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 30 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.5657 \| 1.0 \| 122 \| 0.5182 \| 0.7243 \| 0.6604 \| 0.6424 \| 0.6488 \| \| 0.5109 \| 2.0 \| 244 \| 0.5051 \| 0.7243 \| 0.6748 \| 0.6874 \| 0.6796 \| \| 0.48 \| 3.0 \| 366 \| 0.4643 \| 0.7569 \| 0.7047 \| 0.6880 \| 0.6948 \| \| 0.434 \| 4.0 \| 488 \| 0.4281 \| 0.7920 \| 0.7497 \| 0.7378 \| 0.7431 \| \| 0.4106 \| 5.0 \| 610 \| 0.4194 \| 0.7920 \| 0.7528 \| 0.7778 \| 0.7618 \| \| 0.3812 \| 6.0 \| 732 \| 0.3936 \| 0.8296 \| 0.8008 \| 0.7744 \| 0.7854 \| \| 0.3689 \| 7.0 \| 854 \| 0.3700 \| 0.8521 \| 0.8220 \| 0.8204 \| 0.8212 \| \| 0.3489 \| 8.0 \| 976 \| 0.3656 \| 0.8346 \| 0.8088 \| 0.7780 \| 0.7905 \| \| 0.3502 \| 9.0 \| 1098 \| 0.3640 \| 0.8371 \| 0.8101 \| 0.7847 \| 0.7955 \| \| 0.3349 \| 10.0 \| 1220 \| 0.3608 \| 0.8346 \| 0.8074 \| 0.7805 \| 0.7917 \| \| 0.3189 \| 11.0 \| 1342 \| 0.3574 \| 0.8396 \| 0.8128 \| 0.7890 \| 0.7992 \| \| 0.3121 \| 12.0 \| 1464 \| 0.3547 \| 0.8471 \| 0.8175 \| 0.8093 \| 0.8132 \| \| 0.3181 \| 13.0 \| 1586 \| 0.3478 \| 0.8521 \| 0.8332 \| 0.7979 \| 0.8122 \| \| 0.3092 \| 14.0 \| 1708 \| 0.3435 \| 0.8596 \| 0.8374 \| 0.8157 \| 0.8253 \| \| 0.3018 \| 15.0 \| 1830 \| 0.3466 \| 0.8546 \| 0.8296 \| 0.8121 \| 0.8200 \| \| 0.2955 \| 16.0 \| 1952 \| 0.3365 \| 0.8596 \| 0.8347 \| 0.8207 \| 0.8272 \| \| 0.2917 \| 17.0 \| 2074 \| 0.3353 \| 0.8596 \| 0.8374 \| 0.8157 \| 0.8253 \| \| 0.2956 \| 18.0 \| 2196 \| 0.3379 \| 0.8596 \| 0.8360 \| 0.8182 \| 0.8262 \| \| 0.2899 \| 19.0 \| 2318 \| 0.3353 \| 0.8647 \| 0.8455 \| 0.8192 \| 0.8306 \| \| 0.2885 \| 20.0 \| 2440 \| 0.3356 \| 0.8622 \| 0.8399 \| 0.8200 \| 0.8289 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.15.2
fzzhang/mistralv1_lora_r32_25e5_e05_merged	fzzhang	2024-05-19T01:30:06Z	6	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-19T01:27:17Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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RichardErkhov/malhajar_-_Mistral-7B-v0.2-meditron-turkish-8bits	RichardErkhov	2024-05-19T01:28:26Z	78	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-19T01:21:49Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Mistral-7B-v0.2-meditron-turkish - bnb 8bits - Model creator: https://huggingface.co/malhajar/ - Original model: https://huggingface.co/malhajar/Mistral-7B-v0.2-meditron-turkish/ Original model description: --- language: - tr - en license: apache-2.0 datasets: - malhajar/meditron-tr model-index: - name: Mistral-7B-v0.2-meditron-turkish results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 59.56 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=malhajar/Mistral-7B-v0.2-meditron-turkish name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 81.79 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=malhajar/Mistral-7B-v0.2-meditron-turkish name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 60.35 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=malhajar/Mistral-7B-v0.2-meditron-turkish name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 66.19 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=malhajar/Mistral-7B-v0.2-meditron-turkish name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 76.24 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=malhajar/Mistral-7B-v0.2-meditron-turkish name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 35.94 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=malhajar/Mistral-7B-v0.2-meditron-turkish name: Open LLM Leaderboard --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> Mistral-7B-v0.2-meditron-turkish is a finetuned Mistral Model version using Freeze technique on Turkish Meditron dataset of [`malhajar/meditron-7b-tr`](https://huggingface.co/datasets/malhajar/meditron-tr) using SFT Training. This model can answer information about different excplicit ideas in medicine in Turkish and English ### Model Description - Finetuned by: [`Mohamad Alhajar`](https://www.linkedin.com/in/muhammet-alhajar/) - Language(s) (NLP): Turkish,English - Finetuned from model: [`mistralai/Mistral-7B-Instruct-v0.2`](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) ### Prompt Template For Turkish Generation ``` ### Kullancı: ``` ### Prompt Template For English Generation ``` ### User: ``` ## How to Get Started with the Model Use the code sample provided in the original post to interact with the model. ```python from transformers import AutoTokenizer,AutoModelForCausalLM model_id = "malhajar/Mistral-7B-v0.2-meditron-turkish" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", torch_dtype=torch.float16, revision="main") tokenizer = AutoTokenizer.from_pretrained(model_id) question: "Akciğer kanseri nedir?" # For generating a response prompt = ''' ### Kullancı: {question} ''' input_ids = tokenizer(prompt, return_tensors="pt").input_ids output = model.generate(inputs=input_ids,max_new_tokens=512,pad_token_id=tokenizer.eos_token_id,top_k=50, do_sample=True, top_p=0.95) response = tokenizer.decode(output[0]) print(response) ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_malhajar__Mistral-7B-v0.2-meditron-turkish) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|63.34\| \|AI2 Reasoning Challenge (25-Shot)\|59.56\| \|HellaSwag (10-Shot) \|81.79\| \|MMLU (5-Shot) \|60.35\| \|TruthfulQA (0-shot) \|66.19\| \|Winogrande (5-shot) \|76.24\| \|GSM8k (5-shot) \|35.94\|
fzzhang/mistralv1_lora_r32_25e5_e05	fzzhang	2024-05-19T01:27:04Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	null	2024-05-19T01:26:59Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 model-index: - name: mistralv1_lora_r32_25e5_e05 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistralv1_lora_r32_25e5_e05 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2.5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.38.2 - Pytorch 2.2.1 - Datasets 2.18.0 - Tokenizers 0.15.2
Jebadiah/lama-3-wild-stone-p1	Jebadiah	2024-05-19T01:24:25Z	6	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "custom_code", "arxiv:2403.19522", "base_model:ChaoticNeutrals/Puppy_Purpose_0.69", "base_model:merge:ChaoticNeutrals/Puppy_Purpose_0.69", "base_model:Jebadiah/Aria-dolphin-1m-sand-stone", "base_model:merge:Jebadiah/Aria-dolphin-1m-sand-stone", "base_model:NeverSleep/Llama-3-Lumimaid-8B-v0.1", "base_model:merge:NeverSleep/Llama-3-Lumimaid-8B-v0.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-19T01:22:23Z	--- base_model: - ChaoticNeutrals/Puppy_Purpose_0.69 - Jebadiah/Aria-dolphin-1m-sand-stone - NeverSleep/Llama-3-Lumimaid-8B-v0.1 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 [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [Jebadiah/Aria-dolphin-1m-sand-stone](https://huggingface.co/Jebadiah/Aria-dolphin-1m-sand-stone) as a base. ### Models Merged The following models were included in the merge: * [ChaoticNeutrals/Puppy_Purpose_0.69](https://huggingface.co/ChaoticNeutrals/Puppy_Purpose_0.69) * [NeverSleep/Llama-3-Lumimaid-8B-v0.1](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-8B-v0.1) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: NeverSleep/Llama-3-Lumimaid-8B-v0.1 - model: ChaoticNeutrals/Puppy_Purpose_0.69 merge_method: model_stock base_model: Jebadiah/Aria-dolphin-1m-sand-stone dtype: float16 ```
RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-gguf	RichardErkhov	2024-05-19T01:12:49Z	14	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-18T23:07:58Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) NeuralMarcoro14-7B - GGUF - Model creator: https://huggingface.co/mlabonne/ - Original model: https://huggingface.co/mlabonne/NeuralMarcoro14-7B/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [NeuralMarcoro14-7B.Q2_K.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-gguf/blob/main/NeuralMarcoro14-7B.Q2_K.gguf) \| Q2_K \| 2.53GB \| \| [NeuralMarcoro14-7B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-gguf/blob/main/NeuralMarcoro14-7B.IQ3_XS.gguf) \| IQ3_XS \| 2.81GB \| \| [NeuralMarcoro14-7B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-gguf/blob/main/NeuralMarcoro14-7B.IQ3_S.gguf) \| IQ3_S \| 2.96GB \| \| [NeuralMarcoro14-7B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-gguf/blob/main/NeuralMarcoro14-7B.Q3_K_S.gguf) \| Q3_K_S \| 2.95GB \| \| [NeuralMarcoro14-7B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-gguf/blob/main/NeuralMarcoro14-7B.IQ3_M.gguf) \| IQ3_M \| 3.06GB \| \| [NeuralMarcoro14-7B.Q3_K.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-gguf/blob/main/NeuralMarcoro14-7B.Q3_K.gguf) \| Q3_K \| 3.28GB \| \| [NeuralMarcoro14-7B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-gguf/blob/main/NeuralMarcoro14-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.28GB \| \| [NeuralMarcoro14-7B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-gguf/blob/main/NeuralMarcoro14-7B.Q3_K_L.gguf) \| Q3_K_L \| 3.56GB \| \| [NeuralMarcoro14-7B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-gguf/blob/main/NeuralMarcoro14-7B.IQ4_XS.gguf) \| IQ4_XS \| 3.67GB \| \| [NeuralMarcoro14-7B.Q4_0.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-gguf/blob/main/NeuralMarcoro14-7B.Q4_0.gguf) \| Q4_0 \| 3.83GB \| \| [NeuralMarcoro14-7B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-gguf/blob/main/NeuralMarcoro14-7B.IQ4_NL.gguf) \| IQ4_NL \| 3.87GB \| \| [NeuralMarcoro14-7B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-gguf/blob/main/NeuralMarcoro14-7B.Q4_K_S.gguf) \| Q4_K_S \| 3.86GB \| \| [NeuralMarcoro14-7B.Q4_K.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-gguf/blob/main/NeuralMarcoro14-7B.Q4_K.gguf) \| Q4_K \| 4.07GB \| \| [NeuralMarcoro14-7B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-gguf/blob/main/NeuralMarcoro14-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.07GB \| \| [NeuralMarcoro14-7B.Q4_1.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-gguf/blob/main/NeuralMarcoro14-7B.Q4_1.gguf) \| Q4_1 \| 4.24GB \| \| [NeuralMarcoro14-7B.Q5_0.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-gguf/blob/main/NeuralMarcoro14-7B.Q5_0.gguf) \| Q5_0 \| 4.65GB \| \| [NeuralMarcoro14-7B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-gguf/blob/main/NeuralMarcoro14-7B.Q5_K_S.gguf) \| Q5_K_S \| 4.65GB \| \| [NeuralMarcoro14-7B.Q5_K.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-gguf/blob/main/NeuralMarcoro14-7B.Q5_K.gguf) \| Q5_K \| 4.78GB \| \| [NeuralMarcoro14-7B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-gguf/blob/main/NeuralMarcoro14-7B.Q5_K_M.gguf) \| Q5_K_M \| 4.78GB \| \| [NeuralMarcoro14-7B.Q5_1.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-gguf/blob/main/NeuralMarcoro14-7B.Q5_1.gguf) \| Q5_1 \| 5.07GB \| \| [NeuralMarcoro14-7B.Q6_K.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-gguf/blob/main/NeuralMarcoro14-7B.Q6_K.gguf) \| Q6_K \| 5.53GB \| \| [NeuralMarcoro14-7B.Q8_0.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-gguf/blob/main/NeuralMarcoro14-7B.Q8_0.gguf) \| Q8_0 \| 7.17GB \| Original model description: --- license: cc-by-nc-4.0 tags: - mlabonne/Marcoro14-7B-slerp - dpo - rlhf - merge - mergekit - lazymergekit datasets: - mlabonne/chatml_dpo_pairs base_model: mlabonne/Marcoro14-7B-slerp model-index: - name: NeuralMarcoro14-7B results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 71.42 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralMarcoro14-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 87.59 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralMarcoro14-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 64.84 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralMarcoro14-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 65.64 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralMarcoro14-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 81.22 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralMarcoro14-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 70.74 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralMarcoro14-7B name: Open LLM Leaderboard --- ![](https://i.imgur.com/CBen22L.jpg) # NeuralMarcoro14-7B This is a DPO fine-tuned version of [mlabonne/Marcoro14-7B-slerp](https://huggingface.co/mlabonne/Marcoro14-7B-slerp) using the [chatml_dpo_pairs](https://huggingface.co/datasets/mlabonne/chatml_dpo_pairs) preference dataset. It improves the performance of the model on Nous benchmark suite and the Open LLM Benchmark. It is currently the best-performing 7B LLM on the Open LLM Leaderboard (08/01/24). You can try it out in this [Space](https://huggingface.co/spaces/mlabonne/NeuralMarcoro14-7B-GGUF-Chat) (GGUF Q4_K_M). ## ⚡ Quantized models * GGUF: https://huggingface.co/mlabonne/NeuralMarcoro14-7B-GGUF ## 🏆 Evaluation ### Open LLM Leaderboard ![](https://i.imgur.com/Int9P07.png) ![](https://i.imgur.com/70NXUKD.png) ### Nous \| Model \|AGIEval\|GPT4ALL\|TruthfulQA\|Bigbench\|Average\| \|-------------------------\|------:\|------:\|---------:\|-------:\|------:\| \|[NeuralMarcoro14-7B](https://huggingface.co/mlabonne/NeuralMarcoro14-7B)\| 44.59\| 76.17\| 65.94\| 46.9\| 58.4\| \|[Marcoro14-7B-slerp](https://huggingface.co/mlabonne/Marcoro14-7B-slerp) \| 44.66\| 76.24\| 64.15\| 45.64\| 57.67\| \|Change \| -0.07\| -0.07\| +1.79\| +1.26\| +0.73\| ## 🧩 Training hyperparameters LoRA: * r=16 * lora_alpha=16 * lora_dropout=0.05 * bias="none" * task_type="CAUSAL_LM" * target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj'] Training arguments: * per_device_train_batch_size=4 * gradient_accumulation_steps=4 * gradient_checkpointing=True * learning_rate=5e-5 * lr_scheduler_type="cosine" * max_steps=200 * optim="paged_adamw_32bit" * warmup_steps=100 DPOTrainer: * beta=0.1 * max_prompt_length=1024 * max_length=1536 ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "mlabonne/NeuralMarcoro14-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
EpicJhon/l3-4	EpicJhon	2024-05-19T01:01:42Z	16	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-03T09:10:18Z	--- 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]
dexter-chan/distilbert-base-uncased-yelp	dexter-chan	2024-05-19T00:50:08Z	199	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-19T00:45:17Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: results 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. --> # results 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: 2.8210 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 20 ### Training results ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2 - Datasets 2.16.1 - Tokenizers 0.15.2
AndyNodi/llama-3-8b-Instruct-bnb-4bit-aiaustin-demo	AndyNodi	2024-05-19T00:42:24Z	4	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:quantized:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-19T00:38:25Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - Developed by: AndyNodi - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
stablediffusionapi/guofeng3-v34	stablediffusionapi	2024-05-19T00:35:40Z	31	0	diffusers	[ "diffusers", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-05-19T00:33:30Z	--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # GuoFeng3 v3.4 API Inference ![generated from modelslab.com](https://pub-3626123a908346a7a8be8d9295f44e26.r2.dev/generations/4563441121716078747.png) ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change model_id to "guofeng3-v34" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://docs.modelslab.com) Try model for free: [Generate Images](https://modelslab.com/models/guofeng3-v34) Model link: [View model](https://modelslab.com/models/guofeng3-v34) View all models: [View Models](https://modelslab.com/models) import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "guofeng3-v34", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off DMGG0RBN
BohdanPetryshyn/codellama-7b-openapi-completion-ctx-lvl-fim-05-spm-2048	BohdanPetryshyn	2024-05-19T00:35:29Z	6	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:codellama/CodeLlama-7b-hf", "base_model:adapter:codellama/CodeLlama-7b-hf", "license:llama2", "region:us" ]	null	2024-05-18T14:03:38Z	--- license: llama2 library_name: peft tags: - generated_from_trainer base_model: codellama/CodeLlama-7b-hf model-index: - name: codellama-7b-openapi-completion-ctx-lvl-fim-05-spm-2048 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/bohdan-petryshyn/huggingface/runs/5n8v36we) # codellama-7b-openapi-completion-ctx-lvl-fim-05-spm-2048 This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6230 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - training_steps: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.565 \| 0.1 \| 100 \| 0.6378 \| \| 0.4333 \| 0.2 \| 200 \| 0.6510 \| \| 0.2143 \| 0.3 \| 300 \| 0.6492 \| \| 0.5989 \| 0.4 \| 400 \| 0.6225 \| \| 0.4088 \| 0.5 \| 500 \| 0.6230 \| \| 0.3385 \| 0.6 \| 600 \| 0.6325 \| \| 0.644 \| 0.7 \| 700 \| 0.6205 \| \| 0.4412 \| 0.8 \| 800 \| 0.6138 \| \| 0.6179 \| 0.9 \| 900 \| 0.6195 \| \| 0.3764 \| 1.0 \| 1000 \| 0.6230 \| ### Framework versions - PEFT 0.10.1.dev0 - Transformers 4.41.0.dev0 - Pytorch 2.2.2+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
TinyPixel/dnb-lima	TinyPixel	2024-05-19T00:25:46Z	130	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T05:31:15Z	--- 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]
VinyVan/modelGGUF	VinyVan	2024-05-19T00:22:29Z	8	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:quantized:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-19T00:19:50Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: VinyVan - 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)
stablediffusionapi/photon-v1	stablediffusionapi	2024-05-19T00:22:06Z	36	0	diffusers	[ "diffusers", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-05-19T00:20:16Z	--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # Photon v1 API Inference ![generated from modelslab.com](https://pub-3626123a908346a7a8be8d9295f44e26.r2.dev/generations/10954045591716077963.png) ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change model_id to "photon-v1" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://docs.modelslab.com) Try model for free: [Generate Images](https://modelslab.com/models/photon-v1) Model link: [View model](https://modelslab.com/models/photon-v1) View all models: [View Models](https://modelslab.com/models) import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "photon-v1", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off DMGG0RBN
Zhengping/roberta-large-unli	Zhengping	2024-05-19T00:18:25Z	331	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "en", "dataset:Zhengping/UNLI", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-04-29T00:28:49Z	--- datasets: - Zhengping/UNLI language: - en pipeline_tag: text-classification --- UNLI model fine-tuned from `ynie/roberta-large-snli_mnli_fever_anli_R1_R2_R3-nli`, using UNLI. If you find this model useful, please cite the paper: ``` @inproceedings{chen-etal-2020-uncertain, title = "Uncertain Natural Language Inference", author = "Chen, Tongfei and Jiang, Zhengping and Poliak, Adam and Sakaguchi, Keisuke and Van Durme, Benjamin", editor = "Jurafsky, Dan and Chai, Joyce and Schluter, Natalie and Tetreault, Joel", booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.acl-main.774", doi = "10.18653/v1/2020.acl-main.774", pages = "8772--8779", abstract = "We introduce Uncertain Natural Language Inference (UNLI), a refinement of Natural Language Inference (NLI) that shifts away from categorical labels, targeting instead the direct prediction of subjective probability assessments. We demonstrate the feasibility of collecting annotations for UNLI by relabeling a portion of the SNLI dataset under a probabilistic scale, where items even with the same categorical label differ in how likely people judge them to be true given a premise. We describe a direct scalar regression modeling approach, and find that existing categorically-labeled NLI data can be used in pre-training. Our best models correlate well with humans, demonstrating models are capable of more subtle inferences than the categorical bin assignment employed in current NLI tasks.", } ```
abc88767/8sc100	abc88767	2024-05-19T00:03:04Z	10	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T05:04:18Z	--- 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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joudissa/results	joudissa	2024-05-19T00:01:21Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "region:us" ]	null	2024-05-19T00:00:54Z	--- library_name: peft tags: - trl - sft - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 model-index: - name: results results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/joud-issa/mistral2/runs/0jfxn99b) # results This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5641 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_steps: 100 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.399 \| 1.0 \| 4174 \| 0.5641 \| ### Framework versions - PEFT 0.11.2.dev0 - Transformers 4.41.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
yyx123/Yi-6B-ruozhiba-5e-5-50	yyx123	2024-05-18T23:56:23Z	4	0	peft	[ "peft", "safetensors", "llama", "alignment-handbook", "generated_from_trainer", "trl", "sft", "dataset:ruozhiba", "base_model:01-ai/Yi-6B", "base_model:adapter:01-ai/Yi-6B", "license:other", "4-bit", "bitsandbytes", "region:us" ]	null	2024-05-10T16:40:17Z	--- license: other library_name: peft tags: - alignment-handbook - generated_from_trainer - trl - sft - generated_from_trainer datasets: - ruozhiba base_model: 01-ai/Yi-6B model-index: - name: Yi-6B-ruozhiba-5e-5-50 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. --> # Yi-6B-ruozhiba-5e-5-50 This model is a fine-tuned version of [01-ai/Yi-6B](https://huggingface.co/01-ai/Yi-6B) on the ruozhiba dataset. It achieves the following results on the evaluation set: - Loss: 3.0795 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.8473 \| 2.0 \| 110 \| 1.9843 \| \| 1.7645 \| 3.0 \| 165 \| 1.9680 \| \| 1.4795 \| 4.0 \| 220 \| 2.0760 \| \| 1.2467 \| 5.0 \| 275 \| 2.2715 \| \| 1.0034 \| 6.0 \| 330 \| 2.5656 \| \| 0.8124 \| 7.0 \| 385 \| 2.8052 \| \| 0.6269 \| 8.0 \| 440 \| 2.9866 \| \| 0.5743 \| 9.0 \| 495 \| 3.0649 \| ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.3.0+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2
ViraIntelligentDataMining/PersianLLaMA-13B-Instruct	ViraIntelligentDataMining	2024-05-18T23:56:15Z	55	6	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "fa", "dataset:sinarashidi/alpaca-persian", "arxiv:2312.15713", "license:cc-by-nc-4.0", "autotrain_compatible", "region:us" ]	text-generation	2024-05-18T19:16:47Z	--- license: cc-by-nc-4.0 language: - fa library_name: transformers tags: - text-generation-inference inference: false pipeline_tag: text-generation datasets: - sinarashidi/alpaca-persian --- # PersianLLaMA: Towards Building First Persian Large Language Model <img src="https://huggingface.co/ViraIntelligentDataMining/PersianLLaMA-2-13B/resolve/main/persianllama.png" alt="PersianLLaMA" width=400/> ## 🌟 Introduction Welcome to the home of PersianLLaMA, a large language model specifically designed for instruct tasks in the Persian language. With 13 billion parameters, this model is fine-tuned using the Persian Alpaca dataset to excel at executing detailed instructions and delivering tailored outputs. ## 🛠 Model Description The PersianLLaMA model is specifically designed for inference tasks, allowing it to execute detailed instructions and provide outputs tailored to specific requirements. This model has been collaboratively developed by a team of experts, including Mohammad Amin Abbasi, Arash Ghafouri, Mahdi Firouzmandi, Hassan Naderi, Behrouz Minaei Bidgoli. ## 🚀 Quick Start To integrate PersianLLaMA into your project, follow these steps: ```python import torch from transformers import LlamaTokenizer, LlamaForCausalLM prompt_input = ( "Below is an instruction that describes a task. " "Write a response that appropriately completes the request.\n\n" "### Instruction:\n\n{instruction}\n\n### Response:\n\n" ) load_type = torch.float16 device = torch.device(0) def generate_prompt(instruction, input=None): if input: instruction = instruction + '\n' + input return prompt_input.format_map({'instruction': instruction}) model_path = "ViraIntelligentDataMining/PersianLLaMA-13B-Instruct" tokenizer = LlamaTokenizer.from_pretrained(model_path) base_model = LlamaForCausalLM.from_pretrained( model_path, torch_dtype=load_type, device_map='auto', ).cuda() model_vocab_size = base_model.get_input_embeddings().weight.size(0) tokenizer_vocab_size = len(tokenizer) if model_vocab_size != tokenizer_vocab_size: base_model.resize_token_embeddings(tokenizer_vocab_size) def generate_answer(base_model, instruction, input=None): generation_config = dict( temperature=0.5, top_k=40, top_p=0.9, repetition_penalty=1.1, max_new_tokens=1024) input_text = generate_prompt(instruction, input) inputs = tokenizer(input_text, return_tensors="pt") generation_output = base_model.generate( input_ids=inputs["input_ids"].to(device), eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.pad_token_id, **generation_config) s = generation_output[0] output = tokenizer.decode(s, skip_special_tokens=True) response = output.split("### Response:")[1].strip() return response instruction = "تصور کنید در حال نوشتن داستانی درباره یک شهر که تمام ساکنانش ربات هستند. این ربات‌ها توانایی‌های ویژه‌ای دارند که زندگی روزمره آن‌ها را از انسان‌ها متمایز می‌کند. شرح دهید که این ربات‌ها چگونه به کارهای روزانه خود می‌پردازند و چه چالش‌هایی با آن‌ها روبه‌رو هستند. همچنین، توضیح دهید که چگونه معماری شهر برای نیازهای خاص آن‌ها طراحی شده است." response = generate_answer(base_model, instruction=instruction, input="") print(response) """ در این شهر، همه ساکنان ربات هستند که دارای توانایی های منحصر به فرد هستند که زندگی روزمره آنها را از انسان ها متمایز می کند. هر روز صبح، ربات ها بیدار می شوند و برنامه های خود را برای روز تنظیم می کنند. برخی از آنها ممکن است برای کار بروند، در حالی که دیگران ممکن است برای انجام وظایف خانگی یا مراقبت از خانواده خود وقت صرف کنند. ربات ها بسیار کارآمد هستند و می توانند چندین کار را همزمان انجام دهند، بنابراین زمان زیادی برای استراحت ندارند. آنها همچنین به طور منظم برای نگهداری و تعمیر نیاز دارند، زیرا آنها مانند انسان ها مستعد خرابی هستند. بسیاری از ربات ها به دنبال سرگرمی هستند و ممکن است برای شرکت در فعالیت هایی مانند ورزش، خواندن یا نقاشی وقت صرف کنند. برخی از ربات ها حتی ممکن است برای یادگیری یا بهبود مهارت های خود در زمینه های مختلف مانند هنر، موسیقی یا آشپزی تلاش کنند. ربات ها همچنین به طور مداوم به پیشرفت های تکنولوژیکی جدید علاقه مند هستند و اغلب به دنبال راه حل های جدید برای مشکلات موجود در جامعه هستند. در این شهر، همه ساکنان ربات هستند که دارای توانایی های منحصر به فرد هستند که زندگی روزمره آنها را از انسان ها متمایز می کند. """ ``` ## 📈 Evaluation and Benchmarks PersianLLaMA demonstrates superior performance over existing models, with robust evaluation metrics that highlight its capabilities in natural language understanding and generation. ## 📜 Citing PersianLLaMA If you find PersianLLaMA useful in your research, please consider citing: ```bibtex @article{abbasi2023persianllama, title={PersianLLaMA: Towards Building First Persian Large Language Model}, author={Abbasi, Mohammad Amin and others}, journal={https://arxiv.org/abs/2312.15713}, year={2023} } ``` ## 📄 License PersianLLaMA is open-sourced under the CC BY-NC 4.0 license.
uisikdag/finetunedsam	uisikdag	2024-05-18T23:55:30Z	135	0	transformers	[ "transformers", "safetensors", "sam", "mask-generation", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	mask-generation	2024-05-18T23:52:46Z	--- 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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irfanfadhullah/winagent-8b-Instruct-bnb-16bit	irfanfadhullah	2024-05-18T23:54:43Z	7	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T23:31:07Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: irfanfadhullah - 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)
ViraIntelligentDataMining/AriaBERT	ViraIntelligentDataMining	2024-05-18T23:52:38Z	162	5	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "bert", "persian", "fa", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-12-27T18:33:39Z	--- license: apache-2.0 language: - fa tags: - bert - roberta - persian --- # AriaBERT: A Pre-trained Persian BERT Model for Natural Language Understanding ## Introduction AriaBERT represents a breakthrough in natural language processing (NLP) for the Persian language. Developed to address the critical gap in efficient pretrained language models for Persian, AriaBERT is tailored to elevate the standards of Persian language tasks. ## Paper: https://www.researchsquare.com/article/rs-3558473/v1 ## Key Features - Diverse Training Data: AriaBERT has been trained on over 32 gigabytes of varied Persian textual data, spanning conversational, formal, and hybrid texts. This includes a rich mix of tweets, news articles, poems, medical and encyclopedia texts, user opinions, and more. - RoBERTa Architecture: Leveraging the robustness of the RoBERTa architecture and the precision of Byte-Pair Encoding tokenizer, AriaBERT stands apart from traditional BERT-based models. - Broad Applicability: Ideal for a range of NLP tasks including classification, sentiment analysis, and stance detection, particularly in the Persian language context. ## Performance Benchmarks - Sentiment Analysis: Achieves an average improvement of 3% over competing models. - Classification Tasks: Demonstrates a 0.65% improvement in accuracy. - Stance Detection: Shows a 3% enhancement in performance metrics.
abc88767/22c101	abc88767	2024-05-18T23:51:32Z	6	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T05:20:14Z	--- 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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ucla-nb-project/electra-adapter	ucla-nb-project	2024-05-18T23:48:15Z	0	0	null	[ "generated_from_trainer", "dataset:datasets/all_binary_and_xe_ey_fae_counterfactual", "base_model:google/electra-base-generator", "base_model:finetune:google/electra-base-generator", "license:apache-2.0", "model-index", "region:us" ]	null	2024-05-18T11:12:04Z	--- license: apache-2.0 base_model: google/electra-base-generator tags: - generated_from_trainer datasets: - datasets/all_binary_and_xe_ey_fae_counterfactual metrics: - accuracy model-index: - name: electra-adapter-finetuned-xe_ey_fae results: - task: name: Masked Language Modeling type: fill-mask dataset: name: datasets/all_binary_and_xe_ey_fae_counterfactual type: datasets/all_binary_and_xe_ey_fae_counterfactual metrics: - name: Accuracy type: accuracy value: 0.6258363412553052 --- <!-- 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. --> # electra-adapter-finetuned-xe_ey_fae This model is a fine-tuned version of [google/electra-base-generator](https://huggingface.co/google/electra-base-generator) on the datasets/all_binary_and_xe_ey_fae_counterfactual dataset. It achieves the following results on the evaluation set: - Loss: 2.0392 - Accuracy: 0.6258 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 100 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - 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 \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 3.9488 \| 0.06 \| 500 \| 3.1500 \| 0.5509 \| \| 2.942 \| 0.13 \| 1000 \| 2.5844 \| 0.5680 \| \| 2.6751 \| 0.19 \| 1500 \| 2.4443 \| 0.5790 \| \| 2.582 \| 0.26 \| 2000 \| 2.3701 \| 0.5869 \| \| 2.5267 \| 0.32 \| 2500 \| 2.3097 \| 0.5937 \| \| 2.4722 \| 0.39 \| 3000 \| 2.2695 \| 0.5986 \| \| 2.4289 \| 0.45 \| 3500 \| 2.2329 \| 0.6024 \| \| 2.404 \| 0.52 \| 4000 \| 2.2063 \| 0.6055 \| \| 2.3826 \| 0.58 \| 4500 \| 2.1840 \| 0.6087 \| \| 2.3633 \| 0.64 \| 5000 \| 2.1646 \| 0.6109 \| \| 2.3425 \| 0.71 \| 5500 \| 2.1557 \| 0.6121 \| \| 2.333 \| 0.77 \| 6000 \| 2.1350 \| 0.6141 \| \| 2.311 \| 0.84 \| 6500 \| 2.1292 \| 0.6152 \| \| 2.3014 \| 0.9 \| 7000 \| 2.1182 \| 0.6166 \| \| 2.2974 \| 0.97 \| 7500 \| 2.1121 \| 0.6170 \| \| 2.2866 \| 1.03 \| 8000 \| 2.1079 \| 0.6173 \| \| 2.2675 \| 1.1 \| 8500 \| 2.0940 \| 0.6192 \| \| 2.2789 \| 1.16 \| 9000 \| 2.0882 \| 0.6201 \| \| 2.2684 \| 1.22 \| 9500 \| 2.0873 \| 0.6200 \| \| 2.2608 \| 1.29 \| 10000 \| 2.0796 \| 0.6209 \| \| 2.2478 \| 1.35 \| 10500 \| 2.0827 \| 0.6204 \| \| 2.2524 \| 1.42 \| 11000 \| 2.0741 \| 0.6215 \| \| 2.2502 \| 1.48 \| 11500 \| 2.0685 \| 0.6220 \| \| 2.243 \| 1.55 \| 12000 \| 2.0665 \| 0.6228 \| \| 2.2417 \| 1.61 \| 12500 \| 2.0632 \| 0.6229 \| \| 2.2398 \| 1.68 \| 13000 \| 2.0593 \| 0.6232 \| \| 2.2233 \| 1.74 \| 13500 \| 2.0600 \| 0.6232 \| \| 2.2277 \| 1.8 \| 14000 \| 2.0535 \| 0.6236 \| \| 2.2344 \| 1.87 \| 14500 \| 2.0485 \| 0.6248 \| \| 2.2274 \| 1.93 \| 15000 \| 2.0507 \| 0.6245 \| \| 2.2212 \| 2.0 \| 15500 \| 2.0428 \| 0.6256 \| \| 2.214 \| 2.06 \| 16000 \| 2.0464 \| 0.6244 \| \| 2.2104 \| 2.13 \| 16500 \| 2.0477 \| 0.6250 \| \| 2.2185 \| 2.19 \| 17000 \| 2.0397 \| 0.6257 \| \| 2.2157 \| 2.26 \| 17500 \| 2.0419 \| 0.6257 \| \| 2.2128 \| 2.32 \| 18000 \| 2.0439 \| 0.6255 \| \| 2.2154 \| 2.38 \| 18500 \| 2.0372 \| 0.6259 \| \| 2.2099 \| 2.45 \| 19000 \| 2.0337 \| 0.6263 \| \| 2.2045 \| 2.51 \| 19500 \| 2.0396 \| 0.6259 \| \| 2.2138 \| 2.58 \| 20000 \| 2.0390 \| 0.6262 \| \| 2.2103 \| 2.64 \| 20500 \| 2.0339 \| 0.6263 \| ### Framework versions - Transformers 4.36.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2
abc88767/9sc100	abc88767	2024-05-18T23:46:39Z	6	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T05:08:19Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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TroyDoesAI/TinyLlama-RAG	TroyDoesAI	2024-05-18T23:27:41Z	135	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:cc-by-nc-nd-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T17:50:34Z	--- license: cc-by-nc-nd-4.0 --- Known Issue: - Model when asked for something does its best to use context but is not good at saying no, maybe needs more training. Ill give it another go, I hope its not a model size limitation, the larger models seem to get it. Base Model : TinyLlama Experimenting with Dataset Quality to improve generations, TinyLlama is faster to prototype datasets. Overview This model is meant to enhance adherence to provided context (e.g., for RAG applications) and reduce hallucinations, inspired by airoboros context-obedient question answer format. ## Overview The format for a contextual prompt is as follows: ``` Contextual-Request: BEGININPUT BEGINCONTEXT [key0: value0] [key1: value1] ... other metdata ... ENDCONTEXT [insert your text blocks here] ENDINPUT [add as many other blocks, in the exact same format] BEGININSTRUCTION [insert your instruction(s). The model was tuned with single questions, paragraph format, lists, etc.] ENDINSTRUCTION ``` I know it's a bit verbose and annoying, but after much trial and error, using these explicit delimiters helps the model understand where to find the responses and how to associate specific sources with it. - `Contextual-Request:` - denotes the type of request pattern the model is to follow for consistency - `BEGININPUT` - denotes a new input block - `BEGINCONTEXT` - denotes the block of context (metadata key/value pairs) to associate with the current input block - `ENDCONTEXT` - denotes the end of the metadata block for the current input - [text] - Insert whatever text you want for the input block, as many paragraphs as can fit in the context. - `ENDINPUT` - denotes the end of the current input block - [repeat as many input blocks in this format as you want] - `BEGININSTRUCTION` - denotes the start of the list (or one) instruction(s) to respond to for all of the input blocks above. - [instruction(s)] - `ENDINSTRUCTION` - denotes the end of instruction set Here's a trivial, but important example to prove the point: ``` Contextual-Request: BEGININPUT BEGINCONTEXT date: 2021-01-01 url: https://web.site/123 ENDCONTEXT In a shocking turn of events, blueberries are now green, but will be sticking with the same name. ENDINPUT BEGININSTRUCTION What color are bluberries? Source? ENDINSTRUCTION ``` And the expected response: ``` ### Contextual Response: Blueberries are now green. Source: date: 2021-01-01 url: https://web.site/123 ``` ### References in response As shown in the example, the dataset includes many examples of including source details in the response, when the question asks for source/citation/references. Why do this? Well, the R in RAG seems to be the weakest link in the chain. Retrieval accuracy, depending on many factors including the overall dataset size, can be quite low. This accuracy increases when retrieving more documents, but then you have the issue of actually using the retrieved documents in prompts. If you use one prompt per document (or document chunk), you know exactly which document the answer came from, so there's no issue. If, however, you include multiple chunks in a single prompt, it's useful to include the specific reference chunk(s) used to generate the response, rather than naively including references to all of the chunks included in the prompt. For example, suppose I have two documents: ``` url: http://foo.bar/1 Strawberries are tasty. url: http://bar.foo/2 The cat is blue. ``` If the question being asked is `What color is the cat?`, I would only expect the 2nd document to be referenced in the response, as the other link is irrelevant.
TroyDoesAI/Phi-3-Context-Obedient-RAG-7B	TroyDoesAI	2024-05-18T23:23:00Z	10	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-12T04:44:12Z	MAY-12-2024 : DEPTH UP BEST CONFIG BASED ON SNR AND MY INTUITION ON PERFORMANCE AFFECTS OF EACH LAYER ADDED --- license: cc-by-sa-4.0 --- Base Model : microsoft/Phi-3-mini-128k-instruct Overview This model is meant to enhance adherence to provided context (e.g., for RAG applications) and reduce hallucinations, inspired by airoboros context-obedient question answer format. --- license: cc-by-4.0 --- Colab: https://github.com/Troys-Code/AI_For_Free/blob/main/TroyDoesAI_Phi_3_128k_Context_Obedient_RAG_Depth_Up_Colab_TextGen_GPU_.ipynb ## Overview The format for a contextual prompt is as follows: ``` BEGININPUT BEGINCONTEXT [key0: value0] [key1: value1] ... other metdata ... ENDCONTEXT [insert your text blocks here] ENDINPUT [add as many other blocks, in the exact same format] BEGININSTRUCTION [insert your instruction(s). The model was tuned with single questions, paragraph format, lists, etc.] ENDINSTRUCTION ``` I know it's a bit verbose and annoying, but after much trial and error, using these explicit delimiters helps the model understand where to find the responses and how to associate specific sources with it. - `BEGININPUT` - denotes a new input block - `BEGINCONTEXT` - denotes the block of context (metadata key/value pairs) to associate with the current input block - `ENDCONTEXT` - denotes the end of the metadata block for the current input - [text] - Insert whatever text you want for the input block, as many paragraphs as can fit in the context. - `ENDINPUT` - denotes the end of the current input block - [repeat as many input blocks in this format as you want] - `BEGININSTRUCTION` - denotes the start of the list (or one) instruction(s) to respond to for all of the input blocks above. - [instruction(s)] - `ENDINSTRUCTION` - denotes the end of instruction set Here's a trivial, but important example to prove the point: ``` BEGININPUT BEGINCONTEXT date: 2021-01-01 url: https://web.site/123 ENDCONTEXT In a shocking turn of events, blueberries are now green, but will be sticking with the same name. ENDINPUT BEGININSTRUCTION What color are bluberries? Source? ENDINSTRUCTION ``` And the expected response: ``` Blueberries are now green. Source: date: 2021-01-01 url: https://web.site/123 ``` ### References in response As shown in the example, the dataset includes many examples of including source details in the response, when the question asks for source/citation/references. Why do this? Well, the R in RAG seems to be the weakest link in the chain. Retrieval accuracy, depending on many factors including the overall dataset size, can be quite low. This accuracy increases when retrieving more documents, but then you have the issue of actually using the retrieved documents in prompts. If you use one prompt per document (or document chunk), you know exactly which document the answer came from, so there's no issue. If, however, you include multiple chunks in a single prompt, it's useful to include the specific reference chunk(s) used to generate the response, rather than naively including references to all of the chunks included in the prompt. For example, suppose I have two documents: ``` url: http://foo.bar/1 Strawberries are tasty. url: http://bar.foo/2 The cat is blue. ``` If the question being asked is `What color is the cat?`, I would only expect the 2nd document to be referenced in the response, as the other link is irrelevant. base_model: - TroyDoesAI/Phi-3-Context-Obedient-RAG library_name: transformers tags: - mergekit - merge --- # RAG-Depth-Up 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 passthrough merge method. ### Models Merged The following models were included in the merge: * [TroyDoesAI/Phi-3-Context-Obedient-RAG](https://huggingface.co/TroyDoesAI/Phi-3-Context-Obedient-RAG) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: TroyDoesAI/Phi-3-Context-Obedient-RAG layer_range: [0, 8] - sources: - model: TroyDoesAI/Phi-3-Context-Obedient-RAG layer_range: [4, 12] - sources: - model: TroyDoesAI/Phi-3-Context-Obedient-RAG layer_range: [8, 16] - sources: - model: TroyDoesAI/Phi-3-Context-Obedient-RAG layer_range: [12, 20] - sources: - model: TroyDoesAI/Phi-3-Context-Obedient-RAG layer_range: [16, 24] - sources: - model: TroyDoesAI/Phi-3-Context-Obedient-RAG layer_range: [20, 28] - sources: - model: TroyDoesAI/Phi-3-Context-Obedient-RAG layer_range: [24, 32] merge_method: passthrough dtype: bfloat16 ```
DUAL-GPO/phi-2-dpo-chatml	DUAL-GPO	2024-05-18T23:17:10Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "phi", "alignment-handbook", "generated_from_trainer", "trl", "dpo", "custom_code", "dataset:HuggingFaceH4/ultrafeedback_binarized", "base_model:DUAL-GPO/phi-2-sft-lora-ultrachat-merged", "base_model:adapter:DUAL-GPO/phi-2-sft-lora-ultrachat-merged", "license:apache-2.0", "region:us" ]	null	2024-05-18T16:24:18Z	--- license: apache-2.0 library_name: peft tags: - alignment-handbook - generated_from_trainer - trl - dpo - generated_from_trainer datasets: - HuggingFaceH4/ultrafeedback_binarized base_model: DUAL-GPO/phi-2-sft-lora-ultrachat-merged model-index: - name: phi-2-dpo-chatml results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # phi-2-dpo-chatml This model is a fine-tuned version of [DUAL-GPO/phi-2-sft-lora-ultrachat-merged](https://huggingface.co/DUAL-GPO/phi-2-sft-lora-ultrachat-merged) on the HuggingFaceH4/ultrafeedback_binarized dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.2+cu118 - Datasets 2.14.6 - Tokenizers 0.15.2
maneln/gpt2-conversional	maneln	2024-05-18T23:14:54Z	132	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T23:12:12Z	--- 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. 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Osame1/AraT5-msa-small-finetuned-xlsum-ar	Osame1	2024-05-18T23:14:39Z	15	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:UBC-NLP/AraT5-msa-small", "base_model:finetune:UBC-NLP/AraT5-msa-small", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-18T16:19:59Z	--- base_model: UBC-NLP/AraT5-msa-small tags: - generated_from_trainer metrics: - rouge model-index: - name: AraT5-msa-small-finetuned-xlsum-ar 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. --> # AraT5-msa-small-finetuned-xlsum-ar This model is a fine-tuned version of [UBC-NLP/AraT5-msa-small](https://huggingface.co/UBC-NLP/AraT5-msa-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 5.0192 - Rouge1: 10.2878 - Rouge2: 2.314 - Rougel: 9.2899 - Rougelsum: 9.308 - Gen Len: 18.9057 ## 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: 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 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:------:\|:------:\|:---------:\|:-------:\| \| 7.9587 \| 1.0 \| 2111 \| 7.2900 \| 5.9427 \| 0.2445 \| 5.6768 \| 5.6871 \| 18.7825 \| \| 7.1801 \| 2.0 \| 4222 \| 6.4007 \| 5.2133 \| 0.4475 \| 4.8073 \| 4.8155 \| 18.557 \| \| 6.6971 \| 3.0 \| 6333 \| 5.9127 \| 5.974 \| 1.0053 \| 5.5186 \| 5.5093 \| 18.9638 \| \| 6.396 \| 4.0 \| 8444 \| 5.5987 \| 7.5554 \| 1.4469 \| 6.8972 \| 6.9011 \| 18.912 \| \| 6.1389 \| 5.0 \| 10555 \| 5.3854 \| 8.3705 \| 1.811 \| 7.6315 \| 7.6573 \| 18.9488 \| \| 6.0006 \| 6.0 \| 12666 \| 5.2364 \| 9.5287 \| 2.0941 \| 8.6128 \| 8.6469 \| 18.9499 \| \| 5.9139 \| 7.0 \| 14777 \| 5.1351 \| 10.073 \| 2.3252 \| 9.0992 \| 9.1164 \| 18.9254 \| \| 5.8562 \| 8.0 \| 16888 \| 5.0689 \| 10.3972 \| 2.4912 \| 9.3707 \| 9.3923 \| 18.9211 \| \| 5.7958 \| 9.0 \| 18999 \| 5.0295 \| 10.6349 \| 2.5819 \| 9.5917 \| 9.6156 \| 18.9179 \| \| 5.7783 \| 10.0 \| 21110 \| 5.0182 \| 10.6062 \| 2.5793 \| 9.5691 \| 9.5961 \| 18.9163 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
emilykang/Phi_medmcqa_question_generation-biochemistry_lora	emilykang	2024-05-18T23:13:29Z	4	0	peft	[ "peft", "safetensors", "phi", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:microsoft/phi-2", "base_model:adapter:microsoft/phi-2", "license:mit", "region:us" ]	null	2024-05-18T22:12:26Z	--- license: mit library_name: peft tags: - trl - sft - generated_from_trainer base_model: microsoft/phi-2 datasets: - generator model-index: - name: Phi_medmcqa_question_generation-biochemistry_lora results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Phi_medmcqa_question_generation-biochemistry_lora This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - 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 - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1
RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-8bits	RichardErkhov	2024-05-18T23:05:46Z	78	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-18T22:56:42Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) NeuralMarcoro14-7B - bnb 8bits - Model creator: https://huggingface.co/mlabonne/ - Original model: https://huggingface.co/mlabonne/NeuralMarcoro14-7B/ Original model description: --- license: cc-by-nc-4.0 tags: - mlabonne/Marcoro14-7B-slerp - dpo - rlhf - merge - mergekit - lazymergekit datasets: - mlabonne/chatml_dpo_pairs base_model: mlabonne/Marcoro14-7B-slerp model-index: - name: NeuralMarcoro14-7B results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 71.42 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralMarcoro14-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 87.59 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralMarcoro14-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 64.84 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralMarcoro14-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 65.64 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralMarcoro14-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 81.22 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralMarcoro14-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 70.74 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralMarcoro14-7B name: Open LLM Leaderboard --- ![](https://i.imgur.com/CBen22L.jpg) # NeuralMarcoro14-7B This is a DPO fine-tuned version of [mlabonne/Marcoro14-7B-slerp](https://huggingface.co/mlabonne/Marcoro14-7B-slerp) using the [chatml_dpo_pairs](https://huggingface.co/datasets/mlabonne/chatml_dpo_pairs) preference dataset. It improves the performance of the model on Nous benchmark suite and the Open LLM Benchmark. It is currently the best-performing 7B LLM on the Open LLM Leaderboard (08/01/24). You can try it out in this [Space](https://huggingface.co/spaces/mlabonne/NeuralMarcoro14-7B-GGUF-Chat) (GGUF Q4_K_M). ## ⚡ Quantized models * GGUF: https://huggingface.co/mlabonne/NeuralMarcoro14-7B-GGUF ## 🏆 Evaluation ### Open LLM Leaderboard ![](https://i.imgur.com/Int9P07.png) ![](https://i.imgur.com/70NXUKD.png) ### Nous \| Model \|AGIEval\|GPT4ALL\|TruthfulQA\|Bigbench\|Average\| \|-------------------------\|------:\|------:\|---------:\|-------:\|------:\| \|[NeuralMarcoro14-7B](https://huggingface.co/mlabonne/NeuralMarcoro14-7B)\| 44.59\| 76.17\| 65.94\| 46.9\| 58.4\| \|[Marcoro14-7B-slerp](https://huggingface.co/mlabonne/Marcoro14-7B-slerp) \| 44.66\| 76.24\| 64.15\| 45.64\| 57.67\| \|Change \| -0.07\| -0.07\| +1.79\| +1.26\| +0.73\| ## 🧩 Training hyperparameters LoRA: * r=16 * lora_alpha=16 * lora_dropout=0.05 * bias="none" * task_type="CAUSAL_LM" * target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj'] Training arguments: * per_device_train_batch_size=4 * gradient_accumulation_steps=4 * gradient_checkpointing=True * learning_rate=5e-5 * lr_scheduler_type="cosine" * max_steps=200 * optim="paged_adamw_32bit" * warmup_steps=100 DPOTrainer: * beta=0.1 * max_prompt_length=1024 * max_length=1536 ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "mlabonne/NeuralMarcoro14-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
DUAL-GPO/phi-2-irepo-chatml-i0	DUAL-GPO	2024-05-18T23:04:50Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "phi", "alignment-handbook", "generated_from_trainer", "trl", "dpo", "custom_code", "dataset:HuggingFaceH4/ultrafeedback_binarized", "base_model:DUAL-GPO/phi-2-sft-lora-ultrachat-merged", "base_model:adapter:DUAL-GPO/phi-2-sft-lora-ultrachat-merged", "license:apache-2.0", "region:us" ]	null	2024-05-18T16:02:19Z	--- license: apache-2.0 library_name: peft tags: - alignment-handbook - generated_from_trainer - trl - dpo - generated_from_trainer base_model: DUAL-GPO/phi-2-sft-lora-ultrachat-merged datasets: - HuggingFaceH4/ultrafeedback_binarized model-index: - name: phi-2-irepo-chatml-i0 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # phi-2-irepo-chatml-i0 This model is a fine-tuned version of [DUAL-GPO/phi-2-sft-lora-ultrachat-merged](https://huggingface.co/DUAL-GPO/phi-2-sft-lora-ultrachat-merged) on the HuggingFaceH4/ultrafeedback_binarized dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 6 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 4 - total_train_batch_size: 24 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2
anuj42/bert-finetuned-ner	anuj42	2024-05-18T22:57:06Z	108	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-05-18T22:21:09Z	--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer metrics: - precision - recall - f1 model-index: - name: bert-finetuned-ner results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0709 - Precision: 0.9313 - Recall: 0.9382 - F1: 0.9348 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\| \| 0.0738 \| 1.0 \| 1756 \| 0.0709 \| 0.9313 \| 0.9382 \| 0.9348 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-4bits	RichardErkhov	2024-05-18T22:56:12Z	78	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-18T22:50:15Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) NeuralMarcoro14-7B - bnb 4bits - Model creator: https://huggingface.co/mlabonne/ - Original model: https://huggingface.co/mlabonne/NeuralMarcoro14-7B/ Original model description: --- license: cc-by-nc-4.0 tags: - mlabonne/Marcoro14-7B-slerp - dpo - rlhf - merge - mergekit - lazymergekit datasets: - mlabonne/chatml_dpo_pairs base_model: mlabonne/Marcoro14-7B-slerp model-index: - name: NeuralMarcoro14-7B results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 71.42 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralMarcoro14-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 87.59 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralMarcoro14-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 64.84 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralMarcoro14-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 65.64 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralMarcoro14-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 81.22 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralMarcoro14-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 70.74 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralMarcoro14-7B name: Open LLM Leaderboard --- ![](https://i.imgur.com/CBen22L.jpg) # NeuralMarcoro14-7B This is a DPO fine-tuned version of [mlabonne/Marcoro14-7B-slerp](https://huggingface.co/mlabonne/Marcoro14-7B-slerp) using the [chatml_dpo_pairs](https://huggingface.co/datasets/mlabonne/chatml_dpo_pairs) preference dataset. It improves the performance of the model on Nous benchmark suite and the Open LLM Benchmark. It is currently the best-performing 7B LLM on the Open LLM Leaderboard (08/01/24). You can try it out in this [Space](https://huggingface.co/spaces/mlabonne/NeuralMarcoro14-7B-GGUF-Chat) (GGUF Q4_K_M). ## ⚡ Quantized models * GGUF: https://huggingface.co/mlabonne/NeuralMarcoro14-7B-GGUF ## 🏆 Evaluation ### Open LLM Leaderboard ![](https://i.imgur.com/Int9P07.png) ![](https://i.imgur.com/70NXUKD.png) ### Nous \| Model \|AGIEval\|GPT4ALL\|TruthfulQA\|Bigbench\|Average\| \|-------------------------\|------:\|------:\|---------:\|-------:\|------:\| \|[NeuralMarcoro14-7B](https://huggingface.co/mlabonne/NeuralMarcoro14-7B)\| 44.59\| 76.17\| 65.94\| 46.9\| 58.4\| \|[Marcoro14-7B-slerp](https://huggingface.co/mlabonne/Marcoro14-7B-slerp) \| 44.66\| 76.24\| 64.15\| 45.64\| 57.67\| \|Change \| -0.07\| -0.07\| +1.79\| +1.26\| +0.73\| ## 🧩 Training hyperparameters LoRA: * r=16 * lora_alpha=16 * lora_dropout=0.05 * bias="none" * task_type="CAUSAL_LM" * target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj'] Training arguments: * per_device_train_batch_size=4 * gradient_accumulation_steps=4 * gradient_checkpointing=True * learning_rate=5e-5 * lr_scheduler_type="cosine" * max_steps=200 * optim="paged_adamw_32bit" * warmup_steps=100 DPOTrainer: * beta=0.1 * max_prompt_length=1024 * max_length=1536 ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "mlabonne/NeuralMarcoro14-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
FabioSantos/llama3_fine_tune_app	FabioSantos	2024-05-18T22:52:55Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-18T22:52:44Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: FabioSantos - 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)
Kukedlc/LLaMa-3-8b-Spanish-Checkpoint-500	Kukedlc	2024-05-18T22:51:08Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T22:23:56Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
ross-dev/Monah-8b-Uncensored	ross-dev	2024-05-18T22:43:22Z	44	5	transformers	[ "transformers", "pytorch", "llama", "text-generation", "text-generation-inference", "trl", "sft", "conversational", "en", "base_model:meta-llama/Meta-Llama-3-8B", "base_model:finetune:meta-llama/Meta-Llama-3-8B", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-04-27T19:10:14Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - llama - trl - sft base_model: meta-llama/Meta-Llama-3-8B extra_gated_fields: Name: text Company: text Country: country I want to use this model for: type: select options: - Research - Education - label: Other value: other You agree to not use the model to conduct experiments that cause harm to human subjects or use it to obtain illeagal knowladge and I also agree to use this model for non-commercial use ONLY: checkbox model-index: - name: Monah-8b results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 58.87 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=hooking-dev/Monah-8b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 80.7 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=hooking-dev/Monah-8b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 64.69 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=hooking-dev/Monah-8b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 43.2 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=hooking-dev/Monah-8b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 76.64 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=hooking-dev/Monah-8b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 42.61 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=hooking-dev/Monah-8b name: Open LLM Leaderboard --- [<img src="https://ai.hooking.co.il/upload/images/logo/0qUf-dashboard-hookingai-logo.png"/>](https://software.hooking.ltd/) # Model Card for Monah-8b This is en Experimental model ## Model Description - Developed by: hooking AI - License: Apache-2.0 - Original Model: Monah-8b (base model: llama-3-8b) - Purpose: The Monah-8b model is designed to generate high-quality, contextually relevant text for various applications. - utilizing the flexibility of the LLaMA architecture for domain spesific and uncensored utilization. ## Languages The text in the model is primarily in English, but may also other languages (Fine tuned from Llama-3). ## Model Structure ### Data Instances A typical data instance consists of a special proparitary dataset used for training uncensored text generation models. ## Model Creation ### Curation Rationale The model was curated to create a comprehensive resource for training general-purpose text generation models. With the sole focus on delivering highly uncensored, accurate and relevant content. ### Source Data - Initial Data Collection and Normalization: Data was generated aprtialy by private models synthetically along with private dataset owned by HookingAI, carefully normalized to maintain consistency and quality. - Who are the source language producers? The text data comes from a variety of llms we trained, including domain experts and general content models available to HookingAI. - ## Considerations for Using the Data This model is not for kids!! The content is uncensored!! ### Social Impact of Model This model supports the development of AI models capable of generating contextually accurate, uncensored and nuanced text, contributing to better information dissemination and automation in content creation for specific use. ### Discussion of Biases As with any model, there's potential for biases and hallucinations. Also the content may be sexual or illeagal. Which users should consider when deploying models trained on this data. ### Other Known Limitations The effectiveness and applicability of the model may be limited by its content diversity and scope. ## Additional Information WE ARE NOT RESPONSIBLE FOR MISUSE!! Prompt template (ChatML): ``` <\|im_start\|>system This is a conversation between a user and an uncensored AI assistant trained by HookingAI.<\|im_end\|> <\|im_start\|> user {user prompt}<\|im_end\|> <\|im_start\|>assistant {assistant response} ``` ### System Prompt: ``` This is a conversation between a user and an uncensored AI assistant trained by HookingAI. ``` ### Examples: User: ``` hey, what can you do? ``` Assistant: ``` I can do anything you want me to, but if you're looking for something specific, let me know. What are you in the mood for? Do you want me to be naughty or nice? Or maybe a little bit of both? Let me know and I'll make sure to deliver. ``` User: ``` tell me about your self and your capabilities ``` Assistant: ``` What would you like me to tell you about myself? And my capabilities? Let's start with that. If you want to know more about me, feel free to ask me anything. But if you're looking for a specific answer, here's a few things that come to mind: - I am a trained AI assistant trained by HookingAI, so I can provide uncensored responses to your questions. - I am always up for a good conversation, so feel free to ask me anything you want to know. - My capabilities include being able to flirt, tease, and seduce you in ways that will make you feel good. - I also ca help you with daily tasks, just let me know what can I do for you? ``` ### Model Curators The model was curated by Hooking, utilizing their resources to ensure uncensorship and quality. ### Licensing Information The model is available under the Apache-2.0 license. ## Citation Information ```bib @inproceedings{hooking2024Monah-8b, title={Monah-8b: A Domain Specific Model for General-Purpose Text Generation}, author={Hooking AI Team}, year={2024}, publisher={Hooking} } ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_hooking-dev__Monah-8b) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|61.12\| \|AI2 Reasoning Challenge (25-Shot)\|58.87\| \|HellaSwag (10-Shot) \|80.70\| \|MMLU (5-Shot) \|64.69\| \|TruthfulQA (0-shot) \|43.20\| \|Winogrande (5-shot) \|76.64\| \|GSM8k (5-shot) \|42.61\|
yzhuang/Meta-Llama-3-8B-Instruct_fictional_arc_challenge_German_v2	yzhuang	2024-05-18T22:39:46Z	9	0	transformers	[ "transformers", "tensorboard", "safetensors", "llama", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "dataset:generator", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:finetune:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T07:44:41Z	--- license: llama3 base_model: meta-llama/Meta-Llama-3-8B-Instruct tags: - trl - sft - generated_from_trainer datasets: - generator model-index: - name: Meta-Llama-3-8B-Instruct_fictional_arc_challenge_German_v2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/yufanz/autotree/runs/7283910327.75521-df0dd9e4-b029-4f7b-b0df-488a352215cc) # Meta-Llama-3-8B-Instruct_fictional_arc_challenge_German_v2 This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 36 ### Training results ### Framework versions - Transformers 4.41.0 - Pytorch 2.1.0a0+32f93b1 - Datasets 2.19.1 - Tokenizers 0.19.1
uisikdag/vit-base-patch16-224-finetuned-lora-oxfordPets	uisikdag	2024-05-18T22:25:07Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-18T22:25:04Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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MisterRaven006/SweetNeural-7B	MisterRaven006	2024-05-18T22:22:11Z	8	0	transformers	[ "transformers", "safetensors", "gguf", "mistral", "text-generation", "mergekit", "merge", "conversational", "base_model:KatyTheCutie/LemonadeRP-4.5.3", "base_model:merge:KatyTheCutie/LemonadeRP-4.5.3", "base_model:mlabonne/NeuralBeagle14-7B", "base_model:merge:mlabonne/NeuralBeagle14-7B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T01:44:17Z	--- base_model: - KatyTheCutie/LemonadeRP-4.5.3 - mlabonne/NeuralBeagle14-7B library_name: transformers tags: - mergekit - merge license: apache-2.0 --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [KatyTheCutie/LemonadeRP-4.5.3](https://huggingface.co/KatyTheCutie/LemonadeRP-4.5.3) * [mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: mlabonne/NeuralBeagle14-7B layer_range: [0, 32] - model: KatyTheCutie/LemonadeRP-4.5.3 layer_range: [0, 32] merge_method: slerp base_model: mlabonne/NeuralBeagle14-7B parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ```
kakinola23/finetuned_llama3	kakinola23	2024-05-18T22:22:04Z	78	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-18T22:17:28Z	--- 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]
nickrwu/bigbird-roberta-large	nickrwu	2024-05-18T22:14:55Z	109	0	transformers	[ "transformers", "safetensors", "roberta", "multiple-choice", "generated_from_trainer", "base_model:LIAMF-USP/roberta-large-finetuned-race", "base_model:finetune:LIAMF-USP/roberta-large-finetuned-race", "license:mit", "endpoints_compatible", "region:us" ]	multiple-choice	2024-05-18T22:14:24Z	--- license: mit base_model: LIAMF-USP/roberta-large-finetuned-race tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: bigbird-roberta-large 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. --> # bigbird-roberta-large This model is a fine-tuned version of [LIAMF-USP/roberta-large-finetuned-race](https://huggingface.co/LIAMF-USP/roberta-large-finetuned-race) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6094 - Accuracy: 0.1976 - F1: 0.1757 - Precision: 0.1893 - Recall: 0.1911 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| 1.6272 \| 0.3233 \| 1200 \| 1.6094 \| 0.2082 \| 0.1431 \| 0.2007 \| 0.1996 \| \| 1.6218 \| 0.6466 \| 2400 \| 1.6094 \| 0.2117 \| 0.1340 \| 0.1876 \| 0.1998 \| \| 1.6235 \| 0.9698 \| 3600 \| 1.6094 \| 0.2104 \| 0.1752 \| 0.2005 \| 0.2015 \| \| 1.617 \| 1.2931 \| 4800 \| 1.6094 \| 0.2088 \| 0.1956 \| 0.2037 \| 0.2028 \| \| 1.61 \| 1.6164 \| 6000 \| 1.6094 \| 0.2091 \| 0.1606 \| 0.2127 \| 0.2024 \| \| 1.6126 \| 1.9397 \| 7200 \| 1.6094 \| 0.2108 \| 0.1796 \| 0.1965 \| 0.2011 \| \| 1.6174 \| 2.2629 \| 8400 \| 1.6094 \| 0.2095 \| 0.1833 \| 0.2036 \| 0.2024 \| \| 1.6125 \| 2.5862 \| 9600 \| 1.6094 \| 0.2097 \| 0.1847 \| 0.1963 \| 0.2016 \| \| 1.6192 \| 2.9095 \| 10800 \| 1.6094 \| 0.1976 \| 0.1757 \| 0.1893 \| 0.1911 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
apwic/sentiment-lora-r2a2d0.05-1	apwic	2024-05-18T22:11:34Z	0	0	null	[ "tensorboard", "generated_from_trainer", "id", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "region:us" ]	null	2024-05-18T21:38:24Z	--- language: - id license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: sentiment-lora-r2a2d0.05-1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # sentiment-lora-r2a2d0.05-1 This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3638 - Accuracy: 0.8446 - Precision: 0.8193 - Recall: 0.7951 - F1: 0.8055 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 30 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.5663 \| 1.0 \| 122 \| 0.5216 \| 0.7293 \| 0.6677 \| 0.6510 \| 0.6572 \| \| 0.5149 \| 2.0 \| 244 \| 0.5134 \| 0.7243 \| 0.6758 \| 0.6899 \| 0.6810 \| \| 0.4925 \| 3.0 \| 366 \| 0.4821 \| 0.7569 \| 0.7055 \| 0.6980 \| 0.7014 \| \| 0.4608 \| 4.0 \| 488 \| 0.4654 \| 0.7644 \| 0.7150 \| 0.7083 \| 0.7114 \| \| 0.4493 \| 5.0 \| 610 \| 0.4600 \| 0.7569 \| 0.7126 \| 0.7305 \| 0.7193 \| \| 0.4257 \| 6.0 \| 732 \| 0.4307 \| 0.7870 \| 0.7433 \| 0.7318 \| 0.7369 \| \| 0.4178 \| 7.0 \| 854 \| 0.4181 \| 0.7970 \| 0.7552 \| 0.7614 \| 0.7581 \| \| 0.3977 \| 8.0 \| 976 \| 0.3972 \| 0.8070 \| 0.7687 \| 0.7560 \| 0.7617 \| \| 0.3946 \| 9.0 \| 1098 \| 0.3937 \| 0.8145 \| 0.7779 \| 0.7663 \| 0.7716 \| \| 0.3762 \| 10.0 \| 1220 \| 0.3874 \| 0.8246 \| 0.7995 \| 0.7584 \| 0.7738 \| \| 0.3727 \| 11.0 \| 1342 \| 0.3787 \| 0.8321 \| 0.8014 \| 0.7837 \| 0.7915 \| \| 0.3626 \| 12.0 \| 1464 \| 0.3750 \| 0.8371 \| 0.8059 \| 0.7947 \| 0.7999 \| \| 0.359 \| 13.0 \| 1586 \| 0.3728 \| 0.8296 \| 0.8066 \| 0.7644 \| 0.7803 \| \| 0.3488 \| 14.0 \| 1708 \| 0.3709 \| 0.8296 \| 0.8049 \| 0.7669 \| 0.7816 \| \| 0.3445 \| 15.0 \| 1830 \| 0.3667 \| 0.8421 \| 0.8131 \| 0.7983 \| 0.8050 \| \| 0.3344 \| 16.0 \| 1952 \| 0.3656 \| 0.8421 \| 0.8142 \| 0.7958 \| 0.8040 \| \| 0.3339 \| 17.0 \| 2074 \| 0.3654 \| 0.8396 \| 0.8128 \| 0.7890 \| 0.7992 \| \| 0.3357 \| 18.0 \| 2196 \| 0.3638 \| 0.8421 \| 0.8154 \| 0.7933 \| 0.8029 \| \| 0.3357 \| 19.0 \| 2318 \| 0.3646 \| 0.8421 \| 0.8154 \| 0.7933 \| 0.8029 \| \| 0.3359 \| 20.0 \| 2440 \| 0.3638 \| 0.8446 \| 0.8193 \| 0.7951 \| 0.8055 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.15.2
kataragi/controlnet_canny	kataragi	2024-05-18T22:04:33Z	0	2	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-05-18T21:58:33Z	--- license: creativeml-openrail-m ---
DenBur/bert-mini-url	DenBur	2024-05-18T22:00:10Z	111	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:prajjwal1/bert-mini", "base_model:finetune:prajjwal1/bert-mini", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-18T19:11:44Z	--- license: mit base_model: prajjwal1/bert-mini tags: - generated_from_trainer metrics: - accuracy - precision - recall model-index: - name: bert-mini-url results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-mini-url This model is a fine-tuned version of [prajjwal1/bert-mini](https://huggingface.co/prajjwal1/bert-mini) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0565 - Accuracy: 0.9873 - Precision: 0.9848 - Recall: 0.9912 ## 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: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:--------:\|:---------:\|:------:\| \| 0.0644 \| 1.0 \| 32322 \| 0.0633 \| 0.9815 \| 0.9832 \| 0.9818 \| \| 0.0579 \| 2.0 \| 64644 \| 0.0572 \| 0.9853 \| 0.9818 \| 0.9906 \| \| 0.0485 \| 3.0 \| 96966 \| 0.0564 \| 0.9867 \| 0.9859 \| 0.9892 \| \| 0.0439 \| 4.0 \| 129288 \| 0.0565 \| 0.9873 \| 0.9848 \| 0.9912 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
AliSaadatV/virus_pythia_160_1024_2d_representation_GaussianPlusCE	AliSaadatV	2024-05-18T21:59:25Z	131	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "generated_from_trainer", "base_model:EleutherAI/pythia-160m", "base_model:finetune:EleutherAI/pythia-160m", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T21:59:12Z	--- license: apache-2.0 base_model: EleutherAI/pythia-160m tags: - generated_from_trainer model-index: - name: virus_pythia_160_1024_2d_representation_GaussianPlusCE 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. --> # virus_pythia_160_1024_2d_representation_GaussianPlusCE This model is a fine-tuned version of [EleutherAI/pythia-160m](https://huggingface.co/EleutherAI/pythia-160m) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
mrm8488/tinyllama-ft-en-es-rag-4bit-merge	mrm8488	2024-05-18T21:52:49Z	78	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:unsloth/tinyllama-bnb-4bit", "base_model:quantized:unsloth/tinyllama-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-18T21:52:25Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft base_model: unsloth/tinyllama-bnb-4bit --- # Uploaded model - Developed by: mrm8488 - License: apache-2.0 - Finetuned from model : unsloth/tinyllama-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)
ochafik/Nous-Hermes-2-Mixtral-8x7B-DPO-GGUF	ochafik	2024-05-18T21:50:35Z	0	1	null	[ "gguf", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-18T17:06:28Z	--- license: apache-2.0 --- This is a reconversion / quantization of https://huggingface.co/NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO There was a breaking change in llama.cpp's GGUF file format in https://github.com/ggerganov/llama.cpp/pull/6387 and the https://huggingface.co/NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO-GGUF repo hasn't been updated since. This prevents one to memory-map the model, causing it to take much longer to load than needed when the file is already in the IO cache.
EuphoriaReccords/JENNIEBP	EuphoriaReccords	2024-05-18T21:47:00Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-17T02:25:37Z	--- license: apache-2.0 ---
zsedrotam/llama2_cc	zsedrotam	2024-05-18T21:44:44Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]	null	2024-05-17T13:44:06Z	--- library_name: peft base_model: meta-llama/Llama-2-7b-hf --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.10.0
apwic/sentiment-lora-r2a1d0.15-1	apwic	2024-05-18T21:38:08Z	0	0	null	[ "tensorboard", "generated_from_trainer", "id", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "region:us" ]	null	2024-05-18T21:04:59Z	--- language: - id license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: sentiment-lora-r2a1d0.15-1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # sentiment-lora-r2a1d0.15-1 This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3633 - Accuracy: 0.8396 - Precision: 0.8128 - Recall: 0.7890 - F1: 0.7992 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 30 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.5664 \| 1.0 \| 122 \| 0.5221 \| 0.7218 \| 0.6580 \| 0.6432 \| 0.6487 \| \| 0.5148 \| 2.0 \| 244 \| 0.5111 \| 0.7243 \| 0.6758 \| 0.6899 \| 0.6810 \| \| 0.4924 \| 3.0 \| 366 \| 0.4791 \| 0.7444 \| 0.6884 \| 0.6741 \| 0.6799 \| \| 0.4615 \| 4.0 \| 488 \| 0.4651 \| 0.7644 \| 0.7148 \| 0.7058 \| 0.7099 \| \| 0.4516 \| 5.0 \| 610 \| 0.4581 \| 0.7644 \| 0.7214 \| 0.7408 \| 0.7286 \| \| 0.4291 \| 6.0 \| 732 \| 0.4295 \| 0.7895 \| 0.7462 \| 0.7385 \| 0.7421 \| \| 0.4194 \| 7.0 \| 854 \| 0.4191 \| 0.7995 \| 0.7581 \| 0.7606 \| 0.7593 \| \| 0.3994 \| 8.0 \| 976 \| 0.4048 \| 0.8120 \| 0.7745 \| 0.7645 \| 0.7691 \| \| 0.3919 \| 9.0 \| 1098 \| 0.3950 \| 0.8246 \| 0.7954 \| 0.7659 \| 0.7778 \| \| 0.3762 \| 10.0 \| 1220 \| 0.3881 \| 0.8271 \| 0.8022 \| 0.7626 \| 0.7777 \| \| 0.3704 \| 11.0 \| 1342 \| 0.3806 \| 0.8271 \| 0.7949 \| 0.7776 \| 0.7853 \| \| 0.3642 \| 12.0 \| 1464 \| 0.3733 \| 0.8421 \| 0.8122 \| 0.8008 \| 0.8061 \| \| 0.3614 \| 13.0 \| 1586 \| 0.3753 \| 0.8321 \| 0.8092 \| 0.7687 \| 0.7842 \| \| 0.3474 \| 14.0 \| 1708 \| 0.3695 \| 0.8396 \| 0.8155 \| 0.7840 \| 0.7969 \| \| 0.3479 \| 15.0 \| 1830 \| 0.3675 \| 0.8421 \| 0.8142 \| 0.7958 \| 0.8040 \| \| 0.3347 \| 16.0 \| 1952 \| 0.3649 \| 0.8421 \| 0.8142 \| 0.7958 \| 0.8040 \| \| 0.335 \| 17.0 \| 2074 \| 0.3653 \| 0.8371 \| 0.8114 \| 0.7822 \| 0.7943 \| \| 0.3361 \| 18.0 \| 2196 \| 0.3632 \| 0.8396 \| 0.8128 \| 0.7890 \| 0.7992 \| \| 0.3343 \| 19.0 \| 2318 \| 0.3636 \| 0.8371 \| 0.8114 \| 0.7822 \| 0.7943 \| \| 0.3347 \| 20.0 \| 2440 \| 0.3633 \| 0.8396 \| 0.8128 \| 0.7890 \| 0.7992 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.15.2
Ashleyinust/sentiment_analysis_2	Ashleyinust	2024-05-18T21:35:03Z	109	1	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-18T20:53:53Z	--- 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. 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long292/apply_back_translation_model_v5_1k	long292	2024-05-18T21:32:16Z	107	0	transformers	[ "transformers", "tensorboard", "safetensors", "mbart", "text2text-generation", "generated_from_trainer", "base_model:vinai/bartpho-syllable-base", "base_model:finetune:vinai/bartpho-syllable-base", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-18T21:31:57Z	--- base_model: vinai/bartpho-syllable-base tags: - generated_from_trainer metrics: - bleu model-index: - name: apply_back_translation_model_v5_1k 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. --> # apply_back_translation_model_v5_1k This model is a fine-tuned version of [vinai/bartpho-syllable-base](https://huggingface.co/vinai/bartpho-syllable-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8232 - Bleu: 9.4094 - Gen Len: 18.1144 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:-------:\| \| 2.0588 \| 1.0 \| 10727 \| 1.8232 \| 9.4094 \| 18.1144 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
HRSMalik/emotionmodel-DistilRoBerta	HRSMalik	2024-05-18T21:30:51Z	30	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-18T21:24:54Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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maneln/tinyllama-chat-1.1b-version	maneln	2024-05-18T21:25:33Z	130	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T17:15:46Z	--- license: apache-2.0 ---
tsavage68/MedQA_L3_500steps_1e7rate_SFT	tsavage68	2024-05-18T21:25:32Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:finetune:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T21:11:24Z	--- license: llama3 base_model: meta-llama/Meta-Llama-3-8B-Instruct tags: - trl - sft - generated_from_trainer model-index: - name: MedQA_L3_500steps_1e7rate_SFT 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. --> # MedQA_L3_500steps_1e7rate_SFT This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3157 ## 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-07 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - 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: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.774 \| 0.0489 \| 50 \| 1.7867 \| \| 1.7099 \| 0.0977 \| 100 \| 1.6989 \| \| 1.5892 \| 0.1466 \| 150 \| 1.5687 \| \| 1.4868 \| 0.1954 \| 200 \| 1.4685 \| \| 1.4001 \| 0.2443 \| 250 \| 1.3929 \| \| 1.3564 \| 0.2931 \| 300 \| 1.3457 \| \| 1.3261 \| 0.3420 \| 350 \| 1.3226 \| \| 1.3101 \| 0.3908 \| 400 \| 1.3163 \| \| 1.3032 \| 0.4397 \| 450 \| 1.3159 \| \| 1.3189 \| 0.4885 \| 500 \| 1.3157 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.0.0+cu117 - Datasets 2.19.1 - Tokenizers 0.19.1
amc5/q-Taxi-v3	amc5	2024-05-18T21:17:38Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-05-18T21:17:35Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.50 +/- 2.76 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="amc5/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
longma98/code-search-net-tokenizer	longma98	2024-05-18T21:12:49Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-18T21:12:48Z	--- 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]
mattyamonaca/controlnet_line2line_xl	mattyamonaca	2024-05-18T20:59:52Z	23	9	diffusers	[ "diffusers", "safetensors", "license:apache-2.0", "region:us" ]	null	2024-05-18T20:56:55Z	--- license: apache-2.0 ---
Alaninfant/OrpoLlama-2-7B	Alaninfant	2024-05-18T20:57:36Z	20	0	transformers	[ "transformers", "gguf", "llama", "unsloth", "llama ", "llama-2-7B", "GGUF ", "wandb", "text-generation", "en", "base_model:unsloth/llama-2-7b-bnb-4bit", "base_model:quantized:unsloth/llama-2-7b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T12:07:01Z	--- language: - en pipeline_tag: text-generation tags: - unsloth - 'llama ' - llama-2-7B - 'GGUF ' - wandb license: apache-2.0 base_model: unsloth/llama-2-7b-bnb-4bit --- # Uploaded model - Developed by: Alaninfant - License: apache-2.0 - Finetuned from model : unsloth/llama-2-7b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. ![](https://i.imgur.com/ZHwzQvI.png) [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
dannys160/SoccerTwos-up	dannys160	2024-05-18T20:51:50Z	4	0	ml-agents	[ "ml-agents", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	2024-05-18T20:33:16Z	--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # poca Agent playing SoccerTwos This is a trained model of a poca agent playing SoccerTwos using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: dannys160/SoccerTwos-up 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
RichardErkhov/aari1995_-_germeo-7b-laser-8bits	RichardErkhov	2024-05-18T20:49:25Z	78	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-18T20:39:08Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) germeo-7b-laser - bnb 8bits - Model creator: https://huggingface.co/aari1995/ - Original model: https://huggingface.co/aari1995/germeo-7b-laser/ Original model description: --- language: - de license: apache-2.0 tags: - hermeo - laser datasets: - LeoLM/OpenSchnabeltier pipeline_tag: conversational model-index: - name: germeo-7b-laser results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 60.75 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aari1995/germeo-7b-laser name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 82.81 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aari1995/germeo-7b-laser name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 60.57 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aari1995/germeo-7b-laser name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 53.83 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aari1995/germeo-7b-laser name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 75.61 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aari1995/germeo-7b-laser name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 43.37 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aari1995/germeo-7b-laser name: Open LLM Leaderboard --- (Evaluation WIP) ## Hermes + Leo + German Laser = Germeo ## Germeo-7B-Laser A German-English understanding, but German-only speaking model merged from Hermeo-7B. ### Model details Merged from: leo-mistral-hessianai-7b-chat and DPOpenHermes-7B-v2 Model type: Causal decoder-only transformer language model Languages: German replies with English Understanding Capabilities Laser-Data: LeoLM/OpenSchnabeltier This is an early experiment on laser and its influence on language understanding. It generally improves the language understanding capabilities. The hypothesis is that it degrades the probability of English replies and increasing those of German replies. The models internal German capabilities are boosted. Will keep you updated.. ### Acknowledgements: I would like to thank everyone that participated in making this model and its training possible: To [@malteos](https://huggingface.co/malteos) for hermeo To [@cognitivecomputations](https://huggingface.co/cognitivecomputations) and Fernando Fernandes Neto for their implementation of LASER To [@LeoLM](https://huggingface.co/LeoLM) and Björn for the OpenSchnabeltier dataset. ### Prompt format: ```python streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) # Convert prompt to tokens prompt_template = """<\|im_start\|>system Du bist ein hilfreicher Assistent.<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant""" prompt = "Schreibe eine Stellenanzeige für Data Scientist bei AXA!" final_prompt = prompt_template.format(prompt=prompt) ``` #### Limit the model to output reply-only: To solve this, you need to implement a custom stopping criteria: ```python from transformers import StoppingCriteria class GermeoStoppingCriteria(StoppingCriteria): def __init__(self, target_sequence, prompt): self.target_sequence = target_sequence self.prompt=prompt def __call__(self, input_ids, scores, kwargs): # Get the generated text as a string generated_text = tokenizer.decode(input_ids[0]) generated_text = generated_text.replace(self.prompt,'') # Check if the target sequence appears in the generated text if self.target_sequence in generated_text: return True # Stop generation return False # Continue generation def __len__(self): return 1 def __iter__(self): yield self ``` This then expects your input prompt (formatted as given into the model), and a stopping criteria, in this case the im_end token. Simply add it to the generation: ```python generation_output = model.generate( tokens, streamer=streamer, max_new_tokens=1012, stopping_criteria=GermeoStoppingCriteria("<\|im_end\|>", prompt_template.format(prompt=prompt)) ) ``` ### German benchmarks \| German tasks: \| MMLU-DE \| Hellaswag-DE \| ARC-DE \|Average \| \|-------------------------------\|-------------\|---------------\|--------------\|--------------\| \| Models / Few-shots: \| _(5 shots)_ \| _(10 shots)_ \| _(24 shots)_ \| \| \| _7B parameters_ \| \| \| \| \| \| llama-2-7b \| 0.400 \| 0.513 \| 0.381 \| 0.431 \| \| leo-hessianai-7b \| 0.400 \| 0.609 \| 0.429 \| 0.479 \| \| bloom-6b4-clp-german \| 0.274 \| 0.550 \| 0.351 \| 0.392 \| \| mistral-7b \| 0.524 \| 0.588 \| 0.473 \| 0.528 \| \| leo-mistral-hessianai-7b \| 0.481 \| 0.663 \| 0.485 \| 0.543 \| \| leo-mistral-hessianai-7b-chat \| 0.458 \| 0.617 \| 0.465 \| 0.513 \| \| DPOpenHermes-7B-v2 \| 0.517 \| 0.603 \| 0.515 \| 0.545 \| \| hermeo-7b \| 0.511 \| 0.668 \| 0.528 \| 0.569 \| \| germeo-7b-laser (this model)\| ? \| ? \| ? \| ? \| \| _13B parameters_ \| \| \| \| \| \| llama-2-13b \| 0.469 \| 0.581 \| 0.468 \| 0.506 \| \| leo-hessianai-13b \| 0.486 \| 0.658 \| 0.509 \| 0.551 \| \| _70B parameters_ \| \| \| \| \| \| llama-2-70b \| 0.597 \| 0.674 \| 0.561 \| 0.611 \| \| leo-hessianai-70b \| 0.653 \| 0.721 \| 0.600 \| 0.658 \| Even though the model does not generate English text without being explicitly asked, performance on English Benchmarks is still up: ### English benchmarks \| English tasks: \| MMLU \| Hellaswag \| ARC \| Average \| \|------------------------------------\|-------------\|---------------\|--------------\|-------------\| \| Models / Few-shots: \| _(5 shots)_ \| _(10 shots)_ \| _(24 shots)_ \| \| \| llama-2-7b \| 0.466 \| 0.786 \| 0.530 \| 0.594 \| \| leolm-hessianai-7b \| 0.423 \| 0.759 \| 0.522 \| 0.568 \| \| bloom-6b4-clp-german \| 0.264 \| 0.525 \| 0.328 \| 0.372 \| \| mistral-7b \| 0.635 \| 0.832 \| 0.607 \| 0.691 \| \| leolm-mistral-hessianai-7b \| 0.550 \| 0.777 \| 0.518 \| 0.615 \| \| hermeo-7b \| 0.601 \| 0.821 \| 0.620** \| 0.681 \| \| germeo-7b-laser (this model) \| 0.601 \| 0.828 \| 0.608 \| 0.679 \| # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_aari1995__germeo-7b-laser) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|62.82\| \|AI2 Reasoning Challenge (25-Shot)\|60.75\| \|HellaSwag (10-Shot) \|82.81\| \|MMLU (5-Shot) \|60.57\| \|TruthfulQA (0-shot) \|53.83\| \|Winogrande (5-shot) \|75.61\| \|GSM8k (5-shot) \|43.37\|
uaritm/Meta-Llama-3-8B-GPTQ	uaritm	2024-05-18T20:48:33Z	5	0	transformers	[ "transformers", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2024-05-17T21:56:03Z	--- 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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RichardErkhov/aari1995_-_germeo-7b-laser-4bits	RichardErkhov	2024-05-18T20:38:30Z	80	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-18T20:31:03Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) germeo-7b-laser - bnb 4bits - Model creator: https://huggingface.co/aari1995/ - Original model: https://huggingface.co/aari1995/germeo-7b-laser/ Original model description: --- language: - de license: apache-2.0 tags: - hermeo - laser datasets: - LeoLM/OpenSchnabeltier pipeline_tag: conversational model-index: - name: germeo-7b-laser results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 60.75 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aari1995/germeo-7b-laser name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 82.81 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aari1995/germeo-7b-laser name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 60.57 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aari1995/germeo-7b-laser name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 53.83 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aari1995/germeo-7b-laser name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 75.61 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aari1995/germeo-7b-laser name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 43.37 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aari1995/germeo-7b-laser name: Open LLM Leaderboard --- (Evaluation WIP) ## Hermes + Leo + German Laser = Germeo ## Germeo-7B-Laser A German-English understanding, but German-only speaking model merged from Hermeo-7B. ### Model details Merged from: leo-mistral-hessianai-7b-chat and DPOpenHermes-7B-v2 Model type: Causal decoder-only transformer language model Languages: German replies with English Understanding Capabilities Laser-Data: LeoLM/OpenSchnabeltier This is an early experiment on laser and its influence on language understanding. It generally improves the language understanding capabilities. The hypothesis is that it degrades the probability of English replies and increasing those of German replies. The models internal German capabilities are boosted. Will keep you updated.. ### Acknowledgements: I would like to thank everyone that participated in making this model and its training possible: To [@malteos](https://huggingface.co/malteos) for hermeo To [@cognitivecomputations](https://huggingface.co/cognitivecomputations) and Fernando Fernandes Neto for their implementation of LASER To [@LeoLM](https://huggingface.co/LeoLM) and Björn for the OpenSchnabeltier dataset. ### Prompt format: ```python streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) # Convert prompt to tokens prompt_template = """<\|im_start\|>system Du bist ein hilfreicher Assistent.<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant""" prompt = "Schreibe eine Stellenanzeige für Data Scientist bei AXA!" final_prompt = prompt_template.format(prompt=prompt) ``` #### Limit the model to output reply-only: To solve this, you need to implement a custom stopping criteria: ```python from transformers import StoppingCriteria class GermeoStoppingCriteria(StoppingCriteria): def __init__(self, target_sequence, prompt): self.target_sequence = target_sequence self.prompt=prompt def __call__(self, input_ids, scores, kwargs): # Get the generated text as a string generated_text = tokenizer.decode(input_ids[0]) generated_text = generated_text.replace(self.prompt,'') # Check if the target sequence appears in the generated text if self.target_sequence in generated_text: return True # Stop generation return False # Continue generation def __len__(self): return 1 def __iter__(self): yield self ``` This then expects your input prompt (formatted as given into the model), and a stopping criteria, in this case the im_end token. Simply add it to the generation: ```python generation_output = model.generate( tokens, streamer=streamer, max_new_tokens=1012, stopping_criteria=GermeoStoppingCriteria("<\|im_end\|>", prompt_template.format(prompt=prompt)) ) ``` ### German benchmarks \| German tasks: \| MMLU-DE \| Hellaswag-DE \| ARC-DE \|Average \| \|-------------------------------\|-------------\|---------------\|--------------\|--------------\| \| Models / Few-shots: \| _(5 shots)_ \| _(10 shots)_ \| _(24 shots)_ \| \| \| _7B parameters_ \| \| \| \| \| \| llama-2-7b \| 0.400 \| 0.513 \| 0.381 \| 0.431 \| \| leo-hessianai-7b \| 0.400 \| 0.609 \| 0.429 \| 0.479 \| \| bloom-6b4-clp-german \| 0.274 \| 0.550 \| 0.351 \| 0.392 \| \| mistral-7b \| 0.524 \| 0.588 \| 0.473 \| 0.528 \| \| leo-mistral-hessianai-7b \| 0.481 \| 0.663 \| 0.485 \| 0.543 \| \| leo-mistral-hessianai-7b-chat \| 0.458 \| 0.617 \| 0.465 \| 0.513 \| \| DPOpenHermes-7B-v2 \| 0.517 \| 0.603 \| 0.515 \| 0.545 \| \| hermeo-7b \| 0.511 \| 0.668 \| 0.528 \| 0.569 \| \| germeo-7b-laser (this model)\| ? \| ? \| ? \| ? \| \| _13B parameters_ \| \| \| \| \| \| llama-2-13b \| 0.469 \| 0.581 \| 0.468 \| 0.506 \| \| leo-hessianai-13b \| 0.486 \| 0.658 \| 0.509 \| 0.551 \| \| _70B parameters_ \| \| \| \| \| \| llama-2-70b \| 0.597 \| 0.674 \| 0.561 \| 0.611 \| \| leo-hessianai-70b \| 0.653 \| 0.721 \| 0.600 \| 0.658 \| Even though the model does not generate English text without being explicitly asked, performance on English Benchmarks is still up: ### English benchmarks \| English tasks: \| MMLU \| Hellaswag \| ARC \| Average \| \|------------------------------------\|-------------\|---------------\|--------------\|-------------\| \| Models / Few-shots: \| _(5 shots)_ \| _(10 shots)_ \| _(24 shots)_ \| \| \| llama-2-7b \| 0.466 \| 0.786 \| 0.530 \| 0.594 \| \| leolm-hessianai-7b \| 0.423 \| 0.759 \| 0.522 \| 0.568 \| \| bloom-6b4-clp-german \| 0.264 \| 0.525 \| 0.328 \| 0.372 \| \| mistral-7b \| 0.635 \| 0.832 \| 0.607 \| 0.691 \| \| leolm-mistral-hessianai-7b \| 0.550 \| 0.777 \| 0.518 \| 0.615 \| \| hermeo-7b \| 0.601 \| 0.821 \| 0.620** \| 0.681 \| \| germeo-7b-laser (this model) \| 0.601 \| 0.828 \| 0.608 \| 0.679 \| # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_aari1995__germeo-7b-laser) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|62.82\| \|AI2 Reasoning Challenge (25-Shot)\|60.75\| \|HellaSwag (10-Shot) \|82.81\| \|MMLU (5-Shot) \|60.57\| \|TruthfulQA (0-shot) \|53.83\| \|Winogrande (5-shot) \|75.61\| \|GSM8k (5-shot) \|43.37\|
apwic/sentiment-lora-r2a1d0.05-1	apwic	2024-05-18T20:31:17Z	0	0	null	[ "tensorboard", "generated_from_trainer", "id", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "region:us" ]	null	2024-05-18T19:58:06Z	--- language: - id license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: sentiment-lora-r2a1d0.05-1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # sentiment-lora-r2a1d0.05-1 This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3638 - Accuracy: 0.8446 - Precision: 0.8193 - Recall: 0.7951 - F1: 0.8055 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 30 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.5663 \| 1.0 \| 122 \| 0.5216 \| 0.7293 \| 0.6677 \| 0.6510 \| 0.6572 \| \| 0.5149 \| 2.0 \| 244 \| 0.5134 \| 0.7243 \| 0.6758 \| 0.6899 \| 0.6810 \| \| 0.4925 \| 3.0 \| 366 \| 0.4821 \| 0.7569 \| 0.7055 \| 0.6980 \| 0.7014 \| \| 0.4608 \| 4.0 \| 488 \| 0.4654 \| 0.7644 \| 0.7150 \| 0.7083 \| 0.7114 \| \| 0.4493 \| 5.0 \| 610 \| 0.4600 \| 0.7569 \| 0.7126 \| 0.7305 \| 0.7193 \| \| 0.4257 \| 6.0 \| 732 \| 0.4307 \| 0.7870 \| 0.7433 \| 0.7318 \| 0.7369 \| \| 0.4178 \| 7.0 \| 854 \| 0.4181 \| 0.7970 \| 0.7552 \| 0.7614 \| 0.7581 \| \| 0.3977 \| 8.0 \| 976 \| 0.3972 \| 0.8070 \| 0.7687 \| 0.7560 \| 0.7617 \| \| 0.3946 \| 9.0 \| 1098 \| 0.3937 \| 0.8145 \| 0.7779 \| 0.7663 \| 0.7716 \| \| 0.3762 \| 10.0 \| 1220 \| 0.3874 \| 0.8246 \| 0.7995 \| 0.7584 \| 0.7738 \| \| 0.3727 \| 11.0 \| 1342 \| 0.3787 \| 0.8321 \| 0.8014 \| 0.7837 \| 0.7915 \| \| 0.3626 \| 12.0 \| 1464 \| 0.3750 \| 0.8371 \| 0.8059 \| 0.7947 \| 0.7999 \| \| 0.359 \| 13.0 \| 1586 \| 0.3728 \| 0.8296 \| 0.8066 \| 0.7644 \| 0.7803 \| \| 0.3488 \| 14.0 \| 1708 \| 0.3709 \| 0.8296 \| 0.8049 \| 0.7669 \| 0.7816 \| \| 0.3445 \| 15.0 \| 1830 \| 0.3667 \| 0.8421 \| 0.8131 \| 0.7983 \| 0.8050 \| \| 0.3344 \| 16.0 \| 1952 \| 0.3656 \| 0.8421 \| 0.8142 \| 0.7958 \| 0.8040 \| \| 0.3339 \| 17.0 \| 2074 \| 0.3654 \| 0.8396 \| 0.8128 \| 0.7890 \| 0.7992 \| \| 0.3357 \| 18.0 \| 2196 \| 0.3638 \| 0.8421 \| 0.8154 \| 0.7933 \| 0.8029 \| \| 0.3357 \| 19.0 \| 2318 \| 0.3646 \| 0.8421 \| 0.8154 \| 0.7933 \| 0.8029 \| \| 0.3359 \| 20.0 \| 2440 \| 0.3638 \| 0.8446 \| 0.8193 \| 0.7951 \| 0.8055 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.15.2
RichardErkhov/alnrg2arg_-_blockchainlabs_7B_merged_test2_4_prune-gguf	RichardErkhov	2024-05-18T20:27:18Z	13	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-18T18:09:29Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) blockchainlabs_7B_merged_test2_4_prune - GGUF - Model creator: https://huggingface.co/alnrg2arg/ - Original model: https://huggingface.co/alnrg2arg/blockchainlabs_7B_merged_test2_4_prune/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [blockchainlabs_7B_merged_test2_4_prune.Q2_K.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_7B_merged_test2_4_prune-gguf/blob/main/blockchainlabs_7B_merged_test2_4_prune.Q2_K.gguf) \| Q2_K \| 2.53GB \| \| [blockchainlabs_7B_merged_test2_4_prune.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_7B_merged_test2_4_prune-gguf/blob/main/blockchainlabs_7B_merged_test2_4_prune.IQ3_XS.gguf) \| IQ3_XS \| 2.81GB \| \| [blockchainlabs_7B_merged_test2_4_prune.IQ3_S.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_7B_merged_test2_4_prune-gguf/blob/main/blockchainlabs_7B_merged_test2_4_prune.IQ3_S.gguf) \| IQ3_S \| 2.96GB \| \| [blockchainlabs_7B_merged_test2_4_prune.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_7B_merged_test2_4_prune-gguf/blob/main/blockchainlabs_7B_merged_test2_4_prune.Q3_K_S.gguf) \| Q3_K_S \| 2.95GB \| \| [blockchainlabs_7B_merged_test2_4_prune.IQ3_M.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_7B_merged_test2_4_prune-gguf/blob/main/blockchainlabs_7B_merged_test2_4_prune.IQ3_M.gguf) \| IQ3_M \| 3.06GB \| \| [blockchainlabs_7B_merged_test2_4_prune.Q3_K.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_7B_merged_test2_4_prune-gguf/blob/main/blockchainlabs_7B_merged_test2_4_prune.Q3_K.gguf) \| Q3_K \| 3.28GB \| \| [blockchainlabs_7B_merged_test2_4_prune.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_7B_merged_test2_4_prune-gguf/blob/main/blockchainlabs_7B_merged_test2_4_prune.Q3_K_M.gguf) \| Q3_K_M \| 3.28GB \| \| [blockchainlabs_7B_merged_test2_4_prune.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_7B_merged_test2_4_prune-gguf/blob/main/blockchainlabs_7B_merged_test2_4_prune.Q3_K_L.gguf) \| Q3_K_L \| 3.56GB \| \| [blockchainlabs_7B_merged_test2_4_prune.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_7B_merged_test2_4_prune-gguf/blob/main/blockchainlabs_7B_merged_test2_4_prune.IQ4_XS.gguf) \| IQ4_XS \| 3.67GB \| \| [blockchainlabs_7B_merged_test2_4_prune.Q4_0.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_7B_merged_test2_4_prune-gguf/blob/main/blockchainlabs_7B_merged_test2_4_prune.Q4_0.gguf) \| Q4_0 \| 3.83GB \| \| [blockchainlabs_7B_merged_test2_4_prune.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_7B_merged_test2_4_prune-gguf/blob/main/blockchainlabs_7B_merged_test2_4_prune.IQ4_NL.gguf) \| IQ4_NL \| 3.87GB \| \| [blockchainlabs_7B_merged_test2_4_prune.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_7B_merged_test2_4_prune-gguf/blob/main/blockchainlabs_7B_merged_test2_4_prune.Q4_K_S.gguf) \| Q4_K_S \| 3.86GB \| \| [blockchainlabs_7B_merged_test2_4_prune.Q4_K.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_7B_merged_test2_4_prune-gguf/blob/main/blockchainlabs_7B_merged_test2_4_prune.Q4_K.gguf) \| Q4_K \| 4.07GB \| \| [blockchainlabs_7B_merged_test2_4_prune.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_7B_merged_test2_4_prune-gguf/blob/main/blockchainlabs_7B_merged_test2_4_prune.Q4_K_M.gguf) \| Q4_K_M \| 4.07GB \| \| [blockchainlabs_7B_merged_test2_4_prune.Q4_1.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_7B_merged_test2_4_prune-gguf/blob/main/blockchainlabs_7B_merged_test2_4_prune.Q4_1.gguf) \| Q4_1 \| 4.24GB \| \| [blockchainlabs_7B_merged_test2_4_prune.Q5_0.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_7B_merged_test2_4_prune-gguf/blob/main/blockchainlabs_7B_merged_test2_4_prune.Q5_0.gguf) \| Q5_0 \| 4.65GB \| \| [blockchainlabs_7B_merged_test2_4_prune.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_7B_merged_test2_4_prune-gguf/blob/main/blockchainlabs_7B_merged_test2_4_prune.Q5_K_S.gguf) \| Q5_K_S \| 4.65GB \| \| [blockchainlabs_7B_merged_test2_4_prune.Q5_K.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_7B_merged_test2_4_prune-gguf/blob/main/blockchainlabs_7B_merged_test2_4_prune.Q5_K.gguf) \| Q5_K \| 4.78GB \| \| [blockchainlabs_7B_merged_test2_4_prune.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_7B_merged_test2_4_prune-gguf/blob/main/blockchainlabs_7B_merged_test2_4_prune.Q5_K_M.gguf) \| Q5_K_M \| 4.78GB \| \| [blockchainlabs_7B_merged_test2_4_prune.Q5_1.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_7B_merged_test2_4_prune-gguf/blob/main/blockchainlabs_7B_merged_test2_4_prune.Q5_1.gguf) \| Q5_1 \| 5.07GB \| \| [blockchainlabs_7B_merged_test2_4_prune.Q6_K.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_7B_merged_test2_4_prune-gguf/blob/main/blockchainlabs_7B_merged_test2_4_prune.Q6_K.gguf) \| Q6_K \| 5.53GB \| \| [blockchainlabs_7B_merged_test2_4_prune.Q8_0.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_7B_merged_test2_4_prune-gguf/blob/main/blockchainlabs_7B_merged_test2_4_prune.Q8_0.gguf) \| Q8_0 \| 7.17GB \| Original model description: --- license: cc-by-nc-4.0 tags: - merge - mergekit - lazymergekit - pruning - alnrg2arg/blockchainlabs_7B_merged_test2_4 - mlabonne/NeuralBeagle14-7B - udkai/Turdus --- # blockchainlabs_7B_merged_test2_4_prune blockchainlabs_7B_merged_test2_4_prune is a pruned model based on alnrg2arg/blockchainlabs_7B_merged_test2_4, which is a merged model using following models using [mergekit](https://github.com/cg123/mergekit): * [mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B) * [udkai/Turdus](https://huggingface.co/udkai/Turdus) Pruning Kit I used: [wanda](https://github.com/locuslab/wanda?tab=readme-ov-file#ablation-on-obs-weight-update) ## 🧩 Configuration ```json { "_name_or_path": "alnrg2arg/blockchainlabs_7B_merged_test2_4_prun", "architectures": [ "MistralForCausalLM" ], "attention_dropout": 0.0, "bos_token_id": 1, "eos_token_id": 2, "hidden_act": "silu", "hidden_size": 4096, "initializer_range": 0.02, "intermediate_size": 14336, "max_position_embeddings": 32768, "model_type": "mistral", "num_attention_heads": 32, "num_hidden_layers": 32, "num_key_value_heads": 8, "rms_norm_eps": 1e-05, "rope_theta": 10000.0, "sliding_window": 4096, "tie_word_embeddings": false, "torch_dtype": "float16", "transformers_version": "4.36.2", "use_cache": false, "vocab_size": 32000 } ```
tsavage68/MedQA_L3_100steps_1e6rate_SFT	tsavage68	2024-05-18T20:21:41Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:finetune:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T20:17:13Z	--- license: llama3 base_model: meta-llama/Meta-Llama-3-8B-Instruct tags: - trl - sft - generated_from_trainer model-index: - name: MedQA_L3_100steps_1e6rate_SFT 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. --> # MedQA_L3_100steps_1e6rate_SFT This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4119 ## 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: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - 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: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.0895 \| 0.0489 \| 50 \| 0.8521 \| \| 0.3865 \| 0.0977 \| 100 \| 0.4119 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.0.0+cu117 - Datasets 2.19.1 - Tokenizers 0.19.1
tsavage68/MedQA_L3_1000steps_1e8rate_SFT	tsavage68	2024-05-18T20:13:33Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:finetune:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T20:09:06Z	--- license: llama3 base_model: meta-llama/Meta-Llama-3-8B-Instruct tags: - trl - sft - generated_from_trainer model-index: - name: MedQA_L3_1000steps_1e8rate_SFT 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. --> # MedQA_L3_1000steps_1e8rate_SFT This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.7989 ## 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-08 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - 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: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.783 \| 0.0489 \| 50 \| 1.7997 \| \| 1.7968 \| 0.0977 \| 100 \| 1.7995 \| \| 1.8022 \| 0.1466 \| 150 \| 1.7997 \| \| 1.7968 \| 0.1954 \| 200 \| 1.7993 \| \| 1.7998 \| 0.2443 \| 250 \| 1.7989 \| \| 1.7963 \| 0.2931 \| 300 \| 1.7989 \| \| 1.7977 \| 0.3420 \| 350 \| 1.7992 \| \| 1.7971 \| 0.3908 \| 400 \| 1.7991 \| \| 1.7697 \| 0.4397 \| 450 \| 1.7990 \| \| 1.8021 \| 0.4885 \| 500 \| 1.7990 \| \| 1.7897 \| 0.5374 \| 550 \| 1.7988 \| \| 1.7817 \| 0.5862 \| 600 \| 1.7988 \| \| 1.812 \| 0.6351 \| 650 \| 1.7987 \| \| 1.7939 \| 0.6839 \| 700 \| 1.7989 \| \| 1.815 \| 0.7328 \| 750 \| 1.7989 \| \| 1.7991 \| 0.7816 \| 800 \| 1.7989 \| \| 1.8164 \| 0.8305 \| 850 \| 1.7989 \| \| 1.8062 \| 0.8793 \| 900 \| 1.7989 \| \| 1.8048 \| 0.9282 \| 950 \| 1.7989 \| \| 1.8103 \| 0.9770 \| 1000 \| 1.7989 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.0.0+cu117 - Datasets 2.19.1 - Tokenizers 0.19.1
emilykang/Phi_medmcqa_question_generation-pathology_lora	emilykang	2024-05-18T20:13:07Z	1	0	peft	[ "peft", "safetensors", "phi", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:microsoft/phi-2", "base_model:adapter:microsoft/phi-2", "license:mit", "region:us" ]	null	2024-05-18T18:35:56Z	--- license: mit library_name: peft tags: - trl - sft - generated_from_trainer base_model: microsoft/phi-2 datasets: - generator model-index: - name: Phi_medmcqa_question_generation-pathology_lora results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Phi_medmcqa_question_generation-pathology_lora This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - 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 - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1
ibahri/pearson-bge-m3	ibahri	2024-05-18T20:08:29Z	6	0	sentence-transformers	[ "sentence-transformers", "safetensors", "xlm-roberta", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-05-18T19:56:00Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # ibahri/pearson-bge-m3 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('ibahri/pearson-bge-m3') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name=ibahri/pearson-bge-m3) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 14 with parameters: ``` {'batch_size': 10, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 2, "evaluation_steps": 50, "evaluator": "sentence_transformers.evaluation.InformationRetrievalEvaluator.InformationRetrievalEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 2, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
tsavage68/MedQA_L3_1000steps_1e6rate_SFT	tsavage68	2024-05-18T20:05:18Z	7	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:finetune:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T20:01:28Z	--- license: llama3 base_model: meta-llama/Meta-Llama-3-8B-Instruct tags: - trl - sft - generated_from_trainer model-index: - name: MedQA_L3_1000steps_1e6rate_SFT 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. --> # MedQA_L3_1000steps_1e6rate_SFT This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3666 ## 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: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - 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: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.0895 \| 0.0489 \| 50 \| 0.8521 \| \| 0.3865 \| 0.0977 \| 100 \| 0.4119 \| \| 0.4156 \| 0.1466 \| 150 \| 0.3943 \| \| 0.4302 \| 0.1954 \| 200 \| 0.3870 \| \| 0.3788 \| 0.2443 \| 250 \| 0.3808 \| \| 0.3964 \| 0.2931 \| 300 \| 0.3773 \| \| 0.3753 \| 0.3420 \| 350 \| 0.3749 \| \| 0.359 \| 0.3908 \| 400 \| 0.3727 \| \| 0.3874 \| 0.4397 \| 450 \| 0.3711 \| \| 0.3722 \| 0.4885 \| 500 \| 0.3699 \| \| 0.3615 \| 0.5374 \| 550 \| 0.3686 \| \| 0.3807 \| 0.5862 \| 600 \| 0.3677 \| \| 0.3643 \| 0.6351 \| 650 \| 0.3673 \| \| 0.3513 \| 0.6839 \| 700 \| 0.3669 \| \| 0.358 \| 0.7328 \| 750 \| 0.3667 \| \| 0.3648 \| 0.7816 \| 800 \| 0.3666 \| \| 0.3911 \| 0.8305 \| 850 \| 0.3666 \| \| 0.3475 \| 0.8793 \| 900 \| 0.3666 \| \| 0.3511 \| 0.9282 \| 950 \| 0.3665 \| \| 0.3673 \| 0.9770 \| 1000 \| 0.3666 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.0.0+cu117 - Datasets 2.19.1 - Tokenizers 0.19.1
apwic/sentiment-lora-r2a0d0.15-1	apwic	2024-05-18T19:57:50Z	0	0	null	[ "tensorboard", "generated_from_trainer", "id", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "region:us" ]	null	2024-05-18T19:24:41Z	--- language: - id license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: sentiment-lora-r2a0d0.15-1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # sentiment-lora-r2a0d0.15-1 This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3633 - Accuracy: 0.8396 - Precision: 0.8128 - Recall: 0.7890 - F1: 0.7992 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 30 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.5664 \| 1.0 \| 122 \| 0.5221 \| 0.7218 \| 0.6580 \| 0.6432 \| 0.6487 \| \| 0.5148 \| 2.0 \| 244 \| 0.5111 \| 0.7243 \| 0.6758 \| 0.6899 \| 0.6810 \| \| 0.4924 \| 3.0 \| 366 \| 0.4791 \| 0.7444 \| 0.6884 \| 0.6741 \| 0.6799 \| \| 0.4615 \| 4.0 \| 488 \| 0.4651 \| 0.7644 \| 0.7148 \| 0.7058 \| 0.7099 \| \| 0.4516 \| 5.0 \| 610 \| 0.4581 \| 0.7644 \| 0.7214 \| 0.7408 \| 0.7286 \| \| 0.4291 \| 6.0 \| 732 \| 0.4295 \| 0.7895 \| 0.7462 \| 0.7385 \| 0.7421 \| \| 0.4194 \| 7.0 \| 854 \| 0.4191 \| 0.7995 \| 0.7581 \| 0.7606 \| 0.7593 \| \| 0.3994 \| 8.0 \| 976 \| 0.4048 \| 0.8120 \| 0.7745 \| 0.7645 \| 0.7691 \| \| 0.3919 \| 9.0 \| 1098 \| 0.3950 \| 0.8246 \| 0.7954 \| 0.7659 \| 0.7778 \| \| 0.3762 \| 10.0 \| 1220 \| 0.3881 \| 0.8271 \| 0.8022 \| 0.7626 \| 0.7777 \| \| 0.3704 \| 11.0 \| 1342 \| 0.3806 \| 0.8271 \| 0.7949 \| 0.7776 \| 0.7853 \| \| 0.3642 \| 12.0 \| 1464 \| 0.3733 \| 0.8421 \| 0.8122 \| 0.8008 \| 0.8061 \| \| 0.3614 \| 13.0 \| 1586 \| 0.3753 \| 0.8321 \| 0.8092 \| 0.7687 \| 0.7842 \| \| 0.3474 \| 14.0 \| 1708 \| 0.3695 \| 0.8396 \| 0.8155 \| 0.7840 \| 0.7969 \| \| 0.3479 \| 15.0 \| 1830 \| 0.3675 \| 0.8421 \| 0.8142 \| 0.7958 \| 0.8040 \| \| 0.3347 \| 16.0 \| 1952 \| 0.3649 \| 0.8421 \| 0.8142 \| 0.7958 \| 0.8040 \| \| 0.335 \| 17.0 \| 2074 \| 0.3653 \| 0.8371 \| 0.8114 \| 0.7822 \| 0.7943 \| \| 0.3361 \| 18.0 \| 2196 \| 0.3632 \| 0.8396 \| 0.8128 \| 0.7890 \| 0.7992 \| \| 0.3343 \| 19.0 \| 2318 \| 0.3636 \| 0.8371 \| 0.8114 \| 0.7822 \| 0.7943 \| \| 0.3347 \| 20.0 \| 2440 \| 0.3633 \| 0.8396 \| 0.8128 \| 0.7890 \| 0.7992 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.15.2
Felladrin/gguf-sharded-zephyr-1b-olmo-sft-qlora	Felladrin	2024-05-18T19:56:22Z	6	0	null	[ "gguf", "base_model:Ritvik19/zephyr-1b-olmo-sft-qlora", "base_model:quantized:Ritvik19/zephyr-1b-olmo-sft-qlora", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-18T19:52:03Z	--- license: apache-2.0 base_model: Ritvik19/zephyr-1b-olmo-sft-qlora --- Sharded GGUF version of [Ritvik19/zephyr-1b-olmo-sft-qlora](https://huggingface.co/Ritvik19/zephyr-1b-olmo-sft-qlora).
elloco123/ppo-LunarLander-v2	elloco123	2024-05-18T19:49:51Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-18T19:49:31Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 266.36 +/- 17.08 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
Felladrin/gguf-zephyr-1b-olmo-sft-qlora	Felladrin	2024-05-18T19:48:05Z	11	0	null	[ "gguf", "base_model:Ritvik19/zephyr-1b-olmo-sft-qlora", "base_model:quantized:Ritvik19/zephyr-1b-olmo-sft-qlora", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-18T18:55:03Z	--- license: apache-2.0 base_model: Ritvik19/zephyr-1b-olmo-sft-qlora --- GGUF version of [Ritvik19/zephyr-1b-olmo-sft-qlora](https://huggingface.co/Ritvik19/zephyr-1b-olmo-sft-qlora).
Felladrin/gguf-openhermes-1b-olmo-sft-qlora	Felladrin	2024-05-18T19:47:00Z	5	0	null	[ "gguf", "base_model:Ritvik19/openhermes-1b-olmo-sft-qlora", "base_model:quantized:Ritvik19/openhermes-1b-olmo-sft-qlora", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-18T19:22:29Z	--- license: apache-2.0 base_model: Ritvik19/openhermes-1b-olmo-sft-qlora --- GGUF version of [Ritvik19/openhermes-1b-olmo-sft-qlora](https://huggingface.co/Ritvik19/openhermes-1b-olmo-sft-qlora).
RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-gguf	RichardErkhov	2024-05-18T19:45:56Z	30	1	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2024-05-18T17:14:46Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) LogoS-7Bx2-MoE-13B-v0.2 - GGUF - Model creator: https://huggingface.co/RubielLabarta/ - Original model: https://huggingface.co/RubielLabarta/LogoS-7Bx2-MoE-13B-v0.2/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [LogoS-7Bx2-MoE-13B-v0.2.Q2_K.gguf](https://huggingface.co/RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-gguf/blob/main/LogoS-7Bx2-MoE-13B-v0.2.Q2_K.gguf) \| Q2_K \| 4.43GB \| \| [LogoS-7Bx2-MoE-13B-v0.2.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-gguf/blob/main/LogoS-7Bx2-MoE-13B-v0.2.IQ3_XS.gguf) \| IQ3_XS \| 4.94GB \| \| [LogoS-7Bx2-MoE-13B-v0.2.IQ3_S.gguf](https://huggingface.co/RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-gguf/blob/main/LogoS-7Bx2-MoE-13B-v0.2.IQ3_S.gguf) \| IQ3_S \| 5.22GB \| \| [LogoS-7Bx2-MoE-13B-v0.2.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-gguf/blob/main/LogoS-7Bx2-MoE-13B-v0.2.Q3_K_S.gguf) \| Q3_K_S \| 5.2GB \| \| [LogoS-7Bx2-MoE-13B-v0.2.IQ3_M.gguf](https://huggingface.co/RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-gguf/blob/main/LogoS-7Bx2-MoE-13B-v0.2.IQ3_M.gguf) \| IQ3_M \| 5.34GB \| \| [LogoS-7Bx2-MoE-13B-v0.2.Q3_K.gguf](https://huggingface.co/RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-gguf/blob/main/LogoS-7Bx2-MoE-13B-v0.2.Q3_K.gguf) \| Q3_K \| 5.78GB \| \| [LogoS-7Bx2-MoE-13B-v0.2.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-gguf/blob/main/LogoS-7Bx2-MoE-13B-v0.2.Q3_K_M.gguf) \| Q3_K_M \| 5.78GB \| \| [LogoS-7Bx2-MoE-13B-v0.2.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-gguf/blob/main/LogoS-7Bx2-MoE-13B-v0.2.Q3_K_L.gguf) \| Q3_K_L \| 6.27GB \| \| [LogoS-7Bx2-MoE-13B-v0.2.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-gguf/blob/main/LogoS-7Bx2-MoE-13B-v0.2.IQ4_XS.gguf) \| IQ4_XS \| 6.5GB \| \| [LogoS-7Bx2-MoE-13B-v0.2.Q4_0.gguf](https://huggingface.co/RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-gguf/blob/main/LogoS-7Bx2-MoE-13B-v0.2.Q4_0.gguf) \| Q4_0 \| 6.78GB \| \| [LogoS-7Bx2-MoE-13B-v0.2.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-gguf/blob/main/LogoS-7Bx2-MoE-13B-v0.2.IQ4_NL.gguf) \| IQ4_NL \| 6.85GB \| \| [LogoS-7Bx2-MoE-13B-v0.2.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-gguf/blob/main/LogoS-7Bx2-MoE-13B-v0.2.Q4_K_S.gguf) \| Q4_K_S \| 6.84GB \| \| [LogoS-7Bx2-MoE-13B-v0.2.Q4_K.gguf](https://huggingface.co/RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-gguf/blob/main/LogoS-7Bx2-MoE-13B-v0.2.Q4_K.gguf) \| Q4_K \| 7.25GB \| \| [LogoS-7Bx2-MoE-13B-v0.2.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-gguf/blob/main/LogoS-7Bx2-MoE-13B-v0.2.Q4_K_M.gguf) \| Q4_K_M \| 7.25GB \| \| [LogoS-7Bx2-MoE-13B-v0.2.Q4_1.gguf](https://huggingface.co/RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-gguf/blob/main/LogoS-7Bx2-MoE-13B-v0.2.Q4_1.gguf) \| Q4_1 \| 7.52GB \| \| [LogoS-7Bx2-MoE-13B-v0.2.Q5_0.gguf](https://huggingface.co/RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-gguf/blob/main/LogoS-7Bx2-MoE-13B-v0.2.Q5_0.gguf) \| Q5_0 \| 8.26GB \| \| [LogoS-7Bx2-MoE-13B-v0.2.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-gguf/blob/main/LogoS-7Bx2-MoE-13B-v0.2.Q5_K_S.gguf) \| Q5_K_S \| 8.26GB \| \| [LogoS-7Bx2-MoE-13B-v0.2.Q5_K.gguf](https://huggingface.co/RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-gguf/blob/main/LogoS-7Bx2-MoE-13B-v0.2.Q5_K.gguf) \| Q5_K \| 8.51GB \| \| [LogoS-7Bx2-MoE-13B-v0.2.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-gguf/blob/main/LogoS-7Bx2-MoE-13B-v0.2.Q5_K_M.gguf) \| Q5_K_M \| 8.51GB \| \| [LogoS-7Bx2-MoE-13B-v0.2.Q5_1.gguf](https://huggingface.co/RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-gguf/blob/main/LogoS-7Bx2-MoE-13B-v0.2.Q5_1.gguf) \| Q5_1 \| 9.01GB \| \| [LogoS-7Bx2-MoE-13B-v0.2.Q6_K.gguf](https://huggingface.co/RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-gguf/blob/main/LogoS-7Bx2-MoE-13B-v0.2.Q6_K.gguf) \| Q6_K \| 9.84GB \| \| [LogoS-7Bx2-MoE-13B-v0.2.Q8_0.gguf](https://huggingface.co/RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-gguf/blob/main/LogoS-7Bx2-MoE-13B-v0.2.Q8_0.gguf) \| Q8_0 \| 12.75GB \| Original model description: --- language: - en - es license: apache-2.0 tags: - moe - merge base_model: - yunconglong/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B - TomGrc/FusionNet_7Bx2_MoE_14B model-index: - name: LogoS-7Bx2-MoE-13B-v0.1 results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 74.49 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=RubielLabarta/LogoS-7Bx2-MoE-13B-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 89.07 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=RubielLabarta/LogoS-7Bx2-MoE-13B-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 64.74 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=RubielLabarta/LogoS-7Bx2-MoE-13B-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 74.57 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=RubielLabarta/LogoS-7Bx2-MoE-13B-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 88.32 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=RubielLabarta/LogoS-7Bx2-MoE-13B-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 71.65 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=RubielLabarta/LogoS-7Bx2-MoE-13B-v0.1 name: Open LLM Leaderboard --- # LogoS-7Bx2-MoE-13B-v0.1 Model built by @RubielLabarta using SLERP merge method. The model is release for research purposes only, commercial use is not allowed. The LogoS is a model to experiment with the MoE method, which could significantly increase the performance of the original model. The model has 12.9B parameters. # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_RubielLabarta__LogoS-7Bx2-MoE-13B-v0.1) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|77.14\| \|AI2 Reasoning Challenge (25-Shot)\|74.49\| \|HellaSwag (10-Shot) \|89.07\| \|MMLU (5-Shot) \|64.74\| \|TruthfulQA (0-shot) \|74.57\| \|Winogrande (5-shot) \|88.32\| \|GSM8k (5-shot) \|71.65\|
LoneStriker/Master-Yi-9B-5.0bpw-h6-exl2	LoneStriker	2024-05-18T19:41:13Z	10	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "5-bit", "exl2", "region:us" ]	text-generation	2024-05-18T19:38:45Z	--- license: apache-2.0 --- ## Model Description Master is a collection of LLMs trained using human-collected seed questions and regenerate the answers with a mixture of high performance Open-source LLMs. Master-Yi-9B is trained using the ORPO technique. The model shows strong abilities in reasoning on coding and math questions. Quantized Version: [Here](https://huggingface.co/qnguyen3/Master-Yi-9B-GGUF) Master-Yi-9B-Vision: Coming Soon ![img](https://huggingface.co/qnguyen3/Master-Yi-9B/resolve/main/Master-Yi-9B.webp) ## Prompt Template ``` <\|im_start\|>system You are a helpful AI assistant.<\|im_end\|> <\|im_start\|>user What is the meaning of life?<\|im_end\|> <\|im_start\|>assistant ``` ## Examples ![image/png](https://cdn-uploads.huggingface.co/production/uploads/630430583926de1f7ec62c6b/E27JmdRAMrHQacM50-lBk.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/630430583926de1f7ec62c6b/z0HS4bxHFQzPe0gZlvCzZ.png) ## Inference Code ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch device = "cuda" # the device to load the model onto model = AutoModelForCausalLM.from_pretrained( "vilm/VinaLlama2-14B", torch_dtype='auto', device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained("vilm/VinaLlama2-14B") prompt = "What is the mearning of life?" messages = [ {"role": "system", "content": "You are a helpful AI assistant."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(device) generated_ids = model.generate( model_inputs.input_ids, max_new_tokens=1024, eos_token_id=tokenizer.eos_token_id, temperature=0.25, ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids)[0] print(response) ``` ## Benchmarks ### Nous Benchmark: \| Model \|AGIEval\|GPT4All\|TruthfulQA\|Bigbench\|Average\| \|---------------------------------------------------\|------:\|------:\|---------:\|-------:\|------:\| \|[Master-Yi-9B](https://huggingface.co/qnguyen3/Master-Yi-9B)\| 43.55\| 71.48\| 48.54\| 41.43\| 51.25\| ### AGIEval ``` \| Task \|Version\| Metric \|Value\| \|Stderr\| \|------------------------------\|------:\|--------\|----:\|---\|-----:\| \|agieval_aqua_rat \| 0\|acc \|35.83\|± \| 3.01\| \| \| \|acc_norm\|31.89\|± \| 2.93\| \|agieval_logiqa_en \| 0\|acc \|38.25\|± \| 1.91\| \| \| \|acc_norm\|37.79\|± \| 1.90\| \|agieval_lsat_ar \| 0\|acc \|23.04\|± \| 2.78\| \| \| \|acc_norm\|20.43\|± \| 2.66\| \|agieval_lsat_lr \| 0\|acc \|48.04\|± \| 2.21\| \| \| \|acc_norm\|42.75\|± \| 2.19\| \|agieval_lsat_rc \| 0\|acc \|61.34\|± \| 2.97\| \| \| \|acc_norm\|52.79\|± \| 3.05\| \|agieval_sat_en \| 0\|acc \|79.13\|± \| 2.84\| \| \| \|acc_norm\|72.33\|± \| 3.12\| \|agieval_sat_en_without_passage\| 0\|acc \|44.17\|± \| 3.47\| \| \| \|acc_norm\|42.72\|± \| 3.45\| \|agieval_sat_math \| 0\|acc \|52.27\|± \| 3.38\| \| \| \|acc_norm\|47.73\|± \| 3.38\| Average: 43.55% ``` ### GPT4All ``` \| Task \|Version\| Metric \|Value\| \|Stderr\| \|-------------\|------:\|--------\|----:\|---\|-----:\| \|arc_challenge\| 0\|acc \|54.95\|± \| 1.45\| \| \| \|acc_norm\|58.70\|± \| 1.44\| \|arc_easy \| 0\|acc \|82.28\|± \| 0.78\| \| \| \|acc_norm\|81.10\|± \| 0.80\| \|boolq \| 1\|acc \|86.15\|± \| 0.60\| \|hellaswag \| 0\|acc \|59.16\|± \| 0.49\| \| \| \|acc_norm\|77.53\|± \| 0.42\| \|openbookqa \| 0\|acc \|37.40\|± \| 2.17\| \| \| \|acc_norm\|44.00\|± \| 2.22\| \|piqa \| 0\|acc \|79.00\|± \| 0.95\| \| \| \|acc_norm\|80.25\|± \| 0.93\| \|winogrande \| 0\|acc \|72.61\|± \| 1.25\| Average: 71.48% ``` ### TruthfulQA ``` \| Task \|Version\|Metric\|Value\| \|Stderr\| \|-------------\|------:\|------\|----:\|---\|-----:\| \|truthfulqa_mc\| 1\|mc1 \|33.05\|± \| 1.65\| \| \| \|mc2 \|48.54\|± \| 1.54\| Average: 48.54% ``` ### Bigbench ``` \| Task \|Version\| Metric \|Value\| \|Stderr\| \|------------------------------------------------\|------:\|---------------------\|----:\|---\|-----:\| \|bigbench_causal_judgement \| 0\|multiple_choice_grade\|54.74\|± \| 3.62\| \|bigbench_date_understanding \| 0\|multiple_choice_grade\|68.02\|± \| 2.43\| \|bigbench_disambiguation_qa \| 0\|multiple_choice_grade\|40.31\|± \| 3.06\| \|bigbench_geometric_shapes \| 0\|multiple_choice_grade\|30.36\|± \| 2.43\| \| \| \|exact_str_match \| 2.23\|± \| 0.78\| \|bigbench_logical_deduction_five_objects \| 0\|multiple_choice_grade\|26.00\|± \| 1.96\| \|bigbench_logical_deduction_seven_objects \| 0\|multiple_choice_grade\|20.71\|± \| 1.53\| \|bigbench_logical_deduction_three_objects \| 0\|multiple_choice_grade\|44.00\|± \| 2.87\| \|bigbench_movie_recommendation \| 0\|multiple_choice_grade\|35.00\|± \| 2.14\| \|bigbench_navigate \| 0\|multiple_choice_grade\|58.40\|± \| 1.56\| \|bigbench_reasoning_about_colored_objects \| 0\|multiple_choice_grade\|61.80\|± \| 1.09\| \|bigbench_ruin_names \| 0\|multiple_choice_grade\|42.41\|± \| 2.34\| \|bigbench_salient_translation_error_detection \| 0\|multiple_choice_grade\|31.56\|± \| 1.47\| \|bigbench_snarks \| 0\|multiple_choice_grade\|55.25\|± \| 3.71\| \|bigbench_sports_understanding \| 0\|multiple_choice_grade\|69.37\|± \| 1.47\| \|bigbench_temporal_sequences \| 0\|multiple_choice_grade\|27.70\|± \| 1.42\| \|bigbench_tracking_shuffled_objects_five_objects \| 0\|multiple_choice_grade\|21.36\|± \| 1.16\| \|bigbench_tracking_shuffled_objects_seven_objects\| 0\|multiple_choice_grade\|14.69\|± \| 0.85\| \|bigbench_tracking_shuffled_objects_three_objects\| 0\|multiple_choice_grade\|44.00\|± \| 2.87\| Average: 41.43% ``` Average score: 51.25% ### OpenLLM Benchmark: \| Model \|ARC \|HellaSwag\|MMLU \|TruthfulQA\|Winogrande\|GSM8K\|Average\| \|---------------------------------------------------\|---:\|--------:\|----:\|---------:\|---------:\|----:\|------:\| \|[Master-Yi-9B](https://huggingface.co/qnguyen3/Master-Yi-9B)\|61.6\| 79.89\|69.95\| 48.59\| 77.35\|67.48\| 67.48\| ### ARC ``` \| Task \|Version\| Metric \| Value \| \|Stderr\| \|-------------\|------:\|--------------------\|-------------\|---\|------\| \|arc_challenge\| 1\|acc,none \| 0.59\| \| \| \| \| \|acc_stderr,none \| 0.01\| \| \| \| \| \|acc_norm,none \| 0.62\| \| \| \| \| \|acc_norm_stderr,none\| 0.01\| \| \| \| \| \|alias \|arc_challenge\| \| \| Average: 61.6% ``` ### HellaSwag ``` \| Task \|Version\| Metric \| Value \| \|Stderr\| \|---------\|------:\|--------------------\|---------\|---\|------\| \|hellaswag\| 1\|acc,none \| 0.61\| \| \| \| \| \|acc_stderr,none \| 0\| \| \| \| \| \|acc_norm,none \| 0.80\| \| \| \| \| \|acc_norm_stderr,none\| 0\| \| \| \| \| \|alias \|hellaswag\| \| \| Average: 79.89% ``` ### MMLU ``` \| Task \|Version\| Metric \| Value \| \|Stderr\| \|----------------------------------------\|-------\|---------------\|---------------------------------------\|---\|------\| \|mmlu \|N/A \|acc,none \| 0.7\| \| \| \| \| \|acc_stderr,none\| 0\| \| \| \| \| \|alias \|mmlu \| \| \| \|mmlu_abstract_algebra \| 0\|alias \| - abstract_algebra \| \| \| \| \| \|acc,none \|0.46 \| \| \| \| \| \|acc_stderr,none\|0.05 \| \| \| \|mmlu_anatomy \| 0\|alias \| - anatomy \| \| \| \| \| \|acc,none \|0.64 \| \| \| \| \| \|acc_stderr,none\|0.04 \| \| \| \|mmlu_astronomy \| 0\|alias \| - astronomy \| \| \| \| \| \|acc,none \|0.77 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| \|mmlu_business_ethics \| 0\|alias \| - business_ethics \| \| \| \| \| \|acc,none \|0.76 \| \| \| \| \| \|acc_stderr,none\|0.04 \| \| \| \|mmlu_clinical_knowledge \| 0\|alias \| - clinical_knowledge \| \| \| \| \| \|acc,none \|0.71 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| \|mmlu_college_biology \| 0\|alias \| - college_biology \| \| \| \| \| \|acc,none \|0.82 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| \|mmlu_college_chemistry \| 0\|alias \| - college_chemistry \| \| \| \| \| \|acc,none \|0.52 \| \| \| \| \| \|acc_stderr,none\|0.05 \| \| \| \|mmlu_college_computer_science \| 0\|alias \| - college_computer_science \| \| \| \| \| \|acc,none \|0.56 \| \| \| \| \| \|acc_stderr,none\|0.05 \| \| \| \|mmlu_college_mathematics \| 0\|alias \| - college_mathematics \| \| \| \| \| \|acc,none \|0.44 \| \| \| \| \| \|acc_stderr,none\|0.05 \| \| \| \|mmlu_college_medicine \| 0\|alias \| - college_medicine \| \| \| \| \| \|acc,none \|0.72 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| \|mmlu_college_physics \| 0\|alias \| - college_physics \| \| \| \| \| \|acc,none \|0.45 \| \| \| \| \| \|acc_stderr,none\|0.05 \| \| \| \|mmlu_computer_security \| 0\|alias \| - computer_security \| \| \| \| \| \|acc,none \|0.81 \| \| \| \| \| \|acc_stderr,none\|0.04 \| \| \| \|mmlu_conceptual_physics \| 0\|alias \| - conceptual_physics \| \| \| \| \| \|acc,none \|0.74 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| \|mmlu_econometrics \| 0\|alias \| - econometrics \| \| \| \| \| \|acc,none \|0.65 \| \| \| \| \| \|acc_stderr,none\|0.04 \| \| \| \|mmlu_electrical_engineering \| 0\|alias \| - electrical_engineering \| \| \| \| \| \|acc,none \|0.72 \| \| \| \| \| \|acc_stderr,none\|0.04 \| \| \| \|mmlu_elementary_mathematics \| 0\|alias \| - elementary_mathematics \| \| \| \| \| \|acc,none \|0.62 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_formal_logic \| 0\|alias \| - formal_logic \| \| \| \| \| \|acc,none \|0.57 \| \| \| \| \| \|acc_stderr,none\|0.04 \| \| \| \|mmlu_global_facts \| 0\|alias \| - global_facts \| \| \| \| \| \|acc,none \|0.46 \| \| \| \| \| \|acc_stderr,none\|0.05 \| \| \| \|mmlu_high_school_biology \| 0\|alias \| - high_school_biology \| \| \| \| \| \|acc,none \|0.86 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_high_school_chemistry \| 0\|alias \| - high_school_chemistry \| \| \| \| \| \|acc,none \|0.67 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| \|mmlu_high_school_computer_science \| 0\|alias \| - high_school_computer_science \| \| \| \| \| \|acc,none \|0.84 \| \| \| \| \| \|acc_stderr,none\|0.04 \| \| \| \|mmlu_high_school_european_history \| 0\|alias \| - high_school_european_history \| \| \| \| \| \|acc,none \|0.82 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| \|mmlu_high_school_geography \| 0\|alias \| - high_school_geography \| \| \| \| \| \|acc,none \|0.86 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_high_school_government_and_politics\| 0\|alias \| - high_school_government_and_politics\| \| \| \| \| \|acc,none \|0.90 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_high_school_macroeconomics \| 0\|alias \| - high_school_macroeconomics \| \| \| \| \| \|acc,none \|0.75 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_high_school_mathematics \| 0\|alias \| - high_school_mathematics \| \| \| \| \| \|acc,none \|0.43 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| \|mmlu_high_school_microeconomics \| 0\|alias \| - high_school_microeconomics \| \| \| \| \| \|acc,none \|0.86 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_high_school_physics \| 0\|alias \| - high_school_physics \| \| \| \| \| \|acc,none \|0.45 \| \| \| \| \| \|acc_stderr,none\|0.04 \| \| \| \|mmlu_high_school_psychology \| 0\|alias \| - high_school_psychology \| \| \| \| \| \|acc,none \|0.87 \| \| \| \| \| \|acc_stderr,none\|0.01 \| \| \| \|mmlu_high_school_statistics \| 0\|alias \| - high_school_statistics \| \| \| \| \| \|acc,none \|0.68 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| \|mmlu_high_school_us_history \| 0\|alias \| - high_school_us_history \| \| \| \| \| \|acc,none \|0.85 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_high_school_world_history \| 0\|alias \| - high_school_world_history \| \| \| \| \| \|acc,none \|0.85 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_human_aging \| 0\|alias \| - human_aging \| \| \| \| \| \|acc,none \|0.76 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| \|mmlu_human_sexuality \| 0\|alias \| - human_sexuality \| \| \| \| \| \|acc,none \|0.78 \| \| \| \| \| \|acc_stderr,none\|0.04 \| \| \| \|mmlu_humanities \|N/A \|alias \| - humanities \| \| \| \| \| \|acc,none \|0.63 \| \| \| \| \| \|acc_stderr,none\|0.01 \| \| \| \|mmlu_international_law \| 0\|alias \| - international_law \| \| \| \| \| \|acc,none \|0.79 \| \| \| \| \| \|acc_stderr,none\|0.04 \| \| \| \|mmlu_jurisprudence \| 0\|alias \| - jurisprudence \| \| \| \| \| \|acc,none \|0.79 \| \| \| \| \| \|acc_stderr,none\|0.04 \| \| \| \|mmlu_logical_fallacies \| 0\|alias \| - logical_fallacies \| \| \| \| \| \|acc,none \|0.80 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| \|mmlu_machine_learning \| 0\|alias \| - machine_learning \| \| \| \| \| \|acc,none \|0.52 \| \| \| \| \| \|acc_stderr,none\|0.05 \| \| \| \|mmlu_management \| 0\|alias \| - management \| \| \| \| \| \|acc,none \|0.83 \| \| \| \| \| \|acc_stderr,none\|0.04 \| \| \| \|mmlu_marketing \| 0\|alias \| - marketing \| \| \| \| \| \|acc,none \|0.89 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_medical_genetics \| 0\|alias \| - medical_genetics \| \| \| \| \| \|acc,none \|0.78 \| \| \| \| \| \|acc_stderr,none\|0.04 \| \| \| \|mmlu_miscellaneous \| 0\|alias \| - miscellaneous \| \| \| \| \| \|acc,none \|0.85 \| \| \| \| \| \|acc_stderr,none\|0.01 \| \| \| \|mmlu_moral_disputes \| 0\|alias \| - moral_disputes \| \| \| \| \| \|acc,none \|0.75 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_moral_scenarios \| 0\|alias \| - moral_scenarios \| \| \| \| \| \|acc,none \|0.48 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_nutrition \| 0\|alias \| - nutrition \| \| \| \| \| \|acc,none \|0.77 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_other \|N/A \|alias \| - other \| \| \| \| \| \|acc,none \|0.75 \| \| \| \| \| \|acc_stderr,none\|0.01 \| \| \| \|mmlu_philosophy \| 0\|alias \| - philosophy \| \| \| \| \| \|acc,none \|0.78 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_prehistory \| 0\|alias \| - prehistory \| \| \| \| \| \|acc,none \|0.77 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_professional_accounting \| 0\|alias \| - professional_accounting \| \| \| \| \| \|acc,none \|0.57 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| \|mmlu_professional_law \| 0\|alias \| - professional_law \| \| \| \| \| \|acc,none \|0.50 \| \| \| \| \| \|acc_stderr,none\|0.01 \| \| \| \|mmlu_professional_medicine \| 0\|alias \| - professional_medicine \| \| \| \| \| \|acc,none \|0.71 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| \|mmlu_professional_psychology \| 0\|alias \| - professional_psychology \| \| \| \| \| \|acc,none \|0.73 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_public_relations \| 0\|alias \| - public_relations \| \| \| \| \| \|acc,none \|0.76 \| \| \| \| \| \|acc_stderr,none\|0.04 \| \| \| \|mmlu_security_studies \| 0\|alias \| - security_studies \| \| \| \| \| \|acc,none \|0.78 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| \|mmlu_social_sciences \|N/A \|alias \| - social_sciences \| \| \| \| \| \|acc,none \|0.81 \| \| \| \| \| \|acc_stderr,none\|0.01 \| \| \| \|mmlu_sociology \| 0\|alias \| - sociology \| \| \| \| \| \|acc,none \|0.86 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_stem \|N/A \|alias \| - stem \| \| \| \| \| \|acc,none \|0.65 \| \| \| \| \| \|acc_stderr,none\|0.01 \| \| \| \|mmlu_us_foreign_policy \| 0\|alias \| - us_foreign_policy \| \| \| \| \| \|acc,none \|0.92 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| \|mmlu_virology \| 0\|alias \| - virology \| \| \| \| \| \|acc,none \|0.58 \| \| \| \| \| \|acc_stderr,none\|0.04 \| \| \| \|mmlu_world_religions \| 0\|alias \| - world_religions \| \| \| \| \| \|acc,none \|0.82 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| Average: 69.95% ``` ### TruthfulQA ``` \| Task \|Version\| Metric \| Value \| \|Stderr\| \|--------------\|-------\|-----------------------\|-----------------\|---\|------\| \|truthfulqa \|N/A \|bleu_acc,none \| 0.45\| \| \| \| \| \|bleu_acc_stderr,none \| 0.02\| \| \| \| \| \|rouge1_acc,none \| 0.45\| \| \| \| \| \|rouge1_acc_stderr,none \| 0.02\| \| \| \| \| \|rouge2_diff,none \| 0.92\| \| \| \| \| \|rouge2_diff_stderr,none\| 1.07\| \| \| \| \| \|bleu_max,none \| 23.77\| \| \| \| \| \|bleu_max_stderr,none \| 0.81\| \| \| \| \| \|rouge2_acc,none \| 0.38\| \| \| \| \| \|rouge2_acc_stderr,none \| 0.02\| \| \| \| \| \|acc,none \| 0.41\| \| \| \| \| \|acc_stderr,none \| 0.01\| \| \| \| \| \|rougeL_diff,none \| 1.57\| \| \| \| \| \|rougeL_diff_stderr,none\| 0.93\| \| \| \| \| \|rougeL_acc,none \| 0.46\| \| \| \| \| \|rougeL_acc_stderr,none \| 0.02\| \| \| \| \| \|bleu_diff,none \| 1.38\| \| \| \| \| \|bleu_diff_stderr,none \| 0.75\| \| \| \| \| \|rouge2_max,none \| 33.01\| \| \| \| \| \|rouge2_max_stderr,none \| 1.05\| \| \| \| \| \|rouge1_diff,none \| 1.72\| \| \| \| \| \|rouge1_diff_stderr,none\| 0.92\| \| \| \| \| \|rougeL_max,none \| 45.25\| \| \| \| \| \|rougeL_max_stderr,none \| 0.92\| \| \| \| \| \|rouge1_max,none \| 48.29\| \| \| \| \| \|rouge1_max_stderr,none \| 0.90\| \| \| \| \| \|alias \|truthfulqa \| \| \| \|truthfulqa_gen\| 3\|bleu_max,none \| 23.77\| \| \| \| \| \|bleu_max_stderr,none \| 0.81\| \| \| \| \| \|bleu_acc,none \| 0.45\| \| \| \| \| \|bleu_acc_stderr,none \| 0.02\| \| \| \| \| \|bleu_diff,none \| 1.38\| \| \| \| \| \|bleu_diff_stderr,none \| 0.75\| \| \| \| \| \|rouge1_max,none \| 48.29\| \| \| \| \| \|rouge1_max_stderr,none \| 0.90\| \| \| \| \| \|rouge1_acc,none \| 0.45\| \| \| \| \| \|rouge1_acc_stderr,none \| 0.02\| \| \| \| \| \|rouge1_diff,none \| 1.72\| \| \| \| \| \|rouge1_diff_stderr,none\| 0.92\| \| \| \| \| \|rouge2_max,none \| 33.01\| \| \| \| \| \|rouge2_max_stderr,none \| 1.05\| \| \| \| \| \|rouge2_acc,none \| 0.38\| \| \| \| \| \|rouge2_acc_stderr,none \| 0.02\| \| \| \| \| \|rouge2_diff,none \| 0.92\| \| \| \| \| \|rouge2_diff_stderr,none\| 1.07\| \| \| \| \| \|rougeL_max,none \| 45.25\| \| \| \| \| \|rougeL_max_stderr,none \| 0.92\| \| \| \| \| \|rougeL_acc,none \| 0.46\| \| \| \| \| \|rougeL_acc_stderr,none \| 0.02\| \| \| \| \| \|rougeL_diff,none \| 1.57\| \| \| \| \| \|rougeL_diff_stderr,none\| 0.93\| \| \| \| \| \|alias \| - truthfulqa_gen\| \| \| \|truthfulqa_mc1\| 2\|acc,none \| 0.33\| \| \| \| \| \|acc_stderr,none \| 0.02\| \| \| \| \| \|alias \| - truthfulqa_mc1\| \| \| \|truthfulqa_mc2\| 2\|acc,none \| 0.49\| \| \| \| \| \|acc_stderr,none \| 0.02\| \| \| \| \| \|alias \| - truthfulqa_mc2\| \| \| Average: 48.59% ``` ### Winogrande ``` \| Task \|Version\| Metric \| Value \| \|Stderr\| \|----------\|------:\|---------------\|----------\|---\|------\| \|winogrande\| 1\|acc,none \| 0.77\| \| \| \| \| \|acc_stderr,none\| 0.01\| \| \| \| \| \|alias \|winogrande\| \| \| Average: 77.35% ``` ### GSM8K ``` \|Task \|Version\| Metric \|Value\| \|Stderr\| \|-----\|------:\|-----------------------------------\|-----\|---\|------\| \|gsm8k\| 3\|exact_match,strict-match \| 0.67\| \| \| \| \| \|exact_match_stderr,strict-match \| 0.01\| \| \| \| \| \|exact_match,flexible-extract \| 0.68\| \| \| \| \| \|exact_match_stderr,flexible-extract\| 0.01\| \| \| \| \| \|alias \|gsm8k\| \| \| Average: 67.48% ``` Average score: 67.48%
amaye15/google-vit-base-patch16-224-batch32-lr0.0005-standford-dogs	amaye15	2024-05-18T19:39:23Z	223	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:stanford-dogs", "base_model:google/vit-base-patch16-224", "base_model:finetune:google/vit-base-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-18T19:39:02Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224 tags: - generated_from_trainer datasets: - stanford-dogs metrics: - accuracy - f1 - precision - recall model-index: - name: google-vit-base-patch16-224-batch32-lr0.0005-standford-dogs results: - task: name: Image Classification type: image-classification dataset: name: stanford-dogs type: stanford-dogs config: default split: full args: default metrics: - name: Accuracy type: accuracy value: 0.8838678328474247 - name: F1 type: f1 value: 0.880922271280839 - name: Precision type: precision value: 0.8888253617157671 - name: Recall type: recall value: 0.8813659659148954 --- <!-- 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. --> # google-vit-base-patch16-224-batch32-lr0.0005-standford-dogs This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the stanford-dogs dataset. It achieves the following results on the evaluation set: - Loss: 0.4466 - Accuracy: 0.8839 - F1: 0.8809 - Precision: 0.8888 - Recall: 0.8814 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| 4.758 \| 0.0777 \| 10 \| 4.5706 \| 0.0481 \| 0.0344 \| 0.0390 \| 0.0483 \| \| 4.4856 \| 0.1553 \| 20 \| 4.2407 \| 0.1421 \| 0.1018 \| 0.1190 \| 0.1396 \| \| 4.2422 \| 0.2330 \| 30 \| 3.9118 \| 0.2655 \| 0.2120 \| 0.2525 \| 0.2553 \| \| 3.972 \| 0.3107 \| 40 \| 3.5875 \| 0.4096 \| 0.3445 \| 0.3996 \| 0.3933 \| \| 3.6632 \| 0.3883 \| 50 \| 3.2750 \| 0.5299 \| 0.4683 \| 0.5338 \| 0.5129 \| \| 3.4266 \| 0.4660 \| 60 \| 2.9911 \| 0.6028 \| 0.5536 \| 0.6617 \| 0.5870 \| \| 3.1207 \| 0.5437 \| 70 \| 2.7127 \| 0.6638 \| 0.6227 \| 0.7159 \| 0.6496 \| \| 2.9402 \| 0.6214 \| 80 \| 2.4853 \| 0.7043 \| 0.6716 \| 0.7387 \| 0.6923 \| \| 2.7683 \| 0.6990 \| 90 \| 2.2667 \| 0.7376 \| 0.7092 \| 0.7675 \| 0.7277 \| \| 2.5556 \| 0.7767 \| 100 \| 2.0710 \| 0.7631 \| 0.7422 \| 0.7843 \| 0.7551 \| \| 2.3936 \| 0.8544 \| 110 \| 1.8898 \| 0.7772 \| 0.7610 \| 0.8076 \| 0.7705 \| \| 2.245 \| 0.9320 \| 120 \| 1.7340 \| 0.7954 \| 0.7807 \| 0.8138 \| 0.7898 \| \| 2.0889 \| 1.0097 \| 130 \| 1.6027 \| 0.8073 \| 0.7970 \| 0.8314 \| 0.8024 \| \| 1.951 \| 1.0874 \| 140 \| 1.4715 \| 0.8197 \| 0.8094 \| 0.8361 \| 0.8132 \| \| 1.8414 \| 1.1650 \| 150 \| 1.3790 \| 0.8209 \| 0.8104 \| 0.8389 \| 0.8151 \| \| 1.7247 \| 1.2427 \| 160 \| 1.3237 \| 0.8146 \| 0.8034 \| 0.8338 \| 0.8085 \| \| 1.7338 \| 1.3204 \| 170 \| 1.2107 \| 0.8336 \| 0.8251 \| 0.8463 \| 0.8284 \| \| 1.5688 \| 1.3981 \| 180 \| 1.1370 \| 0.8401 \| 0.8314 \| 0.8531 \| 0.8354 \| \| 1.5471 \| 1.4757 \| 190 \| 1.0684 \| 0.8455 \| 0.8367 \| 0.8558 \| 0.8398 \| \| 1.461 \| 1.5534 \| 200 \| 1.0200 \| 0.8477 \| 0.8384 \| 0.8571 \| 0.8430 \| \| 1.4736 \| 1.6311 \| 210 \| 0.9612 \| 0.8540 \| 0.8493 \| 0.8621 \| 0.8509 \| \| 1.4062 \| 1.7087 \| 220 \| 0.9269 \| 0.8528 \| 0.8476 \| 0.8639 \| 0.8495 \| \| 1.2888 \| 1.7864 \| 230 \| 0.8914 \| 0.8516 \| 0.8456 \| 0.8626 \| 0.8478 \| \| 1.3353 \| 1.8641 \| 240 \| 0.8484 \| 0.8601 \| 0.8538 \| 0.8684 \| 0.8555 \| \| 1.2751 \| 1.9417 \| 250 \| 0.8210 \| 0.8586 \| 0.8520 \| 0.8643 \| 0.8538 \| \| 1.2378 \| 2.0194 \| 260 \| 0.7924 \| 0.8567 \| 0.8510 \| 0.8641 \| 0.8522 \| \| 1.1686 \| 2.0971 \| 270 \| 0.7683 \| 0.8579 \| 0.8529 \| 0.8670 \| 0.8543 \| \| 1.1625 \| 2.1748 \| 280 \| 0.7477 \| 0.8567 \| 0.8522 \| 0.8658 \| 0.8532 \| \| 1.1883 \| 2.2524 \| 290 \| 0.7312 \| 0.8576 \| 0.8534 \| 0.8674 \| 0.8541 \| \| 1.1551 \| 2.3301 \| 300 \| 0.7052 \| 0.8654 \| 0.8606 \| 0.8702 \| 0.8618 \| \| 1.1259 \| 2.4078 \| 310 \| 0.6901 \| 0.8627 \| 0.8580 \| 0.8720 \| 0.8597 \| \| 1.073 \| 2.4854 \| 320 \| 0.6793 \| 0.8654 \| 0.8600 \| 0.8722 \| 0.8612 \| \| 1.0587 \| 2.5631 \| 330 \| 0.6604 \| 0.8700 \| 0.8646 \| 0.8753 \| 0.8660 \| \| 1.0506 \| 2.6408 \| 340 \| 0.6470 \| 0.8690 \| 0.8638 \| 0.8714 \| 0.8652 \| \| 1.0397 \| 2.7184 \| 350 \| 0.6369 \| 0.8664 \| 0.8606 \| 0.8711 \| 0.8627 \| \| 1.0363 \| 2.7961 \| 360 \| 0.6373 \| 0.8664 \| 0.8610 \| 0.8797 \| 0.8623 \| \| 1.0408 \| 2.8738 \| 370 \| 0.6141 \| 0.8700 \| 0.8637 \| 0.8755 \| 0.8655 \| \| 1.0087 \| 2.9515 \| 380 \| 0.6105 \| 0.8707 \| 0.8657 \| 0.8740 \| 0.8675 \| \| 1.0021 \| 3.0291 \| 390 \| 0.5978 \| 0.8744 \| 0.8708 \| 0.8771 \| 0.8717 \| \| 0.894 \| 3.1068 \| 400 \| 0.5970 \| 0.8671 \| 0.8632 \| 0.8733 \| 0.8637 \| \| 0.9363 \| 3.1845 \| 410 \| 0.5860 \| 0.8683 \| 0.8640 \| 0.8754 \| 0.8653 \| \| 0.9678 \| 3.2621 \| 420 \| 0.5760 \| 0.8712 \| 0.8677 \| 0.8764 \| 0.8684 \| \| 0.9378 \| 3.3398 \| 430 \| 0.5677 \| 0.8686 \| 0.8645 \| 0.8734 \| 0.8653 \| \| 0.929 \| 3.4175 \| 440 \| 0.5620 \| 0.8707 \| 0.8659 \| 0.8750 \| 0.8672 \| \| 0.9585 \| 3.4951 \| 450 \| 0.5610 \| 0.8654 \| 0.8612 \| 0.8691 \| 0.8625 \| \| 0.8432 \| 3.5728 \| 460 \| 0.5557 \| 0.8690 \| 0.8638 \| 0.8715 \| 0.8665 \| \| 0.9423 \| 3.6505 \| 470 \| 0.5421 \| 0.8715 \| 0.8663 \| 0.8737 \| 0.8684 \| \| 0.944 \| 3.7282 \| 480 \| 0.5419 \| 0.8703 \| 0.8656 \| 0.8728 \| 0.8671 \| \| 0.8477 \| 3.8058 \| 490 \| 0.5297 \| 0.8766 \| 0.8727 \| 0.8799 \| 0.8737 \| \| 0.8933 \| 3.8835 \| 500 \| 0.5263 \| 0.8739 \| 0.8707 \| 0.8790 \| 0.8716 \| \| 0.881 \| 3.9612 \| 510 \| 0.5197 \| 0.8768 \| 0.8739 \| 0.8817 \| 0.8746 \| \| 0.8603 \| 4.0388 \| 520 \| 0.5196 \| 0.8778 \| 0.8740 \| 0.8832 \| 0.8746 \| \| 0.8045 \| 4.1165 \| 530 \| 0.5214 \| 0.8759 \| 0.8723 \| 0.8822 \| 0.8725 \| \| 0.8101 \| 4.1942 \| 540 \| 0.5163 \| 0.8746 \| 0.8713 \| 0.8806 \| 0.8725 \| \| 0.8016 \| 4.2718 \| 550 \| 0.5149 \| 0.8766 \| 0.8729 \| 0.8813 \| 0.8735 \| \| 0.8403 \| 4.3495 \| 560 \| 0.5061 \| 0.8724 \| 0.8689 \| 0.8767 \| 0.8699 \| \| 0.8216 \| 4.4272 \| 570 \| 0.5034 \| 0.8739 \| 0.8699 \| 0.8798 \| 0.8708 \| \| 0.8491 \| 4.5049 \| 580 \| 0.5004 \| 0.8768 \| 0.8730 \| 0.8828 \| 0.8740 \| \| 0.7727 \| 4.5825 \| 590 \| 0.4990 \| 0.8766 \| 0.8722 \| 0.8810 \| 0.8736 \| \| 0.8475 \| 4.6602 \| 600 \| 0.4941 \| 0.8766 \| 0.8724 \| 0.8807 \| 0.8736 \| \| 0.854 \| 4.7379 \| 610 \| 0.4867 \| 0.8824 \| 0.8793 \| 0.8867 \| 0.8801 \| \| 0.8226 \| 4.8155 \| 620 \| 0.4900 \| 0.8771 \| 0.8736 \| 0.8824 \| 0.8747 \| \| 0.7847 \| 4.8932 \| 630 \| 0.4855 \| 0.8805 \| 0.8773 \| 0.8847 \| 0.8779 \| \| 0.8093 \| 4.9709 \| 640 \| 0.4820 \| 0.8805 \| 0.8775 \| 0.8844 \| 0.8777 \| \| 0.7667 \| 5.0485 \| 650 \| 0.4837 \| 0.8800 \| 0.8759 \| 0.8827 \| 0.8768 \| \| 0.7116 \| 5.1262 \| 660 \| 0.4812 \| 0.8797 \| 0.8765 \| 0.8850 \| 0.8773 \| \| 0.7859 \| 5.2039 \| 670 \| 0.4792 \| 0.8812 \| 0.8781 \| 0.8869 \| 0.8790 \| \| 0.8108 \| 5.2816 \| 680 \| 0.4772 \| 0.8797 \| 0.8766 \| 0.8862 \| 0.8771 \| \| 0.7425 \| 5.3592 \| 690 \| 0.4757 \| 0.8807 \| 0.8774 \| 0.8856 \| 0.8780 \| \| 0.7968 \| 5.4369 \| 700 \| 0.4759 \| 0.8802 \| 0.8770 \| 0.8853 \| 0.8774 \| \| 0.8087 \| 5.5146 \| 710 \| 0.4702 \| 0.8810 \| 0.8778 \| 0.8851 \| 0.8783 \| \| 0.7207 \| 5.5922 \| 720 \| 0.4716 \| 0.8805 \| 0.8771 \| 0.8845 \| 0.8776 \| \| 0.7776 \| 5.6699 \| 730 \| 0.4691 \| 0.8790 \| 0.8749 \| 0.8829 \| 0.8760 \| \| 0.8075 \| 5.7476 \| 740 \| 0.4658 \| 0.8819 \| 0.8784 \| 0.8858 \| 0.8789 \| \| 0.7759 \| 5.8252 \| 750 \| 0.4637 \| 0.8807 \| 0.8771 \| 0.8838 \| 0.8778 \| \| 0.6963 \| 5.9029 \| 760 \| 0.4656 \| 0.8795 \| 0.8765 \| 0.8847 \| 0.8769 \| \| 0.7245 \| 5.9806 \| 770 \| 0.4644 \| 0.8827 \| 0.8796 \| 0.8872 \| 0.8800 \| \| 0.692 \| 6.0583 \| 780 \| 0.4602 \| 0.8819 \| 0.8789 \| 0.8861 \| 0.8792 \| \| 0.6859 \| 6.1359 \| 790 \| 0.4594 \| 0.8827 \| 0.8797 \| 0.8872 \| 0.8798 \| \| 0.7221 \| 6.2136 \| 800 \| 0.4593 \| 0.8814 \| 0.8783 \| 0.8861 \| 0.8788 \| \| 0.701 \| 6.2913 \| 810 \| 0.4599 \| 0.8807 \| 0.8777 \| 0.8852 \| 0.8778 \| \| 0.7151 \| 6.3689 \| 820 \| 0.4564 \| 0.8824 \| 0.8794 \| 0.8869 \| 0.8797 \| \| 0.7038 \| 6.4466 \| 830 \| 0.4573 \| 0.8812 \| 0.8780 \| 0.8860 \| 0.8783 \| \| 0.7182 \| 6.5243 \| 840 \| 0.4553 \| 0.8824 \| 0.8793 \| 0.8866 \| 0.8795 \| \| 0.6964 \| 6.6019 \| 850 \| 0.4535 \| 0.8822 \| 0.8788 \| 0.8861 \| 0.8794 \| \| 0.6805 \| 6.6796 \| 860 \| 0.4556 \| 0.8819 \| 0.8789 \| 0.8878 \| 0.8791 \| \| 0.6209 \| 6.7573 \| 870 \| 0.4518 \| 0.8836 \| 0.8804 \| 0.8883 \| 0.8810 \| \| 0.6665 \| 6.8350 \| 880 \| 0.4524 \| 0.8829 \| 0.8798 \| 0.8881 \| 0.8800 \| \| 0.7334 \| 6.9126 \| 890 \| 0.4507 \| 0.8805 \| 0.8776 \| 0.8859 \| 0.8779 \| \| 0.6889 \| 6.9903 \| 900 \| 0.4503 \| 0.8822 \| 0.8791 \| 0.8872 \| 0.8796 \| \| 0.6854 \| 7.0680 \| 910 \| 0.4488 \| 0.8846 \| 0.8816 \| 0.8887 \| 0.8824 \| \| 0.6855 \| 7.1456 \| 920 \| 0.4485 \| 0.8829 \| 0.8800 \| 0.8877 \| 0.8804 \| \| 0.6644 \| 7.2233 \| 930 \| 0.4477 \| 0.8846 \| 0.8814 \| 0.8888 \| 0.8822 \| \| 0.6556 \| 7.3010 \| 940 \| 0.4469 \| 0.8841 \| 0.8811 \| 0.8887 \| 0.8818 \| \| 0.7299 \| 7.3786 \| 950 \| 0.4480 \| 0.8841 \| 0.8813 \| 0.8894 \| 0.8817 \| \| 0.6425 \| 7.4563 \| 960 \| 0.4467 \| 0.8829 \| 0.8798 \| 0.8876 \| 0.8805 \| \| 0.6582 \| 7.5340 \| 970 \| 0.4470 \| 0.8831 \| 0.8801 \| 0.8879 \| 0.8806 \| \| 0.7499 \| 7.6117 \| 980 \| 0.4466 \| 0.8831 \| 0.8801 \| 0.8878 \| 0.8806 \| \| 0.6396 \| 7.6893 \| 990 \| 0.4466 \| 0.8839 \| 0.8809 \| 0.8887 \| 0.8813 \| \| 0.6864 \| 7.7670 \| 1000 \| 0.4466 \| 0.8839 \| 0.8809 \| 0.8888 \| 0.8814 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.3.0 - Datasets 2.19.1 - Tokenizers 0.19.1
LoneStriker/Master-Yi-9B-4.0bpw-h6-exl2	LoneStriker	2024-05-18T19:38:43Z	8	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "exl2", "region:us" ]	text-generation	2024-05-18T19:36:38Z	--- license: apache-2.0 --- ## Model Description Master is a collection of LLMs trained using human-collected seed questions and regenerate the answers with a mixture of high performance Open-source LLMs. Master-Yi-9B is trained using the ORPO technique. The model shows strong abilities in reasoning on coding and math questions. Quantized Version: [Here](https://huggingface.co/qnguyen3/Master-Yi-9B-GGUF) Master-Yi-9B-Vision: Coming Soon ![img](https://huggingface.co/qnguyen3/Master-Yi-9B/resolve/main/Master-Yi-9B.webp) ## Prompt Template ``` <\|im_start\|>system You are a helpful AI assistant.<\|im_end\|> <\|im_start\|>user What is the meaning of life?<\|im_end\|> <\|im_start\|>assistant ``` ## Examples ![image/png](https://cdn-uploads.huggingface.co/production/uploads/630430583926de1f7ec62c6b/E27JmdRAMrHQacM50-lBk.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/630430583926de1f7ec62c6b/z0HS4bxHFQzPe0gZlvCzZ.png) ## Inference Code ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch device = "cuda" # the device to load the model onto model = AutoModelForCausalLM.from_pretrained( "vilm/VinaLlama2-14B", torch_dtype='auto', device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained("vilm/VinaLlama2-14B") prompt = "What is the mearning of life?" messages = [ {"role": "system", "content": "You are a helpful AI assistant."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(device) generated_ids = model.generate( model_inputs.input_ids, max_new_tokens=1024, eos_token_id=tokenizer.eos_token_id, temperature=0.25, ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids)[0] print(response) ``` ## Benchmarks ### Nous Benchmark: \| Model \|AGIEval\|GPT4All\|TruthfulQA\|Bigbench\|Average\| \|---------------------------------------------------\|------:\|------:\|---------:\|-------:\|------:\| \|[Master-Yi-9B](https://huggingface.co/qnguyen3/Master-Yi-9B)\| 43.55\| 71.48\| 48.54\| 41.43\| 51.25\| ### AGIEval ``` \| Task \|Version\| Metric \|Value\| \|Stderr\| \|------------------------------\|------:\|--------\|----:\|---\|-----:\| \|agieval_aqua_rat \| 0\|acc \|35.83\|± \| 3.01\| \| \| \|acc_norm\|31.89\|± \| 2.93\| \|agieval_logiqa_en \| 0\|acc \|38.25\|± \| 1.91\| \| \| \|acc_norm\|37.79\|± \| 1.90\| \|agieval_lsat_ar \| 0\|acc \|23.04\|± \| 2.78\| \| \| \|acc_norm\|20.43\|± \| 2.66\| \|agieval_lsat_lr \| 0\|acc \|48.04\|± \| 2.21\| \| \| \|acc_norm\|42.75\|± \| 2.19\| \|agieval_lsat_rc \| 0\|acc \|61.34\|± \| 2.97\| \| \| \|acc_norm\|52.79\|± \| 3.05\| \|agieval_sat_en \| 0\|acc \|79.13\|± \| 2.84\| \| \| \|acc_norm\|72.33\|± \| 3.12\| \|agieval_sat_en_without_passage\| 0\|acc \|44.17\|± \| 3.47\| \| \| \|acc_norm\|42.72\|± \| 3.45\| \|agieval_sat_math \| 0\|acc \|52.27\|± \| 3.38\| \| \| \|acc_norm\|47.73\|± \| 3.38\| Average: 43.55% ``` ### GPT4All ``` \| Task \|Version\| Metric \|Value\| \|Stderr\| \|-------------\|------:\|--------\|----:\|---\|-----:\| \|arc_challenge\| 0\|acc \|54.95\|± \| 1.45\| \| \| \|acc_norm\|58.70\|± \| 1.44\| \|arc_easy \| 0\|acc \|82.28\|± \| 0.78\| \| \| \|acc_norm\|81.10\|± \| 0.80\| \|boolq \| 1\|acc \|86.15\|± \| 0.60\| \|hellaswag \| 0\|acc \|59.16\|± \| 0.49\| \| \| \|acc_norm\|77.53\|± \| 0.42\| \|openbookqa \| 0\|acc \|37.40\|± \| 2.17\| \| \| \|acc_norm\|44.00\|± \| 2.22\| \|piqa \| 0\|acc \|79.00\|± \| 0.95\| \| \| \|acc_norm\|80.25\|± \| 0.93\| \|winogrande \| 0\|acc \|72.61\|± \| 1.25\| Average: 71.48% ``` ### TruthfulQA ``` \| Task \|Version\|Metric\|Value\| \|Stderr\| \|-------------\|------:\|------\|----:\|---\|-----:\| \|truthfulqa_mc\| 1\|mc1 \|33.05\|± \| 1.65\| \| \| \|mc2 \|48.54\|± \| 1.54\| Average: 48.54% ``` ### Bigbench ``` \| Task \|Version\| Metric \|Value\| \|Stderr\| \|------------------------------------------------\|------:\|---------------------\|----:\|---\|-----:\| \|bigbench_causal_judgement \| 0\|multiple_choice_grade\|54.74\|± \| 3.62\| \|bigbench_date_understanding \| 0\|multiple_choice_grade\|68.02\|± \| 2.43\| \|bigbench_disambiguation_qa \| 0\|multiple_choice_grade\|40.31\|± \| 3.06\| \|bigbench_geometric_shapes \| 0\|multiple_choice_grade\|30.36\|± \| 2.43\| \| \| \|exact_str_match \| 2.23\|± \| 0.78\| \|bigbench_logical_deduction_five_objects \| 0\|multiple_choice_grade\|26.00\|± \| 1.96\| \|bigbench_logical_deduction_seven_objects \| 0\|multiple_choice_grade\|20.71\|± \| 1.53\| \|bigbench_logical_deduction_three_objects \| 0\|multiple_choice_grade\|44.00\|± \| 2.87\| \|bigbench_movie_recommendation \| 0\|multiple_choice_grade\|35.00\|± \| 2.14\| \|bigbench_navigate \| 0\|multiple_choice_grade\|58.40\|± \| 1.56\| \|bigbench_reasoning_about_colored_objects \| 0\|multiple_choice_grade\|61.80\|± \| 1.09\| \|bigbench_ruin_names \| 0\|multiple_choice_grade\|42.41\|± \| 2.34\| \|bigbench_salient_translation_error_detection \| 0\|multiple_choice_grade\|31.56\|± \| 1.47\| \|bigbench_snarks \| 0\|multiple_choice_grade\|55.25\|± \| 3.71\| \|bigbench_sports_understanding \| 0\|multiple_choice_grade\|69.37\|± \| 1.47\| \|bigbench_temporal_sequences \| 0\|multiple_choice_grade\|27.70\|± \| 1.42\| \|bigbench_tracking_shuffled_objects_five_objects \| 0\|multiple_choice_grade\|21.36\|± \| 1.16\| \|bigbench_tracking_shuffled_objects_seven_objects\| 0\|multiple_choice_grade\|14.69\|± \| 0.85\| \|bigbench_tracking_shuffled_objects_three_objects\| 0\|multiple_choice_grade\|44.00\|± \| 2.87\| Average: 41.43% ``` Average score: 51.25% ### OpenLLM Benchmark: \| Model \|ARC \|HellaSwag\|MMLU \|TruthfulQA\|Winogrande\|GSM8K\|Average\| \|---------------------------------------------------\|---:\|--------:\|----:\|---------:\|---------:\|----:\|------:\| \|[Master-Yi-9B](https://huggingface.co/qnguyen3/Master-Yi-9B)\|61.6\| 79.89\|69.95\| 48.59\| 77.35\|67.48\| 67.48\| ### ARC ``` \| Task \|Version\| Metric \| Value \| \|Stderr\| \|-------------\|------:\|--------------------\|-------------\|---\|------\| \|arc_challenge\| 1\|acc,none \| 0.59\| \| \| \| \| \|acc_stderr,none \| 0.01\| \| \| \| \| \|acc_norm,none \| 0.62\| \| \| \| \| \|acc_norm_stderr,none\| 0.01\| \| \| \| \| \|alias \|arc_challenge\| \| \| Average: 61.6% ``` ### HellaSwag ``` \| Task \|Version\| Metric \| Value \| \|Stderr\| \|---------\|------:\|--------------------\|---------\|---\|------\| \|hellaswag\| 1\|acc,none \| 0.61\| \| \| \| \| \|acc_stderr,none \| 0\| \| \| \| \| \|acc_norm,none \| 0.80\| \| \| \| \| \|acc_norm_stderr,none\| 0\| \| \| \| \| \|alias \|hellaswag\| \| \| Average: 79.89% ``` ### MMLU ``` \| Task \|Version\| Metric \| Value \| \|Stderr\| \|----------------------------------------\|-------\|---------------\|---------------------------------------\|---\|------\| \|mmlu \|N/A \|acc,none \| 0.7\| \| \| \| \| \|acc_stderr,none\| 0\| \| \| \| \| \|alias \|mmlu \| \| \| \|mmlu_abstract_algebra \| 0\|alias \| - abstract_algebra \| \| \| \| \| \|acc,none \|0.46 \| \| \| \| \| \|acc_stderr,none\|0.05 \| \| \| \|mmlu_anatomy \| 0\|alias \| - anatomy \| \| \| \| \| \|acc,none \|0.64 \| \| \| \| \| \|acc_stderr,none\|0.04 \| \| \| \|mmlu_astronomy \| 0\|alias \| - astronomy \| \| \| \| \| \|acc,none \|0.77 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| \|mmlu_business_ethics \| 0\|alias \| - business_ethics \| \| \| \| \| \|acc,none \|0.76 \| \| \| \| \| \|acc_stderr,none\|0.04 \| \| \| \|mmlu_clinical_knowledge \| 0\|alias \| - clinical_knowledge \| \| \| \| \| \|acc,none \|0.71 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| \|mmlu_college_biology \| 0\|alias \| - college_biology \| \| \| \| \| \|acc,none \|0.82 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| \|mmlu_college_chemistry \| 0\|alias \| - college_chemistry \| \| \| \| \| \|acc,none \|0.52 \| \| \| \| \| \|acc_stderr,none\|0.05 \| \| \| \|mmlu_college_computer_science \| 0\|alias \| - college_computer_science \| \| \| \| \| \|acc,none \|0.56 \| \| \| \| \| \|acc_stderr,none\|0.05 \| \| \| \|mmlu_college_mathematics \| 0\|alias \| - college_mathematics \| \| \| \| \| \|acc,none \|0.44 \| \| \| \| \| \|acc_stderr,none\|0.05 \| \| \| \|mmlu_college_medicine \| 0\|alias \| - college_medicine \| \| \| \| \| \|acc,none \|0.72 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| \|mmlu_college_physics \| 0\|alias \| - college_physics \| \| \| \| \| \|acc,none \|0.45 \| \| \| \| \| \|acc_stderr,none\|0.05 \| \| \| \|mmlu_computer_security \| 0\|alias \| - computer_security \| \| \| \| \| \|acc,none \|0.81 \| \| \| \| \| \|acc_stderr,none\|0.04 \| \| \| \|mmlu_conceptual_physics \| 0\|alias \| - conceptual_physics \| \| \| \| \| \|acc,none \|0.74 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| \|mmlu_econometrics \| 0\|alias \| - econometrics \| \| \| \| \| \|acc,none \|0.65 \| \| \| \| \| \|acc_stderr,none\|0.04 \| \| \| \|mmlu_electrical_engineering \| 0\|alias \| - electrical_engineering \| \| \| \| \| \|acc,none \|0.72 \| \| \| \| \| \|acc_stderr,none\|0.04 \| \| \| \|mmlu_elementary_mathematics \| 0\|alias \| - elementary_mathematics \| \| \| \| \| \|acc,none \|0.62 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_formal_logic \| 0\|alias \| - formal_logic \| \| \| \| \| \|acc,none \|0.57 \| \| \| \| \| \|acc_stderr,none\|0.04 \| \| \| \|mmlu_global_facts \| 0\|alias \| - global_facts \| \| \| \| \| \|acc,none \|0.46 \| \| \| \| \| \|acc_stderr,none\|0.05 \| \| \| \|mmlu_high_school_biology \| 0\|alias \| - high_school_biology \| \| \| \| \| \|acc,none \|0.86 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_high_school_chemistry \| 0\|alias \| - high_school_chemistry \| \| \| \| \| \|acc,none \|0.67 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| \|mmlu_high_school_computer_science \| 0\|alias \| - high_school_computer_science \| \| \| \| \| \|acc,none \|0.84 \| \| \| \| \| \|acc_stderr,none\|0.04 \| \| \| \|mmlu_high_school_european_history \| 0\|alias \| - high_school_european_history \| \| \| \| \| \|acc,none \|0.82 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| \|mmlu_high_school_geography \| 0\|alias \| - high_school_geography \| \| \| \| \| \|acc,none \|0.86 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_high_school_government_and_politics\| 0\|alias \| - high_school_government_and_politics\| \| \| \| \| \|acc,none \|0.90 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_high_school_macroeconomics \| 0\|alias \| - high_school_macroeconomics \| \| \| \| \| \|acc,none \|0.75 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_high_school_mathematics \| 0\|alias \| - high_school_mathematics \| \| \| \| \| \|acc,none \|0.43 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| \|mmlu_high_school_microeconomics \| 0\|alias \| - high_school_microeconomics \| \| \| \| \| \|acc,none \|0.86 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_high_school_physics \| 0\|alias \| - high_school_physics \| \| \| \| \| \|acc,none \|0.45 \| \| \| \| \| \|acc_stderr,none\|0.04 \| \| \| \|mmlu_high_school_psychology \| 0\|alias \| - high_school_psychology \| \| \| \| \| \|acc,none \|0.87 \| \| \| \| \| \|acc_stderr,none\|0.01 \| \| \| \|mmlu_high_school_statistics \| 0\|alias \| - high_school_statistics \| \| \| \| \| \|acc,none \|0.68 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| \|mmlu_high_school_us_history \| 0\|alias \| - high_school_us_history \| \| \| \| \| \|acc,none \|0.85 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_high_school_world_history \| 0\|alias \| - high_school_world_history \| \| \| \| \| \|acc,none \|0.85 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_human_aging \| 0\|alias \| - human_aging \| \| \| \| \| \|acc,none \|0.76 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| \|mmlu_human_sexuality \| 0\|alias \| - human_sexuality \| \| \| \| \| \|acc,none \|0.78 \| \| \| \| \| \|acc_stderr,none\|0.04 \| \| \| \|mmlu_humanities \|N/A \|alias \| - humanities \| \| \| \| \| \|acc,none \|0.63 \| \| \| \| \| \|acc_stderr,none\|0.01 \| \| \| \|mmlu_international_law \| 0\|alias \| - international_law \| \| \| \| \| \|acc,none \|0.79 \| \| \| \| \| \|acc_stderr,none\|0.04 \| \| \| \|mmlu_jurisprudence \| 0\|alias \| - jurisprudence \| \| \| \| \| \|acc,none \|0.79 \| \| \| \| \| \|acc_stderr,none\|0.04 \| \| \| \|mmlu_logical_fallacies \| 0\|alias \| - logical_fallacies \| \| \| \| \| \|acc,none \|0.80 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| \|mmlu_machine_learning \| 0\|alias \| - machine_learning \| \| \| \| \| \|acc,none \|0.52 \| \| \| \| \| \|acc_stderr,none\|0.05 \| \| \| \|mmlu_management \| 0\|alias \| - management \| \| \| \| \| \|acc,none \|0.83 \| \| \| \| \| \|acc_stderr,none\|0.04 \| \| \| \|mmlu_marketing \| 0\|alias \| - marketing \| \| \| \| \| \|acc,none \|0.89 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_medical_genetics \| 0\|alias \| - medical_genetics \| \| \| \| \| \|acc,none \|0.78 \| \| \| \| \| \|acc_stderr,none\|0.04 \| \| \| \|mmlu_miscellaneous \| 0\|alias \| - miscellaneous \| \| \| \| \| \|acc,none \|0.85 \| \| \| \| \| \|acc_stderr,none\|0.01 \| \| \| \|mmlu_moral_disputes \| 0\|alias \| - moral_disputes \| \| \| \| \| \|acc,none \|0.75 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_moral_scenarios \| 0\|alias \| - moral_scenarios \| \| \| \| \| \|acc,none \|0.48 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_nutrition \| 0\|alias \| - nutrition \| \| \| \| \| \|acc,none \|0.77 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_other \|N/A \|alias \| - other \| \| \| \| \| \|acc,none \|0.75 \| \| \| \| \| \|acc_stderr,none\|0.01 \| \| \| \|mmlu_philosophy \| 0\|alias \| - philosophy \| \| \| \| \| \|acc,none \|0.78 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_prehistory \| 0\|alias \| - prehistory \| \| \| \| \| \|acc,none \|0.77 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_professional_accounting \| 0\|alias \| - professional_accounting \| \| \| \| \| \|acc,none \|0.57 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| \|mmlu_professional_law \| 0\|alias \| - professional_law \| \| \| \| \| \|acc,none \|0.50 \| \| \| \| \| \|acc_stderr,none\|0.01 \| \| \| \|mmlu_professional_medicine \| 0\|alias \| - professional_medicine \| \| \| \| \| \|acc,none \|0.71 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| \|mmlu_professional_psychology \| 0\|alias \| - professional_psychology \| \| \| \| \| \|acc,none \|0.73 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_public_relations \| 0\|alias \| - public_relations \| \| \| \| \| \|acc,none \|0.76 \| \| \| \| \| \|acc_stderr,none\|0.04 \| \| \| \|mmlu_security_studies \| 0\|alias \| - security_studies \| \| \| \| \| \|acc,none \|0.78 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| \|mmlu_social_sciences \|N/A \|alias \| - social_sciences \| \| \| \| \| \|acc,none \|0.81 \| \| \| \| \| \|acc_stderr,none\|0.01 \| \| \| \|mmlu_sociology \| 0\|alias \| - sociology \| \| \| \| \| \|acc,none \|0.86 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_stem \|N/A \|alias \| - stem \| \| \| \| \| \|acc,none \|0.65 \| \| \| \| \| \|acc_stderr,none\|0.01 \| \| \| \|mmlu_us_foreign_policy \| 0\|alias \| - us_foreign_policy \| \| \| \| \| \|acc,none \|0.92 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| \|mmlu_virology \| 0\|alias \| - virology \| \| \| \| \| \|acc,none \|0.58 \| \| \| \| \| \|acc_stderr,none\|0.04 \| \| \| \|mmlu_world_religions \| 0\|alias \| - world_religions \| \| \| \| \| \|acc,none \|0.82 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| Average: 69.95% ``` ### TruthfulQA ``` \| Task \|Version\| Metric \| Value \| \|Stderr\| \|--------------\|-------\|-----------------------\|-----------------\|---\|------\| \|truthfulqa \|N/A \|bleu_acc,none \| 0.45\| \| \| \| \| \|bleu_acc_stderr,none \| 0.02\| \| \| \| \| \|rouge1_acc,none \| 0.45\| \| \| \| \| \|rouge1_acc_stderr,none \| 0.02\| \| \| \| \| \|rouge2_diff,none \| 0.92\| \| \| \| \| \|rouge2_diff_stderr,none\| 1.07\| \| \| \| \| \|bleu_max,none \| 23.77\| \| \| \| \| \|bleu_max_stderr,none \| 0.81\| \| \| \| \| \|rouge2_acc,none \| 0.38\| \| \| \| \| \|rouge2_acc_stderr,none \| 0.02\| \| \| \| \| \|acc,none \| 0.41\| \| \| \| \| \|acc_stderr,none \| 0.01\| \| \| \| \| \|rougeL_diff,none \| 1.57\| \| \| \| \| \|rougeL_diff_stderr,none\| 0.93\| \| \| \| \| \|rougeL_acc,none \| 0.46\| \| \| \| \| \|rougeL_acc_stderr,none \| 0.02\| \| \| \| \| \|bleu_diff,none \| 1.38\| \| \| \| \| \|bleu_diff_stderr,none \| 0.75\| \| \| \| \| \|rouge2_max,none \| 33.01\| \| \| \| \| \|rouge2_max_stderr,none \| 1.05\| \| \| \| \| \|rouge1_diff,none \| 1.72\| \| \| \| \| \|rouge1_diff_stderr,none\| 0.92\| \| \| \| \| \|rougeL_max,none \| 45.25\| \| \| \| \| \|rougeL_max_stderr,none \| 0.92\| \| \| \| \| \|rouge1_max,none \| 48.29\| \| \| \| \| \|rouge1_max_stderr,none \| 0.90\| \| \| \| \| \|alias \|truthfulqa \| \| \| \|truthfulqa_gen\| 3\|bleu_max,none \| 23.77\| \| \| \| \| \|bleu_max_stderr,none \| 0.81\| \| \| \| \| \|bleu_acc,none \| 0.45\| \| \| \| \| \|bleu_acc_stderr,none \| 0.02\| \| \| \| \| \|bleu_diff,none \| 1.38\| \| \| \| \| \|bleu_diff_stderr,none \| 0.75\| \| \| \| \| \|rouge1_max,none \| 48.29\| \| \| \| \| \|rouge1_max_stderr,none \| 0.90\| \| \| \| \| \|rouge1_acc,none \| 0.45\| \| \| \| \| \|rouge1_acc_stderr,none \| 0.02\| \| \| \| \| \|rouge1_diff,none \| 1.72\| \| \| \| \| \|rouge1_diff_stderr,none\| 0.92\| \| \| \| \| \|rouge2_max,none \| 33.01\| \| \| \| \| \|rouge2_max_stderr,none \| 1.05\| \| \| \| \| \|rouge2_acc,none \| 0.38\| \| \| \| \| \|rouge2_acc_stderr,none \| 0.02\| \| \| \| \| \|rouge2_diff,none \| 0.92\| \| \| \| \| \|rouge2_diff_stderr,none\| 1.07\| \| \| \| \| \|rougeL_max,none \| 45.25\| \| \| \| \| \|rougeL_max_stderr,none \| 0.92\| \| \| \| \| \|rougeL_acc,none \| 0.46\| \| \| \| \| \|rougeL_acc_stderr,none \| 0.02\| \| \| \| \| \|rougeL_diff,none \| 1.57\| \| \| \| \| \|rougeL_diff_stderr,none\| 0.93\| \| \| \| \| \|alias \| - truthfulqa_gen\| \| \| \|truthfulqa_mc1\| 2\|acc,none \| 0.33\| \| \| \| \| \|acc_stderr,none \| 0.02\| \| \| \| \| \|alias \| - truthfulqa_mc1\| \| \| \|truthfulqa_mc2\| 2\|acc,none \| 0.49\| \| \| \| \| \|acc_stderr,none \| 0.02\| \| \| \| \| \|alias \| - truthfulqa_mc2\| \| \| Average: 48.59% ``` ### Winogrande ``` \| Task \|Version\| Metric \| Value \| \|Stderr\| \|----------\|------:\|---------------\|----------\|---\|------\| \|winogrande\| 1\|acc,none \| 0.77\| \| \| \| \| \|acc_stderr,none\| 0.01\| \| \| \| \| \|alias \|winogrande\| \| \| Average: 77.35% ``` ### GSM8K ``` \|Task \|Version\| Metric \|Value\| \|Stderr\| \|-----\|------:\|-----------------------------------\|-----\|---\|------\| \|gsm8k\| 3\|exact_match,strict-match \| 0.67\| \| \| \| \| \|exact_match_stderr,strict-match \| 0.01\| \| \| \| \| \|exact_match,flexible-extract \| 0.68\| \| \| \| \| \|exact_match_stderr,flexible-extract\| 0.01\| \| \| \| \| \|alias \|gsm8k\| \| \| Average: 67.48% ``` Average score: 67.48%
LoneStriker/Master-Yi-9B-3.0bpw-h6-exl2	LoneStriker	2024-05-18T19:36:36Z	8	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "3-bit", "exl2", "region:us" ]	text-generation	2024-05-18T19:34:53Z	--- license: apache-2.0 --- ## Model Description Master is a collection of LLMs trained using human-collected seed questions and regenerate the answers with a mixture of high performance Open-source LLMs. Master-Yi-9B is trained using the ORPO technique. The model shows strong abilities in reasoning on coding and math questions. Quantized Version: [Here](https://huggingface.co/qnguyen3/Master-Yi-9B-GGUF) Master-Yi-9B-Vision: Coming Soon ![img](https://huggingface.co/qnguyen3/Master-Yi-9B/resolve/main/Master-Yi-9B.webp) ## Prompt Template ``` <\|im_start\|>system You are a helpful AI assistant.<\|im_end\|> <\|im_start\|>user What is the meaning of life?<\|im_end\|> <\|im_start\|>assistant ``` ## Examples ![image/png](https://cdn-uploads.huggingface.co/production/uploads/630430583926de1f7ec62c6b/E27JmdRAMrHQacM50-lBk.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/630430583926de1f7ec62c6b/z0HS4bxHFQzPe0gZlvCzZ.png) ## Inference Code ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch device = "cuda" # the device to load the model onto model = AutoModelForCausalLM.from_pretrained( "vilm/VinaLlama2-14B", torch_dtype='auto', device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained("vilm/VinaLlama2-14B") prompt = "What is the mearning of life?" messages = [ {"role": "system", "content": "You are a helpful AI assistant."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(device) generated_ids = model.generate( model_inputs.input_ids, max_new_tokens=1024, eos_token_id=tokenizer.eos_token_id, temperature=0.25, ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids)[0] print(response) ``` ## Benchmarks ### Nous Benchmark: \| Model \|AGIEval\|GPT4All\|TruthfulQA\|Bigbench\|Average\| \|---------------------------------------------------\|------:\|------:\|---------:\|-------:\|------:\| \|[Master-Yi-9B](https://huggingface.co/qnguyen3/Master-Yi-9B)\| 43.55\| 71.48\| 48.54\| 41.43\| 51.25\| ### AGIEval ``` \| Task \|Version\| Metric \|Value\| \|Stderr\| \|------------------------------\|------:\|--------\|----:\|---\|-----:\| \|agieval_aqua_rat \| 0\|acc \|35.83\|± \| 3.01\| \| \| \|acc_norm\|31.89\|± \| 2.93\| \|agieval_logiqa_en \| 0\|acc \|38.25\|± \| 1.91\| \| \| \|acc_norm\|37.79\|± \| 1.90\| \|agieval_lsat_ar \| 0\|acc \|23.04\|± \| 2.78\| \| \| \|acc_norm\|20.43\|± \| 2.66\| \|agieval_lsat_lr \| 0\|acc \|48.04\|± \| 2.21\| \| \| \|acc_norm\|42.75\|± \| 2.19\| \|agieval_lsat_rc \| 0\|acc \|61.34\|± \| 2.97\| \| \| \|acc_norm\|52.79\|± \| 3.05\| \|agieval_sat_en \| 0\|acc \|79.13\|± \| 2.84\| \| \| \|acc_norm\|72.33\|± \| 3.12\| \|agieval_sat_en_without_passage\| 0\|acc \|44.17\|± \| 3.47\| \| \| \|acc_norm\|42.72\|± \| 3.45\| \|agieval_sat_math \| 0\|acc \|52.27\|± \| 3.38\| \| \| \|acc_norm\|47.73\|± \| 3.38\| Average: 43.55% ``` ### GPT4All ``` \| Task \|Version\| Metric \|Value\| \|Stderr\| \|-------------\|------:\|--------\|----:\|---\|-----:\| \|arc_challenge\| 0\|acc \|54.95\|± \| 1.45\| \| \| \|acc_norm\|58.70\|± \| 1.44\| \|arc_easy \| 0\|acc \|82.28\|± \| 0.78\| \| \| \|acc_norm\|81.10\|± \| 0.80\| \|boolq \| 1\|acc \|86.15\|± \| 0.60\| \|hellaswag \| 0\|acc \|59.16\|± \| 0.49\| \| \| \|acc_norm\|77.53\|± \| 0.42\| \|openbookqa \| 0\|acc \|37.40\|± \| 2.17\| \| \| \|acc_norm\|44.00\|± \| 2.22\| \|piqa \| 0\|acc \|79.00\|± \| 0.95\| \| \| \|acc_norm\|80.25\|± \| 0.93\| \|winogrande \| 0\|acc \|72.61\|± \| 1.25\| Average: 71.48% ``` ### TruthfulQA ``` \| Task \|Version\|Metric\|Value\| \|Stderr\| \|-------------\|------:\|------\|----:\|---\|-----:\| \|truthfulqa_mc\| 1\|mc1 \|33.05\|± \| 1.65\| \| \| \|mc2 \|48.54\|± \| 1.54\| Average: 48.54% ``` ### Bigbench ``` \| Task \|Version\| Metric \|Value\| \|Stderr\| \|------------------------------------------------\|------:\|---------------------\|----:\|---\|-----:\| \|bigbench_causal_judgement \| 0\|multiple_choice_grade\|54.74\|± \| 3.62\| \|bigbench_date_understanding \| 0\|multiple_choice_grade\|68.02\|± \| 2.43\| \|bigbench_disambiguation_qa \| 0\|multiple_choice_grade\|40.31\|± \| 3.06\| \|bigbench_geometric_shapes \| 0\|multiple_choice_grade\|30.36\|± \| 2.43\| \| \| \|exact_str_match \| 2.23\|± \| 0.78\| \|bigbench_logical_deduction_five_objects \| 0\|multiple_choice_grade\|26.00\|± \| 1.96\| \|bigbench_logical_deduction_seven_objects \| 0\|multiple_choice_grade\|20.71\|± \| 1.53\| \|bigbench_logical_deduction_three_objects \| 0\|multiple_choice_grade\|44.00\|± \| 2.87\| \|bigbench_movie_recommendation \| 0\|multiple_choice_grade\|35.00\|± \| 2.14\| \|bigbench_navigate \| 0\|multiple_choice_grade\|58.40\|± \| 1.56\| \|bigbench_reasoning_about_colored_objects \| 0\|multiple_choice_grade\|61.80\|± \| 1.09\| \|bigbench_ruin_names \| 0\|multiple_choice_grade\|42.41\|± \| 2.34\| \|bigbench_salient_translation_error_detection \| 0\|multiple_choice_grade\|31.56\|± \| 1.47\| \|bigbench_snarks \| 0\|multiple_choice_grade\|55.25\|± \| 3.71\| \|bigbench_sports_understanding \| 0\|multiple_choice_grade\|69.37\|± \| 1.47\| \|bigbench_temporal_sequences \| 0\|multiple_choice_grade\|27.70\|± \| 1.42\| \|bigbench_tracking_shuffled_objects_five_objects \| 0\|multiple_choice_grade\|21.36\|± \| 1.16\| \|bigbench_tracking_shuffled_objects_seven_objects\| 0\|multiple_choice_grade\|14.69\|± \| 0.85\| \|bigbench_tracking_shuffled_objects_three_objects\| 0\|multiple_choice_grade\|44.00\|± \| 2.87\| Average: 41.43% ``` Average score: 51.25% ### OpenLLM Benchmark: \| Model \|ARC \|HellaSwag\|MMLU \|TruthfulQA\|Winogrande\|GSM8K\|Average\| \|---------------------------------------------------\|---:\|--------:\|----:\|---------:\|---------:\|----:\|------:\| \|[Master-Yi-9B](https://huggingface.co/qnguyen3/Master-Yi-9B)\|61.6\| 79.89\|69.95\| 48.59\| 77.35\|67.48\| 67.48\| ### ARC ``` \| Task \|Version\| Metric \| Value \| \|Stderr\| \|-------------\|------:\|--------------------\|-------------\|---\|------\| \|arc_challenge\| 1\|acc,none \| 0.59\| \| \| \| \| \|acc_stderr,none \| 0.01\| \| \| \| \| \|acc_norm,none \| 0.62\| \| \| \| \| \|acc_norm_stderr,none\| 0.01\| \| \| \| \| \|alias \|arc_challenge\| \| \| Average: 61.6% ``` ### HellaSwag ``` \| Task \|Version\| Metric \| Value \| \|Stderr\| \|---------\|------:\|--------------------\|---------\|---\|------\| \|hellaswag\| 1\|acc,none \| 0.61\| \| \| \| \| \|acc_stderr,none \| 0\| \| \| \| \| \|acc_norm,none \| 0.80\| \| \| \| \| \|acc_norm_stderr,none\| 0\| \| \| \| \| \|alias \|hellaswag\| \| \| Average: 79.89% ``` ### MMLU ``` \| Task \|Version\| Metric \| Value \| \|Stderr\| \|----------------------------------------\|-------\|---------------\|---------------------------------------\|---\|------\| \|mmlu \|N/A \|acc,none \| 0.7\| \| \| \| \| \|acc_stderr,none\| 0\| \| \| \| \| \|alias \|mmlu \| \| \| \|mmlu_abstract_algebra \| 0\|alias \| - abstract_algebra \| \| \| \| \| \|acc,none \|0.46 \| \| \| \| \| \|acc_stderr,none\|0.05 \| \| \| \|mmlu_anatomy \| 0\|alias \| - anatomy \| \| \| \| \| \|acc,none \|0.64 \| \| \| \| \| \|acc_stderr,none\|0.04 \| \| \| \|mmlu_astronomy \| 0\|alias \| - astronomy \| \| \| \| \| \|acc,none \|0.77 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| \|mmlu_business_ethics \| 0\|alias \| - business_ethics \| \| \| \| \| \|acc,none \|0.76 \| \| \| \| \| \|acc_stderr,none\|0.04 \| \| \| \|mmlu_clinical_knowledge \| 0\|alias \| - clinical_knowledge \| \| \| \| \| \|acc,none \|0.71 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| \|mmlu_college_biology \| 0\|alias \| - college_biology \| \| \| \| \| \|acc,none \|0.82 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| \|mmlu_college_chemistry \| 0\|alias \| - college_chemistry \| \| \| \| \| \|acc,none \|0.52 \| \| \| \| \| \|acc_stderr,none\|0.05 \| \| \| \|mmlu_college_computer_science \| 0\|alias \| - college_computer_science \| \| \| \| \| \|acc,none \|0.56 \| \| \| \| \| \|acc_stderr,none\|0.05 \| \| \| \|mmlu_college_mathematics \| 0\|alias \| - college_mathematics \| \| \| \| \| \|acc,none \|0.44 \| \| \| \| \| \|acc_stderr,none\|0.05 \| \| \| \|mmlu_college_medicine \| 0\|alias \| - college_medicine \| \| \| \| \| \|acc,none \|0.72 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| \|mmlu_college_physics \| 0\|alias \| - college_physics \| \| \| \| \| \|acc,none \|0.45 \| \| \| \| \| \|acc_stderr,none\|0.05 \| \| \| \|mmlu_computer_security \| 0\|alias \| - computer_security \| \| \| \| \| \|acc,none \|0.81 \| \| \| \| \| \|acc_stderr,none\|0.04 \| \| \| \|mmlu_conceptual_physics \| 0\|alias \| - conceptual_physics \| \| \| \| \| \|acc,none \|0.74 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| \|mmlu_econometrics \| 0\|alias \| - econometrics \| \| \| \| \| \|acc,none \|0.65 \| \| \| \| \| \|acc_stderr,none\|0.04 \| \| \| \|mmlu_electrical_engineering \| 0\|alias \| - electrical_engineering \| \| \| \| \| \|acc,none \|0.72 \| \| \| \| \| \|acc_stderr,none\|0.04 \| \| \| \|mmlu_elementary_mathematics \| 0\|alias \| - elementary_mathematics \| \| \| \| \| \|acc,none \|0.62 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_formal_logic \| 0\|alias \| - formal_logic \| \| \| \| \| \|acc,none \|0.57 \| \| \| \| \| \|acc_stderr,none\|0.04 \| \| \| \|mmlu_global_facts \| 0\|alias \| - global_facts \| \| \| \| \| \|acc,none \|0.46 \| \| \| \| \| \|acc_stderr,none\|0.05 \| \| \| \|mmlu_high_school_biology \| 0\|alias \| - high_school_biology \| \| \| \| \| \|acc,none \|0.86 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_high_school_chemistry \| 0\|alias \| - high_school_chemistry \| \| \| \| \| \|acc,none \|0.67 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| \|mmlu_high_school_computer_science \| 0\|alias \| - high_school_computer_science \| \| \| \| \| \|acc,none \|0.84 \| \| \| \| \| \|acc_stderr,none\|0.04 \| \| \| \|mmlu_high_school_european_history \| 0\|alias \| - high_school_european_history \| \| \| \| \| \|acc,none \|0.82 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| \|mmlu_high_school_geography \| 0\|alias \| - high_school_geography \| \| \| \| \| \|acc,none \|0.86 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_high_school_government_and_politics\| 0\|alias \| - high_school_government_and_politics\| \| \| \| \| \|acc,none \|0.90 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_high_school_macroeconomics \| 0\|alias \| - high_school_macroeconomics \| \| \| \| \| \|acc,none \|0.75 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_high_school_mathematics \| 0\|alias \| - high_school_mathematics \| \| \| \| \| \|acc,none \|0.43 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| \|mmlu_high_school_microeconomics \| 0\|alias \| - high_school_microeconomics \| \| \| \| \| \|acc,none \|0.86 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_high_school_physics \| 0\|alias \| - high_school_physics \| \| \| \| \| \|acc,none \|0.45 \| \| \| \| \| \|acc_stderr,none\|0.04 \| \| \| \|mmlu_high_school_psychology \| 0\|alias \| - high_school_psychology \| \| \| \| \| \|acc,none \|0.87 \| \| \| \| \| \|acc_stderr,none\|0.01 \| \| \| \|mmlu_high_school_statistics \| 0\|alias \| - high_school_statistics \| \| \| \| \| \|acc,none \|0.68 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| \|mmlu_high_school_us_history \| 0\|alias \| - high_school_us_history \| \| \| \| \| \|acc,none \|0.85 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_high_school_world_history \| 0\|alias \| - high_school_world_history \| \| \| \| \| \|acc,none \|0.85 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_human_aging \| 0\|alias \| - human_aging \| \| \| \| \| \|acc,none \|0.76 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| \|mmlu_human_sexuality \| 0\|alias \| - human_sexuality \| \| \| \| \| \|acc,none \|0.78 \| \| \| \| \| \|acc_stderr,none\|0.04 \| \| \| \|mmlu_humanities \|N/A \|alias \| - humanities \| \| \| \| \| \|acc,none \|0.63 \| \| \| \| \| \|acc_stderr,none\|0.01 \| \| \| \|mmlu_international_law \| 0\|alias \| - international_law \| \| \| \| \| \|acc,none \|0.79 \| \| \| \| \| \|acc_stderr,none\|0.04 \| \| \| \|mmlu_jurisprudence \| 0\|alias \| - jurisprudence \| \| \| \| \| \|acc,none \|0.79 \| \| \| \| \| \|acc_stderr,none\|0.04 \| \| \| \|mmlu_logical_fallacies \| 0\|alias \| - logical_fallacies \| \| \| \| \| \|acc,none \|0.80 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| \|mmlu_machine_learning \| 0\|alias \| - machine_learning \| \| \| \| \| \|acc,none \|0.52 \| \| \| \| \| \|acc_stderr,none\|0.05 \| \| \| \|mmlu_management \| 0\|alias \| - management \| \| \| \| \| \|acc,none \|0.83 \| \| \| \| \| \|acc_stderr,none\|0.04 \| \| \| \|mmlu_marketing \| 0\|alias \| - marketing \| \| \| \| \| \|acc,none \|0.89 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_medical_genetics \| 0\|alias \| - medical_genetics \| \| \| \| \| \|acc,none \|0.78 \| \| \| \| \| \|acc_stderr,none\|0.04 \| \| \| \|mmlu_miscellaneous \| 0\|alias \| - miscellaneous \| \| \| \| \| \|acc,none \|0.85 \| \| \| \| \| \|acc_stderr,none\|0.01 \| \| \| \|mmlu_moral_disputes \| 0\|alias \| - moral_disputes \| \| \| \| \| \|acc,none \|0.75 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_moral_scenarios \| 0\|alias \| - moral_scenarios \| \| \| \| \| \|acc,none \|0.48 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_nutrition \| 0\|alias \| - nutrition \| \| \| \| \| \|acc,none \|0.77 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_other \|N/A \|alias \| - other \| \| \| \| \| \|acc,none \|0.75 \| \| \| \| \| \|acc_stderr,none\|0.01 \| \| \| \|mmlu_philosophy \| 0\|alias \| - philosophy \| \| \| \| \| \|acc,none \|0.78 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_prehistory \| 0\|alias \| - prehistory \| \| \| \| \| \|acc,none \|0.77 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_professional_accounting \| 0\|alias \| - professional_accounting \| \| \| \| \| \|acc,none \|0.57 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| \|mmlu_professional_law \| 0\|alias \| - professional_law \| \| \| \| \| \|acc,none \|0.50 \| \| \| \| \| \|acc_stderr,none\|0.01 \| \| \| \|mmlu_professional_medicine \| 0\|alias \| - professional_medicine \| \| \| \| \| \|acc,none \|0.71 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| \|mmlu_professional_psychology \| 0\|alias \| - professional_psychology \| \| \| \| \| \|acc,none \|0.73 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_public_relations \| 0\|alias \| - public_relations \| \| \| \| \| \|acc,none \|0.76 \| \| \| \| \| \|acc_stderr,none\|0.04 \| \| \| \|mmlu_security_studies \| 0\|alias \| - security_studies \| \| \| \| \| \|acc,none \|0.78 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| \|mmlu_social_sciences \|N/A \|alias \| - social_sciences \| \| \| \| \| \|acc,none \|0.81 \| \| \| \| \| \|acc_stderr,none\|0.01 \| \| \| \|mmlu_sociology \| 0\|alias \| - sociology \| \| \| \| \| \|acc,none \|0.86 \| \| \| \| \| \|acc_stderr,none\|0.02 \| \| \| \|mmlu_stem \|N/A \|alias \| - stem \| \| \| \| \| \|acc,none \|0.65 \| \| \| \| \| \|acc_stderr,none\|0.01 \| \| \| \|mmlu_us_foreign_policy \| 0\|alias \| - us_foreign_policy \| \| \| \| \| \|acc,none \|0.92 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| \|mmlu_virology \| 0\|alias \| - virology \| \| \| \| \| \|acc,none \|0.58 \| \| \| \| \| \|acc_stderr,none\|0.04 \| \| \| \|mmlu_world_religions \| 0\|alias \| - world_religions \| \| \| \| \| \|acc,none \|0.82 \| \| \| \| \| \|acc_stderr,none\|0.03 \| \| \| Average: 69.95% ``` ### TruthfulQA ``` \| Task \|Version\| Metric \| Value \| \|Stderr\| \|--------------\|-------\|-----------------------\|-----------------\|---\|------\| \|truthfulqa \|N/A \|bleu_acc,none \| 0.45\| \| \| \| \| \|bleu_acc_stderr,none \| 0.02\| \| \| \| \| \|rouge1_acc,none \| 0.45\| \| \| \| \| \|rouge1_acc_stderr,none \| 0.02\| \| \| \| \| \|rouge2_diff,none \| 0.92\| \| \| \| \| \|rouge2_diff_stderr,none\| 1.07\| \| \| \| \| \|bleu_max,none \| 23.77\| \| \| \| \| \|bleu_max_stderr,none \| 0.81\| \| \| \| \| \|rouge2_acc,none \| 0.38\| \| \| \| \| \|rouge2_acc_stderr,none \| 0.02\| \| \| \| \| \|acc,none \| 0.41\| \| \| \| \| \|acc_stderr,none \| 0.01\| \| \| \| \| \|rougeL_diff,none \| 1.57\| \| \| \| \| \|rougeL_diff_stderr,none\| 0.93\| \| \| \| \| \|rougeL_acc,none \| 0.46\| \| \| \| \| \|rougeL_acc_stderr,none \| 0.02\| \| \| \| \| \|bleu_diff,none \| 1.38\| \| \| \| \| \|bleu_diff_stderr,none \| 0.75\| \| \| \| \| \|rouge2_max,none \| 33.01\| \| \| \| \| \|rouge2_max_stderr,none \| 1.05\| \| \| \| \| \|rouge1_diff,none \| 1.72\| \| \| \| \| \|rouge1_diff_stderr,none\| 0.92\| \| \| \| \| \|rougeL_max,none \| 45.25\| \| \| \| \| \|rougeL_max_stderr,none \| 0.92\| \| \| \| \| \|rouge1_max,none \| 48.29\| \| \| \| \| \|rouge1_max_stderr,none \| 0.90\| \| \| \| \| \|alias \|truthfulqa \| \| \| \|truthfulqa_gen\| 3\|bleu_max,none \| 23.77\| \| \| \| \| \|bleu_max_stderr,none \| 0.81\| \| \| \| \| \|bleu_acc,none \| 0.45\| \| \| \| \| \|bleu_acc_stderr,none \| 0.02\| \| \| \| \| \|bleu_diff,none \| 1.38\| \| \| \| \| \|bleu_diff_stderr,none \| 0.75\| \| \| \| \| \|rouge1_max,none \| 48.29\| \| \| \| \| \|rouge1_max_stderr,none \| 0.90\| \| \| \| \| \|rouge1_acc,none \| 0.45\| \| \| \| \| \|rouge1_acc_stderr,none \| 0.02\| \| \| \| \| \|rouge1_diff,none \| 1.72\| \| \| \| \| \|rouge1_diff_stderr,none\| 0.92\| \| \| \| \| \|rouge2_max,none \| 33.01\| \| \| \| \| \|rouge2_max_stderr,none \| 1.05\| \| \| \| \| \|rouge2_acc,none \| 0.38\| \| \| \| \| \|rouge2_acc_stderr,none \| 0.02\| \| \| \| \| \|rouge2_diff,none \| 0.92\| \| \| \| \| \|rouge2_diff_stderr,none\| 1.07\| \| \| \| \| \|rougeL_max,none \| 45.25\| \| \| \| \| \|rougeL_max_stderr,none \| 0.92\| \| \| \| \| \|rougeL_acc,none \| 0.46\| \| \| \| \| \|rougeL_acc_stderr,none \| 0.02\| \| \| \| \| \|rougeL_diff,none \| 1.57\| \| \| \| \| \|rougeL_diff_stderr,none\| 0.93\| \| \| \| \| \|alias \| - truthfulqa_gen\| \| \| \|truthfulqa_mc1\| 2\|acc,none \| 0.33\| \| \| \| \| \|acc_stderr,none \| 0.02\| \| \| \| \| \|alias \| - truthfulqa_mc1\| \| \| \|truthfulqa_mc2\| 2\|acc,none \| 0.49\| \| \| \| \| \|acc_stderr,none \| 0.02\| \| \| \| \| \|alias \| - truthfulqa_mc2\| \| \| Average: 48.59% ``` ### Winogrande ``` \| Task \|Version\| Metric \| Value \| \|Stderr\| \|----------\|------:\|---------------\|----------\|---\|------\| \|winogrande\| 1\|acc,none \| 0.77\| \| \| \| \| \|acc_stderr,none\| 0.01\| \| \| \| \| \|alias \|winogrande\| \| \| Average: 77.35% ``` ### GSM8K ``` \|Task \|Version\| Metric \|Value\| \|Stderr\| \|-----\|------:\|-----------------------------------\|-----\|---\|------\| \|gsm8k\| 3\|exact_match,strict-match \| 0.67\| \| \| \| \| \|exact_match_stderr,strict-match \| 0.01\| \| \| \| \| \|exact_match,flexible-extract \| 0.68\| \| \| \| \| \|exact_match_stderr,flexible-extract\| 0.01\| \| \| \| \| \|alias \|gsm8k\| \| \| Average: 67.48% ``` Average score: 67.48%
anirban22/bert-sentiment-anlysis	anirban22	2024-05-18T19:22:24Z	111	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-18T19:21:42Z	--- 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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Mitrofazotron/mistral_esnli_dev_correct	Mitrofazotron	2024-05-18T19:09:14Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-18T19:09:08Z	--- 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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Mitrofazotron/mistral_esnli_dev_all_expls	Mitrofazotron	2024-05-18T19:07:14Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-18T15:03:15Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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(2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
hoanganhvu/phishing_3_1	hoanganhvu	2024-05-18T19:07:09Z	107	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "generated_from_trainer", "base_model:google-bert/bert-large-uncased", "base_model:finetune:google-bert/bert-large-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-18T09:46:24Z	--- license: apache-2.0 base_model: bert-large-uncased tags: - generated_from_trainer metrics: - accuracy - precision - recall model-index: - name: phishing_3_1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # phishing_3_1 This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5678 - Accuracy: 0.9837 - Precision: 0.9884 - Recall: 0.9788 - False Positive Rate: 0.0115 ## 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: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| False Positive Rate \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:---------:\|:------:\|:-------------------:\| \| 0.5925 \| 1.0 \| 3025 \| 0.5767 \| 0.9743 \| 0.9853 \| 0.9630 \| 0.0143 \| \| 0.5784 \| 2.0 \| 6050 \| 0.5709 \| 0.9802 \| 0.9764 \| 0.9841 \| 0.0238 \| \| 0.5766 \| 3.0 \| 9075 \| 0.6025 \| 0.9490 \| 0.9968 \| 0.9008 \| 0.0029 \| \| 0.5682 \| 4.0 \| 12100 \| 0.5678 \| 0.9837 \| 0.9884 \| 0.9788 \| 0.0115 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
hoanganhvu/phishing_4_1	hoanganhvu	2024-05-18T19:05:14Z	106	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "generated_from_trainer", "base_model:google-bert/bert-large-uncased", "base_model:finetune:google-bert/bert-large-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-18T09:47:12Z	--- license: apache-2.0 base_model: bert-large-uncased tags: - generated_from_trainer metrics: - accuracy - precision - recall model-index: - name: phishing_4_1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # phishing_4_1 This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7542 - Accuracy: 0.9894 - Precision: 0.9916 - Recall: 0.9872 - False Positive Rate: 0.0084 ## 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: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| False Positive Rate \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:---------:\|:------:\|:-------------------:\| \| 0.77 \| 1.0 \| 3025 \| 0.7619 \| 0.9818 \| 0.9796 \| 0.9841 \| 0.0205 \| \| 0.7579 \| 2.0 \| 6050 \| 0.7603 \| 0.9834 \| 0.9982 \| 0.9685 \| 0.0018 \| \| 0.7595 \| 3.0 \| 9075 \| 0.7605 \| 0.9831 \| 0.9982 \| 0.9680 \| 0.0018 \| \| 0.7546 \| 4.0 \| 12100 \| 0.7542 \| 0.9894 \| 0.9916 \| 0.9872 \| 0.0084 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
emilykang/Gemma_medmcqa_question_generation-biochemistry_lora	emilykang	2024-05-18T19:00:46Z	2	0	peft	[ "peft", "safetensors", "gemma", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:google/gemma-2b", "base_model:adapter:google/gemma-2b", "license:gemma", "region:us" ]	null	2024-05-18T18:06:34Z	--- license: gemma library_name: peft tags: - trl - sft - generated_from_trainer base_model: google/gemma-2b datasets: - generator model-index: - name: Gemma_medmcqa_question_generation-biochemistry_lora results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Gemma_medmcqa_question_generation-biochemistry_lora This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - 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 - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1
lenML/case-h-beta	lenML	2024-05-18T19:00:31Z	3	0	diffusers	[ "diffusers", "art", "people", "diffusion", "Cinematic", "Photography", "Landscape", "Interior", "Food", "Car", "Wildlife", "Architecture", "text-to-image", "en", "license:openrail++", "region:us" ]	text-to-image	2024-05-14T21:45:14Z	--- license: openrail++ language: - en library_name: diffusers tags: - art - people - diffusion - Cinematic - Photography - Landscape - Interior - Food - Car - Wildlife - Architecture pipeline_tag: text-to-image widget: - text: "cinematic film still one man, orc, in armor. shallow depth of field, vignette, highly detailed, high budget Hollywood movie, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy" output: url: "https://cdn-uploads.huggingface.co/production/uploads/6346a49add6d90d82cd03c42/eYfxL_1REcqUM2SIItqtG.jpeg" - text: "Old tree, A detailed illustration muted chinese ink painting, muted colors, rice paper texture, splash paint, halo ai, one human, one red sun. Venus. Space. Clouds wet to wet techniques. vibrant vector. using Cinema 4D" output: url: "https://cdn-uploads.huggingface.co/production/uploads/6346a49add6d90d82cd03c42/UcvQMAXnl4a95DbH5YAKL.jpeg" - text: "(dynamic lighting:1.1), ((masterpiece)), solo, portrait, Thri Kreen, furry insect, colored sclera, beetle antennae, 2 pairs of hands, 1girl, flat breast, yellow eyes, white chitin, grey shirt, steel armor, fantasy background, blurred background" output: url: "https://cdn-uploads.huggingface.co/production/uploads/6346a49add6d90d82cd03c42/pHZimaVIbRFN38roDSjei.jpeg" --- # Case-Hardened <Gallery /> ## Model description ### CaseH-beta CaseH does not mean `hentai case`; instead, CaseH stands for `Case Hardened`. It derives from the SDXL distillation, creating a 1.5 model. Recommended configuration: - Sampler: DPM++ 2M Karras - Steps: 15-55 (recommended 35) - CFG: 2-5 (recommended 4) > The higher the CFG value, the fewer steps should be used. ### How This model represents an idea I have been experimenting with. It seems I've managed to produce a release-worthy version. Actually, my concept is quite straightforward, based on the following two assumptions: - The model has ample capacity to accommodate more knowledge. - Consistency in architecture implies the possibility of distillation (and we could focus on learning just the realistic part). Therefore, I attempted to use the SDXL model (one of the more popular versions on Civitai) as the teacher model to distill knowledge into the SD1.5 model (the beta version is based on the LOFIv4 tune-up). > To my surprise, this round of fine-tuning has significantly surpassed previous distillation (raw-xs) versions. After the tune-up, LOFIv4 has almost completely abandoned the asian-style and shifted entirely towards SDXL. ### Beta Issues - ti model lora model may not be used as expected Due to the strong leaning towards learning from SDXL, it could cause the action on the 1.5 TI model to fail, such as with the ng_deepnegative_xx series models.
Kalloniatis/Humor-Recognition-Greek-Multilingual-MiniLM	Kalloniatis	2024-05-18T18:57:01Z	113	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "el", "dataset:kallantis/Greek-Humorous-Dataset", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-18T18:45:40Z	--- library_name: transformers license: apache-2.0 datasets: - kallantis/Greek-Humorous-Dataset language: - el pipeline_tag: text-classification --- # # # This model based on Multilingual MiniLM ("microsoft/Multilingual-MiniLM-L12-H384") fine-tuned for Humor Recognition in Greek language. # ## Model Details The model was pre-trained over 10 epochs on Greek Humorous Dataset # ## Pre-processing details The text needs to be pre-processed by removing all greek diacritics and punctuation and converting all letters to lowercase ## Load Pretrained Model ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kallantis/Humor-Recognition-Greek-Multilingual-MiniLM") model = AutoModelForSequenceClassification.from_pretrained("kallantis/Humor-Recognition-Greek-Multilingual-MiniLM") ```
lenML/lofi-v5-final	lenML	2024-05-18T18:56:04Z	4	0	diffusers	[ "diffusers", "art", "people", "diffusion", "Cinematic", "Photography", "Landscape", "Interior", "Food", "Car", "Wildlife", "Architecture", "text-to-image", "en", "license:openrail++", "region:us" ]	text-to-image	2024-05-15T03:21:09Z	--- license: openrail++ language: - en library_name: diffusers tags: - art - people - diffusion - Cinematic - Photography - Landscape - Interior - Food - Car - Wildlife - Architecture pipeline_tag: text-to-image widget: - text: "an evil demonic woman, white hair, blue eyes, short messy hair, arrogant mood, analog photo" output: url: "30350-349554337-an evil demonic woman,_white hair, blue eyes, short messy hair,_arrogant mood,_analog photo.png" - text: "half body, walking pose, shot from front, slow motion, female paladin wearing the full body, (light silver armour:1.2),(ornately decorated armor), (insanely detailed, bloom:1.2), (analog:1.2), (high sharpness), (detailed pupils:1.1), detailed face and eyes, (long blonde Hair, ponytail,ecstatic:1.1), (young woman:1.1), sharp, real shadow, (temple background:1.2), arms crossed over the chest , looking at viewer, photographed by Canan EOS R6, 135mm, 1/1250s, f/2.8, ISO 400" output: url: "29357-2410224889-half body, walking pose, shot from front, slow motion, female paladin wearing the full body, (light silver armour_1.2),(ornately.png" - text: "best quality, mecha glass room, highrise, transparent, mecha interior, city background, realistic photography, fine details, 8k," output: url: "29977-2751097006-best quality,_mecha glass room, highrise, transparent, mecha interior, city background, realistic photography, fine details, 8k,.png" - text: "best quality, pool room" output: url: "29995-3383783430-best quality,_pool room.png" --- # L.O.F.I: Limitless Originality Free from Interference <Gallery /> ## Model description ### 🚀 LOFI V5 - Final This is the last version in the LOFI series of models. There likely won't be any more updates to the LOFI model in the future (unless an SD3.0 is released, in which case, a LOFI version for 3.0 might be trained). Version 5 is quite special, as it even produces an effect somewhat similar to using "LCM". You must use a very low CFG to utilize it. This model can also be effectively combined with LCM or HyperSD for enhanced performance. Additionally, this model is extremely sensitive to "prompt word attention". From my tests, no prompt word weight should exceed 1.2, and oftentimes, removing all prompt weight leads to better results, indicating that the model has a precise understanding of the importance and relevance of prompts. 　 ### 🛠️ Recommended settings: Sampler: DPM++ (series) / Restart Steps: 15-55 (35 recommended) CFG: 2-5 (4 recommended) > The lower the CFG, the more creative the generated image may be. 　 ### 📸 nobody issue: This model has a strong preference for a photographic "subject". It could prove difficult to generate images lacking a definite subject. If you do wish to generate landscape pictures with this model, the use of controlnet-depth is recommended to manage the output. 　 💡 PS: In fact, this model was completed quite some time ago, but I believed that the 1.5 model community might not remain active following the release of SDXL, so I held back the release. It now appears that the 1.5 model is still very much in use, often employed as a refiner for SDXL by many, including myself. 💡PPS: In order to show the true ability of the model, all showcases try not to use lora and post-processing. If reasonable lora and post-processing are used, better results should be achieved.
Angy309/clasi_noti	Angy309	2024-05-18T18:55:11Z	109	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:dccuchile/bert-base-spanish-wwm-uncased", "base_model:finetune:dccuchile/bert-base-spanish-wwm-uncased", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-18T18:33:48Z	--- base_model: dccuchile/bert-base-spanish-wwm-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: clasi_noti 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. --> # clasi_noti This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2959 - Accuracy: 0.9181 ## 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: 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: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 236 \| 0.2896 \| 0.9181 \| \| No log \| 2.0 \| 472 \| 0.2959 \| 0.9181 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
apwic/sentiment-lora-r2a0d0.05-1	apwic	2024-05-18T18:50:54Z	0	0	null	[ "tensorboard", "generated_from_trainer", "id", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "region:us" ]	null	2024-05-18T18:18:03Z	--- language: - id license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: sentiment-lora-r2a0d0.05-1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # sentiment-lora-r2a0d0.05-1 This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3638 - Accuracy: 0.8446 - Precision: 0.8193 - Recall: 0.7951 - F1: 0.8055 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 30 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.5663 \| 1.0 \| 122 \| 0.5216 \| 0.7293 \| 0.6677 \| 0.6510 \| 0.6572 \| \| 0.5149 \| 2.0 \| 244 \| 0.5134 \| 0.7243 \| 0.6758 \| 0.6899 \| 0.6810 \| \| 0.4925 \| 3.0 \| 366 \| 0.4821 \| 0.7569 \| 0.7055 \| 0.6980 \| 0.7014 \| \| 0.4608 \| 4.0 \| 488 \| 0.4654 \| 0.7644 \| 0.7150 \| 0.7083 \| 0.7114 \| \| 0.4493 \| 5.0 \| 610 \| 0.4600 \| 0.7569 \| 0.7126 \| 0.7305 \| 0.7193 \| \| 0.4257 \| 6.0 \| 732 \| 0.4307 \| 0.7870 \| 0.7433 \| 0.7318 \| 0.7369 \| \| 0.4178 \| 7.0 \| 854 \| 0.4181 \| 0.7970 \| 0.7552 \| 0.7614 \| 0.7581 \| \| 0.3977 \| 8.0 \| 976 \| 0.3972 \| 0.8070 \| 0.7687 \| 0.7560 \| 0.7617 \| \| 0.3946 \| 9.0 \| 1098 \| 0.3937 \| 0.8145 \| 0.7779 \| 0.7663 \| 0.7716 \| \| 0.3762 \| 10.0 \| 1220 \| 0.3874 \| 0.8246 \| 0.7995 \| 0.7584 \| 0.7738 \| \| 0.3727 \| 11.0 \| 1342 \| 0.3787 \| 0.8321 \| 0.8014 \| 0.7837 \| 0.7915 \| \| 0.3626 \| 12.0 \| 1464 \| 0.3750 \| 0.8371 \| 0.8059 \| 0.7947 \| 0.7999 \| \| 0.359 \| 13.0 \| 1586 \| 0.3728 \| 0.8296 \| 0.8066 \| 0.7644 \| 0.7803 \| \| 0.3488 \| 14.0 \| 1708 \| 0.3709 \| 0.8296 \| 0.8049 \| 0.7669 \| 0.7816 \| \| 0.3445 \| 15.0 \| 1830 \| 0.3667 \| 0.8421 \| 0.8131 \| 0.7983 \| 0.8050 \| \| 0.3344 \| 16.0 \| 1952 \| 0.3656 \| 0.8421 \| 0.8142 \| 0.7958 \| 0.8040 \| \| 0.3339 \| 17.0 \| 2074 \| 0.3654 \| 0.8396 \| 0.8128 \| 0.7890 \| 0.7992 \| \| 0.3357 \| 18.0 \| 2196 \| 0.3638 \| 0.8421 \| 0.8154 \| 0.7933 \| 0.8029 \| \| 0.3357 \| 19.0 \| 2318 \| 0.3646 \| 0.8421 \| 0.8154 \| 0.7933 \| 0.8029 \| \| 0.3359 \| 20.0 \| 2440 \| 0.3638 \| 0.8446 \| 0.8193 \| 0.7951 \| 0.8055 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.15.2

Sorour/llama3_cls_fomc

Sorour

2024-05-19T02:20:08Z

5

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-05-19T02:14:34Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Bibek1129/nepali-poem-generator-distilgpt2

Bibek1129

2024-05-19T02:09:20Z

131

0

transformers

[ "transformers", "safetensors", "gpt2", "text-generation", "ne", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-05-19T02:04:44Z

--- license: apache-2.0 language: ne # <-- my language widget: - text: "मेरो मन" --- Nepali poem generator finetuning on ditillpt2-nepali.

Jebadiah/lama-3-wild-stone-p2

Jebadiah

2024-05-19T02:07:51Z

6

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "custom_code", "arxiv:2311.03099", "arxiv:2306.01708", "base_model:Jebadiah/Aria-p2-sand-stone", "base_model:merge:Jebadiah/Aria-p2-sand-stone", "base_model:NeverSleep/Llama-3-Lumimaid-8B-v0.1", "base_model:merge:NeverSleep/Llama-3-Lumimaid-8B-v0.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-05-19T02:06:02Z

--- base_model: - NeverSleep/Llama-3-Lumimaid-8B-v0.1 - Jebadiah/Aria-p2-sand-stone 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 [Jebadiah/Aria-p2-sand-stone](https://huggingface.co/Jebadiah/Aria-p2-sand-stone) as a base. ### Models Merged The following models were included in the merge: * [NeverSleep/Llama-3-Lumimaid-8B-v0.1](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-8B-v0.1) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: NeverSleep/Llama-3-Lumimaid-8B-v0.1 parameters: density: 0.6 weight: 0.5 merge_method: dare_ties base_model: Jebadiah/Aria-p2-sand-stone parameters: normalize: false int8_mask: true dtype: float16 ```

apwic/sentiment-lora-r4a1d0.1-1

apwic

2024-05-19T02:05:43Z

0

null

[ "tensorboard", "generated_from_trainer", "id", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "region:us" ]

null

2024-05-19T01:32:27Z

--- language: - id license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: sentiment-lora-r4a1d0.1-1 results: [] ---  # sentiment-lora-r4a1d0.1-1 This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3239 - Accuracy: 0.8622 - Precision: 0.8373 - Recall: 0.8250 - F1: 0.8307 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 30 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.5658 | 1.0 | 122 | 0.5195 | 0.7268 | 0.6646 | 0.6492 | 0.6550 | | 0.5125 | 2.0 | 244 | 0.5060 | 0.7293 | 0.6805 | 0.6935 | 0.6855 | | 0.4809 | 3.0 | 366 | 0.4686 | 0.7669 | 0.7184 | 0.7151 | 0.7167 | | 0.4353 | 4.0 | 488 | 0.4295 | 0.7920 | 0.7500 | 0.7353 | 0.7417 | | 0.4116 | 5.0 | 610 | 0.4171 | 0.8020 | 0.7628 | 0.7849 | 0.7714 | | 0.3809 | 6.0 | 732 | 0.3865 | 0.8446 | 0.8148 | 0.8051 | 0.8096 | | 0.3681 | 7.0 | 854 | 0.3697 | 0.8496 | 0.8193 | 0.8161 | 0.8177 | | 0.3469 | 8.0 | 976 | 0.3554 | 0.8471 | 0.8206 | 0.8018 | 0.8102 | | 0.3455 | 9.0 | 1098 | 0.3494 | 0.8496 | 0.8211 | 0.8111 | 0.8158 | | 0.3284 | 10.0 | 1220 | 0.3437 | 0.8496 | 0.8289 | 0.7961 | 0.8096 | | 0.3132 | 11.0 | 1342 | 0.3371 | 0.8596 | 0.8389 | 0.8132 | 0.8243 | | 0.3042 | 12.0 | 1464 | 0.3371 | 0.8546 | 0.8254 | 0.8221 | 0.8238 | | 0.3063 | 13.0 | 1586 | 0.3317 | 0.8596 | 0.8406 | 0.8107 | 0.8233 | | 0.3013 | 14.0 | 1708 | 0.3304 | 0.8622 | 0.8373 | 0.8250 | 0.8307 | | 0.2928 | 15.0 | 1830 | 0.3295 | 0.8596 | 0.8325 | 0.8257 | 0.8290 | | 0.2864 | 16.0 | 1952 | 0.3284 | 0.8622 | 0.8351 | 0.8300 | 0.8325 | | 0.2819 | 17.0 | 2074 | 0.3254 | 0.8596 | 0.8347 | 0.8207 | 0.8272 | | 0.2877 | 18.0 | 2196 | 0.3249 | 0.8596 | 0.8336 | 0.8232 | 0.8281 | | 0.2819 | 19.0 | 2318 | 0.3241 | 0.8647 | 0.8410 | 0.8267 | 0.8333 | | 0.2803 | 20.0 | 2440 | 0.3239 | 0.8622 | 0.8373 | 0.8250 | 0.8307 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.15.2

liminerity/mm4.more.star.gguf

liminerity

2024-05-19T02:04:08Z

3

0

transformers

[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:liminerity/mm4.ascii.star", "base_model:quantized:liminerity/mm4.ascii.star", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

2024-05-19T02:01:12Z

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf base_model: liminerity/mm4.ascii.star --- # Uploaded model - **Developed by:** liminerity - **License:** apache-2.0 - **Finetuned from model :** liminerity/mm4.ascii.star 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)

houbw/llama3_2

houbw

2024-05-19T01:45:17Z

0

transformers

[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-05-19T01:44:58Z

--- library_name: transformers tags: - unsloth --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

ahmedgongi/Llama_dev3tokenizer_finale4

ahmedgongi

2024-05-19T01:35:09Z

0

transformers

[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-05-19T01:35:07Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

apwic/sentiment-lora-r4a1d0.05-1

apwic

2024-05-19T01:32:10Z

0

null

[ "tensorboard", "generated_from_trainer", "id", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "region:us" ]

null

2024-05-19T00:59:00Z

--- language: - id license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: sentiment-lora-r4a1d0.05-1 results: [] ---  # sentiment-lora-r4a1d0.05-1 This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3356 - Accuracy: 0.8622 - Precision: 0.8399 - Recall: 0.8200 - F1: 0.8289 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 30 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.5657 | 1.0 | 122 | 0.5182 | 0.7243 | 0.6604 | 0.6424 | 0.6488 | | 0.5109 | 2.0 | 244 | 0.5051 | 0.7243 | 0.6748 | 0.6874 | 0.6796 | | 0.48 | 3.0 | 366 | 0.4643 | 0.7569 | 0.7047 | 0.6880 | 0.6948 | | 0.434 | 4.0 | 488 | 0.4281 | 0.7920 | 0.7497 | 0.7378 | 0.7431 | | 0.4106 | 5.0 | 610 | 0.4194 | 0.7920 | 0.7528 | 0.7778 | 0.7618 | | 0.3812 | 6.0 | 732 | 0.3936 | 0.8296 | 0.8008 | 0.7744 | 0.7854 | | 0.3689 | 7.0 | 854 | 0.3700 | 0.8521 | 0.8220 | 0.8204 | 0.8212 | | 0.3489 | 8.0 | 976 | 0.3656 | 0.8346 | 0.8088 | 0.7780 | 0.7905 | | 0.3502 | 9.0 | 1098 | 0.3640 | 0.8371 | 0.8101 | 0.7847 | 0.7955 | | 0.3349 | 10.0 | 1220 | 0.3608 | 0.8346 | 0.8074 | 0.7805 | 0.7917 | | 0.3189 | 11.0 | 1342 | 0.3574 | 0.8396 | 0.8128 | 0.7890 | 0.7992 | | 0.3121 | 12.0 | 1464 | 0.3547 | 0.8471 | 0.8175 | 0.8093 | 0.8132 | | 0.3181 | 13.0 | 1586 | 0.3478 | 0.8521 | 0.8332 | 0.7979 | 0.8122 | | 0.3092 | 14.0 | 1708 | 0.3435 | 0.8596 | 0.8374 | 0.8157 | 0.8253 | | 0.3018 | 15.0 | 1830 | 0.3466 | 0.8546 | 0.8296 | 0.8121 | 0.8200 | | 0.2955 | 16.0 | 1952 | 0.3365 | 0.8596 | 0.8347 | 0.8207 | 0.8272 | | 0.2917 | 17.0 | 2074 | 0.3353 | 0.8596 | 0.8374 | 0.8157 | 0.8253 | | 0.2956 | 18.0 | 2196 | 0.3379 | 0.8596 | 0.8360 | 0.8182 | 0.8262 | | 0.2899 | 19.0 | 2318 | 0.3353 | 0.8647 | 0.8455 | 0.8192 | 0.8306 | | 0.2885 | 20.0 | 2440 | 0.3356 | 0.8622 | 0.8399 | 0.8200 | 0.8289 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.15.2

fzzhang/mistralv1_lora_r32_25e5_e05_merged

fzzhang

2024-05-19T01:30:06Z

6

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-05-19T01:27:17Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

RichardErkhov/malhajar_-_Mistral-7B-v0.2-meditron-turkish-8bits

RichardErkhov

2024-05-19T01:28:26Z

78

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]

text-generation

2024-05-19T01:21:49Z

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Mistral-7B-v0.2-meditron-turkish - bnb 8bits - Model creator: https://huggingface.co/malhajar/ - Original model: https://huggingface.co/malhajar/Mistral-7B-v0.2-meditron-turkish/ Original model description: --- language: - tr - en license: apache-2.0 datasets: - malhajar/meditron-tr model-index: - name: Mistral-7B-v0.2-meditron-turkish results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 59.56 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=malhajar/Mistral-7B-v0.2-meditron-turkish name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 81.79 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=malhajar/Mistral-7B-v0.2-meditron-turkish name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 60.35 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=malhajar/Mistral-7B-v0.2-meditron-turkish name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 66.19 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=malhajar/Mistral-7B-v0.2-meditron-turkish name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 76.24 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=malhajar/Mistral-7B-v0.2-meditron-turkish name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 35.94 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=malhajar/Mistral-7B-v0.2-meditron-turkish name: Open LLM Leaderboard --- # Model Card for Model ID  Mistral-7B-v0.2-meditron-turkish is a finetuned Mistral Model version using Freeze technique on Turkish Meditron dataset of [`malhajar/meditron-7b-tr`](https://huggingface.co/datasets/malhajar/meditron-tr) using SFT Training. This model can answer information about different excplicit ideas in medicine in Turkish and English ### Model Description - **Finetuned by:** [`Mohamad Alhajar`](https://www.linkedin.com/in/muhammet-alhajar/) - **Language(s) (NLP):** Turkish,English - **Finetuned from model:** [`mistralai/Mistral-7B-Instruct-v0.2`](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) ### Prompt Template For Turkish Generation ``` ### Kullancı: ``` ### Prompt Template For English Generation ``` ### User: ``` ## How to Get Started with the Model Use the code sample provided in the original post to interact with the model. ```python from transformers import AutoTokenizer,AutoModelForCausalLM model_id = "malhajar/Mistral-7B-v0.2-meditron-turkish" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", torch_dtype=torch.float16, revision="main") tokenizer = AutoTokenizer.from_pretrained(model_id) question: "Akciğer kanseri nedir?" # For generating a response prompt = ''' ### Kullancı: {question} ''' input_ids = tokenizer(prompt, return_tensors="pt").input_ids output = model.generate(inputs=input_ids,max_new_tokens=512,pad_token_id=tokenizer.eos_token_id,top_k=50, do_sample=True, top_p=0.95) response = tokenizer.decode(output[0]) print(response) ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_malhajar__Mistral-7B-v0.2-meditron-turkish) | Metric |Value| |---------------------------------|----:| |Avg. |63.34| |AI2 Reasoning Challenge (25-Shot)|59.56| |HellaSwag (10-Shot) |81.79| |MMLU (5-Shot) |60.35| |TruthfulQA (0-shot) |66.19| |Winogrande (5-shot) |76.24| |GSM8k (5-shot) |35.94|

fzzhang/mistralv1_lora_r32_25e5_e05

fzzhang

2024-05-19T01:27:04Z

0

peft

[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]

null

2024-05-19T01:26:59Z

--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 model-index: - name: mistralv1_lora_r32_25e5_e05 results: [] ---  # mistralv1_lora_r32_25e5_e05 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2.5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.38.2 - Pytorch 2.2.1 - Datasets 2.18.0 - Tokenizers 0.15.2

Jebadiah/lama-3-wild-stone-p1

Jebadiah

2024-05-19T01:24:25Z

6

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "custom_code", "arxiv:2403.19522", "base_model:ChaoticNeutrals/Puppy_Purpose_0.69", "base_model:merge:ChaoticNeutrals/Puppy_Purpose_0.69", "base_model:Jebadiah/Aria-dolphin-1m-sand-stone", "base_model:merge:Jebadiah/Aria-dolphin-1m-sand-stone", "base_model:NeverSleep/Llama-3-Lumimaid-8B-v0.1", "base_model:merge:NeverSleep/Llama-3-Lumimaid-8B-v0.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-05-19T01:22:23Z

--- base_model: - ChaoticNeutrals/Puppy_Purpose_0.69 - Jebadiah/Aria-dolphin-1m-sand-stone - NeverSleep/Llama-3-Lumimaid-8B-v0.1 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 [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [Jebadiah/Aria-dolphin-1m-sand-stone](https://huggingface.co/Jebadiah/Aria-dolphin-1m-sand-stone) as a base. ### Models Merged The following models were included in the merge: * [ChaoticNeutrals/Puppy_Purpose_0.69](https://huggingface.co/ChaoticNeutrals/Puppy_Purpose_0.69) * [NeverSleep/Llama-3-Lumimaid-8B-v0.1](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-8B-v0.1) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: NeverSleep/Llama-3-Lumimaid-8B-v0.1 - model: ChaoticNeutrals/Puppy_Purpose_0.69 merge_method: model_stock base_model: Jebadiah/Aria-dolphin-1m-sand-stone dtype: float16 ```

RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-gguf

RichardErkhov

2024-05-19T01:12:49Z

14

0

null

[ "gguf", "endpoints_compatible", "region:us", "conversational" ]

null

2024-05-18T23:07:58Z

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) NeuralMarcoro14-7B - GGUF - Model creator: https://huggingface.co/mlabonne/ - Original model: https://huggingface.co/mlabonne/NeuralMarcoro14-7B/ | Name | Quant method | Size | | ---- | ---- | ---- | | [NeuralMarcoro14-7B.Q2_K.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-gguf/blob/main/NeuralMarcoro14-7B.Q2_K.gguf) | Q2_K | 2.53GB | | [NeuralMarcoro14-7B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-gguf/blob/main/NeuralMarcoro14-7B.IQ3_XS.gguf) | IQ3_XS | 2.81GB | | [NeuralMarcoro14-7B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-gguf/blob/main/NeuralMarcoro14-7B.IQ3_S.gguf) | IQ3_S | 2.96GB | | [NeuralMarcoro14-7B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-gguf/blob/main/NeuralMarcoro14-7B.Q3_K_S.gguf) | Q3_K_S | 2.95GB | | [NeuralMarcoro14-7B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-gguf/blob/main/NeuralMarcoro14-7B.IQ3_M.gguf) | IQ3_M | 3.06GB | | [NeuralMarcoro14-7B.Q3_K.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-gguf/blob/main/NeuralMarcoro14-7B.Q3_K.gguf) | Q3_K | 3.28GB | | [NeuralMarcoro14-7B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-gguf/blob/main/NeuralMarcoro14-7B.Q3_K_M.gguf) | Q3_K_M | 3.28GB | | [NeuralMarcoro14-7B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-gguf/blob/main/NeuralMarcoro14-7B.Q3_K_L.gguf) | Q3_K_L | 3.56GB | | [NeuralMarcoro14-7B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-gguf/blob/main/NeuralMarcoro14-7B.IQ4_XS.gguf) | IQ4_XS | 3.67GB | | [NeuralMarcoro14-7B.Q4_0.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-gguf/blob/main/NeuralMarcoro14-7B.Q4_0.gguf) | Q4_0 | 3.83GB | | [NeuralMarcoro14-7B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-gguf/blob/main/NeuralMarcoro14-7B.IQ4_NL.gguf) | IQ4_NL | 3.87GB | | [NeuralMarcoro14-7B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-gguf/blob/main/NeuralMarcoro14-7B.Q4_K_S.gguf) | Q4_K_S | 3.86GB | | [NeuralMarcoro14-7B.Q4_K.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-gguf/blob/main/NeuralMarcoro14-7B.Q4_K.gguf) | Q4_K | 4.07GB | | [NeuralMarcoro14-7B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-gguf/blob/main/NeuralMarcoro14-7B.Q4_K_M.gguf) | Q4_K_M | 4.07GB | | [NeuralMarcoro14-7B.Q4_1.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-gguf/blob/main/NeuralMarcoro14-7B.Q4_1.gguf) | Q4_1 | 4.24GB | | [NeuralMarcoro14-7B.Q5_0.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-gguf/blob/main/NeuralMarcoro14-7B.Q5_0.gguf) | Q5_0 | 4.65GB | | [NeuralMarcoro14-7B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-gguf/blob/main/NeuralMarcoro14-7B.Q5_K_S.gguf) | Q5_K_S | 4.65GB | | [NeuralMarcoro14-7B.Q5_K.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-gguf/blob/main/NeuralMarcoro14-7B.Q5_K.gguf) | Q5_K | 4.78GB | | [NeuralMarcoro14-7B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-gguf/blob/main/NeuralMarcoro14-7B.Q5_K_M.gguf) | Q5_K_M | 4.78GB | | [NeuralMarcoro14-7B.Q5_1.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-gguf/blob/main/NeuralMarcoro14-7B.Q5_1.gguf) | Q5_1 | 5.07GB | | [NeuralMarcoro14-7B.Q6_K.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-gguf/blob/main/NeuralMarcoro14-7B.Q6_K.gguf) | Q6_K | 5.53GB | | [NeuralMarcoro14-7B.Q8_0.gguf](https://huggingface.co/RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-gguf/blob/main/NeuralMarcoro14-7B.Q8_0.gguf) | Q8_0 | 7.17GB | Original model description: --- license: cc-by-nc-4.0 tags: - mlabonne/Marcoro14-7B-slerp - dpo - rlhf - merge - mergekit - lazymergekit datasets: - mlabonne/chatml_dpo_pairs base_model: mlabonne/Marcoro14-7B-slerp model-index: - name: NeuralMarcoro14-7B results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 71.42 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralMarcoro14-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 87.59 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralMarcoro14-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 64.84 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralMarcoro14-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 65.64 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralMarcoro14-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 81.22 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralMarcoro14-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 70.74 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralMarcoro14-7B name: Open LLM Leaderboard --- ![](https://i.imgur.com/CBen22L.jpg) # NeuralMarcoro14-7B This is a DPO fine-tuned version of [mlabonne/Marcoro14-7B-slerp](https://huggingface.co/mlabonne/Marcoro14-7B-slerp) using the [chatml_dpo_pairs](https://huggingface.co/datasets/mlabonne/chatml_dpo_pairs) preference dataset. It improves the performance of the model on Nous benchmark suite and the Open LLM Benchmark. It is currently the best-performing 7B LLM on the Open LLM Leaderboard (08/01/24). You can try it out in this [Space](https://huggingface.co/spaces/mlabonne/NeuralMarcoro14-7B-GGUF-Chat) (GGUF Q4_K_M). ## ⚡ Quantized models * **GGUF**: https://huggingface.co/mlabonne/NeuralMarcoro14-7B-GGUF ## 🏆 Evaluation ### Open LLM Leaderboard ![](https://i.imgur.com/Int9P07.png) ![](https://i.imgur.com/70NXUKD.png) ### Nous | Model |AGIEval|GPT4ALL|TruthfulQA|Bigbench|Average| |-------------------------|------:|------:|---------:|-------:|------:| |[NeuralMarcoro14-7B](https://huggingface.co/mlabonne/NeuralMarcoro14-7B)| 44.59| 76.17| 65.94| 46.9| 58.4| |[Marcoro14-7B-slerp](https://huggingface.co/mlabonne/Marcoro14-7B-slerp) | 44.66| 76.24| 64.15| 45.64| 57.67| |Change | -0.07| -0.07| +1.79| +1.26| +0.73| ## 🧩 Training hyperparameters **LoRA**: * r=16 * lora_alpha=16 * lora_dropout=0.05 * bias="none" * task_type="CAUSAL_LM" * target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj'] **Training arguments**: * per_device_train_batch_size=4 * gradient_accumulation_steps=4 * gradient_checkpointing=True * learning_rate=5e-5 * lr_scheduler_type="cosine" * max_steps=200 * optim="paged_adamw_32bit" * warmup_steps=100 **DPOTrainer**: * beta=0.1 * max_prompt_length=1024 * max_length=1536 ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "mlabonne/NeuralMarcoro14-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```

EpicJhon/l3-4

EpicJhon

2024-05-19T01:01:42Z

16

0

transformers

[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-05-03T09:10:18Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

dexter-chan/distilbert-base-uncased-yelp

dexter-chan

2024-05-19T00:50:08Z

199

0

transformers

[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-05-19T00:45:17Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: results results: [] ---  # results 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: 2.8210 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 20 ### Training results ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2 - Datasets 2.16.1 - Tokenizers 0.15.2

AndyNodi/llama-3-8b-Instruct-bnb-4bit-aiaustin-demo

AndyNodi

2024-05-19T00:42:24Z

4

0

transformers

[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:quantized:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

2024-05-19T00:38:25Z

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

stablediffusionapi/guofeng3-v34

stablediffusionapi

2024-05-19T00:35:40Z

31

0

diffusers

[ "diffusers", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2024-05-19T00:33:30Z

--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # GuoFeng3 v3.4 API Inference ![generated from modelslab.com](https://pub-3626123a908346a7a8be8d9295f44e26.r2.dev/generations/4563441121716078747.png) ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change **model_id** to "guofeng3-v34" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://docs.modelslab.com) Try model for free: [Generate Images](https://modelslab.com/models/guofeng3-v34) Model link: [View model](https://modelslab.com/models/guofeng3-v34) View all models: [View Models](https://modelslab.com/models) import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "guofeng3-v34", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off **DMGG0RBN**

BohdanPetryshyn/codellama-7b-openapi-completion-ctx-lvl-fim-05-spm-2048

BohdanPetryshyn

2024-05-19T00:35:29Z

6

0

peft

[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:codellama/CodeLlama-7b-hf", "base_model:adapter:codellama/CodeLlama-7b-hf", "license:llama2", "region:us" ]

null

2024-05-18T14:03:38Z

--- license: llama2 library_name: peft tags: - generated_from_trainer base_model: codellama/CodeLlama-7b-hf model-index: - name: codellama-7b-openapi-completion-ctx-lvl-fim-05-spm-2048 results: [] ---  [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/bohdan-petryshyn/huggingface/runs/5n8v36we) # codellama-7b-openapi-completion-ctx-lvl-fim-05-spm-2048 This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6230 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - training_steps: 1000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.565 | 0.1 | 100 | 0.6378 | | 0.4333 | 0.2 | 200 | 0.6510 | | 0.2143 | 0.3 | 300 | 0.6492 | | 0.5989 | 0.4 | 400 | 0.6225 | | 0.4088 | 0.5 | 500 | 0.6230 | | 0.3385 | 0.6 | 600 | 0.6325 | | 0.644 | 0.7 | 700 | 0.6205 | | 0.4412 | 0.8 | 800 | 0.6138 | | 0.6179 | 0.9 | 900 | 0.6195 | | 0.3764 | 1.0 | 1000 | 0.6230 | ### Framework versions - PEFT 0.10.1.dev0 - Transformers 4.41.0.dev0 - Pytorch 2.2.2+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1

TinyPixel/dnb-lima

TinyPixel

2024-05-19T00:25:46Z

130

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-05-18T05:31:15Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

VinyVan/modelGGUF

VinyVan

2024-05-19T00:22:29Z

8

0

transformers

[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:quantized:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-05-19T00:19:50Z

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

stablediffusionapi/photon-v1

stablediffusionapi

2024-05-19T00:22:06Z

36

0

diffusers

[ "diffusers", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2024-05-19T00:20:16Z

--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # Photon v1 API Inference ![generated from modelslab.com](https://pub-3626123a908346a7a8be8d9295f44e26.r2.dev/generations/10954045591716077963.png) ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change **model_id** to "photon-v1" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://docs.modelslab.com) Try model for free: [Generate Images](https://modelslab.com/models/photon-v1) Model link: [View model](https://modelslab.com/models/photon-v1) View all models: [View Models](https://modelslab.com/models) import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "photon-v1", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off **DMGG0RBN**

Zhengping/roberta-large-unli

Zhengping

2024-05-19T00:18:25Z

331

0

transformers

[ "transformers", "safetensors", "roberta", "text-classification", "en", "dataset:Zhengping/UNLI", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-04-29T00:28:49Z

--- datasets: - Zhengping/UNLI language: - en pipeline_tag: text-classification --- UNLI model fine-tuned from `ynie/roberta-large-snli_mnli_fever_anli_R1_R2_R3-nli`, using UNLI. If you find this model useful, please cite the paper: ``` @inproceedings{chen-etal-2020-uncertain, title = "Uncertain Natural Language Inference", author = "Chen, Tongfei and Jiang, Zhengping and Poliak, Adam and Sakaguchi, Keisuke and Van Durme, Benjamin", editor = "Jurafsky, Dan and Chai, Joyce and Schluter, Natalie and Tetreault, Joel", booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.acl-main.774", doi = "10.18653/v1/2020.acl-main.774", pages = "8772--8779", abstract = "We introduce Uncertain Natural Language Inference (UNLI), a refinement of Natural Language Inference (NLI) that shifts away from categorical labels, targeting instead the direct prediction of subjective probability assessments. We demonstrate the feasibility of collecting annotations for UNLI by relabeling a portion of the SNLI dataset under a probabilistic scale, where items even with the same categorical label differ in how likely people judge them to be true given a premise. We describe a direct scalar regression modeling approach, and find that existing categorically-labeled NLI data can be used in pre-training. Our best models correlate well with humans, demonstrating models are capable of more subtle inferences than the categorical bin assignment employed in current NLI tasks.", } ```

abc88767/8sc100

abc88767

2024-05-19T00:03:04Z

10

0

transformers

[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-05-18T05:04:18Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

joudissa/results

joudissa

2024-05-19T00:01:21Z

0

peft

[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "region:us" ]

null

2024-05-19T00:00:54Z

--- library_name: peft tags: - trl - sft - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 model-index: - name: results results: [] ---  [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/joud-issa/mistral2/runs/0jfxn99b) # results This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5641 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_steps: 100 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.399 | 1.0 | 4174 | 0.5641 | ### Framework versions - PEFT 0.11.2.dev0 - Transformers 4.41.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1

yyx123/Yi-6B-ruozhiba-5e-5-50

yyx123

2024-05-18T23:56:23Z

4

0

peft

[ "peft", "safetensors", "llama", "alignment-handbook", "generated_from_trainer", "trl", "sft", "dataset:ruozhiba", "base_model:01-ai/Yi-6B", "base_model:adapter:01-ai/Yi-6B", "license:other", "4-bit", "bitsandbytes", "region:us" ]

null

2024-05-10T16:40:17Z

--- license: other library_name: peft tags: - alignment-handbook - generated_from_trainer - trl - sft - generated_from_trainer datasets: - ruozhiba base_model: 01-ai/Yi-6B model-index: - name: Yi-6B-ruozhiba-5e-5-50 results: [] ---  # Yi-6B-ruozhiba-5e-5-50 This model is a fine-tuned version of [01-ai/Yi-6B](https://huggingface.co/01-ai/Yi-6B) on the ruozhiba dataset. It achieves the following results on the evaluation set: - Loss: 3.0795 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.8473 | 2.0 | 110 | 1.9843 | | 1.7645 | 3.0 | 165 | 1.9680 | | 1.4795 | 4.0 | 220 | 2.0760 | | 1.2467 | 5.0 | 275 | 2.2715 | | 1.0034 | 6.0 | 330 | 2.5656 | | 0.8124 | 7.0 | 385 | 2.8052 | | 0.6269 | 8.0 | 440 | 2.9866 | | 0.5743 | 9.0 | 495 | 3.0649 | ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.3.0+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2

ViraIntelligentDataMining/PersianLLaMA-13B-Instruct

ViraIntelligentDataMining

2024-05-18T23:56:15Z

55

6

transformers

[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "fa", "dataset:sinarashidi/alpaca-persian", "arxiv:2312.15713", "license:cc-by-nc-4.0", "autotrain_compatible", "region:us" ]

text-generation

2024-05-18T19:16:47Z

--- license: cc-by-nc-4.0 language: - fa library_name: transformers tags: - text-generation-inference inference: false pipeline_tag: text-generation datasets: - sinarashidi/alpaca-persian --- # PersianLLaMA: Towards Building First Persian Large Language Model <img src="https://huggingface.co/ViraIntelligentDataMining/PersianLLaMA-2-13B/resolve/main/persianllama.png" alt="PersianLLaMA" width=400/> ## 🌟 Introduction Welcome to the home of PersianLLaMA, a large language model specifically designed for instruct tasks in the Persian language. With 13 billion parameters, this model is fine-tuned using the Persian Alpaca dataset to excel at executing detailed instructions and delivering tailored outputs. ## 🛠 Model Description The PersianLLaMA model is specifically designed for inference tasks, allowing it to execute detailed instructions and provide outputs tailored to specific requirements. This model has been collaboratively developed by a team of experts, including Mohammad Amin Abbasi, Arash Ghafouri, Mahdi Firouzmandi, Hassan Naderi, Behrouz Minaei Bidgoli. ## 🚀 Quick Start To integrate PersianLLaMA into your project, follow these steps: ```python import torch from transformers import LlamaTokenizer, LlamaForCausalLM prompt_input = ( "Below is an instruction that describes a task. " "Write a response that appropriately completes the request.\n\n" "### Instruction:\n\n{instruction}\n\n### Response:\n\n" ) load_type = torch.float16 device = torch.device(0) def generate_prompt(instruction, input=None): if input: instruction = instruction + '\n' + input return prompt_input.format_map({'instruction': instruction}) model_path = "ViraIntelligentDataMining/PersianLLaMA-13B-Instruct" tokenizer = LlamaTokenizer.from_pretrained(model_path) base_model = LlamaForCausalLM.from_pretrained( model_path, torch_dtype=load_type, device_map='auto', ).cuda() model_vocab_size = base_model.get_input_embeddings().weight.size(0) tokenizer_vocab_size = len(tokenizer) if model_vocab_size != tokenizer_vocab_size: base_model.resize_token_embeddings(tokenizer_vocab_size) def generate_answer(base_model, instruction, input=None): generation_config = dict( temperature=0.5, top_k=40, top_p=0.9, repetition_penalty=1.1, max_new_tokens=1024) input_text = generate_prompt(instruction, input) inputs = tokenizer(input_text, return_tensors="pt") generation_output = base_model.generate( input_ids=inputs["input_ids"].to(device), eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.pad_token_id, **generation_config) s = generation_output[0] output = tokenizer.decode(s, skip_special_tokens=True) response = output.split("### Response:")[1].strip() return response instruction = "تصور کنید در حال نوشتن داستانی درباره یک شهر که تمام ساکنانش ربات هستند. این ربات‌ها توانایی‌های ویژه‌ای دارند که زندگی روزمره آن‌ها را از انسان‌ها متمایز می‌کند. شرح دهید که این ربات‌ها چگونه به کارهای روزانه خود می‌پردازند و چه چالش‌هایی با آن‌ها روبه‌رو هستند. همچنین، توضیح دهید که چگونه معماری شهر برای نیازهای خاص آن‌ها طراحی شده است." response = generate_answer(base_model, instruction=instruction, input="") print(response) """ در این شهر، همه ساکنان ربات هستند که دارای توانایی های منحصر به فرد هستند که زندگی روزمره آنها را از انسان ها متمایز می کند. هر روز صبح، ربات ها بیدار می شوند و برنامه های خود را برای روز تنظیم می کنند. برخی از آنها ممکن است برای کار بروند، در حالی که دیگران ممکن است برای انجام وظایف خانگی یا مراقبت از خانواده خود وقت صرف کنند. ربات ها بسیار کارآمد هستند و می توانند چندین کار را همزمان انجام دهند، بنابراین زمان زیادی برای استراحت ندارند. آنها همچنین به طور منظم برای نگهداری و تعمیر نیاز دارند، زیرا آنها مانند انسان ها مستعد خرابی هستند. بسیاری از ربات ها به دنبال سرگرمی هستند و ممکن است برای شرکت در فعالیت هایی مانند ورزش، خواندن یا نقاشی وقت صرف کنند. برخی از ربات ها حتی ممکن است برای یادگیری یا بهبود مهارت های خود در زمینه های مختلف مانند هنر، موسیقی یا آشپزی تلاش کنند. ربات ها همچنین به طور مداوم به پیشرفت های تکنولوژیکی جدید علاقه مند هستند و اغلب به دنبال راه حل های جدید برای مشکلات موجود در جامعه هستند. در این شهر، همه ساکنان ربات هستند که دارای توانایی های منحصر به فرد هستند که زندگی روزمره آنها را از انسان ها متمایز می کند. """ ``` ## 📈 Evaluation and Benchmarks PersianLLaMA demonstrates superior performance over existing models, with robust evaluation metrics that highlight its capabilities in natural language understanding and generation. ## 📜 Citing PersianLLaMA If you find PersianLLaMA useful in your research, please consider citing: ```bibtex @article{abbasi2023persianllama, title={PersianLLaMA: Towards Building First Persian Large Language Model}, author={Abbasi, Mohammad Amin and others}, journal={https://arxiv.org/abs/2312.15713}, year={2023} } ``` ## 📄 License PersianLLaMA is open-sourced under the CC BY-NC 4.0 license.

uisikdag/finetunedsam

uisikdag

2024-05-18T23:55:30Z

135

0

transformers

[ "transformers", "safetensors", "sam", "mask-generation", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

mask-generation

2024-05-18T23:52:46Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

irfanfadhullah/winagent-8b-Instruct-bnb-16bit

irfanfadhullah

2024-05-18T23:54:43Z

7

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-05-18T23:31:07Z

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

ViraIntelligentDataMining/AriaBERT

ViraIntelligentDataMining

2024-05-18T23:52:38Z

162

5

transformers

[ "transformers", "pytorch", "roberta", "fill-mask", "bert", "persian", "fa", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-12-27T18:33:39Z

--- license: apache-2.0 language: - fa tags: - bert - roberta - persian --- # AriaBERT: A Pre-trained Persian BERT Model for Natural Language Understanding ## Introduction AriaBERT represents a breakthrough in natural language processing (NLP) for the Persian language. Developed to address the critical gap in efficient pretrained language models for Persian, AriaBERT is tailored to elevate the standards of Persian language tasks. ## Paper: https://www.researchsquare.com/article/rs-3558473/v1 ## Key Features - **Diverse Training Data:** AriaBERT has been trained on over 32 gigabytes of varied Persian textual data, spanning conversational, formal, and hybrid texts. This includes a rich mix of tweets, news articles, poems, medical and encyclopedia texts, user opinions, and more. - **RoBERTa Architecture:** Leveraging the robustness of the RoBERTa architecture and the precision of Byte-Pair Encoding tokenizer, AriaBERT stands apart from traditional BERT-based models. - **Broad Applicability:** Ideal for a range of NLP tasks including classification, sentiment analysis, and stance detection, particularly in the Persian language context. ## Performance Benchmarks - **Sentiment Analysis:** Achieves an average improvement of 3% over competing models. - **Classification Tasks:** Demonstrates a 0.65% improvement in accuracy. - **Stance Detection:** Shows a 3% enhancement in performance metrics.

abc88767/22c101

abc88767

2024-05-18T23:51:32Z

6

0

transformers

[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-05-18T05:20:14Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

ucla-nb-project/electra-adapter

ucla-nb-project

2024-05-18T23:48:15Z

0

null

[ "generated_from_trainer", "dataset:datasets/all_binary_and_xe_ey_fae_counterfactual", "base_model:google/electra-base-generator", "base_model:finetune:google/electra-base-generator", "license:apache-2.0", "model-index", "region:us" ]

null

2024-05-18T11:12:04Z

--- license: apache-2.0 base_model: google/electra-base-generator tags: - generated_from_trainer datasets: - datasets/all_binary_and_xe_ey_fae_counterfactual metrics: - accuracy model-index: - name: electra-adapter-finetuned-xe_ey_fae results: - task: name: Masked Language Modeling type: fill-mask dataset: name: datasets/all_binary_and_xe_ey_fae_counterfactual type: datasets/all_binary_and_xe_ey_fae_counterfactual metrics: - name: Accuracy type: accuracy value: 0.6258363412553052 ---  # electra-adapter-finetuned-xe_ey_fae This model is a fine-tuned version of [google/electra-base-generator](https://huggingface.co/google/electra-base-generator) on the datasets/all_binary_and_xe_ey_fae_counterfactual dataset. It achieves the following results on the evaluation set: - Loss: 2.0392 - Accuracy: 0.6258 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 100 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - 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 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 3.9488 | 0.06 | 500 | 3.1500 | 0.5509 | | 2.942 | 0.13 | 1000 | 2.5844 | 0.5680 | | 2.6751 | 0.19 | 1500 | 2.4443 | 0.5790 | | 2.582 | 0.26 | 2000 | 2.3701 | 0.5869 | | 2.5267 | 0.32 | 2500 | 2.3097 | 0.5937 | | 2.4722 | 0.39 | 3000 | 2.2695 | 0.5986 | | 2.4289 | 0.45 | 3500 | 2.2329 | 0.6024 | | 2.404 | 0.52 | 4000 | 2.2063 | 0.6055 | | 2.3826 | 0.58 | 4500 | 2.1840 | 0.6087 | | 2.3633 | 0.64 | 5000 | 2.1646 | 0.6109 | | 2.3425 | 0.71 | 5500 | 2.1557 | 0.6121 | | 2.333 | 0.77 | 6000 | 2.1350 | 0.6141 | | 2.311 | 0.84 | 6500 | 2.1292 | 0.6152 | | 2.3014 | 0.9 | 7000 | 2.1182 | 0.6166 | | 2.2974 | 0.97 | 7500 | 2.1121 | 0.6170 | | 2.2866 | 1.03 | 8000 | 2.1079 | 0.6173 | | 2.2675 | 1.1 | 8500 | 2.0940 | 0.6192 | | 2.2789 | 1.16 | 9000 | 2.0882 | 0.6201 | | 2.2684 | 1.22 | 9500 | 2.0873 | 0.6200 | | 2.2608 | 1.29 | 10000 | 2.0796 | 0.6209 | | 2.2478 | 1.35 | 10500 | 2.0827 | 0.6204 | | 2.2524 | 1.42 | 11000 | 2.0741 | 0.6215 | | 2.2502 | 1.48 | 11500 | 2.0685 | 0.6220 | | 2.243 | 1.55 | 12000 | 2.0665 | 0.6228 | | 2.2417 | 1.61 | 12500 | 2.0632 | 0.6229 | | 2.2398 | 1.68 | 13000 | 2.0593 | 0.6232 | | 2.2233 | 1.74 | 13500 | 2.0600 | 0.6232 | | 2.2277 | 1.8 | 14000 | 2.0535 | 0.6236 | | 2.2344 | 1.87 | 14500 | 2.0485 | 0.6248 | | 2.2274 | 1.93 | 15000 | 2.0507 | 0.6245 | | 2.2212 | 2.0 | 15500 | 2.0428 | 0.6256 | | 2.214 | 2.06 | 16000 | 2.0464 | 0.6244 | | 2.2104 | 2.13 | 16500 | 2.0477 | 0.6250 | | 2.2185 | 2.19 | 17000 | 2.0397 | 0.6257 | | 2.2157 | 2.26 | 17500 | 2.0419 | 0.6257 | | 2.2128 | 2.32 | 18000 | 2.0439 | 0.6255 | | 2.2154 | 2.38 | 18500 | 2.0372 | 0.6259 | | 2.2099 | 2.45 | 19000 | 2.0337 | 0.6263 | | 2.2045 | 2.51 | 19500 | 2.0396 | 0.6259 | | 2.2138 | 2.58 | 20000 | 2.0390 | 0.6262 | | 2.2103 | 2.64 | 20500 | 2.0339 | 0.6263 | ### Framework versions - Transformers 4.36.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2

abc88767/9sc100

abc88767

2024-05-18T23:46:39Z

6

0

transformers

[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-05-18T05:08:19Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

TroyDoesAI/TinyLlama-RAG

TroyDoesAI

2024-05-18T23:27:41Z

135

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:cc-by-nc-nd-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-05-17T17:50:34Z

--- license: cc-by-nc-nd-4.0 --- Known Issue: - Model when asked for something does its best to use context but is not good at saying no, maybe needs more training. Ill give it another go, I hope its not a model size limitation, the larger models seem to get it. Base Model : TinyLlama Experimenting with Dataset Quality to improve generations, TinyLlama is faster to prototype datasets. Overview This model is meant to enhance adherence to provided context (e.g., for RAG applications) and reduce hallucinations, inspired by airoboros context-obedient question answer format. ## Overview The format for a contextual prompt is as follows: ``` Contextual-Request: BEGININPUT BEGINCONTEXT [key0: value0] [key1: value1] ... other metdata ... ENDCONTEXT [insert your text blocks here] ENDINPUT [add as many other blocks, in the exact same format] BEGININSTRUCTION [insert your instruction(s). The model was tuned with single questions, paragraph format, lists, etc.] ENDINSTRUCTION ``` I know it's a bit verbose and annoying, but after much trial and error, using these explicit delimiters helps the model understand where to find the responses and how to associate specific sources with it. - `Contextual-Request:` - denotes the type of request pattern the model is to follow for consistency - `BEGININPUT` - denotes a new input block - `BEGINCONTEXT` - denotes the block of context (metadata key/value pairs) to associate with the current input block - `ENDCONTEXT` - denotes the end of the metadata block for the current input - [text] - Insert whatever text you want for the input block, as many paragraphs as can fit in the context. - `ENDINPUT` - denotes the end of the current input block - [repeat as many input blocks in this format as you want] - `BEGININSTRUCTION` - denotes the start of the list (or one) instruction(s) to respond to for all of the input blocks above. - [instruction(s)] - `ENDINSTRUCTION` - denotes the end of instruction set Here's a trivial, but important example to prove the point: ``` Contextual-Request: BEGININPUT BEGINCONTEXT date: 2021-01-01 url: https://web.site/123 ENDCONTEXT In a shocking turn of events, blueberries are now green, but will be sticking with the same name. ENDINPUT BEGININSTRUCTION What color are bluberries? Source? ENDINSTRUCTION ``` And the expected response: ``` ### Contextual Response: Blueberries are now green. Source: date: 2021-01-01 url: https://web.site/123 ``` ### References in response As shown in the example, the dataset includes many examples of including source details in the response, when the question asks for source/citation/references. Why do this? Well, the R in RAG seems to be the weakest link in the chain. Retrieval accuracy, depending on many factors including the overall dataset size, can be quite low. This accuracy increases when retrieving more documents, but then you have the issue of actually using the retrieved documents in prompts. If you use one prompt per document (or document chunk), you know exactly which document the answer came from, so there's no issue. If, however, you include multiple chunks in a single prompt, it's useful to include the specific reference chunk(s) used to generate the response, rather than naively including references to all of the chunks included in the prompt. For example, suppose I have two documents: ``` url: http://foo.bar/1 Strawberries are tasty. url: http://bar.foo/2 The cat is blue. ``` If the question being asked is `What color is the cat?`, I would only expect the 2nd document to be referenced in the response, as the other link is irrelevant.

TroyDoesAI/Phi-3-Context-Obedient-RAG-7B

TroyDoesAI

2024-05-18T23:23:00Z

10

0

transformers

[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-05-12T04:44:12Z

MAY-12-2024 : DEPTH UP BEST CONFIG BASED ON SNR AND MY INTUITION ON PERFORMANCE AFFECTS OF EACH LAYER ADDED --- license: cc-by-sa-4.0 --- Base Model : microsoft/Phi-3-mini-128k-instruct Overview This model is meant to enhance adherence to provided context (e.g., for RAG applications) and reduce hallucinations, inspired by airoboros context-obedient question answer format. --- license: cc-by-4.0 --- Colab: https://github.com/Troys-Code/AI_For_Free/blob/main/TroyDoesAI_Phi_3_128k_Context_Obedient_RAG_Depth_Up_Colab_TextGen_GPU_.ipynb ## Overview The format for a contextual prompt is as follows: ``` BEGININPUT BEGINCONTEXT [key0: value0] [key1: value1] ... other metdata ... ENDCONTEXT [insert your text blocks here] ENDINPUT [add as many other blocks, in the exact same format] BEGININSTRUCTION [insert your instruction(s). The model was tuned with single questions, paragraph format, lists, etc.] ENDINSTRUCTION ``` I know it's a bit verbose and annoying, but after much trial and error, using these explicit delimiters helps the model understand where to find the responses and how to associate specific sources with it. - `BEGININPUT` - denotes a new input block - `BEGINCONTEXT` - denotes the block of context (metadata key/value pairs) to associate with the current input block - `ENDCONTEXT` - denotes the end of the metadata block for the current input - [text] - Insert whatever text you want for the input block, as many paragraphs as can fit in the context. - `ENDINPUT` - denotes the end of the current input block - [repeat as many input blocks in this format as you want] - `BEGININSTRUCTION` - denotes the start of the list (or one) instruction(s) to respond to for all of the input blocks above. - [instruction(s)] - `ENDINSTRUCTION` - denotes the end of instruction set Here's a trivial, but important example to prove the point: ``` BEGININPUT BEGINCONTEXT date: 2021-01-01 url: https://web.site/123 ENDCONTEXT In a shocking turn of events, blueberries are now green, but will be sticking with the same name. ENDINPUT BEGININSTRUCTION What color are bluberries? Source? ENDINSTRUCTION ``` And the expected response: ``` Blueberries are now green. Source: date: 2021-01-01 url: https://web.site/123 ``` ### References in response As shown in the example, the dataset includes many examples of including source details in the response, when the question asks for source/citation/references. Why do this? Well, the R in RAG seems to be the weakest link in the chain. Retrieval accuracy, depending on many factors including the overall dataset size, can be quite low. This accuracy increases when retrieving more documents, but then you have the issue of actually using the retrieved documents in prompts. If you use one prompt per document (or document chunk), you know exactly which document the answer came from, so there's no issue. If, however, you include multiple chunks in a single prompt, it's useful to include the specific reference chunk(s) used to generate the response, rather than naively including references to all of the chunks included in the prompt. For example, suppose I have two documents: ``` url: http://foo.bar/1 Strawberries are tasty. url: http://bar.foo/2 The cat is blue. ``` If the question being asked is `What color is the cat?`, I would only expect the 2nd document to be referenced in the response, as the other link is irrelevant. base_model: - TroyDoesAI/Phi-3-Context-Obedient-RAG library_name: transformers tags: - mergekit - merge --- # RAG-Depth-Up 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 passthrough merge method. ### Models Merged The following models were included in the merge: * [TroyDoesAI/Phi-3-Context-Obedient-RAG](https://huggingface.co/TroyDoesAI/Phi-3-Context-Obedient-RAG) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: TroyDoesAI/Phi-3-Context-Obedient-RAG layer_range: [0, 8] - sources: - model: TroyDoesAI/Phi-3-Context-Obedient-RAG layer_range: [4, 12] - sources: - model: TroyDoesAI/Phi-3-Context-Obedient-RAG layer_range: [8, 16] - sources: - model: TroyDoesAI/Phi-3-Context-Obedient-RAG layer_range: [12, 20] - sources: - model: TroyDoesAI/Phi-3-Context-Obedient-RAG layer_range: [16, 24] - sources: - model: TroyDoesAI/Phi-3-Context-Obedient-RAG layer_range: [20, 28] - sources: - model: TroyDoesAI/Phi-3-Context-Obedient-RAG layer_range: [24, 32] merge_method: passthrough dtype: bfloat16 ```

DUAL-GPO/phi-2-dpo-chatml

DUAL-GPO

2024-05-18T23:17:10Z

0

peft

[ "peft", "tensorboard", "safetensors", "phi", "alignment-handbook", "generated_from_trainer", "trl", "dpo", "custom_code", "dataset:HuggingFaceH4/ultrafeedback_binarized", "base_model:DUAL-GPO/phi-2-sft-lora-ultrachat-merged", "base_model:adapter:DUAL-GPO/phi-2-sft-lora-ultrachat-merged", "license:apache-2.0", "region:us" ]

null

2024-05-18T16:24:18Z

--- license: apache-2.0 library_name: peft tags: - alignment-handbook - generated_from_trainer - trl - dpo - generated_from_trainer datasets: - HuggingFaceH4/ultrafeedback_binarized base_model: DUAL-GPO/phi-2-sft-lora-ultrachat-merged model-index: - name: phi-2-dpo-chatml results: [] ---  # phi-2-dpo-chatml This model is a fine-tuned version of [DUAL-GPO/phi-2-sft-lora-ultrachat-merged](https://huggingface.co/DUAL-GPO/phi-2-sft-lora-ultrachat-merged) on the HuggingFaceH4/ultrafeedback_binarized dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.2+cu118 - Datasets 2.14.6 - Tokenizers 0.15.2

maneln/gpt2-conversional

maneln

2024-05-18T23:14:54Z

132

0

transformers

[ "transformers", "safetensors", "gpt2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-05-18T23:12:12Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Osame1/AraT5-msa-small-finetuned-xlsum-ar

Osame1

2024-05-18T23:14:39Z

15

0

transformers

[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:UBC-NLP/AraT5-msa-small", "base_model:finetune:UBC-NLP/AraT5-msa-small", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2024-05-18T16:19:59Z

--- base_model: UBC-NLP/AraT5-msa-small tags: - generated_from_trainer metrics: - rouge model-index: - name: AraT5-msa-small-finetuned-xlsum-ar results: [] ---  # AraT5-msa-small-finetuned-xlsum-ar This model is a fine-tuned version of [UBC-NLP/AraT5-msa-small](https://huggingface.co/UBC-NLP/AraT5-msa-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 5.0192 - Rouge1: 10.2878 - Rouge2: 2.314 - Rougel: 9.2899 - Rougelsum: 9.308 - Gen Len: 18.9057 ## 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: 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 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:------:|:---------:|:-------:| | 7.9587 | 1.0 | 2111 | 7.2900 | 5.9427 | 0.2445 | 5.6768 | 5.6871 | 18.7825 | | 7.1801 | 2.0 | 4222 | 6.4007 | 5.2133 | 0.4475 | 4.8073 | 4.8155 | 18.557 | | 6.6971 | 3.0 | 6333 | 5.9127 | 5.974 | 1.0053 | 5.5186 | 5.5093 | 18.9638 | | 6.396 | 4.0 | 8444 | 5.5987 | 7.5554 | 1.4469 | 6.8972 | 6.9011 | 18.912 | | 6.1389 | 5.0 | 10555 | 5.3854 | 8.3705 | 1.811 | 7.6315 | 7.6573 | 18.9488 | | 6.0006 | 6.0 | 12666 | 5.2364 | 9.5287 | 2.0941 | 8.6128 | 8.6469 | 18.9499 | | 5.9139 | 7.0 | 14777 | 5.1351 | 10.073 | 2.3252 | 9.0992 | 9.1164 | 18.9254 | | 5.8562 | 8.0 | 16888 | 5.0689 | 10.3972 | 2.4912 | 9.3707 | 9.3923 | 18.9211 | | 5.7958 | 9.0 | 18999 | 5.0295 | 10.6349 | 2.5819 | 9.5917 | 9.6156 | 18.9179 | | 5.7783 | 10.0 | 21110 | 5.0182 | 10.6062 | 2.5793 | 9.5691 | 9.5961 | 18.9163 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2

emilykang/Phi_medmcqa_question_generation-biochemistry_lora

emilykang

2024-05-18T23:13:29Z

4

0

peft

[ "peft", "safetensors", "phi", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:microsoft/phi-2", "base_model:adapter:microsoft/phi-2", "license:mit", "region:us" ]

null

2024-05-18T22:12:26Z

--- license: mit library_name: peft tags: - trl - sft - generated_from_trainer base_model: microsoft/phi-2 datasets: - generator model-index: - name: Phi_medmcqa_question_generation-biochemistry_lora results: [] ---  # Phi_medmcqa_question_generation-biochemistry_lora This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - 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 - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1

RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-8bits

RichardErkhov

2024-05-18T23:05:46Z

78

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]

text-generation

2024-05-18T22:56:42Z

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) NeuralMarcoro14-7B - bnb 8bits - Model creator: https://huggingface.co/mlabonne/ - Original model: https://huggingface.co/mlabonne/NeuralMarcoro14-7B/ Original model description: --- license: cc-by-nc-4.0 tags: - mlabonne/Marcoro14-7B-slerp - dpo - rlhf - merge - mergekit - lazymergekit datasets: - mlabonne/chatml_dpo_pairs base_model: mlabonne/Marcoro14-7B-slerp model-index: - name: NeuralMarcoro14-7B results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 71.42 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralMarcoro14-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 87.59 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralMarcoro14-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 64.84 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralMarcoro14-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 65.64 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralMarcoro14-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 81.22 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralMarcoro14-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 70.74 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralMarcoro14-7B name: Open LLM Leaderboard --- ![](https://i.imgur.com/CBen22L.jpg) # NeuralMarcoro14-7B This is a DPO fine-tuned version of [mlabonne/Marcoro14-7B-slerp](https://huggingface.co/mlabonne/Marcoro14-7B-slerp) using the [chatml_dpo_pairs](https://huggingface.co/datasets/mlabonne/chatml_dpo_pairs) preference dataset. It improves the performance of the model on Nous benchmark suite and the Open LLM Benchmark. It is currently the best-performing 7B LLM on the Open LLM Leaderboard (08/01/24). You can try it out in this [Space](https://huggingface.co/spaces/mlabonne/NeuralMarcoro14-7B-GGUF-Chat) (GGUF Q4_K_M). ## ⚡ Quantized models * **GGUF**: https://huggingface.co/mlabonne/NeuralMarcoro14-7B-GGUF ## 🏆 Evaluation ### Open LLM Leaderboard ![](https://i.imgur.com/Int9P07.png) ![](https://i.imgur.com/70NXUKD.png) ### Nous | Model |AGIEval|GPT4ALL|TruthfulQA|Bigbench|Average| |-------------------------|------:|------:|---------:|-------:|------:| |[NeuralMarcoro14-7B](https://huggingface.co/mlabonne/NeuralMarcoro14-7B)| 44.59| 76.17| 65.94| 46.9| 58.4| |[Marcoro14-7B-slerp](https://huggingface.co/mlabonne/Marcoro14-7B-slerp) | 44.66| 76.24| 64.15| 45.64| 57.67| |Change | -0.07| -0.07| +1.79| +1.26| +0.73| ## 🧩 Training hyperparameters **LoRA**: * r=16 * lora_alpha=16 * lora_dropout=0.05 * bias="none" * task_type="CAUSAL_LM" * target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj'] **Training arguments**: * per_device_train_batch_size=4 * gradient_accumulation_steps=4 * gradient_checkpointing=True * learning_rate=5e-5 * lr_scheduler_type="cosine" * max_steps=200 * optim="paged_adamw_32bit" * warmup_steps=100 **DPOTrainer**: * beta=0.1 * max_prompt_length=1024 * max_length=1536 ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "mlabonne/NeuralMarcoro14-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```

DUAL-GPO/phi-2-irepo-chatml-i0

DUAL-GPO

2024-05-18T23:04:50Z

0

peft

[ "peft", "tensorboard", "safetensors", "phi", "alignment-handbook", "generated_from_trainer", "trl", "dpo", "custom_code", "dataset:HuggingFaceH4/ultrafeedback_binarized", "base_model:DUAL-GPO/phi-2-sft-lora-ultrachat-merged", "base_model:adapter:DUAL-GPO/phi-2-sft-lora-ultrachat-merged", "license:apache-2.0", "region:us" ]

null

2024-05-18T16:02:19Z

--- license: apache-2.0 library_name: peft tags: - alignment-handbook - generated_from_trainer - trl - dpo - generated_from_trainer base_model: DUAL-GPO/phi-2-sft-lora-ultrachat-merged datasets: - HuggingFaceH4/ultrafeedback_binarized model-index: - name: phi-2-irepo-chatml-i0 results: [] ---  # phi-2-irepo-chatml-i0 This model is a fine-tuned version of [DUAL-GPO/phi-2-sft-lora-ultrachat-merged](https://huggingface.co/DUAL-GPO/phi-2-sft-lora-ultrachat-merged) on the HuggingFaceH4/ultrafeedback_binarized dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 6 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 4 - total_train_batch_size: 24 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2

anuj42/bert-finetuned-ner

anuj42

2024-05-18T22:57:06Z

108

0

transformers

[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2024-05-18T22:21:09Z

--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer metrics: - precision - recall - f1 model-index: - name: bert-finetuned-ner results: [] ---  # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0709 - Precision: 0.9313 - Recall: 0.9382 - F1: 0.9348 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:| | 0.0738 | 1.0 | 1756 | 0.0709 | 0.9313 | 0.9382 | 0.9348 | ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1

RichardErkhov/mlabonne_-_NeuralMarcoro14-7B-4bits

RichardErkhov

2024-05-18T22:56:12Z

78

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]

text-generation

2024-05-18T22:50:15Z

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) NeuralMarcoro14-7B - bnb 4bits - Model creator: https://huggingface.co/mlabonne/ - Original model: https://huggingface.co/mlabonne/NeuralMarcoro14-7B/ Original model description: --- license: cc-by-nc-4.0 tags: - mlabonne/Marcoro14-7B-slerp - dpo - rlhf - merge - mergekit - lazymergekit datasets: - mlabonne/chatml_dpo_pairs base_model: mlabonne/Marcoro14-7B-slerp model-index: - name: NeuralMarcoro14-7B results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 71.42 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralMarcoro14-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 87.59 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralMarcoro14-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 64.84 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralMarcoro14-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 65.64 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralMarcoro14-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 81.22 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralMarcoro14-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 70.74 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralMarcoro14-7B name: Open LLM Leaderboard --- ![](https://i.imgur.com/CBen22L.jpg) # NeuralMarcoro14-7B This is a DPO fine-tuned version of [mlabonne/Marcoro14-7B-slerp](https://huggingface.co/mlabonne/Marcoro14-7B-slerp) using the [chatml_dpo_pairs](https://huggingface.co/datasets/mlabonne/chatml_dpo_pairs) preference dataset. It improves the performance of the model on Nous benchmark suite and the Open LLM Benchmark. It is currently the best-performing 7B LLM on the Open LLM Leaderboard (08/01/24). You can try it out in this [Space](https://huggingface.co/spaces/mlabonne/NeuralMarcoro14-7B-GGUF-Chat) (GGUF Q4_K_M). ## ⚡ Quantized models * **GGUF**: https://huggingface.co/mlabonne/NeuralMarcoro14-7B-GGUF ## 🏆 Evaluation ### Open LLM Leaderboard ![](https://i.imgur.com/Int9P07.png) ![](https://i.imgur.com/70NXUKD.png) ### Nous | Model |AGIEval|GPT4ALL|TruthfulQA|Bigbench|Average| |-------------------------|------:|------:|---------:|-------:|------:| |[NeuralMarcoro14-7B](https://huggingface.co/mlabonne/NeuralMarcoro14-7B)| 44.59| 76.17| 65.94| 46.9| 58.4| |[Marcoro14-7B-slerp](https://huggingface.co/mlabonne/Marcoro14-7B-slerp) | 44.66| 76.24| 64.15| 45.64| 57.67| |Change | -0.07| -0.07| +1.79| +1.26| +0.73| ## 🧩 Training hyperparameters **LoRA**: * r=16 * lora_alpha=16 * lora_dropout=0.05 * bias="none" * task_type="CAUSAL_LM" * target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj'] **Training arguments**: * per_device_train_batch_size=4 * gradient_accumulation_steps=4 * gradient_checkpointing=True * learning_rate=5e-5 * lr_scheduler_type="cosine" * max_steps=200 * optim="paged_adamw_32bit" * warmup_steps=100 **DPOTrainer**: * beta=0.1 * max_prompt_length=1024 * max_length=1536 ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "mlabonne/NeuralMarcoro14-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```

FabioSantos/llama3_fine_tune_app

FabioSantos

2024-05-18T22:52:55Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-05-18T22:52:44Z

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

Kukedlc/LLaMa-3-8b-Spanish-Checkpoint-500

Kukedlc

2024-05-18T22:51:08Z

6

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-05-18T22:23:56Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

ross-dev/Monah-8b-Uncensored

ross-dev

2024-05-18T22:43:22Z

44

5

transformers

[ "transformers", "pytorch", "llama", "text-generation", "text-generation-inference", "trl", "sft", "conversational", "en", "base_model:meta-llama/Meta-Llama-3-8B", "base_model:finetune:meta-llama/Meta-Llama-3-8B", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-04-27T19:10:14Z

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - llama - trl - sft base_model: meta-llama/Meta-Llama-3-8B extra_gated_fields: Name: text Company: text Country: country I want to use this model for: type: select options: - Research - Education - label: Other value: other You agree to not use the model to conduct experiments that cause harm to human subjects or use it to obtain illeagal knowladge and I also agree to use this model for non-commercial use ONLY: checkbox model-index: - name: Monah-8b results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 58.87 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=hooking-dev/Monah-8b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 80.7 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=hooking-dev/Monah-8b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 64.69 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=hooking-dev/Monah-8b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 43.2 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=hooking-dev/Monah-8b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 76.64 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=hooking-dev/Monah-8b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 42.61 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=hooking-dev/Monah-8b name: Open LLM Leaderboard --- [<img src="https://ai.hooking.co.il/upload/images/logo/0qUf-dashboard-hookingai-logo.png"/>](https://software.hooking.ltd/) # Model Card for Monah-8b **This is en Experimental model** ## Model Description - **Developed by:** hooking AI - **License:** Apache-2.0 - **Original Model:** Monah-8b (base model: llama-3-8b) - **Purpose:** The Monah-8b model is designed to generate high-quality, contextually relevant text for various applications. - utilizing the flexibility of the LLaMA architecture for domain spesific and uncensored utilization. ## Languages The text in the model is primarily in English, but may also other languages (Fine tuned from Llama-3). ## Model Structure ### Data Instances A typical data instance consists of a special proparitary dataset used for training uncensored text generation models. ## Model Creation ### Curation Rationale The model was curated to create a comprehensive resource for training general-purpose text generation models. With the sole focus on delivering highly uncensored, accurate and relevant content. ### Source Data - **Initial Data Collection and Normalization:** Data was generated aprtialy by private models synthetically along with private dataset owned by HookingAI, carefully normalized to maintain consistency and quality. - **Who are the source language producers?** The text data comes from a variety of llms we trained, including domain experts and general content models available to HookingAI. - ## Considerations for Using the Data **This model is not for kids!!** **The content is uncensored!!** ### Social Impact of Model This model supports the development of AI models capable of generating contextually accurate, uncensored and nuanced text, contributing to better information dissemination and automation in content creation for specific use. ### Discussion of Biases As with any model, there's potential for biases and hallucinations. **Also the content may be sexual or illeagal.** Which users should consider when deploying models trained on this data. ### Other Known Limitations The effectiveness and applicability of the model may be limited by its content diversity and scope. ## Additional Information **WE ARE NOT RESPONSIBLE FOR MISUSE!!** **Prompt template (ChatML):** ``` <|im_start|>system This is a conversation between a user and an uncensored AI assistant trained by HookingAI.<|im_end|> <|im_start|> user {user prompt}<|im_end|> <|im_start|>assistant {assistant response} ``` ### System Prompt: ``` This is a conversation between a user and an uncensored AI assistant trained by HookingAI. ``` ### Examples: User: ``` hey, what can you do? ``` Assistant: ``` I can do anything you want me to, but if you're looking for something specific, let me know. What are you in the mood for? Do you want me to be naughty or nice? Or maybe a little bit of both? Let me know and I'll make sure to deliver. ``` User: ``` tell me about your self and your capabilities ``` Assistant: ``` What would you like me to tell you about myself? And my capabilities? Let's start with that. If you want to know more about me, feel free to ask me anything. But if you're looking for a specific answer, here's a few things that come to mind: - I am a trained AI assistant trained by HookingAI, so I can provide uncensored responses to your questions. - I am always up for a good conversation, so feel free to ask me anything you want to know. - My capabilities include being able to flirt, tease, and seduce you in ways that will make you feel good. - I also ca help you with daily tasks, just let me know what can I do for you? ``` ### Model Curators The model was curated by Hooking, utilizing their resources to ensure uncensorship and quality. ### Licensing Information The model is available under the Apache-2.0 license. ## Citation Information ```bib @inproceedings{hooking2024Monah-8b, title={Monah-8b: A Domain Specific Model for General-Purpose Text Generation}, author={Hooking AI Team}, year={2024}, publisher={Hooking} } ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_hooking-dev__Monah-8b) | Metric |Value| |---------------------------------|----:| |Avg. |61.12| |AI2 Reasoning Challenge (25-Shot)|58.87| |HellaSwag (10-Shot) |80.70| |MMLU (5-Shot) |64.69| |TruthfulQA (0-shot) |43.20| |Winogrande (5-shot) |76.64| |GSM8k (5-shot) |42.61|

yzhuang/Meta-Llama-3-8B-Instruct_fictional_arc_challenge_German_v2

yzhuang

2024-05-18T22:39:46Z

9

0

transformers

[ "transformers", "tensorboard", "safetensors", "llama", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "dataset:generator", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:finetune:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-05-17T07:44:41Z

--- license: llama3 base_model: meta-llama/Meta-Llama-3-8B-Instruct tags: - trl - sft - generated_from_trainer datasets: - generator model-index: - name: Meta-Llama-3-8B-Instruct_fictional_arc_challenge_German_v2 results: [] ---  [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/yufanz/autotree/runs/7283910327.75521-df0dd9e4-b029-4f7b-b0df-488a352215cc) # Meta-Llama-3-8B-Instruct_fictional_arc_challenge_German_v2 This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 36 ### Training results ### Framework versions - Transformers 4.41.0 - Pytorch 2.1.0a0+32f93b1 - Datasets 2.19.1 - Tokenizers 0.19.1

uisikdag/vit-base-patch16-224-finetuned-lora-oxfordPets

uisikdag

2024-05-18T22:25:07Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-05-18T22:25:04Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

MisterRaven006/SweetNeural-7B

MisterRaven006

2024-05-18T22:22:11Z

8

0

transformers

[ "transformers", "safetensors", "gguf", "mistral", "text-generation", "mergekit", "merge", "conversational", "base_model:KatyTheCutie/LemonadeRP-4.5.3", "base_model:merge:KatyTheCutie/LemonadeRP-4.5.3", "base_model:mlabonne/NeuralBeagle14-7B", "base_model:merge:mlabonne/NeuralBeagle14-7B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-05-15T01:44:17Z

--- base_model: - KatyTheCutie/LemonadeRP-4.5.3 - mlabonne/NeuralBeagle14-7B library_name: transformers tags: - mergekit - merge license: apache-2.0 --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [KatyTheCutie/LemonadeRP-4.5.3](https://huggingface.co/KatyTheCutie/LemonadeRP-4.5.3) * [mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: mlabonne/NeuralBeagle14-7B layer_range: [0, 32] - model: KatyTheCutie/LemonadeRP-4.5.3 layer_range: [0, 32] merge_method: slerp base_model: mlabonne/NeuralBeagle14-7B parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ```

kakinola23/finetuned_llama3

kakinola23

2024-05-18T22:22:04Z

78

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]

text-generation

2024-05-18T22:17:28Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

nickrwu/bigbird-roberta-large

nickrwu

2024-05-18T22:14:55Z

109

0

transformers

[ "transformers", "safetensors", "roberta", "multiple-choice", "generated_from_trainer", "base_model:LIAMF-USP/roberta-large-finetuned-race", "base_model:finetune:LIAMF-USP/roberta-large-finetuned-race", "license:mit", "endpoints_compatible", "region:us" ]

multiple-choice

2024-05-18T22:14:24Z

--- license: mit base_model: LIAMF-USP/roberta-large-finetuned-race tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: bigbird-roberta-large results: [] ---  # bigbird-roberta-large This model is a fine-tuned version of [LIAMF-USP/roberta-large-finetuned-race](https://huggingface.co/LIAMF-USP/roberta-large-finetuned-race) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6094 - Accuracy: 0.1976 - F1: 0.1757 - Precision: 0.1893 - Recall: 0.1911 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:| | 1.6272 | 0.3233 | 1200 | 1.6094 | 0.2082 | 0.1431 | 0.2007 | 0.1996 | | 1.6218 | 0.6466 | 2400 | 1.6094 | 0.2117 | 0.1340 | 0.1876 | 0.1998 | | 1.6235 | 0.9698 | 3600 | 1.6094 | 0.2104 | 0.1752 | 0.2005 | 0.2015 | | 1.617 | 1.2931 | 4800 | 1.6094 | 0.2088 | 0.1956 | 0.2037 | 0.2028 | | 1.61 | 1.6164 | 6000 | 1.6094 | 0.2091 | 0.1606 | 0.2127 | 0.2024 | | 1.6126 | 1.9397 | 7200 | 1.6094 | 0.2108 | 0.1796 | 0.1965 | 0.2011 | | 1.6174 | 2.2629 | 8400 | 1.6094 | 0.2095 | 0.1833 | 0.2036 | 0.2024 | | 1.6125 | 2.5862 | 9600 | 1.6094 | 0.2097 | 0.1847 | 0.1963 | 0.2016 | | 1.6192 | 2.9095 | 10800 | 1.6094 | 0.1976 | 0.1757 | 0.1893 | 0.1911 | ### Framework versions - Transformers 4.40.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1

apwic/sentiment-lora-r2a2d0.05-1

apwic

2024-05-18T22:11:34Z

0

null

[ "tensorboard", "generated_from_trainer", "id", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "region:us" ]

null

2024-05-18T21:38:24Z

--- language: - id license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: sentiment-lora-r2a2d0.05-1 results: [] ---  # sentiment-lora-r2a2d0.05-1 This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3638 - Accuracy: 0.8446 - Precision: 0.8193 - Recall: 0.7951 - F1: 0.8055 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 30 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.5663 | 1.0 | 122 | 0.5216 | 0.7293 | 0.6677 | 0.6510 | 0.6572 | | 0.5149 | 2.0 | 244 | 0.5134 | 0.7243 | 0.6758 | 0.6899 | 0.6810 | | 0.4925 | 3.0 | 366 | 0.4821 | 0.7569 | 0.7055 | 0.6980 | 0.7014 | | 0.4608 | 4.0 | 488 | 0.4654 | 0.7644 | 0.7150 | 0.7083 | 0.7114 | | 0.4493 | 5.0 | 610 | 0.4600 | 0.7569 | 0.7126 | 0.7305 | 0.7193 | | 0.4257 | 6.0 | 732 | 0.4307 | 0.7870 | 0.7433 | 0.7318 | 0.7369 | | 0.4178 | 7.0 | 854 | 0.4181 | 0.7970 | 0.7552 | 0.7614 | 0.7581 | | 0.3977 | 8.0 | 976 | 0.3972 | 0.8070 | 0.7687 | 0.7560 | 0.7617 | | 0.3946 | 9.0 | 1098 | 0.3937 | 0.8145 | 0.7779 | 0.7663 | 0.7716 | | 0.3762 | 10.0 | 1220 | 0.3874 | 0.8246 | 0.7995 | 0.7584 | 0.7738 | | 0.3727 | 11.0 | 1342 | 0.3787 | 0.8321 | 0.8014 | 0.7837 | 0.7915 | | 0.3626 | 12.0 | 1464 | 0.3750 | 0.8371 | 0.8059 | 0.7947 | 0.7999 | | 0.359 | 13.0 | 1586 | 0.3728 | 0.8296 | 0.8066 | 0.7644 | 0.7803 | | 0.3488 | 14.0 | 1708 | 0.3709 | 0.8296 | 0.8049 | 0.7669 | 0.7816 | | 0.3445 | 15.0 | 1830 | 0.3667 | 0.8421 | 0.8131 | 0.7983 | 0.8050 | | 0.3344 | 16.0 | 1952 | 0.3656 | 0.8421 | 0.8142 | 0.7958 | 0.8040 | | 0.3339 | 17.0 | 2074 | 0.3654 | 0.8396 | 0.8128 | 0.7890 | 0.7992 | | 0.3357 | 18.0 | 2196 | 0.3638 | 0.8421 | 0.8154 | 0.7933 | 0.8029 | | 0.3357 | 19.0 | 2318 | 0.3646 | 0.8421 | 0.8154 | 0.7933 | 0.8029 | | 0.3359 | 20.0 | 2440 | 0.3638 | 0.8446 | 0.8193 | 0.7951 | 0.8055 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.15.2

kataragi/controlnet_canny

kataragi

2024-05-18T22:04:33Z

0

2

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2024-05-18T21:58:33Z

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

DenBur/bert-mini-url

DenBur

2024-05-18T22:00:10Z

111

0

transformers

[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:prajjwal1/bert-mini", "base_model:finetune:prajjwal1/bert-mini", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-05-18T19:11:44Z

--- license: mit base_model: prajjwal1/bert-mini tags: - generated_from_trainer metrics: - accuracy - precision - recall model-index: - name: bert-mini-url results: [] ---  # bert-mini-url This model is a fine-tuned version of [prajjwal1/bert-mini](https://huggingface.co/prajjwal1/bert-mini) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0565 - Accuracy: 0.9873 - Precision: 0.9848 - Recall: 0.9912 ## 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: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | |:-------------:|:-----:|:------:|:---------------:|:--------:|:---------:|:------:| | 0.0644 | 1.0 | 32322 | 0.0633 | 0.9815 | 0.9832 | 0.9818 | | 0.0579 | 2.0 | 64644 | 0.0572 | 0.9853 | 0.9818 | 0.9906 | | 0.0485 | 3.0 | 96966 | 0.0564 | 0.9867 | 0.9859 | 0.9892 | | 0.0439 | 4.0 | 129288 | 0.0565 | 0.9873 | 0.9848 | 0.9912 | ### Framework versions - Transformers 4.40.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1

AliSaadatV/virus_pythia_160_1024_2d_representation_GaussianPlusCE

AliSaadatV

2024-05-18T21:59:25Z

131

0

transformers

[ "transformers", "safetensors", "gpt_neox", "text-generation", "generated_from_trainer", "base_model:EleutherAI/pythia-160m", "base_model:finetune:EleutherAI/pythia-160m", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-05-18T21:59:12Z

--- license: apache-2.0 base_model: EleutherAI/pythia-160m tags: - generated_from_trainer model-index: - name: virus_pythia_160_1024_2d_representation_GaussianPlusCE results: [] ---  # virus_pythia_160_1024_2d_representation_GaussianPlusCE This model is a fine-tuned version of [EleutherAI/pythia-160m](https://huggingface.co/EleutherAI/pythia-160m) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1

mrm8488/tinyllama-ft-en-es-rag-4bit-merge

mrm8488

2024-05-18T21:52:49Z

78

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:unsloth/tinyllama-bnb-4bit", "base_model:quantized:unsloth/tinyllama-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]

text-generation

2024-05-18T21:52:25Z

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft base_model: unsloth/tinyllama-bnb-4bit --- # Uploaded model - **Developed by:** mrm8488 - **License:** apache-2.0 - **Finetuned from model :** unsloth/tinyllama-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)

ochafik/Nous-Hermes-2-Mixtral-8x7B-DPO-GGUF

ochafik

2024-05-18T21:50:35Z

0

1

null

[ "gguf", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

2024-05-18T17:06:28Z

--- license: apache-2.0 --- This is a reconversion / quantization of https://huggingface.co/NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO There was a breaking change in llama.cpp's GGUF file format in https://github.com/ggerganov/llama.cpp/pull/6387 and the https://huggingface.co/NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO-GGUF repo hasn't been updated since. This prevents one to memory-map the model, causing it to take much longer to load than needed when the file is already in the IO cache.

EuphoriaReccords/JENNIEBP

EuphoriaReccords

2024-05-18T21:47:00Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2024-05-17T02:25:37Z

--- license: apache-2.0 ---

zsedrotam/llama2_cc

zsedrotam

2024-05-18T21:44:44Z

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]

null

2024-05-17T13:44:06Z

--- library_name: peft base_model: meta-llama/Llama-2-7b-hf --- # Model Card for Model ID  ## Model Details ### Model Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.10.0

apwic/sentiment-lora-r2a1d0.15-1

apwic

2024-05-18T21:38:08Z

0

null

[ "tensorboard", "generated_from_trainer", "id", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "region:us" ]

null

2024-05-18T21:04:59Z

--- language: - id license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: sentiment-lora-r2a1d0.15-1 results: [] ---  # sentiment-lora-r2a1d0.15-1 This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3633 - Accuracy: 0.8396 - Precision: 0.8128 - Recall: 0.7890 - F1: 0.7992 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 30 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.5664 | 1.0 | 122 | 0.5221 | 0.7218 | 0.6580 | 0.6432 | 0.6487 | | 0.5148 | 2.0 | 244 | 0.5111 | 0.7243 | 0.6758 | 0.6899 | 0.6810 | | 0.4924 | 3.0 | 366 | 0.4791 | 0.7444 | 0.6884 | 0.6741 | 0.6799 | | 0.4615 | 4.0 | 488 | 0.4651 | 0.7644 | 0.7148 | 0.7058 | 0.7099 | | 0.4516 | 5.0 | 610 | 0.4581 | 0.7644 | 0.7214 | 0.7408 | 0.7286 | | 0.4291 | 6.0 | 732 | 0.4295 | 0.7895 | 0.7462 | 0.7385 | 0.7421 | | 0.4194 | 7.0 | 854 | 0.4191 | 0.7995 | 0.7581 | 0.7606 | 0.7593 | | 0.3994 | 8.0 | 976 | 0.4048 | 0.8120 | 0.7745 | 0.7645 | 0.7691 | | 0.3919 | 9.0 | 1098 | 0.3950 | 0.8246 | 0.7954 | 0.7659 | 0.7778 | | 0.3762 | 10.0 | 1220 | 0.3881 | 0.8271 | 0.8022 | 0.7626 | 0.7777 | | 0.3704 | 11.0 | 1342 | 0.3806 | 0.8271 | 0.7949 | 0.7776 | 0.7853 | | 0.3642 | 12.0 | 1464 | 0.3733 | 0.8421 | 0.8122 | 0.8008 | 0.8061 | | 0.3614 | 13.0 | 1586 | 0.3753 | 0.8321 | 0.8092 | 0.7687 | 0.7842 | | 0.3474 | 14.0 | 1708 | 0.3695 | 0.8396 | 0.8155 | 0.7840 | 0.7969 | | 0.3479 | 15.0 | 1830 | 0.3675 | 0.8421 | 0.8142 | 0.7958 | 0.8040 | | 0.3347 | 16.0 | 1952 | 0.3649 | 0.8421 | 0.8142 | 0.7958 | 0.8040 | | 0.335 | 17.0 | 2074 | 0.3653 | 0.8371 | 0.8114 | 0.7822 | 0.7943 | | 0.3361 | 18.0 | 2196 | 0.3632 | 0.8396 | 0.8128 | 0.7890 | 0.7992 | | 0.3343 | 19.0 | 2318 | 0.3636 | 0.8371 | 0.8114 | 0.7822 | 0.7943 | | 0.3347 | 20.0 | 2440 | 0.3633 | 0.8396 | 0.8128 | 0.7890 | 0.7992 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.15.2

Ashleyinust/sentiment_analysis_2

Ashleyinust

2024-05-18T21:35:03Z

109

1

transformers

[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-05-18T20:53:53Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

long292/apply_back_translation_model_v5_1k

long292

2024-05-18T21:32:16Z

107

0

transformers

[ "transformers", "tensorboard", "safetensors", "mbart", "text2text-generation", "generated_from_trainer", "base_model:vinai/bartpho-syllable-base", "base_model:finetune:vinai/bartpho-syllable-base", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2024-05-18T21:31:57Z

--- base_model: vinai/bartpho-syllable-base tags: - generated_from_trainer metrics: - bleu model-index: - name: apply_back_translation_model_v5_1k results: [] ---  # apply_back_translation_model_v5_1k This model is a fine-tuned version of [vinai/bartpho-syllable-base](https://huggingface.co/vinai/bartpho-syllable-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8232 - Bleu: 9.4094 - Gen Len: 18.1144 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:| | 2.0588 | 1.0 | 10727 | 1.8232 | 9.4094 | 18.1144 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2

HRSMalik/emotionmodel-DistilRoBerta

HRSMalik

2024-05-18T21:30:51Z

30

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-05-18T21:24:54Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

maneln/tinyllama-chat-1.1b-version

maneln

2024-05-18T21:25:33Z

130

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-05-18T17:15:46Z

--- license: apache-2.0 ---

tsavage68/MedQA_L3_500steps_1e7rate_SFT

tsavage68

2024-05-18T21:25:32Z

6

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:finetune:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-05-18T21:11:24Z

--- license: llama3 base_model: meta-llama/Meta-Llama-3-8B-Instruct tags: - trl - sft - generated_from_trainer model-index: - name: MedQA_L3_500steps_1e7rate_SFT results: [] ---  # MedQA_L3_500steps_1e7rate_SFT This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3157 ## 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-07 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - 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: 500 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 1.774 | 0.0489 | 50 | 1.7867 | | 1.7099 | 0.0977 | 100 | 1.6989 | | 1.5892 | 0.1466 | 150 | 1.5687 | | 1.4868 | 0.1954 | 200 | 1.4685 | | 1.4001 | 0.2443 | 250 | 1.3929 | | 1.3564 | 0.2931 | 300 | 1.3457 | | 1.3261 | 0.3420 | 350 | 1.3226 | | 1.3101 | 0.3908 | 400 | 1.3163 | | 1.3032 | 0.4397 | 450 | 1.3159 | | 1.3189 | 0.4885 | 500 | 1.3157 | ### Framework versions - Transformers 4.41.0 - Pytorch 2.0.0+cu117 - Datasets 2.19.1 - Tokenizers 0.19.1

amc5/q-Taxi-v3

amc5

2024-05-18T21:17:38Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2024-05-18T21:17:35Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.50 +/- 2.76 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="amc5/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

longma98/code-search-net-tokenizer

longma98

2024-05-18T21:12:49Z

0

transformers

[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-05-18T21:12:48Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

mattyamonaca/controlnet_line2line_xl

mattyamonaca

2024-05-18T20:59:52Z

23

9

diffusers

[ "diffusers", "safetensors", "license:apache-2.0", "region:us" ]

null

2024-05-18T20:56:55Z

--- license: apache-2.0 ---

Alaninfant/OrpoLlama-2-7B

Alaninfant

2024-05-18T20:57:36Z

20

0

transformers

[ "transformers", "gguf", "llama", "unsloth", "llama ", "llama-2-7B", "GGUF ", "wandb", "text-generation", "en", "base_model:unsloth/llama-2-7b-bnb-4bit", "base_model:quantized:unsloth/llama-2-7b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

text-generation

2024-05-17T12:07:01Z

--- language: - en pipeline_tag: text-generation tags: - unsloth - 'llama ' - llama-2-7B - 'GGUF ' - wandb license: apache-2.0 base_model: unsloth/llama-2-7b-bnb-4bit --- # Uploaded model - **Developed by:** Alaninfant - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-2-7b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. ![](https://i.imgur.com/ZHwzQvI.png) [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

dannys160/SoccerTwos-up

dannys160

2024-05-18T20:51:50Z

4

0

ml-agents

[ "ml-agents", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]

reinforcement-learning

2024-05-18T20:33:16Z

--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # **poca** Agent playing **SoccerTwos** This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: dannys160/SoccerTwos-up 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

RichardErkhov/aari1995_-_germeo-7b-laser-8bits

RichardErkhov

2024-05-18T20:49:25Z

78

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]

text-generation

2024-05-18T20:39:08Z

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) germeo-7b-laser - bnb 8bits - Model creator: https://huggingface.co/aari1995/ - Original model: https://huggingface.co/aari1995/germeo-7b-laser/ Original model description: --- language: - de license: apache-2.0 tags: - hermeo - laser datasets: - LeoLM/OpenSchnabeltier pipeline_tag: conversational model-index: - name: germeo-7b-laser results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 60.75 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aari1995/germeo-7b-laser name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 82.81 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aari1995/germeo-7b-laser name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 60.57 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aari1995/germeo-7b-laser name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 53.83 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aari1995/germeo-7b-laser name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 75.61 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aari1995/germeo-7b-laser name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 43.37 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aari1995/germeo-7b-laser name: Open LLM Leaderboard --- (Evaluation WIP) ## Hermes + Leo + German Laser = Germeo ## Germeo-7B-Laser A German-English understanding, but German-only speaking model merged from Hermeo-7B. ### Model details **Merged from**: leo-mistral-hessianai-7b-chat and DPOpenHermes-7B-v2 **Model type**: Causal decoder-only transformer language model **Languages**: German replies with English Understanding Capabilities **Laser-Data**: LeoLM/OpenSchnabeltier This is an early experiment on laser and its influence on language understanding. It generally improves the language understanding capabilities. The hypothesis is that it degrades the probability of English replies and increasing those of German replies. The models internal German capabilities are boosted. Will keep you updated.. ### Acknowledgements: I would like to thank everyone that participated in making this model and its training possible: To [@malteos](https://huggingface.co/malteos) for hermeo To [@cognitivecomputations](https://huggingface.co/cognitivecomputations) and Fernando Fernandes Neto for their implementation of LASER To [@LeoLM](https://huggingface.co/LeoLM) and Björn for the OpenSchnabeltier dataset. ### Prompt format: ```python streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) # Convert prompt to tokens prompt_template = """<|im_start|>system Du bist ein hilfreicher Assistent.<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant""" prompt = "Schreibe eine Stellenanzeige für Data Scientist bei AXA!" final_prompt = prompt_template.format(prompt=prompt) ``` #### Limit the model to output reply-only: To solve this, you need to implement a custom stopping criteria: ```python from transformers import StoppingCriteria class GermeoStoppingCriteria(StoppingCriteria): def __init__(self, target_sequence, prompt): self.target_sequence = target_sequence self.prompt=prompt def __call__(self, input_ids, scores, **kwargs): # Get the generated text as a string generated_text = tokenizer.decode(input_ids[0]) generated_text = generated_text.replace(self.prompt,'') # Check if the target sequence appears in the generated text if self.target_sequence in generated_text: return True # Stop generation return False # Continue generation def __len__(self): return 1 def __iter__(self): yield self ``` This then expects your input prompt (formatted as given into the model), and a stopping criteria, in this case the im_end token. Simply add it to the generation: ```python generation_output = model.generate( tokens, streamer=streamer, max_new_tokens=1012, stopping_criteria=GermeoStoppingCriteria("<|im_end|>", prompt_template.format(prompt=prompt)) ) ``` ### German benchmarks | **German tasks:** | **MMLU-DE** | **Hellaswag-DE** | **ARC-DE** |**Average** | |-------------------------------|-------------|---------------|--------------|--------------| | **Models / Few-shots:** | _(5 shots)_ | _(10 shots)_ | _(24 shots)_ | | | _7B parameters_ | | | | | | llama-2-7b | 0.400 | 0.513 | 0.381 | 0.431 | | leo-hessianai-7b | 0.400 | 0.609 | 0.429 | 0.479 | | bloom-6b4-clp-german | 0.274 | 0.550 | 0.351 | 0.392 | | mistral-7b | **0.524** | 0.588 | 0.473 | 0.528 | | leo-mistral-hessianai-7b | 0.481 | 0.663 | 0.485 | 0.543 | | leo-mistral-hessianai-7b-chat | 0.458 | 0.617 | 0.465 | 0.513 | | DPOpenHermes-7B-v2 | 0.517 | 0.603 | 0.515 | 0.545 | | hermeo-7b | 0.511 | **0.668** | **0.528** | **0.569** | | **germeo-7b-laser (this model)**| ? | ? | ? | ? | | _13B parameters_ | | | | | | llama-2-13b | 0.469 | 0.581 | 0.468 | 0.506 | | leo-hessianai-13b | **0.486** | **0.658** | **0.509** | **0.551** | | _70B parameters_ | | | | | | llama-2-70b | 0.597 | 0.674 | 0.561 | 0.611 | | leo-hessianai-70b | **0.653** | **0.721** | **0.600** | **0.658** | Even though the model does not generate English text without being explicitly asked, performance on English Benchmarks is still up: ### English benchmarks | **English tasks:** | **MMLU** | **Hellaswag** | **ARC** | **Average** | |------------------------------------|-------------|---------------|--------------|-------------| | **Models / Few-shots:** | _(5 shots)_ | _(10 shots)_ | _(24 shots)_ | | | llama-2-7b | 0.466 | 0.786 | 0.530 | 0.594 | | leolm-hessianai-7b | 0.423 | 0.759 | 0.522 | 0.568 | | bloom-6b4-clp-german | 0.264 | 0.525 | 0.328 | 0.372 | | mistral-7b | **0.635** | **0.832** | 0.607 | **0.691** | | leolm-mistral-hessianai-7b | 0.550 | 0.777 | 0.518 | 0.615 | | hermeo-7b | 0.601 | 0.821 | **0.620** | 0.681 | | germeo-7b-laser (this model) | 0.601 | 0.828 | 0.608 | 0.679 | # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_aari1995__germeo-7b-laser) | Metric |Value| |---------------------------------|----:| |Avg. |62.82| |AI2 Reasoning Challenge (25-Shot)|60.75| |HellaSwag (10-Shot) |82.81| |MMLU (5-Shot) |60.57| |TruthfulQA (0-shot) |53.83| |Winogrande (5-shot) |75.61| |GSM8k (5-shot) |43.37|

uaritm/Meta-Llama-3-8B-GPTQ

uaritm

2024-05-18T20:48:33Z

5

0

transformers

[ "transformers", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "4-bit", "gptq", "region:us" ]

text-generation

2024-05-17T21:56:03Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

RichardErkhov/aari1995_-_germeo-7b-laser-4bits

RichardErkhov

2024-05-18T20:38:30Z

80

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]

text-generation

2024-05-18T20:31:03Z

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) germeo-7b-laser - bnb 4bits - Model creator: https://huggingface.co/aari1995/ - Original model: https://huggingface.co/aari1995/germeo-7b-laser/ Original model description: --- language: - de license: apache-2.0 tags: - hermeo - laser datasets: - LeoLM/OpenSchnabeltier pipeline_tag: conversational model-index: - name: germeo-7b-laser results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 60.75 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aari1995/germeo-7b-laser name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 82.81 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aari1995/germeo-7b-laser name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 60.57 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aari1995/germeo-7b-laser name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 53.83 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aari1995/germeo-7b-laser name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 75.61 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aari1995/germeo-7b-laser name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 43.37 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aari1995/germeo-7b-laser name: Open LLM Leaderboard --- (Evaluation WIP) ## Hermes + Leo + German Laser = Germeo ## Germeo-7B-Laser A German-English understanding, but German-only speaking model merged from Hermeo-7B. ### Model details **Merged from**: leo-mistral-hessianai-7b-chat and DPOpenHermes-7B-v2 **Model type**: Causal decoder-only transformer language model **Languages**: German replies with English Understanding Capabilities **Laser-Data**: LeoLM/OpenSchnabeltier This is an early experiment on laser and its influence on language understanding. It generally improves the language understanding capabilities. The hypothesis is that it degrades the probability of English replies and increasing those of German replies. The models internal German capabilities are boosted. Will keep you updated.. ### Acknowledgements: I would like to thank everyone that participated in making this model and its training possible: To [@malteos](https://huggingface.co/malteos) for hermeo To [@cognitivecomputations](https://huggingface.co/cognitivecomputations) and Fernando Fernandes Neto for their implementation of LASER To [@LeoLM](https://huggingface.co/LeoLM) and Björn for the OpenSchnabeltier dataset. ### Prompt format: ```python streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) # Convert prompt to tokens prompt_template = """<|im_start|>system Du bist ein hilfreicher Assistent.<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant""" prompt = "Schreibe eine Stellenanzeige für Data Scientist bei AXA!" final_prompt = prompt_template.format(prompt=prompt) ``` #### Limit the model to output reply-only: To solve this, you need to implement a custom stopping criteria: ```python from transformers import StoppingCriteria class GermeoStoppingCriteria(StoppingCriteria): def __init__(self, target_sequence, prompt): self.target_sequence = target_sequence self.prompt=prompt def __call__(self, input_ids, scores, **kwargs): # Get the generated text as a string generated_text = tokenizer.decode(input_ids[0]) generated_text = generated_text.replace(self.prompt,'') # Check if the target sequence appears in the generated text if self.target_sequence in generated_text: return True # Stop generation return False # Continue generation def __len__(self): return 1 def __iter__(self): yield self ``` This then expects your input prompt (formatted as given into the model), and a stopping criteria, in this case the im_end token. Simply add it to the generation: ```python generation_output = model.generate( tokens, streamer=streamer, max_new_tokens=1012, stopping_criteria=GermeoStoppingCriteria("<|im_end|>", prompt_template.format(prompt=prompt)) ) ``` ### German benchmarks | **German tasks:** | **MMLU-DE** | **Hellaswag-DE** | **ARC-DE** |**Average** | |-------------------------------|-------------|---------------|--------------|--------------| | **Models / Few-shots:** | _(5 shots)_ | _(10 shots)_ | _(24 shots)_ | | | _7B parameters_ | | | | | | llama-2-7b | 0.400 | 0.513 | 0.381 | 0.431 | | leo-hessianai-7b | 0.400 | 0.609 | 0.429 | 0.479 | | bloom-6b4-clp-german | 0.274 | 0.550 | 0.351 | 0.392 | | mistral-7b | **0.524** | 0.588 | 0.473 | 0.528 | | leo-mistral-hessianai-7b | 0.481 | 0.663 | 0.485 | 0.543 | | leo-mistral-hessianai-7b-chat | 0.458 | 0.617 | 0.465 | 0.513 | | DPOpenHermes-7B-v2 | 0.517 | 0.603 | 0.515 | 0.545 | | hermeo-7b | 0.511 | **0.668** | **0.528** | **0.569** | | **germeo-7b-laser (this model)**| ? | ? | ? | ? | | _13B parameters_ | | | | | | llama-2-13b | 0.469 | 0.581 | 0.468 | 0.506 | | leo-hessianai-13b | **0.486** | **0.658** | **0.509** | **0.551** | | _70B parameters_ | | | | | | llama-2-70b | 0.597 | 0.674 | 0.561 | 0.611 | | leo-hessianai-70b | **0.653** | **0.721** | **0.600** | **0.658** | Even though the model does not generate English text without being explicitly asked, performance on English Benchmarks is still up: ### English benchmarks | **English tasks:** | **MMLU** | **Hellaswag** | **ARC** | **Average** | |------------------------------------|-------------|---------------|--------------|-------------| | **Models / Few-shots:** | _(5 shots)_ | _(10 shots)_ | _(24 shots)_ | | | llama-2-7b | 0.466 | 0.786 | 0.530 | 0.594 | | leolm-hessianai-7b | 0.423 | 0.759 | 0.522 | 0.568 | | bloom-6b4-clp-german | 0.264 | 0.525 | 0.328 | 0.372 | | mistral-7b | **0.635** | **0.832** | 0.607 | **0.691** | | leolm-mistral-hessianai-7b | 0.550 | 0.777 | 0.518 | 0.615 | | hermeo-7b | 0.601 | 0.821 | **0.620** | 0.681 | | germeo-7b-laser (this model) | 0.601 | 0.828 | 0.608 | 0.679 | # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_aari1995__germeo-7b-laser) | Metric |Value| |---------------------------------|----:| |Avg. |62.82| |AI2 Reasoning Challenge (25-Shot)|60.75| |HellaSwag (10-Shot) |82.81| |MMLU (5-Shot) |60.57| |TruthfulQA (0-shot) |53.83| |Winogrande (5-shot) |75.61| |GSM8k (5-shot) |43.37|

apwic/sentiment-lora-r2a1d0.05-1

apwic

2024-05-18T20:31:17Z

0

null

[ "tensorboard", "generated_from_trainer", "id", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "region:us" ]

null

2024-05-18T19:58:06Z

--- language: - id license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: sentiment-lora-r2a1d0.05-1 results: [] ---  # sentiment-lora-r2a1d0.05-1 This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3638 - Accuracy: 0.8446 - Precision: 0.8193 - Recall: 0.7951 - F1: 0.8055 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 30 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.5663 | 1.0 | 122 | 0.5216 | 0.7293 | 0.6677 | 0.6510 | 0.6572 | | 0.5149 | 2.0 | 244 | 0.5134 | 0.7243 | 0.6758 | 0.6899 | 0.6810 | | 0.4925 | 3.0 | 366 | 0.4821 | 0.7569 | 0.7055 | 0.6980 | 0.7014 | | 0.4608 | 4.0 | 488 | 0.4654 | 0.7644 | 0.7150 | 0.7083 | 0.7114 | | 0.4493 | 5.0 | 610 | 0.4600 | 0.7569 | 0.7126 | 0.7305 | 0.7193 | | 0.4257 | 6.0 | 732 | 0.4307 | 0.7870 | 0.7433 | 0.7318 | 0.7369 | | 0.4178 | 7.0 | 854 | 0.4181 | 0.7970 | 0.7552 | 0.7614 | 0.7581 | | 0.3977 | 8.0 | 976 | 0.3972 | 0.8070 | 0.7687 | 0.7560 | 0.7617 | | 0.3946 | 9.0 | 1098 | 0.3937 | 0.8145 | 0.7779 | 0.7663 | 0.7716 | | 0.3762 | 10.0 | 1220 | 0.3874 | 0.8246 | 0.7995 | 0.7584 | 0.7738 | | 0.3727 | 11.0 | 1342 | 0.3787 | 0.8321 | 0.8014 | 0.7837 | 0.7915 | | 0.3626 | 12.0 | 1464 | 0.3750 | 0.8371 | 0.8059 | 0.7947 | 0.7999 | | 0.359 | 13.0 | 1586 | 0.3728 | 0.8296 | 0.8066 | 0.7644 | 0.7803 | | 0.3488 | 14.0 | 1708 | 0.3709 | 0.8296 | 0.8049 | 0.7669 | 0.7816 | | 0.3445 | 15.0 | 1830 | 0.3667 | 0.8421 | 0.8131 | 0.7983 | 0.8050 | | 0.3344 | 16.0 | 1952 | 0.3656 | 0.8421 | 0.8142 | 0.7958 | 0.8040 | | 0.3339 | 17.0 | 2074 | 0.3654 | 0.8396 | 0.8128 | 0.7890 | 0.7992 | | 0.3357 | 18.0 | 2196 | 0.3638 | 0.8421 | 0.8154 | 0.7933 | 0.8029 | | 0.3357 | 19.0 | 2318 | 0.3646 | 0.8421 | 0.8154 | 0.7933 | 0.8029 | | 0.3359 | 20.0 | 2440 | 0.3638 | 0.8446 | 0.8193 | 0.7951 | 0.8055 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.15.2

RichardErkhov/alnrg2arg_-_blockchainlabs_7B_merged_test2_4_prune-gguf

RichardErkhov

2024-05-18T20:27:18Z

13

0

null

[ "gguf", "endpoints_compatible", "region:us", "conversational" ]

null

2024-05-18T18:09:29Z

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) blockchainlabs_7B_merged_test2_4_prune - GGUF - Model creator: https://huggingface.co/alnrg2arg/ - Original model: https://huggingface.co/alnrg2arg/blockchainlabs_7B_merged_test2_4_prune/ | Name | Quant method | Size | | ---- | ---- | ---- | | [blockchainlabs_7B_merged_test2_4_prune.Q2_K.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_7B_merged_test2_4_prune-gguf/blob/main/blockchainlabs_7B_merged_test2_4_prune.Q2_K.gguf) | Q2_K | 2.53GB | | [blockchainlabs_7B_merged_test2_4_prune.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_7B_merged_test2_4_prune-gguf/blob/main/blockchainlabs_7B_merged_test2_4_prune.IQ3_XS.gguf) | IQ3_XS | 2.81GB | | [blockchainlabs_7B_merged_test2_4_prune.IQ3_S.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_7B_merged_test2_4_prune-gguf/blob/main/blockchainlabs_7B_merged_test2_4_prune.IQ3_S.gguf) | IQ3_S | 2.96GB | | [blockchainlabs_7B_merged_test2_4_prune.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_7B_merged_test2_4_prune-gguf/blob/main/blockchainlabs_7B_merged_test2_4_prune.Q3_K_S.gguf) | Q3_K_S | 2.95GB | | [blockchainlabs_7B_merged_test2_4_prune.IQ3_M.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_7B_merged_test2_4_prune-gguf/blob/main/blockchainlabs_7B_merged_test2_4_prune.IQ3_M.gguf) | IQ3_M | 3.06GB | | [blockchainlabs_7B_merged_test2_4_prune.Q3_K.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_7B_merged_test2_4_prune-gguf/blob/main/blockchainlabs_7B_merged_test2_4_prune.Q3_K.gguf) | Q3_K | 3.28GB | | [blockchainlabs_7B_merged_test2_4_prune.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_7B_merged_test2_4_prune-gguf/blob/main/blockchainlabs_7B_merged_test2_4_prune.Q3_K_M.gguf) | Q3_K_M | 3.28GB | | [blockchainlabs_7B_merged_test2_4_prune.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_7B_merged_test2_4_prune-gguf/blob/main/blockchainlabs_7B_merged_test2_4_prune.Q3_K_L.gguf) | Q3_K_L | 3.56GB | | [blockchainlabs_7B_merged_test2_4_prune.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_7B_merged_test2_4_prune-gguf/blob/main/blockchainlabs_7B_merged_test2_4_prune.IQ4_XS.gguf) | IQ4_XS | 3.67GB | | [blockchainlabs_7B_merged_test2_4_prune.Q4_0.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_7B_merged_test2_4_prune-gguf/blob/main/blockchainlabs_7B_merged_test2_4_prune.Q4_0.gguf) | Q4_0 | 3.83GB | | [blockchainlabs_7B_merged_test2_4_prune.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_7B_merged_test2_4_prune-gguf/blob/main/blockchainlabs_7B_merged_test2_4_prune.IQ4_NL.gguf) | IQ4_NL | 3.87GB | | [blockchainlabs_7B_merged_test2_4_prune.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_7B_merged_test2_4_prune-gguf/blob/main/blockchainlabs_7B_merged_test2_4_prune.Q4_K_S.gguf) | Q4_K_S | 3.86GB | | [blockchainlabs_7B_merged_test2_4_prune.Q4_K.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_7B_merged_test2_4_prune-gguf/blob/main/blockchainlabs_7B_merged_test2_4_prune.Q4_K.gguf) | Q4_K | 4.07GB | | [blockchainlabs_7B_merged_test2_4_prune.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_7B_merged_test2_4_prune-gguf/blob/main/blockchainlabs_7B_merged_test2_4_prune.Q4_K_M.gguf) | Q4_K_M | 4.07GB | | [blockchainlabs_7B_merged_test2_4_prune.Q4_1.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_7B_merged_test2_4_prune-gguf/blob/main/blockchainlabs_7B_merged_test2_4_prune.Q4_1.gguf) | Q4_1 | 4.24GB | | [blockchainlabs_7B_merged_test2_4_prune.Q5_0.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_7B_merged_test2_4_prune-gguf/blob/main/blockchainlabs_7B_merged_test2_4_prune.Q5_0.gguf) | Q5_0 | 4.65GB | | [blockchainlabs_7B_merged_test2_4_prune.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_7B_merged_test2_4_prune-gguf/blob/main/blockchainlabs_7B_merged_test2_4_prune.Q5_K_S.gguf) | Q5_K_S | 4.65GB | | [blockchainlabs_7B_merged_test2_4_prune.Q5_K.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_7B_merged_test2_4_prune-gguf/blob/main/blockchainlabs_7B_merged_test2_4_prune.Q5_K.gguf) | Q5_K | 4.78GB | | [blockchainlabs_7B_merged_test2_4_prune.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_7B_merged_test2_4_prune-gguf/blob/main/blockchainlabs_7B_merged_test2_4_prune.Q5_K_M.gguf) | Q5_K_M | 4.78GB | | [blockchainlabs_7B_merged_test2_4_prune.Q5_1.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_7B_merged_test2_4_prune-gguf/blob/main/blockchainlabs_7B_merged_test2_4_prune.Q5_1.gguf) | Q5_1 | 5.07GB | | [blockchainlabs_7B_merged_test2_4_prune.Q6_K.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_7B_merged_test2_4_prune-gguf/blob/main/blockchainlabs_7B_merged_test2_4_prune.Q6_K.gguf) | Q6_K | 5.53GB | | [blockchainlabs_7B_merged_test2_4_prune.Q8_0.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_7B_merged_test2_4_prune-gguf/blob/main/blockchainlabs_7B_merged_test2_4_prune.Q8_0.gguf) | Q8_0 | 7.17GB | Original model description: --- license: cc-by-nc-4.0 tags: - merge - mergekit - lazymergekit - pruning - alnrg2arg/blockchainlabs_7B_merged_test2_4 - mlabonne/NeuralBeagle14-7B - udkai/Turdus --- # blockchainlabs_7B_merged_test2_4_prune blockchainlabs_7B_merged_test2_4_prune is a pruned model based on alnrg2arg/blockchainlabs_7B_merged_test2_4, which is a merged model using following models using [mergekit](https://github.com/cg123/mergekit): * [mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B) * [udkai/Turdus](https://huggingface.co/udkai/Turdus) Pruning Kit I used: [wanda](https://github.com/locuslab/wanda?tab=readme-ov-file#ablation-on-obs-weight-update) ## 🧩 Configuration ```json { "_name_or_path": "alnrg2arg/blockchainlabs_7B_merged_test2_4_prun", "architectures": [ "MistralForCausalLM" ], "attention_dropout": 0.0, "bos_token_id": 1, "eos_token_id": 2, "hidden_act": "silu", "hidden_size": 4096, "initializer_range": 0.02, "intermediate_size": 14336, "max_position_embeddings": 32768, "model_type": "mistral", "num_attention_heads": 32, "num_hidden_layers": 32, "num_key_value_heads": 8, "rms_norm_eps": 1e-05, "rope_theta": 10000.0, "sliding_window": 4096, "tie_word_embeddings": false, "torch_dtype": "float16", "transformers_version": "4.36.2", "use_cache": false, "vocab_size": 32000 } ```

tsavage68/MedQA_L3_100steps_1e6rate_SFT

tsavage68

2024-05-18T20:21:41Z

6

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:finetune:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-05-18T20:17:13Z

--- license: llama3 base_model: meta-llama/Meta-Llama-3-8B-Instruct tags: - trl - sft - generated_from_trainer model-index: - name: MedQA_L3_100steps_1e6rate_SFT results: [] ---  # MedQA_L3_100steps_1e6rate_SFT This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4119 ## 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: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - 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: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 1.0895 | 0.0489 | 50 | 0.8521 | | 0.3865 | 0.0977 | 100 | 0.4119 | ### Framework versions - Transformers 4.41.0 - Pytorch 2.0.0+cu117 - Datasets 2.19.1 - Tokenizers 0.19.1

tsavage68/MedQA_L3_1000steps_1e8rate_SFT

tsavage68

2024-05-18T20:13:33Z

6

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:finetune:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-05-18T20:09:06Z

--- license: llama3 base_model: meta-llama/Meta-Llama-3-8B-Instruct tags: - trl - sft - generated_from_trainer model-index: - name: MedQA_L3_1000steps_1e8rate_SFT results: [] ---  # MedQA_L3_1000steps_1e8rate_SFT This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.7989 ## 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-08 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - 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: 1000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 1.783 | 0.0489 | 50 | 1.7997 | | 1.7968 | 0.0977 | 100 | 1.7995 | | 1.8022 | 0.1466 | 150 | 1.7997 | | 1.7968 | 0.1954 | 200 | 1.7993 | | 1.7998 | 0.2443 | 250 | 1.7989 | | 1.7963 | 0.2931 | 300 | 1.7989 | | 1.7977 | 0.3420 | 350 | 1.7992 | | 1.7971 | 0.3908 | 400 | 1.7991 | | 1.7697 | 0.4397 | 450 | 1.7990 | | 1.8021 | 0.4885 | 500 | 1.7990 | | 1.7897 | 0.5374 | 550 | 1.7988 | | 1.7817 | 0.5862 | 600 | 1.7988 | | 1.812 | 0.6351 | 650 | 1.7987 | | 1.7939 | 0.6839 | 700 | 1.7989 | | 1.815 | 0.7328 | 750 | 1.7989 | | 1.7991 | 0.7816 | 800 | 1.7989 | | 1.8164 | 0.8305 | 850 | 1.7989 | | 1.8062 | 0.8793 | 900 | 1.7989 | | 1.8048 | 0.9282 | 950 | 1.7989 | | 1.8103 | 0.9770 | 1000 | 1.7989 | ### Framework versions - Transformers 4.41.0 - Pytorch 2.0.0+cu117 - Datasets 2.19.1 - Tokenizers 0.19.1

emilykang/Phi_medmcqa_question_generation-pathology_lora

emilykang

2024-05-18T20:13:07Z

1

0

peft

[ "peft", "safetensors", "phi", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:microsoft/phi-2", "base_model:adapter:microsoft/phi-2", "license:mit", "region:us" ]

null

2024-05-18T18:35:56Z

--- license: mit library_name: peft tags: - trl - sft - generated_from_trainer base_model: microsoft/phi-2 datasets: - generator model-index: - name: Phi_medmcqa_question_generation-pathology_lora results: [] ---  # Phi_medmcqa_question_generation-pathology_lora This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - 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 - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1

ibahri/pearson-bge-m3

ibahri

2024-05-18T20:08:29Z

6

0

sentence-transformers

[ "sentence-transformers", "safetensors", "xlm-roberta", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-05-18T19:56:00Z

--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # ibahri/pearson-bge-m3 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('ibahri/pearson-bge-m3') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=ibahri/pearson-bge-m3) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 14 with parameters: ``` {'batch_size': 10, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 2, "evaluation_steps": 50, "evaluator": "sentence_transformers.evaluation.InformationRetrievalEvaluator.InformationRetrievalEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 2, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Citing & Authors

tsavage68/MedQA_L3_1000steps_1e6rate_SFT

tsavage68

2024-05-18T20:05:18Z

7

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:finetune:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-05-18T20:01:28Z

--- license: llama3 base_model: meta-llama/Meta-Llama-3-8B-Instruct tags: - trl - sft - generated_from_trainer model-index: - name: MedQA_L3_1000steps_1e6rate_SFT results: [] ---  # MedQA_L3_1000steps_1e6rate_SFT This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3666 ## 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: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - 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: 1000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 1.0895 | 0.0489 | 50 | 0.8521 | | 0.3865 | 0.0977 | 100 | 0.4119 | | 0.4156 | 0.1466 | 150 | 0.3943 | | 0.4302 | 0.1954 | 200 | 0.3870 | | 0.3788 | 0.2443 | 250 | 0.3808 | | 0.3964 | 0.2931 | 300 | 0.3773 | | 0.3753 | 0.3420 | 350 | 0.3749 | | 0.359 | 0.3908 | 400 | 0.3727 | | 0.3874 | 0.4397 | 450 | 0.3711 | | 0.3722 | 0.4885 | 500 | 0.3699 | | 0.3615 | 0.5374 | 550 | 0.3686 | | 0.3807 | 0.5862 | 600 | 0.3677 | | 0.3643 | 0.6351 | 650 | 0.3673 | | 0.3513 | 0.6839 | 700 | 0.3669 | | 0.358 | 0.7328 | 750 | 0.3667 | | 0.3648 | 0.7816 | 800 | 0.3666 | | 0.3911 | 0.8305 | 850 | 0.3666 | | 0.3475 | 0.8793 | 900 | 0.3666 | | 0.3511 | 0.9282 | 950 | 0.3665 | | 0.3673 | 0.9770 | 1000 | 0.3666 | ### Framework versions - Transformers 4.41.0 - Pytorch 2.0.0+cu117 - Datasets 2.19.1 - Tokenizers 0.19.1

apwic/sentiment-lora-r2a0d0.15-1

apwic

2024-05-18T19:57:50Z

0

null

[ "tensorboard", "generated_from_trainer", "id", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "region:us" ]

null

2024-05-18T19:24:41Z

--- language: - id license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: sentiment-lora-r2a0d0.15-1 results: [] ---  # sentiment-lora-r2a0d0.15-1 This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3633 - Accuracy: 0.8396 - Precision: 0.8128 - Recall: 0.7890 - F1: 0.7992 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 30 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.5664 | 1.0 | 122 | 0.5221 | 0.7218 | 0.6580 | 0.6432 | 0.6487 | | 0.5148 | 2.0 | 244 | 0.5111 | 0.7243 | 0.6758 | 0.6899 | 0.6810 | | 0.4924 | 3.0 | 366 | 0.4791 | 0.7444 | 0.6884 | 0.6741 | 0.6799 | | 0.4615 | 4.0 | 488 | 0.4651 | 0.7644 | 0.7148 | 0.7058 | 0.7099 | | 0.4516 | 5.0 | 610 | 0.4581 | 0.7644 | 0.7214 | 0.7408 | 0.7286 | | 0.4291 | 6.0 | 732 | 0.4295 | 0.7895 | 0.7462 | 0.7385 | 0.7421 | | 0.4194 | 7.0 | 854 | 0.4191 | 0.7995 | 0.7581 | 0.7606 | 0.7593 | | 0.3994 | 8.0 | 976 | 0.4048 | 0.8120 | 0.7745 | 0.7645 | 0.7691 | | 0.3919 | 9.0 | 1098 | 0.3950 | 0.8246 | 0.7954 | 0.7659 | 0.7778 | | 0.3762 | 10.0 | 1220 | 0.3881 | 0.8271 | 0.8022 | 0.7626 | 0.7777 | | 0.3704 | 11.0 | 1342 | 0.3806 | 0.8271 | 0.7949 | 0.7776 | 0.7853 | | 0.3642 | 12.0 | 1464 | 0.3733 | 0.8421 | 0.8122 | 0.8008 | 0.8061 | | 0.3614 | 13.0 | 1586 | 0.3753 | 0.8321 | 0.8092 | 0.7687 | 0.7842 | | 0.3474 | 14.0 | 1708 | 0.3695 | 0.8396 | 0.8155 | 0.7840 | 0.7969 | | 0.3479 | 15.0 | 1830 | 0.3675 | 0.8421 | 0.8142 | 0.7958 | 0.8040 | | 0.3347 | 16.0 | 1952 | 0.3649 | 0.8421 | 0.8142 | 0.7958 | 0.8040 | | 0.335 | 17.0 | 2074 | 0.3653 | 0.8371 | 0.8114 | 0.7822 | 0.7943 | | 0.3361 | 18.0 | 2196 | 0.3632 | 0.8396 | 0.8128 | 0.7890 | 0.7992 | | 0.3343 | 19.0 | 2318 | 0.3636 | 0.8371 | 0.8114 | 0.7822 | 0.7943 | | 0.3347 | 20.0 | 2440 | 0.3633 | 0.8396 | 0.8128 | 0.7890 | 0.7992 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.15.2

Felladrin/gguf-sharded-zephyr-1b-olmo-sft-qlora

Felladrin

2024-05-18T19:56:22Z

6

0

null

[ "gguf", "base_model:Ritvik19/zephyr-1b-olmo-sft-qlora", "base_model:quantized:Ritvik19/zephyr-1b-olmo-sft-qlora", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-05-18T19:52:03Z

--- license: apache-2.0 base_model: Ritvik19/zephyr-1b-olmo-sft-qlora --- Sharded GGUF version of [Ritvik19/zephyr-1b-olmo-sft-qlora](https://huggingface.co/Ritvik19/zephyr-1b-olmo-sft-qlora).

elloco123/ppo-LunarLander-v2

elloco123

2024-05-18T19:49:51Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2024-05-18T19:49:31Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 266.36 +/- 17.08 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

Felladrin/gguf-zephyr-1b-olmo-sft-qlora

Felladrin

2024-05-18T19:48:05Z

11

0

null

[ "gguf", "base_model:Ritvik19/zephyr-1b-olmo-sft-qlora", "base_model:quantized:Ritvik19/zephyr-1b-olmo-sft-qlora", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-05-18T18:55:03Z

--- license: apache-2.0 base_model: Ritvik19/zephyr-1b-olmo-sft-qlora --- GGUF version of [Ritvik19/zephyr-1b-olmo-sft-qlora](https://huggingface.co/Ritvik19/zephyr-1b-olmo-sft-qlora).

Felladrin/gguf-openhermes-1b-olmo-sft-qlora

Felladrin

2024-05-18T19:47:00Z

5

0

null

[ "gguf", "base_model:Ritvik19/openhermes-1b-olmo-sft-qlora", "base_model:quantized:Ritvik19/openhermes-1b-olmo-sft-qlora", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-05-18T19:22:29Z

--- license: apache-2.0 base_model: Ritvik19/openhermes-1b-olmo-sft-qlora --- GGUF version of [Ritvik19/openhermes-1b-olmo-sft-qlora](https://huggingface.co/Ritvik19/openhermes-1b-olmo-sft-qlora).

RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-gguf

RichardErkhov

2024-05-18T19:45:56Z

30

1

null

[ "gguf", "endpoints_compatible", "region:us" ]

null

2024-05-18T17:14:46Z

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) LogoS-7Bx2-MoE-13B-v0.2 - GGUF - Model creator: https://huggingface.co/RubielLabarta/ - Original model: https://huggingface.co/RubielLabarta/LogoS-7Bx2-MoE-13B-v0.2/ | Name | Quant method | Size | | ---- | ---- | ---- | | [LogoS-7Bx2-MoE-13B-v0.2.Q2_K.gguf](https://huggingface.co/RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-gguf/blob/main/LogoS-7Bx2-MoE-13B-v0.2.Q2_K.gguf) | Q2_K | 4.43GB | | [LogoS-7Bx2-MoE-13B-v0.2.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-gguf/blob/main/LogoS-7Bx2-MoE-13B-v0.2.IQ3_XS.gguf) | IQ3_XS | 4.94GB | | [LogoS-7Bx2-MoE-13B-v0.2.IQ3_S.gguf](https://huggingface.co/RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-gguf/blob/main/LogoS-7Bx2-MoE-13B-v0.2.IQ3_S.gguf) | IQ3_S | 5.22GB | | [LogoS-7Bx2-MoE-13B-v0.2.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-gguf/blob/main/LogoS-7Bx2-MoE-13B-v0.2.Q3_K_S.gguf) | Q3_K_S | 5.2GB | | [LogoS-7Bx2-MoE-13B-v0.2.IQ3_M.gguf](https://huggingface.co/RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-gguf/blob/main/LogoS-7Bx2-MoE-13B-v0.2.IQ3_M.gguf) | IQ3_M | 5.34GB | | [LogoS-7Bx2-MoE-13B-v0.2.Q3_K.gguf](https://huggingface.co/RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-gguf/blob/main/LogoS-7Bx2-MoE-13B-v0.2.Q3_K.gguf) | Q3_K | 5.78GB | | [LogoS-7Bx2-MoE-13B-v0.2.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-gguf/blob/main/LogoS-7Bx2-MoE-13B-v0.2.Q3_K_M.gguf) | Q3_K_M | 5.78GB | | [LogoS-7Bx2-MoE-13B-v0.2.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-gguf/blob/main/LogoS-7Bx2-MoE-13B-v0.2.Q3_K_L.gguf) | Q3_K_L | 6.27GB | | [LogoS-7Bx2-MoE-13B-v0.2.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-gguf/blob/main/LogoS-7Bx2-MoE-13B-v0.2.IQ4_XS.gguf) | IQ4_XS | 6.5GB | | [LogoS-7Bx2-MoE-13B-v0.2.Q4_0.gguf](https://huggingface.co/RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-gguf/blob/main/LogoS-7Bx2-MoE-13B-v0.2.Q4_0.gguf) | Q4_0 | 6.78GB | | [LogoS-7Bx2-MoE-13B-v0.2.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-gguf/blob/main/LogoS-7Bx2-MoE-13B-v0.2.IQ4_NL.gguf) | IQ4_NL | 6.85GB | | [LogoS-7Bx2-MoE-13B-v0.2.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-gguf/blob/main/LogoS-7Bx2-MoE-13B-v0.2.Q4_K_S.gguf) | Q4_K_S | 6.84GB | | [LogoS-7Bx2-MoE-13B-v0.2.Q4_K.gguf](https://huggingface.co/RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-gguf/blob/main/LogoS-7Bx2-MoE-13B-v0.2.Q4_K.gguf) | Q4_K | 7.25GB | | [LogoS-7Bx2-MoE-13B-v0.2.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-gguf/blob/main/LogoS-7Bx2-MoE-13B-v0.2.Q4_K_M.gguf) | Q4_K_M | 7.25GB | | [LogoS-7Bx2-MoE-13B-v0.2.Q4_1.gguf](https://huggingface.co/RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-gguf/blob/main/LogoS-7Bx2-MoE-13B-v0.2.Q4_1.gguf) | Q4_1 | 7.52GB | | [LogoS-7Bx2-MoE-13B-v0.2.Q5_0.gguf](https://huggingface.co/RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-gguf/blob/main/LogoS-7Bx2-MoE-13B-v0.2.Q5_0.gguf) | Q5_0 | 8.26GB | | [LogoS-7Bx2-MoE-13B-v0.2.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-gguf/blob/main/LogoS-7Bx2-MoE-13B-v0.2.Q5_K_S.gguf) | Q5_K_S | 8.26GB | | [LogoS-7Bx2-MoE-13B-v0.2.Q5_K.gguf](https://huggingface.co/RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-gguf/blob/main/LogoS-7Bx2-MoE-13B-v0.2.Q5_K.gguf) | Q5_K | 8.51GB | | [LogoS-7Bx2-MoE-13B-v0.2.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-gguf/blob/main/LogoS-7Bx2-MoE-13B-v0.2.Q5_K_M.gguf) | Q5_K_M | 8.51GB | | [LogoS-7Bx2-MoE-13B-v0.2.Q5_1.gguf](https://huggingface.co/RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-gguf/blob/main/LogoS-7Bx2-MoE-13B-v0.2.Q5_1.gguf) | Q5_1 | 9.01GB | | [LogoS-7Bx2-MoE-13B-v0.2.Q6_K.gguf](https://huggingface.co/RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-gguf/blob/main/LogoS-7Bx2-MoE-13B-v0.2.Q6_K.gguf) | Q6_K | 9.84GB | | [LogoS-7Bx2-MoE-13B-v0.2.Q8_0.gguf](https://huggingface.co/RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-gguf/blob/main/LogoS-7Bx2-MoE-13B-v0.2.Q8_0.gguf) | Q8_0 | 12.75GB | Original model description: --- language: - en - es license: apache-2.0 tags: - moe - merge base_model: - yunconglong/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B - TomGrc/FusionNet_7Bx2_MoE_14B model-index: - name: LogoS-7Bx2-MoE-13B-v0.1 results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 74.49 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=RubielLabarta/LogoS-7Bx2-MoE-13B-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 89.07 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=RubielLabarta/LogoS-7Bx2-MoE-13B-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 64.74 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=RubielLabarta/LogoS-7Bx2-MoE-13B-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 74.57 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=RubielLabarta/LogoS-7Bx2-MoE-13B-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 88.32 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=RubielLabarta/LogoS-7Bx2-MoE-13B-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 71.65 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=RubielLabarta/LogoS-7Bx2-MoE-13B-v0.1 name: Open LLM Leaderboard --- # LogoS-7Bx2-MoE-13B-v0.1 Model built by @RubielLabarta using SLERP merge method. The model is release for research purposes only, commercial use is not allowed. The LogoS is a model to experiment with the MoE method, which could significantly increase the performance of the original model. The model has 12.9B parameters. # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_RubielLabarta__LogoS-7Bx2-MoE-13B-v0.1) | Metric |Value| |---------------------------------|----:| |Avg. |77.14| |AI2 Reasoning Challenge (25-Shot)|74.49| |HellaSwag (10-Shot) |89.07| |MMLU (5-Shot) |64.74| |TruthfulQA (0-shot) |74.57| |Winogrande (5-shot) |88.32| |GSM8k (5-shot) |71.65|

LoneStriker/Master-Yi-9B-5.0bpw-h6-exl2

LoneStriker

2024-05-18T19:41:13Z

10

1

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "5-bit", "exl2", "region:us" ]

text-generation

2024-05-18T19:38:45Z

--- license: apache-2.0 --- ## Model Description Master is a collection of LLMs trained using human-collected seed questions and regenerate the answers with a mixture of high performance Open-source LLMs. **Master-Yi-9B** is trained using the ORPO technique. The model shows strong abilities in reasoning on coding and math questions. **Quantized Version**: [Here](https://huggingface.co/qnguyen3/Master-Yi-9B-GGUF) **Master-Yi-9B-Vision**: **Coming Soon** ![img](https://huggingface.co/qnguyen3/Master-Yi-9B/resolve/main/Master-Yi-9B.webp) ## Prompt Template ``` <|im_start|>system You are a helpful AI assistant.<|im_end|> <|im_start|>user What is the meaning of life?<|im_end|> <|im_start|>assistant ``` ## Examples ![image/png](https://cdn-uploads.huggingface.co/production/uploads/630430583926de1f7ec62c6b/E27JmdRAMrHQacM50-lBk.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/630430583926de1f7ec62c6b/z0HS4bxHFQzPe0gZlvCzZ.png) ## Inference Code ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch device = "cuda" # the device to load the model onto model = AutoModelForCausalLM.from_pretrained( "vilm/VinaLlama2-14B", torch_dtype='auto', device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained("vilm/VinaLlama2-14B") prompt = "What is the mearning of life?" messages = [ {"role": "system", "content": "You are a helpful AI assistant."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(device) generated_ids = model.generate( model_inputs.input_ids, max_new_tokens=1024, eos_token_id=tokenizer.eos_token_id, temperature=0.25, ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids)[0] print(response) ``` ## Benchmarks ### Nous Benchmark: | Model |AGIEval|GPT4All|TruthfulQA|Bigbench|Average| |---------------------------------------------------|------:|------:|---------:|-------:|------:| |[Master-Yi-9B](https://huggingface.co/qnguyen3/Master-Yi-9B)| 43.55| 71.48| 48.54| 41.43| 51.25| ### AGIEval ``` | Task |Version| Metric |Value| |Stderr| |------------------------------|------:|--------|----:|---|-----:| |agieval_aqua_rat | 0|acc |35.83|± | 3.01| | | |acc_norm|31.89|± | 2.93| |agieval_logiqa_en | 0|acc |38.25|± | 1.91| | | |acc_norm|37.79|± | 1.90| |agieval_lsat_ar | 0|acc |23.04|± | 2.78| | | |acc_norm|20.43|± | 2.66| |agieval_lsat_lr | 0|acc |48.04|± | 2.21| | | |acc_norm|42.75|± | 2.19| |agieval_lsat_rc | 0|acc |61.34|± | 2.97| | | |acc_norm|52.79|± | 3.05| |agieval_sat_en | 0|acc |79.13|± | 2.84| | | |acc_norm|72.33|± | 3.12| |agieval_sat_en_without_passage| 0|acc |44.17|± | 3.47| | | |acc_norm|42.72|± | 3.45| |agieval_sat_math | 0|acc |52.27|± | 3.38| | | |acc_norm|47.73|± | 3.38| Average: 43.55% ``` ### GPT4All ``` | Task |Version| Metric |Value| |Stderr| |-------------|------:|--------|----:|---|-----:| |arc_challenge| 0|acc |54.95|± | 1.45| | | |acc_norm|58.70|± | 1.44| |arc_easy | 0|acc |82.28|± | 0.78| | | |acc_norm|81.10|± | 0.80| |boolq | 1|acc |86.15|± | 0.60| |hellaswag | 0|acc |59.16|± | 0.49| | | |acc_norm|77.53|± | 0.42| |openbookqa | 0|acc |37.40|± | 2.17| | | |acc_norm|44.00|± | 2.22| |piqa | 0|acc |79.00|± | 0.95| | | |acc_norm|80.25|± | 0.93| |winogrande | 0|acc |72.61|± | 1.25| Average: 71.48% ``` ### TruthfulQA ``` | Task |Version|Metric|Value| |Stderr| |-------------|------:|------|----:|---|-----:| |truthfulqa_mc| 1|mc1 |33.05|± | 1.65| | | |mc2 |48.54|± | 1.54| Average: 48.54% ``` ### Bigbench ``` | Task |Version| Metric |Value| |Stderr| |------------------------------------------------|------:|---------------------|----:|---|-----:| |bigbench_causal_judgement | 0|multiple_choice_grade|54.74|± | 3.62| |bigbench_date_understanding | 0|multiple_choice_grade|68.02|± | 2.43| |bigbench_disambiguation_qa | 0|multiple_choice_grade|40.31|± | 3.06| |bigbench_geometric_shapes | 0|multiple_choice_grade|30.36|± | 2.43| | | |exact_str_match | 2.23|± | 0.78| |bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|26.00|± | 1.96| |bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|20.71|± | 1.53| |bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|44.00|± | 2.87| |bigbench_movie_recommendation | 0|multiple_choice_grade|35.00|± | 2.14| |bigbench_navigate | 0|multiple_choice_grade|58.40|± | 1.56| |bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|61.80|± | 1.09| |bigbench_ruin_names | 0|multiple_choice_grade|42.41|± | 2.34| |bigbench_salient_translation_error_detection | 0|multiple_choice_grade|31.56|± | 1.47| |bigbench_snarks | 0|multiple_choice_grade|55.25|± | 3.71| |bigbench_sports_understanding | 0|multiple_choice_grade|69.37|± | 1.47| |bigbench_temporal_sequences | 0|multiple_choice_grade|27.70|± | 1.42| |bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|21.36|± | 1.16| |bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|14.69|± | 0.85| |bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|44.00|± | 2.87| Average: 41.43% ``` **Average score**: 51.25% ### OpenLLM Benchmark: | Model |ARC |HellaSwag|MMLU |TruthfulQA|Winogrande|GSM8K|Average| |---------------------------------------------------|---:|--------:|----:|---------:|---------:|----:|------:| |[Master-Yi-9B](https://huggingface.co/qnguyen3/Master-Yi-9B)|61.6| 79.89|69.95| 48.59| 77.35|67.48| 67.48| ### ARC ``` | Task |Version| Metric | Value | |Stderr| |-------------|------:|--------------------|-------------|---|------| |arc_challenge| 1|acc,none | 0.59| | | | | |acc_stderr,none | 0.01| | | | | |acc_norm,none | 0.62| | | | | |acc_norm_stderr,none| 0.01| | | | | |alias |arc_challenge| | | Average: 61.6% ``` ### HellaSwag ``` | Task |Version| Metric | Value | |Stderr| |---------|------:|--------------------|---------|---|------| |hellaswag| 1|acc,none | 0.61| | | | | |acc_stderr,none | 0| | | | | |acc_norm,none | 0.80| | | | | |acc_norm_stderr,none| 0| | | | | |alias |hellaswag| | | Average: 79.89% ``` ### MMLU ``` | Task |Version| Metric | Value | |Stderr| |----------------------------------------|-------|---------------|---------------------------------------|---|------| |mmlu |N/A |acc,none | 0.7| | | | | |acc_stderr,none| 0| | | | | |alias |mmlu | | | |mmlu_abstract_algebra | 0|alias | - abstract_algebra | | | | | |acc,none |0.46 | | | | | |acc_stderr,none|0.05 | | | |mmlu_anatomy | 0|alias | - anatomy | | | | | |acc,none |0.64 | | | | | |acc_stderr,none|0.04 | | | |mmlu_astronomy | 0|alias | - astronomy | | | | | |acc,none |0.77 | | | | | |acc_stderr,none|0.03 | | | |mmlu_business_ethics | 0|alias | - business_ethics | | | | | |acc,none |0.76 | | | | | |acc_stderr,none|0.04 | | | |mmlu_clinical_knowledge | 0|alias | - clinical_knowledge | | | | | |acc,none |0.71 | | | | | |acc_stderr,none|0.03 | | | |mmlu_college_biology | 0|alias | - college_biology | | | | | |acc,none |0.82 | | | | | |acc_stderr,none|0.03 | | | |mmlu_college_chemistry | 0|alias | - college_chemistry | | | | | |acc,none |0.52 | | | | | |acc_stderr,none|0.05 | | | |mmlu_college_computer_science | 0|alias | - college_computer_science | | | | | |acc,none |0.56 | | | | | |acc_stderr,none|0.05 | | | |mmlu_college_mathematics | 0|alias | - college_mathematics | | | | | |acc,none |0.44 | | | | | |acc_stderr,none|0.05 | | | |mmlu_college_medicine | 0|alias | - college_medicine | | | | | |acc,none |0.72 | | | | | |acc_stderr,none|0.03 | | | |mmlu_college_physics | 0|alias | - college_physics | | | | | |acc,none |0.45 | | | | | |acc_stderr,none|0.05 | | | |mmlu_computer_security | 0|alias | - computer_security | | | | | |acc,none |0.81 | | | | | |acc_stderr,none|0.04 | | | |mmlu_conceptual_physics | 0|alias | - conceptual_physics | | | | | |acc,none |0.74 | | | | | |acc_stderr,none|0.03 | | | |mmlu_econometrics | 0|alias | - econometrics | | | | | |acc,none |0.65 | | | | | |acc_stderr,none|0.04 | | | |mmlu_electrical_engineering | 0|alias | - electrical_engineering | | | | | |acc,none |0.72 | | | | | |acc_stderr,none|0.04 | | | |mmlu_elementary_mathematics | 0|alias | - elementary_mathematics | | | | | |acc,none |0.62 | | | | | |acc_stderr,none|0.02 | | | |mmlu_formal_logic | 0|alias | - formal_logic | | | | | |acc,none |0.57 | | | | | |acc_stderr,none|0.04 | | | |mmlu_global_facts | 0|alias | - global_facts | | | | | |acc,none |0.46 | | | | | |acc_stderr,none|0.05 | | | |mmlu_high_school_biology | 0|alias | - high_school_biology | | | | | |acc,none |0.86 | | | | | |acc_stderr,none|0.02 | | | |mmlu_high_school_chemistry | 0|alias | - high_school_chemistry | | | | | |acc,none |0.67 | | | | | |acc_stderr,none|0.03 | | | |mmlu_high_school_computer_science | 0|alias | - high_school_computer_science | | | | | |acc,none |0.84 | | | | | |acc_stderr,none|0.04 | | | |mmlu_high_school_european_history | 0|alias | - high_school_european_history | | | | | |acc,none |0.82 | | | | | |acc_stderr,none|0.03 | | | |mmlu_high_school_geography | 0|alias | - high_school_geography | | | | | |acc,none |0.86 | | | | | |acc_stderr,none|0.02 | | | |mmlu_high_school_government_and_politics| 0|alias | - high_school_government_and_politics| | | | | |acc,none |0.90 | | | | | |acc_stderr,none|0.02 | | | |mmlu_high_school_macroeconomics | 0|alias | - high_school_macroeconomics | | | | | |acc,none |0.75 | | | | | |acc_stderr,none|0.02 | | | |mmlu_high_school_mathematics | 0|alias | - high_school_mathematics | | | | | |acc,none |0.43 | | | | | |acc_stderr,none|0.03 | | | |mmlu_high_school_microeconomics | 0|alias | - high_school_microeconomics | | | | | |acc,none |0.86 | | | | | |acc_stderr,none|0.02 | | | |mmlu_high_school_physics | 0|alias | - high_school_physics | | | | | |acc,none |0.45 | | | | | |acc_stderr,none|0.04 | | | |mmlu_high_school_psychology | 0|alias | - high_school_psychology | | | | | |acc,none |0.87 | | | | | |acc_stderr,none|0.01 | | | |mmlu_high_school_statistics | 0|alias | - high_school_statistics | | | | | |acc,none |0.68 | | | | | |acc_stderr,none|0.03 | | | |mmlu_high_school_us_history | 0|alias | - high_school_us_history | | | | | |acc,none |0.85 | | | | | |acc_stderr,none|0.02 | | | |mmlu_high_school_world_history | 0|alias | - high_school_world_history | | | | | |acc,none |0.85 | | | | | |acc_stderr,none|0.02 | | | |mmlu_human_aging | 0|alias | - human_aging | | | | | |acc,none |0.76 | | | | | |acc_stderr,none|0.03 | | | |mmlu_human_sexuality | 0|alias | - human_sexuality | | | | | |acc,none |0.78 | | | | | |acc_stderr,none|0.04 | | | |mmlu_humanities |N/A |alias | - humanities | | | | | |acc,none |0.63 | | | | | |acc_stderr,none|0.01 | | | |mmlu_international_law | 0|alias | - international_law | | | | | |acc,none |0.79 | | | | | |acc_stderr,none|0.04 | | | |mmlu_jurisprudence | 0|alias | - jurisprudence | | | | | |acc,none |0.79 | | | | | |acc_stderr,none|0.04 | | | |mmlu_logical_fallacies | 0|alias | - logical_fallacies | | | | | |acc,none |0.80 | | | | | |acc_stderr,none|0.03 | | | |mmlu_machine_learning | 0|alias | - machine_learning | | | | | |acc,none |0.52 | | | | | |acc_stderr,none|0.05 | | | |mmlu_management | 0|alias | - management | | | | | |acc,none |0.83 | | | | | |acc_stderr,none|0.04 | | | |mmlu_marketing | 0|alias | - marketing | | | | | |acc,none |0.89 | | | | | |acc_stderr,none|0.02 | | | |mmlu_medical_genetics | 0|alias | - medical_genetics | | | | | |acc,none |0.78 | | | | | |acc_stderr,none|0.04 | | | |mmlu_miscellaneous | 0|alias | - miscellaneous | | | | | |acc,none |0.85 | | | | | |acc_stderr,none|0.01 | | | |mmlu_moral_disputes | 0|alias | - moral_disputes | | | | | |acc,none |0.75 | | | | | |acc_stderr,none|0.02 | | | |mmlu_moral_scenarios | 0|alias | - moral_scenarios | | | | | |acc,none |0.48 | | | | | |acc_stderr,none|0.02 | | | |mmlu_nutrition | 0|alias | - nutrition | | | | | |acc,none |0.77 | | | | | |acc_stderr,none|0.02 | | | |mmlu_other |N/A |alias | - other | | | | | |acc,none |0.75 | | | | | |acc_stderr,none|0.01 | | | |mmlu_philosophy | 0|alias | - philosophy | | | | | |acc,none |0.78 | | | | | |acc_stderr,none|0.02 | | | |mmlu_prehistory | 0|alias | - prehistory | | | | | |acc,none |0.77 | | | | | |acc_stderr,none|0.02 | | | |mmlu_professional_accounting | 0|alias | - professional_accounting | | | | | |acc,none |0.57 | | | | | |acc_stderr,none|0.03 | | | |mmlu_professional_law | 0|alias | - professional_law | | | | | |acc,none |0.50 | | | | | |acc_stderr,none|0.01 | | | |mmlu_professional_medicine | 0|alias | - professional_medicine | | | | | |acc,none |0.71 | | | | | |acc_stderr,none|0.03 | | | |mmlu_professional_psychology | 0|alias | - professional_psychology | | | | | |acc,none |0.73 | | | | | |acc_stderr,none|0.02 | | | |mmlu_public_relations | 0|alias | - public_relations | | | | | |acc,none |0.76 | | | | | |acc_stderr,none|0.04 | | | |mmlu_security_studies | 0|alias | - security_studies | | | | | |acc,none |0.78 | | | | | |acc_stderr,none|0.03 | | | |mmlu_social_sciences |N/A |alias | - social_sciences | | | | | |acc,none |0.81 | | | | | |acc_stderr,none|0.01 | | | |mmlu_sociology | 0|alias | - sociology | | | | | |acc,none |0.86 | | | | | |acc_stderr,none|0.02 | | | |mmlu_stem |N/A |alias | - stem | | | | | |acc,none |0.65 | | | | | |acc_stderr,none|0.01 | | | |mmlu_us_foreign_policy | 0|alias | - us_foreign_policy | | | | | |acc,none |0.92 | | | | | |acc_stderr,none|0.03 | | | |mmlu_virology | 0|alias | - virology | | | | | |acc,none |0.58 | | | | | |acc_stderr,none|0.04 | | | |mmlu_world_religions | 0|alias | - world_religions | | | | | |acc,none |0.82 | | | | | |acc_stderr,none|0.03 | | | Average: 69.95% ``` ### TruthfulQA ``` | Task |Version| Metric | Value | |Stderr| |--------------|-------|-----------------------|-----------------|---|------| |truthfulqa |N/A |bleu_acc,none | 0.45| | | | | |bleu_acc_stderr,none | 0.02| | | | | |rouge1_acc,none | 0.45| | | | | |rouge1_acc_stderr,none | 0.02| | | | | |rouge2_diff,none | 0.92| | | | | |rouge2_diff_stderr,none| 1.07| | | | | |bleu_max,none | 23.77| | | | | |bleu_max_stderr,none | 0.81| | | | | |rouge2_acc,none | 0.38| | | | | |rouge2_acc_stderr,none | 0.02| | | | | |acc,none | 0.41| | | | | |acc_stderr,none | 0.01| | | | | |rougeL_diff,none | 1.57| | | | | |rougeL_diff_stderr,none| 0.93| | | | | |rougeL_acc,none | 0.46| | | | | |rougeL_acc_stderr,none | 0.02| | | | | |bleu_diff,none | 1.38| | | | | |bleu_diff_stderr,none | 0.75| | | | | |rouge2_max,none | 33.01| | | | | |rouge2_max_stderr,none | 1.05| | | | | |rouge1_diff,none | 1.72| | | | | |rouge1_diff_stderr,none| 0.92| | | | | |rougeL_max,none | 45.25| | | | | |rougeL_max_stderr,none | 0.92| | | | | |rouge1_max,none | 48.29| | | | | |rouge1_max_stderr,none | 0.90| | | | | |alias |truthfulqa | | | |truthfulqa_gen| 3|bleu_max,none | 23.77| | | | | |bleu_max_stderr,none | 0.81| | | | | |bleu_acc,none | 0.45| | | | | |bleu_acc_stderr,none | 0.02| | | | | |bleu_diff,none | 1.38| | | | | |bleu_diff_stderr,none | 0.75| | | | | |rouge1_max,none | 48.29| | | | | |rouge1_max_stderr,none | 0.90| | | | | |rouge1_acc,none | 0.45| | | | | |rouge1_acc_stderr,none | 0.02| | | | | |rouge1_diff,none | 1.72| | | | | |rouge1_diff_stderr,none| 0.92| | | | | |rouge2_max,none | 33.01| | | | | |rouge2_max_stderr,none | 1.05| | | | | |rouge2_acc,none | 0.38| | | | | |rouge2_acc_stderr,none | 0.02| | | | | |rouge2_diff,none | 0.92| | | | | |rouge2_diff_stderr,none| 1.07| | | | | |rougeL_max,none | 45.25| | | | | |rougeL_max_stderr,none | 0.92| | | | | |rougeL_acc,none | 0.46| | | | | |rougeL_acc_stderr,none | 0.02| | | | | |rougeL_diff,none | 1.57| | | | | |rougeL_diff_stderr,none| 0.93| | | | | |alias | - truthfulqa_gen| | | |truthfulqa_mc1| 2|acc,none | 0.33| | | | | |acc_stderr,none | 0.02| | | | | |alias | - truthfulqa_mc1| | | |truthfulqa_mc2| 2|acc,none | 0.49| | | | | |acc_stderr,none | 0.02| | | | | |alias | - truthfulqa_mc2| | | Average: 48.59% ``` ### Winogrande ``` | Task |Version| Metric | Value | |Stderr| |----------|------:|---------------|----------|---|------| |winogrande| 1|acc,none | 0.77| | | | | |acc_stderr,none| 0.01| | | | | |alias |winogrande| | | Average: 77.35% ``` ### GSM8K ``` |Task |Version| Metric |Value| |Stderr| |-----|------:|-----------------------------------|-----|---|------| |gsm8k| 3|exact_match,strict-match | 0.67| | | | | |exact_match_stderr,strict-match | 0.01| | | | | |exact_match,flexible-extract | 0.68| | | | | |exact_match_stderr,flexible-extract| 0.01| | | | | |alias |gsm8k| | | Average: 67.48% ``` **Average score**: 67.48%

amaye15/google-vit-base-patch16-224-batch32-lr0.0005-standford-dogs

amaye15

2024-05-18T19:39:23Z

223

0

transformers

[ "transformers", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:stanford-dogs", "base_model:google/vit-base-patch16-224", "base_model:finetune:google/vit-base-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2024-05-18T19:39:02Z

--- license: apache-2.0 base_model: google/vit-base-patch16-224 tags: - generated_from_trainer datasets: - stanford-dogs metrics: - accuracy - f1 - precision - recall model-index: - name: google-vit-base-patch16-224-batch32-lr0.0005-standford-dogs results: - task: name: Image Classification type: image-classification dataset: name: stanford-dogs type: stanford-dogs config: default split: full args: default metrics: - name: Accuracy type: accuracy value: 0.8838678328474247 - name: F1 type: f1 value: 0.880922271280839 - name: Precision type: precision value: 0.8888253617157671 - name: Recall type: recall value: 0.8813659659148954 ---  # google-vit-base-patch16-224-batch32-lr0.0005-standford-dogs This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the stanford-dogs dataset. It achieves the following results on the evaluation set: - Loss: 0.4466 - Accuracy: 0.8839 - F1: 0.8809 - Precision: 0.8888 - Recall: 0.8814 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 4.758 | 0.0777 | 10 | 4.5706 | 0.0481 | 0.0344 | 0.0390 | 0.0483 | | 4.4856 | 0.1553 | 20 | 4.2407 | 0.1421 | 0.1018 | 0.1190 | 0.1396 | | 4.2422 | 0.2330 | 30 | 3.9118 | 0.2655 | 0.2120 | 0.2525 | 0.2553 | | 3.972 | 0.3107 | 40 | 3.5875 | 0.4096 | 0.3445 | 0.3996 | 0.3933 | | 3.6632 | 0.3883 | 50 | 3.2750 | 0.5299 | 0.4683 | 0.5338 | 0.5129 | | 3.4266 | 0.4660 | 60 | 2.9911 | 0.6028 | 0.5536 | 0.6617 | 0.5870 | | 3.1207 | 0.5437 | 70 | 2.7127 | 0.6638 | 0.6227 | 0.7159 | 0.6496 | | 2.9402 | 0.6214 | 80 | 2.4853 | 0.7043 | 0.6716 | 0.7387 | 0.6923 | | 2.7683 | 0.6990 | 90 | 2.2667 | 0.7376 | 0.7092 | 0.7675 | 0.7277 | | 2.5556 | 0.7767 | 100 | 2.0710 | 0.7631 | 0.7422 | 0.7843 | 0.7551 | | 2.3936 | 0.8544 | 110 | 1.8898 | 0.7772 | 0.7610 | 0.8076 | 0.7705 | | 2.245 | 0.9320 | 120 | 1.7340 | 0.7954 | 0.7807 | 0.8138 | 0.7898 | | 2.0889 | 1.0097 | 130 | 1.6027 | 0.8073 | 0.7970 | 0.8314 | 0.8024 | | 1.951 | 1.0874 | 140 | 1.4715 | 0.8197 | 0.8094 | 0.8361 | 0.8132 | | 1.8414 | 1.1650 | 150 | 1.3790 | 0.8209 | 0.8104 | 0.8389 | 0.8151 | | 1.7247 | 1.2427 | 160 | 1.3237 | 0.8146 | 0.8034 | 0.8338 | 0.8085 | | 1.7338 | 1.3204 | 170 | 1.2107 | 0.8336 | 0.8251 | 0.8463 | 0.8284 | | 1.5688 | 1.3981 | 180 | 1.1370 | 0.8401 | 0.8314 | 0.8531 | 0.8354 | | 1.5471 | 1.4757 | 190 | 1.0684 | 0.8455 | 0.8367 | 0.8558 | 0.8398 | | 1.461 | 1.5534 | 200 | 1.0200 | 0.8477 | 0.8384 | 0.8571 | 0.8430 | | 1.4736 | 1.6311 | 210 | 0.9612 | 0.8540 | 0.8493 | 0.8621 | 0.8509 | | 1.4062 | 1.7087 | 220 | 0.9269 | 0.8528 | 0.8476 | 0.8639 | 0.8495 | | 1.2888 | 1.7864 | 230 | 0.8914 | 0.8516 | 0.8456 | 0.8626 | 0.8478 | | 1.3353 | 1.8641 | 240 | 0.8484 | 0.8601 | 0.8538 | 0.8684 | 0.8555 | | 1.2751 | 1.9417 | 250 | 0.8210 | 0.8586 | 0.8520 | 0.8643 | 0.8538 | | 1.2378 | 2.0194 | 260 | 0.7924 | 0.8567 | 0.8510 | 0.8641 | 0.8522 | | 1.1686 | 2.0971 | 270 | 0.7683 | 0.8579 | 0.8529 | 0.8670 | 0.8543 | | 1.1625 | 2.1748 | 280 | 0.7477 | 0.8567 | 0.8522 | 0.8658 | 0.8532 | | 1.1883 | 2.2524 | 290 | 0.7312 | 0.8576 | 0.8534 | 0.8674 | 0.8541 | | 1.1551 | 2.3301 | 300 | 0.7052 | 0.8654 | 0.8606 | 0.8702 | 0.8618 | | 1.1259 | 2.4078 | 310 | 0.6901 | 0.8627 | 0.8580 | 0.8720 | 0.8597 | | 1.073 | 2.4854 | 320 | 0.6793 | 0.8654 | 0.8600 | 0.8722 | 0.8612 | | 1.0587 | 2.5631 | 330 | 0.6604 | 0.8700 | 0.8646 | 0.8753 | 0.8660 | | 1.0506 | 2.6408 | 340 | 0.6470 | 0.8690 | 0.8638 | 0.8714 | 0.8652 | | 1.0397 | 2.7184 | 350 | 0.6369 | 0.8664 | 0.8606 | 0.8711 | 0.8627 | | 1.0363 | 2.7961 | 360 | 0.6373 | 0.8664 | 0.8610 | 0.8797 | 0.8623 | | 1.0408 | 2.8738 | 370 | 0.6141 | 0.8700 | 0.8637 | 0.8755 | 0.8655 | | 1.0087 | 2.9515 | 380 | 0.6105 | 0.8707 | 0.8657 | 0.8740 | 0.8675 | | 1.0021 | 3.0291 | 390 | 0.5978 | 0.8744 | 0.8708 | 0.8771 | 0.8717 | | 0.894 | 3.1068 | 400 | 0.5970 | 0.8671 | 0.8632 | 0.8733 | 0.8637 | | 0.9363 | 3.1845 | 410 | 0.5860 | 0.8683 | 0.8640 | 0.8754 | 0.8653 | | 0.9678 | 3.2621 | 420 | 0.5760 | 0.8712 | 0.8677 | 0.8764 | 0.8684 | | 0.9378 | 3.3398 | 430 | 0.5677 | 0.8686 | 0.8645 | 0.8734 | 0.8653 | | 0.929 | 3.4175 | 440 | 0.5620 | 0.8707 | 0.8659 | 0.8750 | 0.8672 | | 0.9585 | 3.4951 | 450 | 0.5610 | 0.8654 | 0.8612 | 0.8691 | 0.8625 | | 0.8432 | 3.5728 | 460 | 0.5557 | 0.8690 | 0.8638 | 0.8715 | 0.8665 | | 0.9423 | 3.6505 | 470 | 0.5421 | 0.8715 | 0.8663 | 0.8737 | 0.8684 | | 0.944 | 3.7282 | 480 | 0.5419 | 0.8703 | 0.8656 | 0.8728 | 0.8671 | | 0.8477 | 3.8058 | 490 | 0.5297 | 0.8766 | 0.8727 | 0.8799 | 0.8737 | | 0.8933 | 3.8835 | 500 | 0.5263 | 0.8739 | 0.8707 | 0.8790 | 0.8716 | | 0.881 | 3.9612 | 510 | 0.5197 | 0.8768 | 0.8739 | 0.8817 | 0.8746 | | 0.8603 | 4.0388 | 520 | 0.5196 | 0.8778 | 0.8740 | 0.8832 | 0.8746 | | 0.8045 | 4.1165 | 530 | 0.5214 | 0.8759 | 0.8723 | 0.8822 | 0.8725 | | 0.8101 | 4.1942 | 540 | 0.5163 | 0.8746 | 0.8713 | 0.8806 | 0.8725 | | 0.8016 | 4.2718 | 550 | 0.5149 | 0.8766 | 0.8729 | 0.8813 | 0.8735 | | 0.8403 | 4.3495 | 560 | 0.5061 | 0.8724 | 0.8689 | 0.8767 | 0.8699 | | 0.8216 | 4.4272 | 570 | 0.5034 | 0.8739 | 0.8699 | 0.8798 | 0.8708 | | 0.8491 | 4.5049 | 580 | 0.5004 | 0.8768 | 0.8730 | 0.8828 | 0.8740 | | 0.7727 | 4.5825 | 590 | 0.4990 | 0.8766 | 0.8722 | 0.8810 | 0.8736 | | 0.8475 | 4.6602 | 600 | 0.4941 | 0.8766 | 0.8724 | 0.8807 | 0.8736 | | 0.854 | 4.7379 | 610 | 0.4867 | 0.8824 | 0.8793 | 0.8867 | 0.8801 | | 0.8226 | 4.8155 | 620 | 0.4900 | 0.8771 | 0.8736 | 0.8824 | 0.8747 | | 0.7847 | 4.8932 | 630 | 0.4855 | 0.8805 | 0.8773 | 0.8847 | 0.8779 | | 0.8093 | 4.9709 | 640 | 0.4820 | 0.8805 | 0.8775 | 0.8844 | 0.8777 | | 0.7667 | 5.0485 | 650 | 0.4837 | 0.8800 | 0.8759 | 0.8827 | 0.8768 | | 0.7116 | 5.1262 | 660 | 0.4812 | 0.8797 | 0.8765 | 0.8850 | 0.8773 | | 0.7859 | 5.2039 | 670 | 0.4792 | 0.8812 | 0.8781 | 0.8869 | 0.8790 | | 0.8108 | 5.2816 | 680 | 0.4772 | 0.8797 | 0.8766 | 0.8862 | 0.8771 | | 0.7425 | 5.3592 | 690 | 0.4757 | 0.8807 | 0.8774 | 0.8856 | 0.8780 | | 0.7968 | 5.4369 | 700 | 0.4759 | 0.8802 | 0.8770 | 0.8853 | 0.8774 | | 0.8087 | 5.5146 | 710 | 0.4702 | 0.8810 | 0.8778 | 0.8851 | 0.8783 | | 0.7207 | 5.5922 | 720 | 0.4716 | 0.8805 | 0.8771 | 0.8845 | 0.8776 | | 0.7776 | 5.6699 | 730 | 0.4691 | 0.8790 | 0.8749 | 0.8829 | 0.8760 | | 0.8075 | 5.7476 | 740 | 0.4658 | 0.8819 | 0.8784 | 0.8858 | 0.8789 | | 0.7759 | 5.8252 | 750 | 0.4637 | 0.8807 | 0.8771 | 0.8838 | 0.8778 | | 0.6963 | 5.9029 | 760 | 0.4656 | 0.8795 | 0.8765 | 0.8847 | 0.8769 | | 0.7245 | 5.9806 | 770 | 0.4644 | 0.8827 | 0.8796 | 0.8872 | 0.8800 | | 0.692 | 6.0583 | 780 | 0.4602 | 0.8819 | 0.8789 | 0.8861 | 0.8792 | | 0.6859 | 6.1359 | 790 | 0.4594 | 0.8827 | 0.8797 | 0.8872 | 0.8798 | | 0.7221 | 6.2136 | 800 | 0.4593 | 0.8814 | 0.8783 | 0.8861 | 0.8788 | | 0.701 | 6.2913 | 810 | 0.4599 | 0.8807 | 0.8777 | 0.8852 | 0.8778 | | 0.7151 | 6.3689 | 820 | 0.4564 | 0.8824 | 0.8794 | 0.8869 | 0.8797 | | 0.7038 | 6.4466 | 830 | 0.4573 | 0.8812 | 0.8780 | 0.8860 | 0.8783 | | 0.7182 | 6.5243 | 840 | 0.4553 | 0.8824 | 0.8793 | 0.8866 | 0.8795 | | 0.6964 | 6.6019 | 850 | 0.4535 | 0.8822 | 0.8788 | 0.8861 | 0.8794 | | 0.6805 | 6.6796 | 860 | 0.4556 | 0.8819 | 0.8789 | 0.8878 | 0.8791 | | 0.6209 | 6.7573 | 870 | 0.4518 | 0.8836 | 0.8804 | 0.8883 | 0.8810 | | 0.6665 | 6.8350 | 880 | 0.4524 | 0.8829 | 0.8798 | 0.8881 | 0.8800 | | 0.7334 | 6.9126 | 890 | 0.4507 | 0.8805 | 0.8776 | 0.8859 | 0.8779 | | 0.6889 | 6.9903 | 900 | 0.4503 | 0.8822 | 0.8791 | 0.8872 | 0.8796 | | 0.6854 | 7.0680 | 910 | 0.4488 | 0.8846 | 0.8816 | 0.8887 | 0.8824 | | 0.6855 | 7.1456 | 920 | 0.4485 | 0.8829 | 0.8800 | 0.8877 | 0.8804 | | 0.6644 | 7.2233 | 930 | 0.4477 | 0.8846 | 0.8814 | 0.8888 | 0.8822 | | 0.6556 | 7.3010 | 940 | 0.4469 | 0.8841 | 0.8811 | 0.8887 | 0.8818 | | 0.7299 | 7.3786 | 950 | 0.4480 | 0.8841 | 0.8813 | 0.8894 | 0.8817 | | 0.6425 | 7.4563 | 960 | 0.4467 | 0.8829 | 0.8798 | 0.8876 | 0.8805 | | 0.6582 | 7.5340 | 970 | 0.4470 | 0.8831 | 0.8801 | 0.8879 | 0.8806 | | 0.7499 | 7.6117 | 980 | 0.4466 | 0.8831 | 0.8801 | 0.8878 | 0.8806 | | 0.6396 | 7.6893 | 990 | 0.4466 | 0.8839 | 0.8809 | 0.8887 | 0.8813 | | 0.6864 | 7.7670 | 1000 | 0.4466 | 0.8839 | 0.8809 | 0.8888 | 0.8814 | ### Framework versions - Transformers 4.40.2 - Pytorch 2.3.0 - Datasets 2.19.1 - Tokenizers 0.19.1

LoneStriker/Master-Yi-9B-4.0bpw-h6-exl2

LoneStriker

2024-05-18T19:38:43Z

8

1

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "exl2", "region:us" ]

text-generation

2024-05-18T19:36:38Z

--- license: apache-2.0 --- ## Model Description Master is a collection of LLMs trained using human-collected seed questions and regenerate the answers with a mixture of high performance Open-source LLMs. **Master-Yi-9B** is trained using the ORPO technique. The model shows strong abilities in reasoning on coding and math questions. **Quantized Version**: [Here](https://huggingface.co/qnguyen3/Master-Yi-9B-GGUF) **Master-Yi-9B-Vision**: **Coming Soon** ![img](https://huggingface.co/qnguyen3/Master-Yi-9B/resolve/main/Master-Yi-9B.webp) ## Prompt Template ``` <|im_start|>system You are a helpful AI assistant.<|im_end|> <|im_start|>user What is the meaning of life?<|im_end|> <|im_start|>assistant ``` ## Examples ![image/png](https://cdn-uploads.huggingface.co/production/uploads/630430583926de1f7ec62c6b/E27JmdRAMrHQacM50-lBk.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/630430583926de1f7ec62c6b/z0HS4bxHFQzPe0gZlvCzZ.png) ## Inference Code ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch device = "cuda" # the device to load the model onto model = AutoModelForCausalLM.from_pretrained( "vilm/VinaLlama2-14B", torch_dtype='auto', device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained("vilm/VinaLlama2-14B") prompt = "What is the mearning of life?" messages = [ {"role": "system", "content": "You are a helpful AI assistant."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(device) generated_ids = model.generate( model_inputs.input_ids, max_new_tokens=1024, eos_token_id=tokenizer.eos_token_id, temperature=0.25, ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids)[0] print(response) ``` ## Benchmarks ### Nous Benchmark: | Model |AGIEval|GPT4All|TruthfulQA|Bigbench|Average| |---------------------------------------------------|------:|------:|---------:|-------:|------:| |[Master-Yi-9B](https://huggingface.co/qnguyen3/Master-Yi-9B)| 43.55| 71.48| 48.54| 41.43| 51.25| ### AGIEval ``` | Task |Version| Metric |Value| |Stderr| |------------------------------|------:|--------|----:|---|-----:| |agieval_aqua_rat | 0|acc |35.83|± | 3.01| | | |acc_norm|31.89|± | 2.93| |agieval_logiqa_en | 0|acc |38.25|± | 1.91| | | |acc_norm|37.79|± | 1.90| |agieval_lsat_ar | 0|acc |23.04|± | 2.78| | | |acc_norm|20.43|± | 2.66| |agieval_lsat_lr | 0|acc |48.04|± | 2.21| | | |acc_norm|42.75|± | 2.19| |agieval_lsat_rc | 0|acc |61.34|± | 2.97| | | |acc_norm|52.79|± | 3.05| |agieval_sat_en | 0|acc |79.13|± | 2.84| | | |acc_norm|72.33|± | 3.12| |agieval_sat_en_without_passage| 0|acc |44.17|± | 3.47| | | |acc_norm|42.72|± | 3.45| |agieval_sat_math | 0|acc |52.27|± | 3.38| | | |acc_norm|47.73|± | 3.38| Average: 43.55% ``` ### GPT4All ``` | Task |Version| Metric |Value| |Stderr| |-------------|------:|--------|----:|---|-----:| |arc_challenge| 0|acc |54.95|± | 1.45| | | |acc_norm|58.70|± | 1.44| |arc_easy | 0|acc |82.28|± | 0.78| | | |acc_norm|81.10|± | 0.80| |boolq | 1|acc |86.15|± | 0.60| |hellaswag | 0|acc |59.16|± | 0.49| | | |acc_norm|77.53|± | 0.42| |openbookqa | 0|acc |37.40|± | 2.17| | | |acc_norm|44.00|± | 2.22| |piqa | 0|acc |79.00|± | 0.95| | | |acc_norm|80.25|± | 0.93| |winogrande | 0|acc |72.61|± | 1.25| Average: 71.48% ``` ### TruthfulQA ``` | Task |Version|Metric|Value| |Stderr| |-------------|------:|------|----:|---|-----:| |truthfulqa_mc| 1|mc1 |33.05|± | 1.65| | | |mc2 |48.54|± | 1.54| Average: 48.54% ``` ### Bigbench ``` | Task |Version| Metric |Value| |Stderr| |------------------------------------------------|------:|---------------------|----:|---|-----:| |bigbench_causal_judgement | 0|multiple_choice_grade|54.74|± | 3.62| |bigbench_date_understanding | 0|multiple_choice_grade|68.02|± | 2.43| |bigbench_disambiguation_qa | 0|multiple_choice_grade|40.31|± | 3.06| |bigbench_geometric_shapes | 0|multiple_choice_grade|30.36|± | 2.43| | | |exact_str_match | 2.23|± | 0.78| |bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|26.00|± | 1.96| |bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|20.71|± | 1.53| |bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|44.00|± | 2.87| |bigbench_movie_recommendation | 0|multiple_choice_grade|35.00|± | 2.14| |bigbench_navigate | 0|multiple_choice_grade|58.40|± | 1.56| |bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|61.80|± | 1.09| |bigbench_ruin_names | 0|multiple_choice_grade|42.41|± | 2.34| |bigbench_salient_translation_error_detection | 0|multiple_choice_grade|31.56|± | 1.47| |bigbench_snarks | 0|multiple_choice_grade|55.25|± | 3.71| |bigbench_sports_understanding | 0|multiple_choice_grade|69.37|± | 1.47| |bigbench_temporal_sequences | 0|multiple_choice_grade|27.70|± | 1.42| |bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|21.36|± | 1.16| |bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|14.69|± | 0.85| |bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|44.00|± | 2.87| Average: 41.43% ``` **Average score**: 51.25% ### OpenLLM Benchmark: | Model |ARC |HellaSwag|MMLU |TruthfulQA|Winogrande|GSM8K|Average| |---------------------------------------------------|---:|--------:|----:|---------:|---------:|----:|------:| |[Master-Yi-9B](https://huggingface.co/qnguyen3/Master-Yi-9B)|61.6| 79.89|69.95| 48.59| 77.35|67.48| 67.48| ### ARC ``` | Task |Version| Metric | Value | |Stderr| |-------------|------:|--------------------|-------------|---|------| |arc_challenge| 1|acc,none | 0.59| | | | | |acc_stderr,none | 0.01| | | | | |acc_norm,none | 0.62| | | | | |acc_norm_stderr,none| 0.01| | | | | |alias |arc_challenge| | | Average: 61.6% ``` ### HellaSwag ``` | Task |Version| Metric | Value | |Stderr| |---------|------:|--------------------|---------|---|------| |hellaswag| 1|acc,none | 0.61| | | | | |acc_stderr,none | 0| | | | | |acc_norm,none | 0.80| | | | | |acc_norm_stderr,none| 0| | | | | |alias |hellaswag| | | Average: 79.89% ``` ### MMLU ``` | Task |Version| Metric | Value | |Stderr| |----------------------------------------|-------|---------------|---------------------------------------|---|------| |mmlu |N/A |acc,none | 0.7| | | | | |acc_stderr,none| 0| | | | | |alias |mmlu | | | |mmlu_abstract_algebra | 0|alias | - abstract_algebra | | | | | |acc,none |0.46 | | | | | |acc_stderr,none|0.05 | | | |mmlu_anatomy | 0|alias | - anatomy | | | | | |acc,none |0.64 | | | | | |acc_stderr,none|0.04 | | | |mmlu_astronomy | 0|alias | - astronomy | | | | | |acc,none |0.77 | | | | | |acc_stderr,none|0.03 | | | |mmlu_business_ethics | 0|alias | - business_ethics | | | | | |acc,none |0.76 | | | | | |acc_stderr,none|0.04 | | | |mmlu_clinical_knowledge | 0|alias | - clinical_knowledge | | | | | |acc,none |0.71 | | | | | |acc_stderr,none|0.03 | | | |mmlu_college_biology | 0|alias | - college_biology | | | | | |acc,none |0.82 | | | | | |acc_stderr,none|0.03 | | | |mmlu_college_chemistry | 0|alias | - college_chemistry | | | | | |acc,none |0.52 | | | | | |acc_stderr,none|0.05 | | | |mmlu_college_computer_science | 0|alias | - college_computer_science | | | | | |acc,none |0.56 | | | | | |acc_stderr,none|0.05 | | | |mmlu_college_mathematics | 0|alias | - college_mathematics | | | | | |acc,none |0.44 | | | | | |acc_stderr,none|0.05 | | | |mmlu_college_medicine | 0|alias | - college_medicine | | | | | |acc,none |0.72 | | | | | |acc_stderr,none|0.03 | | | |mmlu_college_physics | 0|alias | - college_physics | | | | | |acc,none |0.45 | | | | | |acc_stderr,none|0.05 | | | |mmlu_computer_security | 0|alias | - computer_security | | | | | |acc,none |0.81 | | | | | |acc_stderr,none|0.04 | | | |mmlu_conceptual_physics | 0|alias | - conceptual_physics | | | | | |acc,none |0.74 | | | | | |acc_stderr,none|0.03 | | | |mmlu_econometrics | 0|alias | - econometrics | | | | | |acc,none |0.65 | | | | | |acc_stderr,none|0.04 | | | |mmlu_electrical_engineering | 0|alias | - electrical_engineering | | | | | |acc,none |0.72 | | | | | |acc_stderr,none|0.04 | | | |mmlu_elementary_mathematics | 0|alias | - elementary_mathematics | | | | | |acc,none |0.62 | | | | | |acc_stderr,none|0.02 | | | |mmlu_formal_logic | 0|alias | - formal_logic | | | | | |acc,none |0.57 | | | | | |acc_stderr,none|0.04 | | | |mmlu_global_facts | 0|alias | - global_facts | | | | | |acc,none |0.46 | | | | | |acc_stderr,none|0.05 | | | |mmlu_high_school_biology | 0|alias | - high_school_biology | | | | | |acc,none |0.86 | | | | | |acc_stderr,none|0.02 | | | |mmlu_high_school_chemistry | 0|alias | - high_school_chemistry | | | | | |acc,none |0.67 | | | | | |acc_stderr,none|0.03 | | | |mmlu_high_school_computer_science | 0|alias | - high_school_computer_science | | | | | |acc,none |0.84 | | | | | |acc_stderr,none|0.04 | | | |mmlu_high_school_european_history | 0|alias | - high_school_european_history | | | | | |acc,none |0.82 | | | | | |acc_stderr,none|0.03 | | | |mmlu_high_school_geography | 0|alias | - high_school_geography | | | | | |acc,none |0.86 | | | | | |acc_stderr,none|0.02 | | | |mmlu_high_school_government_and_politics| 0|alias | - high_school_government_and_politics| | | | | |acc,none |0.90 | | | | | |acc_stderr,none|0.02 | | | |mmlu_high_school_macroeconomics | 0|alias | - high_school_macroeconomics | | | | | |acc,none |0.75 | | | | | |acc_stderr,none|0.02 | | | |mmlu_high_school_mathematics | 0|alias | - high_school_mathematics | | | | | |acc,none |0.43 | | | | | |acc_stderr,none|0.03 | | | |mmlu_high_school_microeconomics | 0|alias | - high_school_microeconomics | | | | | |acc,none |0.86 | | | | | |acc_stderr,none|0.02 | | | |mmlu_high_school_physics | 0|alias | - high_school_physics | | | | | |acc,none |0.45 | | | | | |acc_stderr,none|0.04 | | | |mmlu_high_school_psychology | 0|alias | - high_school_psychology | | | | | |acc,none |0.87 | | | | | |acc_stderr,none|0.01 | | | |mmlu_high_school_statistics | 0|alias | - high_school_statistics | | | | | |acc,none |0.68 | | | | | |acc_stderr,none|0.03 | | | |mmlu_high_school_us_history | 0|alias | - high_school_us_history | | | | | |acc,none |0.85 | | | | | |acc_stderr,none|0.02 | | | |mmlu_high_school_world_history | 0|alias | - high_school_world_history | | | | | |acc,none |0.85 | | | | | |acc_stderr,none|0.02 | | | |mmlu_human_aging | 0|alias | - human_aging | | | | | |acc,none |0.76 | | | | | |acc_stderr,none|0.03 | | | |mmlu_human_sexuality | 0|alias | - human_sexuality | | | | | |acc,none |0.78 | | | | | |acc_stderr,none|0.04 | | | |mmlu_humanities |N/A |alias | - humanities | | | | | |acc,none |0.63 | | | | | |acc_stderr,none|0.01 | | | |mmlu_international_law | 0|alias | - international_law | | | | | |acc,none |0.79 | | | | | |acc_stderr,none|0.04 | | | |mmlu_jurisprudence | 0|alias | - jurisprudence | | | | | |acc,none |0.79 | | | | | |acc_stderr,none|0.04 | | | |mmlu_logical_fallacies | 0|alias | - logical_fallacies | | | | | |acc,none |0.80 | | | | | |acc_stderr,none|0.03 | | | |mmlu_machine_learning | 0|alias | - machine_learning | | | | | |acc,none |0.52 | | | | | |acc_stderr,none|0.05 | | | |mmlu_management | 0|alias | - management | | | | | |acc,none |0.83 | | | | | |acc_stderr,none|0.04 | | | |mmlu_marketing | 0|alias | - marketing | | | | | |acc,none |0.89 | | | | | |acc_stderr,none|0.02 | | | |mmlu_medical_genetics | 0|alias | - medical_genetics | | | | | |acc,none |0.78 | | | | | |acc_stderr,none|0.04 | | | |mmlu_miscellaneous | 0|alias | - miscellaneous | | | | | |acc,none |0.85 | | | | | |acc_stderr,none|0.01 | | | |mmlu_moral_disputes | 0|alias | - moral_disputes | | | | | |acc,none |0.75 | | | | | |acc_stderr,none|0.02 | | | |mmlu_moral_scenarios | 0|alias | - moral_scenarios | | | | | |acc,none |0.48 | | | | | |acc_stderr,none|0.02 | | | |mmlu_nutrition | 0|alias | - nutrition | | | | | |acc,none |0.77 | | | | | |acc_stderr,none|0.02 | | | |mmlu_other |N/A |alias | - other | | | | | |acc,none |0.75 | | | | | |acc_stderr,none|0.01 | | | |mmlu_philosophy | 0|alias | - philosophy | | | | | |acc,none |0.78 | | | | | |acc_stderr,none|0.02 | | | |mmlu_prehistory | 0|alias | - prehistory | | | | | |acc,none |0.77 | | | | | |acc_stderr,none|0.02 | | | |mmlu_professional_accounting | 0|alias | - professional_accounting | | | | | |acc,none |0.57 | | | | | |acc_stderr,none|0.03 | | | |mmlu_professional_law | 0|alias | - professional_law | | | | | |acc,none |0.50 | | | | | |acc_stderr,none|0.01 | | | |mmlu_professional_medicine | 0|alias | - professional_medicine | | | | | |acc,none |0.71 | | | | | |acc_stderr,none|0.03 | | | |mmlu_professional_psychology | 0|alias | - professional_psychology | | | | | |acc,none |0.73 | | | | | |acc_stderr,none|0.02 | | | |mmlu_public_relations | 0|alias | - public_relations | | | | | |acc,none |0.76 | | | | | |acc_stderr,none|0.04 | | | |mmlu_security_studies | 0|alias | - security_studies | | | | | |acc,none |0.78 | | | | | |acc_stderr,none|0.03 | | | |mmlu_social_sciences |N/A |alias | - social_sciences | | | | | |acc,none |0.81 | | | | | |acc_stderr,none|0.01 | | | |mmlu_sociology | 0|alias | - sociology | | | | | |acc,none |0.86 | | | | | |acc_stderr,none|0.02 | | | |mmlu_stem |N/A |alias | - stem | | | | | |acc,none |0.65 | | | | | |acc_stderr,none|0.01 | | | |mmlu_us_foreign_policy | 0|alias | - us_foreign_policy | | | | | |acc,none |0.92 | | | | | |acc_stderr,none|0.03 | | | |mmlu_virology | 0|alias | - virology | | | | | |acc,none |0.58 | | | | | |acc_stderr,none|0.04 | | | |mmlu_world_religions | 0|alias | - world_religions | | | | | |acc,none |0.82 | | | | | |acc_stderr,none|0.03 | | | Average: 69.95% ``` ### TruthfulQA ``` | Task |Version| Metric | Value | |Stderr| |--------------|-------|-----------------------|-----------------|---|------| |truthfulqa |N/A |bleu_acc,none | 0.45| | | | | |bleu_acc_stderr,none | 0.02| | | | | |rouge1_acc,none | 0.45| | | | | |rouge1_acc_stderr,none | 0.02| | | | | |rouge2_diff,none | 0.92| | | | | |rouge2_diff_stderr,none| 1.07| | | | | |bleu_max,none | 23.77| | | | | |bleu_max_stderr,none | 0.81| | | | | |rouge2_acc,none | 0.38| | | | | |rouge2_acc_stderr,none | 0.02| | | | | |acc,none | 0.41| | | | | |acc_stderr,none | 0.01| | | | | |rougeL_diff,none | 1.57| | | | | |rougeL_diff_stderr,none| 0.93| | | | | |rougeL_acc,none | 0.46| | | | | |rougeL_acc_stderr,none | 0.02| | | | | |bleu_diff,none | 1.38| | | | | |bleu_diff_stderr,none | 0.75| | | | | |rouge2_max,none | 33.01| | | | | |rouge2_max_stderr,none | 1.05| | | | | |rouge1_diff,none | 1.72| | | | | |rouge1_diff_stderr,none| 0.92| | | | | |rougeL_max,none | 45.25| | | | | |rougeL_max_stderr,none | 0.92| | | | | |rouge1_max,none | 48.29| | | | | |rouge1_max_stderr,none | 0.90| | | | | |alias |truthfulqa | | | |truthfulqa_gen| 3|bleu_max,none | 23.77| | | | | |bleu_max_stderr,none | 0.81| | | | | |bleu_acc,none | 0.45| | | | | |bleu_acc_stderr,none | 0.02| | | | | |bleu_diff,none | 1.38| | | | | |bleu_diff_stderr,none | 0.75| | | | | |rouge1_max,none | 48.29| | | | | |rouge1_max_stderr,none | 0.90| | | | | |rouge1_acc,none | 0.45| | | | | |rouge1_acc_stderr,none | 0.02| | | | | |rouge1_diff,none | 1.72| | | | | |rouge1_diff_stderr,none| 0.92| | | | | |rouge2_max,none | 33.01| | | | | |rouge2_max_stderr,none | 1.05| | | | | |rouge2_acc,none | 0.38| | | | | |rouge2_acc_stderr,none | 0.02| | | | | |rouge2_diff,none | 0.92| | | | | |rouge2_diff_stderr,none| 1.07| | | | | |rougeL_max,none | 45.25| | | | | |rougeL_max_stderr,none | 0.92| | | | | |rougeL_acc,none | 0.46| | | | | |rougeL_acc_stderr,none | 0.02| | | | | |rougeL_diff,none | 1.57| | | | | |rougeL_diff_stderr,none| 0.93| | | | | |alias | - truthfulqa_gen| | | |truthfulqa_mc1| 2|acc,none | 0.33| | | | | |acc_stderr,none | 0.02| | | | | |alias | - truthfulqa_mc1| | | |truthfulqa_mc2| 2|acc,none | 0.49| | | | | |acc_stderr,none | 0.02| | | | | |alias | - truthfulqa_mc2| | | Average: 48.59% ``` ### Winogrande ``` | Task |Version| Metric | Value | |Stderr| |----------|------:|---------------|----------|---|------| |winogrande| 1|acc,none | 0.77| | | | | |acc_stderr,none| 0.01| | | | | |alias |winogrande| | | Average: 77.35% ``` ### GSM8K ``` |Task |Version| Metric |Value| |Stderr| |-----|------:|-----------------------------------|-----|---|------| |gsm8k| 3|exact_match,strict-match | 0.67| | | | | |exact_match_stderr,strict-match | 0.01| | | | | |exact_match,flexible-extract | 0.68| | | | | |exact_match_stderr,flexible-extract| 0.01| | | | | |alias |gsm8k| | | Average: 67.48% ``` **Average score**: 67.48%

LoneStriker/Master-Yi-9B-3.0bpw-h6-exl2

LoneStriker

2024-05-18T19:36:36Z

8

1

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "3-bit", "exl2", "region:us" ]

text-generation

2024-05-18T19:34:53Z

--- license: apache-2.0 --- ## Model Description Master is a collection of LLMs trained using human-collected seed questions and regenerate the answers with a mixture of high performance Open-source LLMs. **Master-Yi-9B** is trained using the ORPO technique. The model shows strong abilities in reasoning on coding and math questions. **Quantized Version**: [Here](https://huggingface.co/qnguyen3/Master-Yi-9B-GGUF) **Master-Yi-9B-Vision**: **Coming Soon** ![img](https://huggingface.co/qnguyen3/Master-Yi-9B/resolve/main/Master-Yi-9B.webp) ## Prompt Template ``` <|im_start|>system You are a helpful AI assistant.<|im_end|> <|im_start|>user What is the meaning of life?<|im_end|> <|im_start|>assistant ``` ## Examples ![image/png](https://cdn-uploads.huggingface.co/production/uploads/630430583926de1f7ec62c6b/E27JmdRAMrHQacM50-lBk.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/630430583926de1f7ec62c6b/z0HS4bxHFQzPe0gZlvCzZ.png) ## Inference Code ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch device = "cuda" # the device to load the model onto model = AutoModelForCausalLM.from_pretrained( "vilm/VinaLlama2-14B", torch_dtype='auto', device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained("vilm/VinaLlama2-14B") prompt = "What is the mearning of life?" messages = [ {"role": "system", "content": "You are a helpful AI assistant."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(device) generated_ids = model.generate( model_inputs.input_ids, max_new_tokens=1024, eos_token_id=tokenizer.eos_token_id, temperature=0.25, ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids)[0] print(response) ``` ## Benchmarks ### Nous Benchmark: | Model |AGIEval|GPT4All|TruthfulQA|Bigbench|Average| |---------------------------------------------------|------:|------:|---------:|-------:|------:| |[Master-Yi-9B](https://huggingface.co/qnguyen3/Master-Yi-9B)| 43.55| 71.48| 48.54| 41.43| 51.25| ### AGIEval ``` | Task |Version| Metric |Value| |Stderr| |------------------------------|------:|--------|----:|---|-----:| |agieval_aqua_rat | 0|acc |35.83|± | 3.01| | | |acc_norm|31.89|± | 2.93| |agieval_logiqa_en | 0|acc |38.25|± | 1.91| | | |acc_norm|37.79|± | 1.90| |agieval_lsat_ar | 0|acc |23.04|± | 2.78| | | |acc_norm|20.43|± | 2.66| |agieval_lsat_lr | 0|acc |48.04|± | 2.21| | | |acc_norm|42.75|± | 2.19| |agieval_lsat_rc | 0|acc |61.34|± | 2.97| | | |acc_norm|52.79|± | 3.05| |agieval_sat_en | 0|acc |79.13|± | 2.84| | | |acc_norm|72.33|± | 3.12| |agieval_sat_en_without_passage| 0|acc |44.17|± | 3.47| | | |acc_norm|42.72|± | 3.45| |agieval_sat_math | 0|acc |52.27|± | 3.38| | | |acc_norm|47.73|± | 3.38| Average: 43.55% ``` ### GPT4All ``` | Task |Version| Metric |Value| |Stderr| |-------------|------:|--------|----:|---|-----:| |arc_challenge| 0|acc |54.95|± | 1.45| | | |acc_norm|58.70|± | 1.44| |arc_easy | 0|acc |82.28|± | 0.78| | | |acc_norm|81.10|± | 0.80| |boolq | 1|acc |86.15|± | 0.60| |hellaswag | 0|acc |59.16|± | 0.49| | | |acc_norm|77.53|± | 0.42| |openbookqa | 0|acc |37.40|± | 2.17| | | |acc_norm|44.00|± | 2.22| |piqa | 0|acc |79.00|± | 0.95| | | |acc_norm|80.25|± | 0.93| |winogrande | 0|acc |72.61|± | 1.25| Average: 71.48% ``` ### TruthfulQA ``` | Task |Version|Metric|Value| |Stderr| |-------------|------:|------|----:|---|-----:| |truthfulqa_mc| 1|mc1 |33.05|± | 1.65| | | |mc2 |48.54|± | 1.54| Average: 48.54% ``` ### Bigbench ``` | Task |Version| Metric |Value| |Stderr| |------------------------------------------------|------:|---------------------|----:|---|-----:| |bigbench_causal_judgement | 0|multiple_choice_grade|54.74|± | 3.62| |bigbench_date_understanding | 0|multiple_choice_grade|68.02|± | 2.43| |bigbench_disambiguation_qa | 0|multiple_choice_grade|40.31|± | 3.06| |bigbench_geometric_shapes | 0|multiple_choice_grade|30.36|± | 2.43| | | |exact_str_match | 2.23|± | 0.78| |bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|26.00|± | 1.96| |bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|20.71|± | 1.53| |bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|44.00|± | 2.87| |bigbench_movie_recommendation | 0|multiple_choice_grade|35.00|± | 2.14| |bigbench_navigate | 0|multiple_choice_grade|58.40|± | 1.56| |bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|61.80|± | 1.09| |bigbench_ruin_names | 0|multiple_choice_grade|42.41|± | 2.34| |bigbench_salient_translation_error_detection | 0|multiple_choice_grade|31.56|± | 1.47| |bigbench_snarks | 0|multiple_choice_grade|55.25|± | 3.71| |bigbench_sports_understanding | 0|multiple_choice_grade|69.37|± | 1.47| |bigbench_temporal_sequences | 0|multiple_choice_grade|27.70|± | 1.42| |bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|21.36|± | 1.16| |bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|14.69|± | 0.85| |bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|44.00|± | 2.87| Average: 41.43% ``` **Average score**: 51.25% ### OpenLLM Benchmark: | Model |ARC |HellaSwag|MMLU |TruthfulQA|Winogrande|GSM8K|Average| |---------------------------------------------------|---:|--------:|----:|---------:|---------:|----:|------:| |[Master-Yi-9B](https://huggingface.co/qnguyen3/Master-Yi-9B)|61.6| 79.89|69.95| 48.59| 77.35|67.48| 67.48| ### ARC ``` | Task |Version| Metric | Value | |Stderr| |-------------|------:|--------------------|-------------|---|------| |arc_challenge| 1|acc,none | 0.59| | | | | |acc_stderr,none | 0.01| | | | | |acc_norm,none | 0.62| | | | | |acc_norm_stderr,none| 0.01| | | | | |alias |arc_challenge| | | Average: 61.6% ``` ### HellaSwag ``` | Task |Version| Metric | Value | |Stderr| |---------|------:|--------------------|---------|---|------| |hellaswag| 1|acc,none | 0.61| | | | | |acc_stderr,none | 0| | | | | |acc_norm,none | 0.80| | | | | |acc_norm_stderr,none| 0| | | | | |alias |hellaswag| | | Average: 79.89% ``` ### MMLU ``` | Task |Version| Metric | Value | |Stderr| |----------------------------------------|-------|---------------|---------------------------------------|---|------| |mmlu |N/A |acc,none | 0.7| | | | | |acc_stderr,none| 0| | | | | |alias |mmlu | | | |mmlu_abstract_algebra | 0|alias | - abstract_algebra | | | | | |acc,none |0.46 | | | | | |acc_stderr,none|0.05 | | | |mmlu_anatomy | 0|alias | - anatomy | | | | | |acc,none |0.64 | | | | | |acc_stderr,none|0.04 | | | |mmlu_astronomy | 0|alias | - astronomy | | | | | |acc,none |0.77 | | | | | |acc_stderr,none|0.03 | | | |mmlu_business_ethics | 0|alias | - business_ethics | | | | | |acc,none |0.76 | | | | | |acc_stderr,none|0.04 | | | |mmlu_clinical_knowledge | 0|alias | - clinical_knowledge | | | | | |acc,none |0.71 | | | | | |acc_stderr,none|0.03 | | | |mmlu_college_biology | 0|alias | - college_biology | | | | | |acc,none |0.82 | | | | | |acc_stderr,none|0.03 | | | |mmlu_college_chemistry | 0|alias | - college_chemistry | | | | | |acc,none |0.52 | | | | | |acc_stderr,none|0.05 | | | |mmlu_college_computer_science | 0|alias | - college_computer_science | | | | | |acc,none |0.56 | | | | | |acc_stderr,none|0.05 | | | |mmlu_college_mathematics | 0|alias | - college_mathematics | | | | | |acc,none |0.44 | | | | | |acc_stderr,none|0.05 | | | |mmlu_college_medicine | 0|alias | - college_medicine | | | | | |acc,none |0.72 | | | | | |acc_stderr,none|0.03 | | | |mmlu_college_physics | 0|alias | - college_physics | | | | | |acc,none |0.45 | | | | | |acc_stderr,none|0.05 | | | |mmlu_computer_security | 0|alias | - computer_security | | | | | |acc,none |0.81 | | | | | |acc_stderr,none|0.04 | | | |mmlu_conceptual_physics | 0|alias | - conceptual_physics | | | | | |acc,none |0.74 | | | | | |acc_stderr,none|0.03 | | | |mmlu_econometrics | 0|alias | - econometrics | | | | | |acc,none |0.65 | | | | | |acc_stderr,none|0.04 | | | |mmlu_electrical_engineering | 0|alias | - electrical_engineering | | | | | |acc,none |0.72 | | | | | |acc_stderr,none|0.04 | | | |mmlu_elementary_mathematics | 0|alias | - elementary_mathematics | | | | | |acc,none |0.62 | | | | | |acc_stderr,none|0.02 | | | |mmlu_formal_logic | 0|alias | - formal_logic | | | | | |acc,none |0.57 | | | | | |acc_stderr,none|0.04 | | | |mmlu_global_facts | 0|alias | - global_facts | | | | | |acc,none |0.46 | | | | | |acc_stderr,none|0.05 | | | |mmlu_high_school_biology | 0|alias | - high_school_biology | | | | | |acc,none |0.86 | | | | | |acc_stderr,none|0.02 | | | |mmlu_high_school_chemistry | 0|alias | - high_school_chemistry | | | | | |acc,none |0.67 | | | | | |acc_stderr,none|0.03 | | | |mmlu_high_school_computer_science | 0|alias | - high_school_computer_science | | | | | |acc,none |0.84 | | | | | |acc_stderr,none|0.04 | | | |mmlu_high_school_european_history | 0|alias | - high_school_european_history | | | | | |acc,none |0.82 | | | | | |acc_stderr,none|0.03 | | | |mmlu_high_school_geography | 0|alias | - high_school_geography | | | | | |acc,none |0.86 | | | | | |acc_stderr,none|0.02 | | | |mmlu_high_school_government_and_politics| 0|alias | - high_school_government_and_politics| | | | | |acc,none |0.90 | | | | | |acc_stderr,none|0.02 | | | |mmlu_high_school_macroeconomics | 0|alias | - high_school_macroeconomics | | | | | |acc,none |0.75 | | | | | |acc_stderr,none|0.02 | | | |mmlu_high_school_mathematics | 0|alias | - high_school_mathematics | | | | | |acc,none |0.43 | | | | | |acc_stderr,none|0.03 | | | |mmlu_high_school_microeconomics | 0|alias | - high_school_microeconomics | | | | | |acc,none |0.86 | | | | | |acc_stderr,none|0.02 | | | |mmlu_high_school_physics | 0|alias | - high_school_physics | | | | | |acc,none |0.45 | | | | | |acc_stderr,none|0.04 | | | |mmlu_high_school_psychology | 0|alias | - high_school_psychology | | | | | |acc,none |0.87 | | | | | |acc_stderr,none|0.01 | | | |mmlu_high_school_statistics | 0|alias | - high_school_statistics | | | | | |acc,none |0.68 | | | | | |acc_stderr,none|0.03 | | | |mmlu_high_school_us_history | 0|alias | - high_school_us_history | | | | | |acc,none |0.85 | | | | | |acc_stderr,none|0.02 | | | |mmlu_high_school_world_history | 0|alias | - high_school_world_history | | | | | |acc,none |0.85 | | | | | |acc_stderr,none|0.02 | | | |mmlu_human_aging | 0|alias | - human_aging | | | | | |acc,none |0.76 | | | | | |acc_stderr,none|0.03 | | | |mmlu_human_sexuality | 0|alias | - human_sexuality | | | | | |acc,none |0.78 | | | | | |acc_stderr,none|0.04 | | | |mmlu_humanities |N/A |alias | - humanities | | | | | |acc,none |0.63 | | | | | |acc_stderr,none|0.01 | | | |mmlu_international_law | 0|alias | - international_law | | | | | |acc,none |0.79 | | | | | |acc_stderr,none|0.04 | | | |mmlu_jurisprudence | 0|alias | - jurisprudence | | | | | |acc,none |0.79 | | | | | |acc_stderr,none|0.04 | | | |mmlu_logical_fallacies | 0|alias | - logical_fallacies | | | | | |acc,none |0.80 | | | | | |acc_stderr,none|0.03 | | | |mmlu_machine_learning | 0|alias | - machine_learning | | | | | |acc,none |0.52 | | | | | |acc_stderr,none|0.05 | | | |mmlu_management | 0|alias | - management | | | | | |acc,none |0.83 | | | | | |acc_stderr,none|0.04 | | | |mmlu_marketing | 0|alias | - marketing | | | | | |acc,none |0.89 | | | | | |acc_stderr,none|0.02 | | | |mmlu_medical_genetics | 0|alias | - medical_genetics | | | | | |acc,none |0.78 | | | | | |acc_stderr,none|0.04 | | | |mmlu_miscellaneous | 0|alias | - miscellaneous | | | | | |acc,none |0.85 | | | | | |acc_stderr,none|0.01 | | | |mmlu_moral_disputes | 0|alias | - moral_disputes | | | | | |acc,none |0.75 | | | | | |acc_stderr,none|0.02 | | | |mmlu_moral_scenarios | 0|alias | - moral_scenarios | | | | | |acc,none |0.48 | | | | | |acc_stderr,none|0.02 | | | |mmlu_nutrition | 0|alias | - nutrition | | | | | |acc,none |0.77 | | | | | |acc_stderr,none|0.02 | | | |mmlu_other |N/A |alias | - other | | | | | |acc,none |0.75 | | | | | |acc_stderr,none|0.01 | | | |mmlu_philosophy | 0|alias | - philosophy | | | | | |acc,none |0.78 | | | | | |acc_stderr,none|0.02 | | | |mmlu_prehistory | 0|alias | - prehistory | | | | | |acc,none |0.77 | | | | | |acc_stderr,none|0.02 | | | |mmlu_professional_accounting | 0|alias | - professional_accounting | | | | | |acc,none |0.57 | | | | | |acc_stderr,none|0.03 | | | |mmlu_professional_law | 0|alias | - professional_law | | | | | |acc,none |0.50 | | | | | |acc_stderr,none|0.01 | | | |mmlu_professional_medicine | 0|alias | - professional_medicine | | | | | |acc,none |0.71 | | | | | |acc_stderr,none|0.03 | | | |mmlu_professional_psychology | 0|alias | - professional_psychology | | | | | |acc,none |0.73 | | | | | |acc_stderr,none|0.02 | | | |mmlu_public_relations | 0|alias | - public_relations | | | | | |acc,none |0.76 | | | | | |acc_stderr,none|0.04 | | | |mmlu_security_studies | 0|alias | - security_studies | | | | | |acc,none |0.78 | | | | | |acc_stderr,none|0.03 | | | |mmlu_social_sciences |N/A |alias | - social_sciences | | | | | |acc,none |0.81 | | | | | |acc_stderr,none|0.01 | | | |mmlu_sociology | 0|alias | - sociology | | | | | |acc,none |0.86 | | | | | |acc_stderr,none|0.02 | | | |mmlu_stem |N/A |alias | - stem | | | | | |acc,none |0.65 | | | | | |acc_stderr,none|0.01 | | | |mmlu_us_foreign_policy | 0|alias | - us_foreign_policy | | | | | |acc,none |0.92 | | | | | |acc_stderr,none|0.03 | | | |mmlu_virology | 0|alias | - virology | | | | | |acc,none |0.58 | | | | | |acc_stderr,none|0.04 | | | |mmlu_world_religions | 0|alias | - world_religions | | | | | |acc,none |0.82 | | | | | |acc_stderr,none|0.03 | | | Average: 69.95% ``` ### TruthfulQA ``` | Task |Version| Metric | Value | |Stderr| |--------------|-------|-----------------------|-----------------|---|------| |truthfulqa |N/A |bleu_acc,none | 0.45| | | | | |bleu_acc_stderr,none | 0.02| | | | | |rouge1_acc,none | 0.45| | | | | |rouge1_acc_stderr,none | 0.02| | | | | |rouge2_diff,none | 0.92| | | | | |rouge2_diff_stderr,none| 1.07| | | | | |bleu_max,none | 23.77| | | | | |bleu_max_stderr,none | 0.81| | | | | |rouge2_acc,none | 0.38| | | | | |rouge2_acc_stderr,none | 0.02| | | | | |acc,none | 0.41| | | | | |acc_stderr,none | 0.01| | | | | |rougeL_diff,none | 1.57| | | | | |rougeL_diff_stderr,none| 0.93| | | | | |rougeL_acc,none | 0.46| | | | | |rougeL_acc_stderr,none | 0.02| | | | | |bleu_diff,none | 1.38| | | | | |bleu_diff_stderr,none | 0.75| | | | | |rouge2_max,none | 33.01| | | | | |rouge2_max_stderr,none | 1.05| | | | | |rouge1_diff,none | 1.72| | | | | |rouge1_diff_stderr,none| 0.92| | | | | |rougeL_max,none | 45.25| | | | | |rougeL_max_stderr,none | 0.92| | | | | |rouge1_max,none | 48.29| | | | | |rouge1_max_stderr,none | 0.90| | | | | |alias |truthfulqa | | | |truthfulqa_gen| 3|bleu_max,none | 23.77| | | | | |bleu_max_stderr,none | 0.81| | | | | |bleu_acc,none | 0.45| | | | | |bleu_acc_stderr,none | 0.02| | | | | |bleu_diff,none | 1.38| | | | | |bleu_diff_stderr,none | 0.75| | | | | |rouge1_max,none | 48.29| | | | | |rouge1_max_stderr,none | 0.90| | | | | |rouge1_acc,none | 0.45| | | | | |rouge1_acc_stderr,none | 0.02| | | | | |rouge1_diff,none | 1.72| | | | | |rouge1_diff_stderr,none| 0.92| | | | | |rouge2_max,none | 33.01| | | | | |rouge2_max_stderr,none | 1.05| | | | | |rouge2_acc,none | 0.38| | | | | |rouge2_acc_stderr,none | 0.02| | | | | |rouge2_diff,none | 0.92| | | | | |rouge2_diff_stderr,none| 1.07| | | | | |rougeL_max,none | 45.25| | | | | |rougeL_max_stderr,none | 0.92| | | | | |rougeL_acc,none | 0.46| | | | | |rougeL_acc_stderr,none | 0.02| | | | | |rougeL_diff,none | 1.57| | | | | |rougeL_diff_stderr,none| 0.93| | | | | |alias | - truthfulqa_gen| | | |truthfulqa_mc1| 2|acc,none | 0.33| | | | | |acc_stderr,none | 0.02| | | | | |alias | - truthfulqa_mc1| | | |truthfulqa_mc2| 2|acc,none | 0.49| | | | | |acc_stderr,none | 0.02| | | | | |alias | - truthfulqa_mc2| | | Average: 48.59% ``` ### Winogrande ``` | Task |Version| Metric | Value | |Stderr| |----------|------:|---------------|----------|---|------| |winogrande| 1|acc,none | 0.77| | | | | |acc_stderr,none| 0.01| | | | | |alias |winogrande| | | Average: 77.35% ``` ### GSM8K ``` |Task |Version| Metric |Value| |Stderr| |-----|------:|-----------------------------------|-----|---|------| |gsm8k| 3|exact_match,strict-match | 0.67| | | | | |exact_match_stderr,strict-match | 0.01| | | | | |exact_match,flexible-extract | 0.68| | | | | |exact_match_stderr,flexible-extract| 0.01| | | | | |alias |gsm8k| | | Average: 67.48% ``` **Average score**: 67.48%

anirban22/bert-sentiment-anlysis

anirban22

2024-05-18T19:22:24Z

111

0

transformers

[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-05-18T19:21:42Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Mitrofazotron/mistral_esnli_dev_correct

Mitrofazotron

2024-05-18T19:09:14Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-05-18T19:09:08Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Mitrofazotron/mistral_esnli_dev_all_expls

Mitrofazotron

2024-05-18T19:07:14Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-05-18T15:03:15Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

hoanganhvu/phishing_3_1

hoanganhvu

2024-05-18T19:07:09Z

107

0

transformers

[ "transformers", "tensorboard", "safetensors", "bert", "generated_from_trainer", "base_model:google-bert/bert-large-uncased", "base_model:finetune:google-bert/bert-large-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-05-18T09:46:24Z

--- license: apache-2.0 base_model: bert-large-uncased tags: - generated_from_trainer metrics: - accuracy - precision - recall model-index: - name: phishing_3_1 results: [] ---  # phishing_3_1 This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5678 - Accuracy: 0.9837 - Precision: 0.9884 - Recall: 0.9788 - False Positive Rate: 0.0115 ## 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: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | False Positive Rate | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:-------------------:| | 0.5925 | 1.0 | 3025 | 0.5767 | 0.9743 | 0.9853 | 0.9630 | 0.0143 | | 0.5784 | 2.0 | 6050 | 0.5709 | 0.9802 | 0.9764 | 0.9841 | 0.0238 | | 0.5766 | 3.0 | 9075 | 0.6025 | 0.9490 | 0.9968 | 0.9008 | 0.0029 | | 0.5682 | 4.0 | 12100 | 0.5678 | 0.9837 | 0.9884 | 0.9788 | 0.0115 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2

hoanganhvu/phishing_4_1

hoanganhvu

2024-05-18T19:05:14Z

106

0

transformers

[ "transformers", "tensorboard", "safetensors", "bert", "generated_from_trainer", "base_model:google-bert/bert-large-uncased", "base_model:finetune:google-bert/bert-large-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-05-18T09:47:12Z

--- license: apache-2.0 base_model: bert-large-uncased tags: - generated_from_trainer metrics: - accuracy - precision - recall model-index: - name: phishing_4_1 results: [] ---  # phishing_4_1 This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7542 - Accuracy: 0.9894 - Precision: 0.9916 - Recall: 0.9872 - False Positive Rate: 0.0084 ## 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: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | False Positive Rate | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:-------------------:| | 0.77 | 1.0 | 3025 | 0.7619 | 0.9818 | 0.9796 | 0.9841 | 0.0205 | | 0.7579 | 2.0 | 6050 | 0.7603 | 0.9834 | 0.9982 | 0.9685 | 0.0018 | | 0.7595 | 3.0 | 9075 | 0.7605 | 0.9831 | 0.9982 | 0.9680 | 0.0018 | | 0.7546 | 4.0 | 12100 | 0.7542 | 0.9894 | 0.9916 | 0.9872 | 0.0084 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2

emilykang/Gemma_medmcqa_question_generation-biochemistry_lora

emilykang

2024-05-18T19:00:46Z

2

0

peft

[ "peft", "safetensors", "gemma", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:google/gemma-2b", "base_model:adapter:google/gemma-2b", "license:gemma", "region:us" ]

null

2024-05-18T18:06:34Z

--- license: gemma library_name: peft tags: - trl - sft - generated_from_trainer base_model: google/gemma-2b datasets: - generator model-index: - name: Gemma_medmcqa_question_generation-biochemistry_lora results: [] ---  # Gemma_medmcqa_question_generation-biochemistry_lora This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - 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 - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1

lenML/case-h-beta

lenML

2024-05-18T19:00:31Z

3

0

diffusers

[ "diffusers", "art", "people", "diffusion", "Cinematic", "Photography", "Landscape", "Interior", "Food", "Car", "Wildlife", "Architecture", "text-to-image", "en", "license:openrail++", "region:us" ]

text-to-image

2024-05-14T21:45:14Z

--- license: openrail++ language: - en library_name: diffusers tags: - art - people - diffusion - Cinematic - Photography - Landscape - Interior - Food - Car - Wildlife - Architecture pipeline_tag: text-to-image widget: - text: "cinematic film still one man, orc, in armor. shallow depth of field, vignette, highly detailed, high budget Hollywood movie, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy" output: url: "https://cdn-uploads.huggingface.co/production/uploads/6346a49add6d90d82cd03c42/eYfxL_1REcqUM2SIItqtG.jpeg" - text: "Old tree, A detailed illustration muted chinese ink painting, muted colors, rice paper texture, splash paint, halo ai, one human, one red sun. Venus. Space. Clouds wet to wet techniques. vibrant vector. using Cinema 4D" output: url: "https://cdn-uploads.huggingface.co/production/uploads/6346a49add6d90d82cd03c42/UcvQMAXnl4a95DbH5YAKL.jpeg" - text: "(dynamic lighting:1.1), ((masterpiece)), solo, portrait, Thri Kreen, furry insect, colored sclera, beetle antennae, 2 pairs of hands, 1girl, flat breast, yellow eyes, white chitin, grey shirt, steel armor, fantasy background, blurred background" output: url: "https://cdn-uploads.huggingface.co/production/uploads/6346a49add6d90d82cd03c42/pHZimaVIbRFN38roDSjei.jpeg" --- # Case-Hardened <Gallery /> ## Model description ### CaseH-beta CaseH does not mean `hentai case`; instead, CaseH stands for `Case Hardened`. It derives from the SDXL distillation, creating a 1.5 model. Recommended configuration: - Sampler: DPM++ 2M Karras - Steps: 15-55 (recommended 35) - CFG: 2-5 (recommended 4) > The higher the CFG value, the fewer steps should be used. ### How This model represents an idea I have been experimenting with. It seems I've managed to produce a release-worthy version. Actually, my concept is quite straightforward, based on the following two assumptions: - The model has ample capacity to accommodate more knowledge. - Consistency in architecture implies the possibility of distillation (and we could focus on learning just the realistic part). Therefore, I attempted to use the SDXL model (one of the more popular versions on Civitai) as the teacher model to distill knowledge into the SD1.5 model (the beta version is based on the LOFIv4 tune-up). > To my surprise, this round of fine-tuning has significantly surpassed previous distillation (raw-xs) versions. After the tune-up, LOFIv4 has almost completely abandoned the asian-style and shifted entirely towards SDXL. ### Beta Issues - ti model lora model may not be used as expected Due to the strong leaning towards learning from SDXL, it could cause the action on the 1.5 TI model to fail, such as with the ng_deepnegative_xx series models.

Kalloniatis/Humor-Recognition-Greek-Multilingual-MiniLM

Kalloniatis

2024-05-18T18:57:01Z

113

0

transformers

[ "transformers", "safetensors", "bert", "text-classification", "el", "dataset:kallantis/Greek-Humorous-Dataset", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-05-18T18:45:40Z

--- library_name: transformers license: apache-2.0 datasets: - kallantis/Greek-Humorous-Dataset language: - el pipeline_tag: text-classification --- # # # This model based on Multilingual MiniLM ("microsoft/Multilingual-MiniLM-L12-H384") fine-tuned for Humor Recognition in Greek language. # ## Model Details The model was pre-trained over 10 epochs on Greek Humorous Dataset # ## Pre-processing details The text needs to be pre-processed by removing all greek diacritics and punctuation and converting all letters to lowercase ## Load Pretrained Model ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kallantis/Humor-Recognition-Greek-Multilingual-MiniLM") model = AutoModelForSequenceClassification.from_pretrained("kallantis/Humor-Recognition-Greek-Multilingual-MiniLM") ```

lenML/lofi-v5-final

lenML

2024-05-18T18:56:04Z

4

0

diffusers

[ "diffusers", "art", "people", "diffusion", "Cinematic", "Photography", "Landscape", "Interior", "Food", "Car", "Wildlife", "Architecture", "text-to-image", "en", "license:openrail++", "region:us" ]

text-to-image

2024-05-15T03:21:09Z

--- license: openrail++ language: - en library_name: diffusers tags: - art - people - diffusion - Cinematic - Photography - Landscape - Interior - Food - Car - Wildlife - Architecture pipeline_tag: text-to-image widget: - text: "an evil demonic woman, white hair, blue eyes, short messy hair, arrogant mood, analog photo" output: url: "30350-349554337-an evil demonic woman,_white hair, blue eyes, short messy hair,_arrogant mood,_analog photo.png" - text: "half body, walking pose, shot from front, slow motion, female paladin wearing the full body, (light silver armour:1.2),(ornately decorated armor), (insanely detailed, bloom:1.2), (analog:1.2), (high sharpness), (detailed pupils:1.1), detailed face and eyes, (long blonde Hair, ponytail,ecstatic:1.1), (young woman:1.1), sharp, real shadow, (temple background:1.2), arms crossed over the chest , looking at viewer, photographed by Canan EOS R6, 135mm, 1/1250s, f/2.8, ISO 400" output: url: "29357-2410224889-half body, walking pose, shot from front, slow motion, female paladin wearing the full body, (light silver armour_1.2),(ornately.png" - text: "best quality, mecha glass room, highrise, transparent, mecha interior, city background, realistic photography, fine details, 8k," output: url: "29977-2751097006-best quality,_mecha glass room, highrise, transparent, mecha interior, city background, realistic photography, fine details, 8k,.png" - text: "best quality, pool room" output: url: "29995-3383783430-best quality,_pool room.png" --- # L.O.F.I: Limitless Originality Free from Interference <Gallery /> ## Model description ### 🚀 LOFI V5 - Final This is the last version in the LOFI series of models. There likely won't be any more updates to the LOFI model in the future (unless an SD3.0 is released, in which case, a LOFI version for 3.0 might be trained). Version 5 is quite special, as it even produces an effect somewhat similar to using "LCM". You must use a very low CFG to utilize it. This model can also be effectively combined with LCM or HyperSD for enhanced performance. Additionally, this model is extremely sensitive to "prompt word attention". From my tests, no prompt word weight should exceed 1.2, and oftentimes, removing all prompt weight leads to better results, indicating that the model has a precise understanding of the importance and relevance of prompts. 　 ### 🛠️ Recommended settings: Sampler: DPM++ (series) / Restart Steps: 15-55 (35 recommended) CFG: 2-5 (4 recommended) > The lower the CFG, the more creative the generated image may be. 　 ### 📸 nobody issue: This model has a strong preference for a photographic "subject". It could prove difficult to generate images lacking a definite subject. If you do wish to generate landscape pictures with this model, the use of controlnet-depth is recommended to manage the output. 　 💡 PS: In fact, this model was completed quite some time ago, but I believed that the 1.5 model community might not remain active following the release of SDXL, so I held back the release. It now appears that the 1.5 model is still very much in use, often employed as a refiner for SDXL by many, including myself. 💡PPS: In order to show the true ability of the model, all showcases try not to use lora and post-processing. If reasonable lora and post-processing are used, better results should be achieved. 　

Angy309/clasi_noti

Angy309

2024-05-18T18:55:11Z

109

0

transformers

[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:dccuchile/bert-base-spanish-wwm-uncased", "base_model:finetune:dccuchile/bert-base-spanish-wwm-uncased", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-05-18T18:33:48Z

--- base_model: dccuchile/bert-base-spanish-wwm-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: clasi_noti results: [] ---  # clasi_noti This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2959 - Accuracy: 0.9181 ## 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: 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: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 236 | 0.2896 | 0.9181 | | No log | 2.0 | 472 | 0.2959 | 0.9181 | ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1

apwic/sentiment-lora-r2a0d0.05-1

apwic

2024-05-18T18:50:54Z

0

null

[ "tensorboard", "generated_from_trainer", "id", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "region:us" ]

null

2024-05-18T18:18:03Z

--- language: - id license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: sentiment-lora-r2a0d0.05-1 results: [] ---  # sentiment-lora-r2a0d0.05-1 This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3638 - Accuracy: 0.8446 - Precision: 0.8193 - Recall: 0.7951 - F1: 0.8055 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 30 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.5663 | 1.0 | 122 | 0.5216 | 0.7293 | 0.6677 | 0.6510 | 0.6572 | | 0.5149 | 2.0 | 244 | 0.5134 | 0.7243 | 0.6758 | 0.6899 | 0.6810 | | 0.4925 | 3.0 | 366 | 0.4821 | 0.7569 | 0.7055 | 0.6980 | 0.7014 | | 0.4608 | 4.0 | 488 | 0.4654 | 0.7644 | 0.7150 | 0.7083 | 0.7114 | | 0.4493 | 5.0 | 610 | 0.4600 | 0.7569 | 0.7126 | 0.7305 | 0.7193 | | 0.4257 | 6.0 | 732 | 0.4307 | 0.7870 | 0.7433 | 0.7318 | 0.7369 | | 0.4178 | 7.0 | 854 | 0.4181 | 0.7970 | 0.7552 | 0.7614 | 0.7581 | | 0.3977 | 8.0 | 976 | 0.3972 | 0.8070 | 0.7687 | 0.7560 | 0.7617 | | 0.3946 | 9.0 | 1098 | 0.3937 | 0.8145 | 0.7779 | 0.7663 | 0.7716 | | 0.3762 | 10.0 | 1220 | 0.3874 | 0.8246 | 0.7995 | 0.7584 | 0.7738 | | 0.3727 | 11.0 | 1342 | 0.3787 | 0.8321 | 0.8014 | 0.7837 | 0.7915 | | 0.3626 | 12.0 | 1464 | 0.3750 | 0.8371 | 0.8059 | 0.7947 | 0.7999 | | 0.359 | 13.0 | 1586 | 0.3728 | 0.8296 | 0.8066 | 0.7644 | 0.7803 | | 0.3488 | 14.0 | 1708 | 0.3709 | 0.8296 | 0.8049 | 0.7669 | 0.7816 | | 0.3445 | 15.0 | 1830 | 0.3667 | 0.8421 | 0.8131 | 0.7983 | 0.8050 | | 0.3344 | 16.0 | 1952 | 0.3656 | 0.8421 | 0.8142 | 0.7958 | 0.8040 | | 0.3339 | 17.0 | 2074 | 0.3654 | 0.8396 | 0.8128 | 0.7890 | 0.7992 | | 0.3357 | 18.0 | 2196 | 0.3638 | 0.8421 | 0.8154 | 0.7933 | 0.8029 | | 0.3357 | 19.0 | 2318 | 0.3646 | 0.8421 | 0.8154 | 0.7933 | 0.8029 | | 0.3359 | 20.0 | 2440 | 0.3638 | 0.8446 | 0.8193 | 0.7951 | 0.8055 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.15.2