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modelId string	author string	last_modified timestamp[us, tz=UTC]	downloads int64	likes int64	library_name string	tags sequence	pipeline_tag string	createdAt timestamp[us, tz=UTC]	card string
RachidAR/AFlow-SegMoe-1Bx3-v0.1	RachidAR	2024-02-07T11:55:35Z	6	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-1.5", "moe", "segmoe", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-02-07T11:10:40Z	--- license: apache-2.0 pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - safetensors - stable-diffusion-1.5 - moe - segmoe language: - en library_name: diffusers --- ## Warning This is an experimental model. It works only with segmoe library! ## Experts - source_model: Lykon/dreamshaper-8 (base) - source_model: Lykon/AAM_AnyLora_AnimeMix - source_model: stablediffusionapi/realistic-vision-51 ## Usage This model can be used via the [segmoe](https://github.com/segmind/segmoe) library. Make sure to install segmoe by running ```bash pip install segmoe ``` ```python from segmoe import SegMoEPipeline pipeline = SegMoEPipeline("RachidAR/AFlow-SegMoe-1Bx3-v0.1", device = "cuda", safety_checker = None) prompt = "cosmic canvas, orange city background, painting of a chubby cat" negative_prompt = "nsfw, bad quality, worse quality" img = pipeline( prompt=prompt, negative_prompt=negative_prompt, height=1024, width=1024, num_inference_steps=25, guidance_scale=7.5, ).images[0] img.save("image.png") ``` ![image/png](https://huggingface.co/RachidAR/AFlow-SegMoe-1Bx3-v0.1/resolve/main/example1.png) ![image/png](https://huggingface.co/RachidAR/AFlow-SegMoe-1Bx3-v0.1/resolve/main/example2.png) ![image/png](https://huggingface.co/RachidAR/AFlow-SegMoe-1Bx3-v0.1/resolve/main/example3.png)
AIJUUD/juud-Mistral-7B-dpo	AIJUUD	2024-02-07T11:47:45Z	3,520	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "en", "arxiv:1910.09700", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-07T10:29:25Z	--- library_name: transformers license: apache-2.0 language: - en --- # 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]
alexgastev/Reinforce-CartPole-v1	alexgastev	2024-02-07T11:46:53Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2024-02-07T11:46:43Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
iDSLR/DeepSilence-Harad-zero-peft-1.3B	iDSLR	2024-02-07T11:40:49Z	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:42dot/42dot_LLM-PLM-1.3B", "base_model:adapter:42dot/42dot_LLM-PLM-1.3B", "region:us" ]	null	2024-02-04T16:48:25Z	--- library_name: peft base_model: 42dot/42dot_LLM-PLM-1.3B --- # 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 0.8.2
PranavInvenics/phi2_v3	PranavInvenics	2024-02-07T11:36:16Z	5	0	transformers	[ "transformers", "safetensors", "phi", "text-generation", "autotrain", "conversational", "custom_code", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-07T10:41:05Z	--- tags: - autotrain - text-generation widget: - text: "I love AutoTrain because " license: other --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
formatec/casenet-tuned-4	formatec	2024-02-07T11:32:53Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-07T11:30:37Z	--- 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]
iamhack/distilhubert-finetuned-ks-ob	iamhack	2024-02-07T11:27:15Z	4	0	transformers	[ "transformers", "tensorboard", "safetensors", "hubert", "audio-classification", "generated_from_trainer", "dataset:audiofolder", "base_model:ntu-spml/distilhubert", "base_model:finetune:ntu-spml/distilhubert", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	audio-classification	2024-02-07T10:29:50Z	--- license: apache-2.0 base_model: ntu-spml/distilhubert tags: - generated_from_trainer datasets: - audiofolder metrics: - accuracy model-index: - name: distilhubert-finetuned-ks-ob results: - task: name: Audio Classification type: audio-classification dataset: name: audiofolder type: audiofolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9998775760048969 --- <!-- 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. --> # distilhubert-finetuned-ks-ob This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the audiofolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0033 - Accuracy: 0.9999 ## 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: 3e-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 - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.1462 \| 1.0 \| 191 \| 0.1376 \| 0.9731 \| \| 0.0317 \| 2.0 \| 383 \| 0.0206 \| 0.9969 \| \| 0.0112 \| 3.0 \| 574 \| 0.0078 \| 0.9990 \| \| 0.0062 \| 4.0 \| 766 \| 0.0040 \| 0.9998 \| \| 0.0063 \| 4.99 \| 955 \| 0.0033 \| 0.9999 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
wahdan99/a2c-PandaReachDense-v3	wahdan99	2024-02-07T11:22:23Z	0	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-02-07T11:18:42Z	--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -0.21 +/- 0.07 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v3 This is a trained model of a A2C agent playing PandaReachDense-v3 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 ... ```
UnaiGurbindo/speecht5_finetuned_voxpopuli_es	UnaiGurbindo	2024-02-07T11:20:40Z	10	0	transformers	[ "transformers", "safetensors", "speecht5", "text-to-audio", "generated_from_trainer", "lt", "dataset:facebook/voxpopuli", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]	text-to-audio	2024-02-07T10:51:49Z	--- language: - lt license: mit base_model: microsoft/speecht5_tts tags: - generated_from_trainer datasets: - facebook/voxpopuli model-index: - name: speec T5 LT - Unai Gurbindo 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. --> # speec T5 LT - Unai Gurbindo This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the Vox Populi LT dataset. It achieves the following results on the evaluation set: - Loss: 0.4978 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.6231 \| 12.7 \| 100 \| 0.5834 \| \| 0.5691 \| 25.4 \| 200 \| 0.5259 \| \| 0.5381 \| 38.1 \| 300 \| 0.5030 \| \| 0.5306 \| 50.79 \| 400 \| 0.5016 \| \| 0.521 \| 63.49 \| 500 \| 0.4978 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
Krithiik/t5-base-gloss-to-sentence	Krithiik	2024-02-07T11:17:10Z	4	0	transformers	[ "transformers", "tf", "safetensors", "t5", "text2text-generation", "generated_from_keras_callback", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-07T11:15:41Z	--- tags: - generated_from_keras_callback model-index: - name: t5-base-gloss-to-sentence results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # t5-base-gloss-to-sentence This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: ## 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: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.37.0 - TensorFlow 2.15.0 - Datasets 2.1.0 - Tokenizers 0.15.1
athmurikarthik/videomae-base-action_detection	athmurikarthik	2024-02-07T11:16:11Z	4	0	transformers	[ "transformers", "tensorboard", "safetensors", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-base", "base_model:finetune:MCG-NJU/videomae-base", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	2024-02-06T10:19:23Z	--- license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: videomae-base-action_detection 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. --> # videomae-base-action_detection This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2662 - Accuracy: 0.7243 ## 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: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 15200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 1.0956 \| 0.02 \| 305 \| 1.3464 \| 0.4774 \| \| 0.683 \| 1.02 \| 610 \| 2.3774 \| 0.3704 \| \| 0.5519 \| 2.02 \| 915 \| 2.1501 \| 0.3128 \| \| 1.5863 \| 3.02 \| 1220 \| 2.7112 \| 0.2387 \| \| 0.8028 \| 4.02 \| 1525 \| 1.5204 \| 0.7037 \| \| 1.1797 \| 5.02 \| 1830 \| 2.6479 \| 0.2963 \| \| 1.185 \| 6.02 \| 2135 \| 0.8982 \| 0.7860 \| \| 0.9516 \| 7.02 \| 2440 \| 1.2030 \| 0.6008 \| \| 0.5755 \| 8.02 \| 2745 \| 0.8003 \| 0.8189 \| \| 0.6815 \| 9.02 \| 3050 \| 2.3653 \| 0.4198 \| \| 1.1649 \| 10.02 \| 3355 \| 3.0645 \| 0.4403 \| \| 1.1024 \| 11.02 \| 3660 \| 2.4187 \| 0.4321 \| \| 1.1158 \| 12.02 \| 3965 \| 2.2631 \| 0.5597 \| \| 0.2375 \| 13.02 \| 4270 \| 2.2977 \| 0.5432 \| \| 0.7445 \| 14.02 \| 4575 \| 1.0086 \| 0.7860 \| \| 0.6555 \| 15.02 \| 4880 \| 0.7161 \| 0.8560 \| \| 0.8807 \| 16.02 \| 5185 \| 1.2404 \| 0.6584 \| \| 1.0477 \| 17.02 \| 5490 \| 1.6849 \| 0.6173 \| \| 0.498 \| 18.02 \| 5795 \| 2.0557 \| 0.5844 \| \| 0.5536 \| 19.02 \| 6100 \| 2.0703 \| 0.5967 \| \| 0.2232 \| 20.02 \| 6405 \| 2.7690 \| 0.4856 \| \| 0.5589 \| 21.02 \| 6710 \| 0.9549 \| 0.7243 \| \| 0.3377 \| 22.02 \| 7015 \| 0.6488 \| 0.8189 \| \| 0.7096 \| 23.02 \| 7320 \| 1.6638 \| 0.5556 \| \| 0.1201 \| 24.02 \| 7625 \| 1.6283 \| 0.5761 \| \| 0.136 \| 25.02 \| 7930 \| 1.4397 \| 0.5926 \| \| 0.2558 \| 26.02 \| 8235 \| 1.7421 \| 0.5350 \| \| 0.3245 \| 27.02 \| 8540 \| 1.2982 \| 0.6132 \| \| 0.0029 \| 28.02 \| 8845 \| 1.0594 \| 0.7202 \| \| 0.3272 \| 29.02 \| 9150 \| 1.0833 \| 0.8272 \| \| 0.0841 \| 30.02 \| 9455 \| 1.3230 \| 0.5926 \| \| 0.5595 \| 31.02 \| 9760 \| 2.5545 \| 0.5844 \| \| 0.0837 \| 32.02 \| 10065 \| 1.5960 \| 0.6296 \| \| 0.0127 \| 33.02 \| 10370 \| 1.8149 \| 0.5720 \| \| 0.3622 \| 34.02 \| 10675 \| 2.4455 \| 0.4938 \| \| 0.0006 \| 35.02 \| 10980 \| 1.6700 \| 0.6461 \| \| 0.0027 \| 36.02 \| 11285 \| 2.2488 \| 0.5720 \| \| 0.0544 \| 37.02 \| 11590 \| 2.6388 \| 0.5514 \| \| 0.2504 \| 38.02 \| 11895 \| 1.5352 \| 0.6379 \| \| 0.0149 \| 39.02 \| 12200 \| 2.2851 \| 0.5391 \| \| 0.4035 \| 40.02 \| 12505 \| 1.8876 \| 0.5556 \| \| 0.0008 \| 41.02 \| 12810 \| 2.4479 \| 0.5473 \| \| 0.3176 \| 42.02 \| 13115 \| 2.0729 \| 0.6049 \| \| 0.0007 \| 43.02 \| 13420 \| 1.5171 \| 0.6255 \| \| 0.3948 \| 44.02 \| 13725 \| 1.4067 \| 0.6132 \| \| 0.0016 \| 45.02 \| 14030 \| 1.0621 \| 0.7325 \| \| 0.2173 \| 46.02 \| 14335 \| 1.5515 \| 0.6132 \| \| 0.0007 \| 47.02 \| 14640 \| 1.2523 \| 0.7284 \| \| 0.2819 \| 48.02 \| 14945 \| 1.5618 \| 0.6461 \| \| 0.0004 \| 49.02 \| 15200 \| 1.2662 \| 0.7243 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
OctavianB/MistralRo	OctavianB	2024-02-07T10:55:24Z	0	1	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-07T10:55:16Z	--- 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]
surya47/medclip-roco	surya47	2024-02-07T10:54:57Z	2	2	transformers	[ "transformers", "jax", "hybrid-clip", "medical", "code", "visual-question-answering", "license:apache-2.0", "endpoints_compatible", "region:us" ]	visual-question-answering	2024-02-07T05:26:24Z	--- license: apache-2.0 metrics: - accuracy pipeline_tag: visual-question-answering tags: - medical - code ---
dariolopez/Llama-2-databricks-dolly-oasst1-es-axolotl-GGUF	dariolopez	2024-02-07T10:52:06Z	0	0	null	[ "es", "license:apache-2.0", "region:us" ]	null	2023-09-05T07:40:24Z	--- license: apache-2.0 language: - es --- Llama 2 (7B) fine-tuned on a [own Spanish instructions dataset](https://huggingface.co/datasets/dariolopez/Llama-2-databricks-dolly-oasst1-es). On this repo you can find 4-bit and 5-bit quantized versions of the [Llama 2 (7B) Spanish fine-tuned](https://huggingface.co/dariolopez/Llama-2-databricks-dolly-oasst1-es-axolotl). # How to use ```sh git clone https://github.com/ggerganov/llama.cpp cd llama.cpp && git pull && make clean && make git clone https://huggingface.co/dariolopez/Llama-2-databricks-dolly-oasst1-es-axolotl-GGUF ./main -m ./llama-2-databricks-dolly-oasst1-es-axolotl.gguf.q4_k_m.bin -n 2048 --color --temp 0 -ngl 35 -p "<s>[INST] Describe 5 lugares para visitar en España: [/INST]" ``` # Based on https://mlabonne.github.io/blog/posts/Quantize_Llama_2_models_using_ggml.html
llmware/slim-nli	llmware	2024-02-07T10:45:05Z	13	7	transformers	[ "transformers", "pytorch", "llama", "text-generation", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-01-08T20:35:04Z	--- license: apache-2.0 inference: false --- # SLIM-NLI <!-- Provide a quick summary of what the model is/does. --> slim-nli is part of the SLIM ("Structured Language Instruction Model") model series, consisting of small, specialized decoder-based models, fine-tuned for function-calling. slim-nli has been fine-tuned for natural language inference (nli) function calls, generating output consisting of a python dictionary corresponding to specified keys, e.g.:     `{"evidence": ["contradicts"]}` SLIM models are designed to generate structured outputs that can be used programmatically as part of a multi-step, multi-model LLM-based automation workflow. Each slim model has a 'quantized tool' version, e.g., ['slim-nli-tool'](https://huggingface.co/llmware/slim-nli-tool). ## Prompt format: `function = "classify"` `params = "nli"` `prompt = "<human> " + {text} + "\n" + `                       `"<{function}> " + {params} + "</{function}>" + "\n<bot>:"` <details> <summary>Transformers Script </summary> model = AutoModelForCausalLM.from_pretrained("llmware/slim-nli") tokenizer = AutoTokenizer.from_pretrained("llmware/slim-nli") function = "classify" params = "evidence" # expects two statements - the first is evidence, and the second is a conclusion text1 = "The stock market declined yesterday as investors worried increasingly about the slowing economy." text2 = "Investors are positive about the market." # the two statements are concatenated with optional/helpful "Evidence: " and "Conclusion: " added text = "Evidence: " + text1 + "\n" + "Conclusion: " + text2 prompt = "<human>: " + text + "\n" + f"<{function}> {params} </{function}>\n<bot>:" inputs = tokenizer(prompt, return_tensors="pt") start_of_input = len(inputs.input_ids[0]) outputs = model.generate( inputs.input_ids.to('cpu'), eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.eos_token_id, do_sample=True, temperature=0.3, max_new_tokens=100 ) output_only = tokenizer.decode(outputs[0][start_of_input:], skip_special_tokens=True) print("output only: ", output_only) # here's the fun part try: output_only = ast.literal_eval(llm_string_output) print("success - converted to python dictionary automatically") except: print("fail - could not convert to python dictionary automatically - ", llm_string_output) </details> <details> <summary>Using as Function Call in LLMWare</summary> from llmware.models import ModelCatalog slim_model = ModelCatalog().load_model("llmware/slim-nli") # input text - expects two statements - the first is evidence, and the second is a conclusion text1 = "The stock market declined yesterday as investors worried increasingly about the slowing economy." text2 = "Investors are positive about the market." text = "Evidence: " + text1 + "\n" + "Conclusion: " + text2 response = slim_model.function_call(text,params=["evidence"], function="classify") print("llmware - llm_response: ", response) </details> ## Model Card Contact Darren Oberst & llmware team [Join us on Discord](https://discord.gg/MhZn5Nc39h)
shidowake/cyber2chat-7B-base-bnb-4bit	shidowake	2024-02-07T10:44:45Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-02-07T10:42:57Z	--- 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]
erfanvaredi/results	erfanvaredi	2024-02-07T10:41:40Z	6	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:mistralai/Mistral-7B-Instruct-v0.1", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.1", "license:apache-2.0", "region:us" ]	null	2024-02-07T10:24:32Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: mistralai/Mistral-7B-Instruct-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. --> # results This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-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: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.3 - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.38.0.dev0 - Pytorch 2.2.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
llmware/slim-ratings-tool	llmware	2024-02-07T10:37:33Z	71	3	transformers	[ "transformers", "gguf", "llama", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-01-24T17:03:40Z	--- license: apache-2.0 --- # SLIM-RATINGS <!-- Provide a quick summary of what the model is/does. --> slim-ratings-tool is a 4_K_M quantized GGUF version of slim-sentiment, providing a small, fast inference implementation, optimized for multi-model concurrent deployment. [slim-ratings](https://huggingface.co/llmware/slim-ratings) is part of the SLIM ("Structured Language Instruction Model") series, providing a set of small, specialized decoder-based LLMs, fine-tuned for function-calling. To pull the model via API: from huggingface_hub import snapshot_download snapshot_download("llmware/slim-ratings-tool", local_dir="/path/on/your/machine/", local_dir_use_symlinks=False) Load in your favorite GGUF inference engine, or try with llmware as follows: from llmware.models import ModelCatalog # to load the model and make a basic inference model = ModelCatalog().load_model("slim-ratings-tool") response = model.function_call(text_sample) # this one line will download the model and run a series of tests ModelCatalog().tool_test_run("slim-ratings-tool", verbose=True) Slim models can also be loaded even more simply as part of a multi-model, multi-step LLMfx calls: from llmware.agents import LLMfx llm_fx = LLMfx() llm_fx.load_tool("ratings") response = llm_fx.ratings(text) Note: please review [config.json](https://huggingface.co/llmware/slim-ratings-tool/blob/main/config.json) in the repository for prompt wrapping information, details on the model, and full test set. ## Model Card Contact Darren Oberst & llmware team [Any questions? Join us on Discord](https://discord.gg/MhZn5Nc39h)
aanaya/rare-puppers	aanaya	2024-02-07T10:37:32Z	6	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "pytorch", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-02-07T09:46:25Z	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: rare-puppers results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.21568627655506134 --- # rare-puppers Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for anything by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### Abelmoschus esculentus leaves ![Abelmoschus esculentus leaves](images/Abelmoschus_esculentus_leaves.jpg) #### Cannabis sativa leaves ![Cannabis sativa leaves](images/Cannabis_sativa_leaves.jpg) #### Crotalaria juncea leaves ![Crotalaria juncea leaves](images/Crotalaria_juncea_leaves.jpg) #### Jatropha multifida leaves ![Jatropha multifida leaves](images/Jatropha_multifida_leaves.jpg) #### Tagetes minuta leaves ![Tagetes minuta leaves](images/Tagetes_minuta_leaves.jpg)
llmware/slim-intent-tool	llmware	2024-02-07T10:24:20Z	70	4	transformers	[ "transformers", "gguf", "llama", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-02-04T21:55:25Z	--- license: apache-2.0 --- # SLIM-INTENT-TOOL <!-- Provide a quick summary of what the model is/does. --> slim-intent-tool is a 4_K_M quantized GGUF version of slim-intent, providing a small, fast inference implementation, optimized for multi-model concurrent deployment. [slim-intent](https://huggingface.co/llmware/slim-intent) is part of the SLIM ("Structured Language Instruction Model") series, providing a set of small, specialized decoder-based LLMs, fine-tuned for function-calling. To pull the model via API: from huggingface_hub import snapshot_download snapshot_download("llmware/slim-intent-tool", local_dir="/path/on/your/machine/", local_dir_use_symlinks=False) Load in your favorite GGUF inference engine, or try with llmware as follows: from llmware.models import ModelCatalog # to load the model and make a basic inference model = ModelCatalog().load_model("slim-intent-tool") response = model.function_call(text_sample) # this one line will download the model and run a series of tests ModelCatalog().tool_test_run("slim-intent-tool", verbose=True) Slim models can also orchestrated as part of a multi-model, multi-step LLMfx calls: from llmware.agents import LLMfx llm_fx = LLMfx() llm_fx.load_tool("intent") response = llm_fx.intent(text) Note: please review [config.json](https://huggingface.co/llmware/slim-intent-tool/blob/main/config.json) in the repository for prompt wrapping information, details on the model, and full test set. ## Model Card Contact Darren Oberst & llmware team [Any questions? Join us on Discord](https://discord.gg/MhZn5Nc39h)
TENRO/Shizuku_Infinity_XX	TENRO	2024-02-07T10:22:39Z	0	0	null	[ "region:us" ]	null	2024-02-04T07:40:31Z	AItuberしずくちゃんのLoRAです。 anything-v4.0系のモデルで作成したものですので、左記モデルやそのマージモデルと相性が良いと思われます。サンプル画像では、VAEに関してもanything-v4.0用のものを使用しています。 LoRAの強度は0.9程度が良いようです。以下にサンプル画像用のプロンプトを提示します。 <lora:Shizuku_Infinity_XX:0.9>, 1girl, solo, milky white hair, ahoge, big bow on the head, headphones, Beautiful detailed gemological eyes, smile, open mouth, upper body, EasyNegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, (negative_hand:1.2), (negative_hand-neg:1.2), (black hair:1.4), (red hair:1.4), (@ @:1.4), (underwear:1.7), (nude:1.7), (worst quality:1.2), (bad quality:1.2), (extra fingers:1.2), (deformed hands:1.2), (bad hands:1.2), (missing fingers:1.2), (over 6 fingers:1.2), (split fingers:1.2), (interlocked fingers:1.2), text, navel, teeth, ![image/png](https://cdn-uploads.huggingface.co/production/uploads/647d37ec1b6ecd15f4bf6e9b/9eh4Wt1TmNIE5AGFiopvV.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/647d37ec1b6ecd15f4bf6e9b/g4ugROIfAw_LeMYE2jr3M.png)
llmware/slim-intent	llmware	2024-02-07T10:20:35Z	11	9	transformers	[ "transformers", "pytorch", "llama", "text-generation", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-02-04T21:54:57Z	--- license: apache-2.0 inference: false --- # SLIM-INTENT <!-- Provide a quick summary of what the model is/does. --> slim-intent is part of the SLIM ("Structured Language Instruction Model") model series, consisting of small, specialized decoder-based models, fine-tuned for function-calling. slim-intent has been fine-tuned for intent analysis function calls, generating output consisting of a python dictionary corresponding to specified keys, e.g.:     `{"intent": ["complaint"]}` SLIM models are designed to generate structured output that can be used programmatically as part of a multi-step, multi-model LLM-based automation workflow. Each slim model has a 'quantized tool' version, e.g., ['slim-intent-tool'](https://huggingface.co/llmware/slim-intent-tool). ## Prompt format: `function = "classify"` `params = "intent"` `prompt = "<human> " + {text} + "\n" + `                       `"<{function}> " + {params} + "</{function}>" + "\n<bot>:"` <details> <summary>Transformers Script </summary> model = AutoModelForCausalLM.from_pretrained("llmware/slim-intent") tokenizer = AutoTokenizer.from_pretrained("llmware/slim-intent") function = "classify" params = "intent" text = "I am really impressed with the quality of the product and the service that I have received so far." prompt = "<human>: " + text + "\n" + f"<{function}> {params} </{function}>\n<bot>:" inputs = tokenizer(prompt, return_tensors="pt") start_of_input = len(inputs.input_ids[0]) outputs = model.generate( inputs.input_ids.to('cpu'), eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.eos_token_id, do_sample=True, temperature=0.3, max_new_tokens=100 ) output_only = tokenizer.decode(outputs[0][start_of_input:], skip_special_tokens=True) print("output only: ", output_only) # here's the fun part try: output_only = ast.literal_eval(llm_string_output) print("success - converted to python dictionary automatically") except: print("fail - could not convert to python dictionary automatically - ", llm_string_output) </details> <details> <summary>Using as Function Call in LLMWare</summary> from llmware.models import ModelCatalog slim_model = ModelCatalog().load_model("llmware/slim-intent") response = slim_model.function_call(text,params=["intent"], function="classify") print("llmware - llm_response: ", response) </details> ## Model Card Contact Darren Oberst & llmware team [Join us on Discord](https://discord.gg/MhZn5Nc39h)
reginaldcoghlan/qa	reginaldcoghlan	2024-02-07T10:19:12Z	0	0	null	[ "region:us" ]	null	2024-02-07T10:16:17Z	In today's digital landscape, the reliability, functionality, and performance of software are paramount to business success. At https://inoxoft.com/service/qa-consulting/, we specialize in revolutionizing your approach to testing, ensuring your products meet exemplary quality standards every step of the way. Our QA consulting services are designed to enhance efficiency, elevate user experience, and propel your business toward greater heights. As an ISO 27001 certified company and esteemed Microsoft Gold Partner, Google Cloud Partner, ISTQB Silver Partner, and recognized member of Clutch Firms that Deliver and Pangea, we bring unparalleled expertise to every project. Proud members of the Lviv IT Cluster, we are committed to setting industry standards and exceeding client expectations. Our comprehensive suite of Quality Assurance consulting services includes: Test Engineering: Our seasoned software QA consultants craft and implement robust testing frameworks tailored to your project's unique requirements. From identifying and addressing defects to verifying system performance, we cover all functional and non-functional aspects with precision. Test Management: Ensure seamless planning, execution, and delivery of QA activities throughout your project lifecycle. Our specialists align testing processes with your company goals, objectives, and quality standards, monitoring progress, and addressing issues proactively. Test Governance & Compliance: Navigating industries with stringent regulations such as healthcare, finance, and government, we define policies, procedures, and guidelines to ensure compliance. Our quality control measures mitigate risks and ensure timely addressing of compliance-related challenges. QA Audit and Improvement: We analyze your existing QA processes to identify areas for improvement, streamlining workflows, and enhancing efficiency. Leveraging automation and continuous integration practices, we optimize your testing processes for maximum efficacy. Pre-certification QA: Prepare your software products for certification and compliance with industry standards and regulations. Our comprehensive assessments, gap analyses, and mock audits ensure your solution meets the necessary criteria.
llmware/slim-category	llmware	2024-02-07T10:13:01Z	9	6	transformers	[ "transformers", "pytorch", "llama", "text-generation", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-02-02T17:07:38Z	--- license: apache-2.0 inference: false --- # SLIM-CATEGORY <!-- Provide a quick summary of what the model is/does. --> slim-category is part of the SLIM ("Structured Language Instruction Model") model series, consisting of small, specialized decoder-based models, fine-tuned for function-calling. slim-category has been fine-tuned for category topic analysis function calls, generating output consisting of a python dictionary corresponding to specified keys, e.g.:     `{"category": ["markets"]}` SLIM models are designed to generate structured outputs that can be used programmatically as part of a multi-step, multi-model LLM-based automation workflow. Each slim model has a 'quantized tool' version, e.g., ['slim-category-tool'](https://huggingface.co/llmware/slim-category-tool). ## Prompt format: `function = "classify"` `params = "category"` `prompt = "<human> " + {text} + "\n" + `                       `"<{function}> " + {params} + "</{function}>" + "\n<bot>:"` <details> <summary>Transformers Script </summary> model = AutoModelForCausalLM.from_pretrained("llmware/slim-category") tokenizer = AutoTokenizer.from_pretrained("llmware/slim-category") function = "classify" params = "category" text = "The stock market declined yesterday as investors worried increasingly about the slowing economy." prompt = "<human>: " + text + "\n" + f"<{function}> {params} </{function}>\n<bot>:" inputs = tokenizer(prompt, return_tensors="pt") start_of_input = len(inputs.input_ids[0]) outputs = model.generate( inputs.input_ids.to('cpu'), eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.eos_token_id, do_sample=True, temperature=0.3, max_new_tokens=100 ) output_only = tokenizer.decode(outputs[0][start_of_input:], skip_special_tokens=True) print("output only: ", output_only) # here's the fun part try: output_only = ast.literal_eval(llm_string_output) print("success - converted to python dictionary automatically") except: print("fail - could not convert to python dictionary automatically - ", llm_string_output) </details> <details> <summary>Using as Function Call in LLMWare</summary> from llmware.models import ModelCatalog slim_model = ModelCatalog().load_model("llmware/slim-category") response = slim_model.function_call(text,params=["category"], function="classify") print("llmware - llm_response: ", response) </details> ## Model Card Contact Darren Oberst & llmware team [Join us on Discord](https://discord.gg/MhZn5Nc39h)
ramsi-k/poca-SoccerTwos	ramsi-k	2024-02-07T10:12:56Z	3	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	2024-02-07T10:11:56Z	--- 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: ramsi-k/poca-SoccerTwos 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Arozhada/dqn-SpaceInvadersNoFrameskip-v4	Arozhada	2024-02-07T10:08:15Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-02-07T10:07:40Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 660.00 +/- 215.20 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Arozhada -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Arozhada -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Arozhada ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
chenhaodev/solar-10b-ocn-v1	chenhaodev	2024-02-07T10:01:49Z	3	1	peft	[ "peft", "safetensors", "llama-factory", "lora", "generated_from_trainer", "base_model:upstage/SOLAR-10.7B-v1.0", "base_model:adapter:upstage/SOLAR-10.7B-v1.0", "license:other", "region:us" ]	null	2024-02-07T09:12:23Z	--- license: other library_name: peft tags: - llama-factory - lora - generated_from_trainer base_model: upstage/SOLAR-10.7B-v1.0 model-index: - name: solar-10b-ocn-v1 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. --> # solar-10b-ocn-v1 This model is a fine-tuned version of upstage/SOLAR-10.7B-v1.0 on the oncc_medqa_instruct dataset. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 1.0 - mixed_precision_training: Native AMP ### Training script CUDA_VISIBLE_DEVICES=0 python src/train_bash.py --stage sft --do_train True --model_name_or_path upstage/SOLAR-10.7B-v1.0 --template solar --finetuning_type lora --quantization_bit 4 --flash_attn True --dataset_dir data --dataset oncc_medqa_instruct --cutoff_len 1024 --learning_rate 0.0005 --num_train_epochs 1.0 --max_samples 5000 --per_device_train_batch_size 4 --gradient_accumulation_steps 4 --lr_scheduler_type cosine --max_grad_norm 1.0 --logging_steps 10 --save_steps 100 --warmup_steps 10 --neftune_noise_alpha 0.5 --lora_rank 8 --lora_dropout 0.2 --lora_target wqkv --output_dir /workspace/solar-10b-ocn-v1 --fp16 True --plot_loss True ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.1.1+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1 ### Performance Test script: lm_eval --model hf --model_args pretrained=upstage/SOLAR-10.7B-v1.0,peft=chenhugging/solar-10b-ocn-v1,trust_remote_code=True,parallelize=True,load_in_4bit=True --tasks ocn,aocnp,medmcqa,pubmedqa,mmlu_clinical_knowledge,mmlu_college_medicine,mmlu_professional_medicine --device cuda:0 --limit 100 hf (pretrained=upstage/SOLAR-10.7B-v1.0,peft=chenhugging/solar-10b-ocn-v1,trust_remote_code=True,parallelize=True,load_in_4bit=True), gen_kwargs: (None), limit: 100.0, num_fewshot: None, batch_size: 1 \| Tasks \|Version\|Filter\|n-shot\| Metric \|Value\| \|Stderr\| \|---------------------\|-------\|------\|-----:\|--------\|----:\|---\|-----:\| \|pubmedqa \| 1\|none \| 0\|acc \| 0.95\|± \|0.0219\| \|medmcqa \|Yaml \|none \| 0\|acc \| 0.42\|± \|0.0496\| \|professional_medicine\| 0\|none \| 0\|acc \| 0.72\|± \|0.0451\| \|college_medicine \| 0\|none \| 0\|acc \| 0.67\|± \|0.0473\| \|clinical_knowledge \| 0\|none \| 0\|acc \| 0.64\|± \|0.0482\| \|ocn \|Yaml \|none \| 0\|acc \| 0.83\|± \|0.0378\| \|aocnp \|Yaml \|none \| 0\|acc \| 0.72\|± \|0.0451\|
ramsi-k/LunarLander-v2-fromscratch-tune	ramsi-k	2024-02-07T09:56:52Z	0	0	null	[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2024-02-07T09:51:41Z	--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -194.56 +/- 121.41 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 50000 'learning_rate': 0.001 'num_envs': 64 'num_steps': 32 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'ramsi-k/LunarLander-v2-fromscratch-tune' 'batch_size': 2048 'minibatch_size': 512} ```
Pankaj001/Flower-Dataset-Resnet50-180	Pankaj001	2024-02-07T09:54:02Z	0	0	tf-keras	[ "tf-keras", "image-classification", "license:apache-2.0", "region:us" ]	image-classification	2024-01-18T08:47:21Z	--- license: apache-2.0 metrics: - accuracy pipeline_tag: image-classification --- # ResNet-50 Model for Flower Classification This model is based on the ResNet-50 architecture and has been trained on a dataset of flower images. ## Model Details - Architecture: ResNet-50 - Input Size: 180x180 pixels with 3 channels (RGB) - Data Preprocessing: The model has been trained on normalized data. - Model Accuracy: 80% - ## Usage You can use this model for flower image classification tasks. Below are some code snippets to help you get started: flowers_url: "https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz" --- license: apache-2.0 language: - en library_name: keras ---
mzbac/phi-2-2x3	mzbac	2024-02-07T09:53:30Z	7	0	transformers	[ "transformers", "safetensors", "phi2moe", "text-generation", "custom_code", "en", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-02-04T05:38:05Z	--- license: mit language: - en --- A Moe model built on top of microsoft/phi-2, g-ronimo/phi-2-OpenHermes-2.5 and mlx-community/phi-2-dpo-7k, random init gates weights ## Example ``` from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline import torch DEV = torch.device('cuda' if torch.cuda.is_available() else 'cpu') model_name_or_path = "mzbac/phi2-2x3" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, trust_remote_code=True, torch_dtype=torch.bfloat16, ) model.to(DEV) tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) prompt = "Instruct: how backpropagation works.\nOutput:" print("\n\n* Generate:") inputs = tokenizer.encode(prompt, return_tensors="pt").to(DEV) generate_kwargs = dict( input_ids=inputs, temperature=0.3, max_new_tokens=500, do_sample=True, ) outputs = model.generate(generate_kwargs) print(tokenizer.decode(outputs[0])) ```
romil9/rvctraintest	romil9	2024-02-07T09:51:46Z	0	0	null	[ "onnx", "license:other", "region:us" ]	null	2024-02-07T06:35:36Z	--- license: other license_name: test license_link: LICENSE ---
varun-v-rao/roberta-base-bn-adapter-895K-snli-model3	varun-v-rao	2024-02-07T09:46:34Z	0	0	null	[ "tensorboard", "generated_from_trainer", "base_model:FacebookAI/roberta-base", "base_model:finetune:FacebookAI/roberta-base", "license:mit", "region:us" ]	null	2024-02-07T08:57:02Z	--- license: mit base_model: roberta-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: roberta-base-bn-adapter-895K-snli-model3 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. --> # roberta-base-bn-adapter-895K-snli-model3 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7710 - Accuracy: 0.7275 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.4273 \| 1.0 \| 8584 \| 0.3416 \| 0.8694 \| \| 0.4019 \| 2.0 \| 17168 \| 0.3206 \| 0.8800 \| \| 0.385 \| 3.0 \| 25752 \| 0.3148 \| 0.8821 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
JiajingChen/c	JiajingChen	2024-02-07T09:42:37Z	1	0	transformers	[ "transformers", "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "endpoints_compatible", "region:us" ]	reinforcement-learning	2024-02-07T09:28:10Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: c results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.50 +/- 2.72 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 ... ```
magus4450/speecht5_finetuned_voxpopuli_cs	magus4450	2024-02-07T09:42:35Z	12	0	transformers	[ "transformers", "tensorboard", "safetensors", "speecht5", "text-to-audio", "generated_from_trainer", "text-to-speech", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]	text-to-speech	2024-02-07T06:06:45Z	--- license: mit base_model: microsoft/speecht5_tts tags: - generated_from_trainer - text-to-speech model-index: - name: speecht5_finetuned_voxpopuli_cs 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. --> # speecht5_finetuned_voxpopuli_cs This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the facebook/voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 0.4251 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 2 - 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: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.4831 \| 7.14 \| 1000 \| 0.4424 \| \| 0.468 \| 14.27 \| 2000 \| 0.4310 \| \| 0.4568 \| 21.41 \| 3000 \| 0.4267 \| \| 0.4604 \| 28.55 \| 4000 \| 0.4251 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.2 - Datasets 2.14.7 - Tokenizers 0.15.0
ramsi-k/LunarLander-v2-fromscratch	ramsi-k	2024-02-07T09:38:06Z	0	0	null	[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2024-02-07T09:38:01Z	--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -117.87 +/- 48.29 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 50000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'ramsi-k/LunarLander-v2-fromscratch' 'batch_size': 512 'minibatch_size': 128} ```
MaziyarPanahi/mistral-7b-finetuned-orca-dpo-v2-Mistral-7B-Instruct-v0.2-slerp-GGUF	MaziyarPanahi	2024-02-07T09:36:53Z	97	5	transformers	[ "transformers", "gguf", "mistral", "quantized", "2-bit", "3-bit", "4-bit", "5-bit", "6-bit", "8-bit", "GGUF", "safetensors", "text-generation", "merge", "mergekit", "7b", "lazymergekit", "mistralai/Mistral-7B-Instruct-v0.2", "lvkaokao/mistral-7b-finetuned-orca-dpo-v2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us", "base_model:MaziyarPanahi/mistral-7b-finetuned-orca-dpo-v2-Mistral-7B-Instruct-v0.2-slerp", "base_model:quantized:MaziyarPanahi/mistral-7b-finetuned-orca-dpo-v2-Mistral-7B-Instruct-v0.2-slerp", "conversational" ]	text-generation	2024-01-24T13:28:23Z	--- license: apache-2.0 tags: - quantized - 2-bit - 3-bit - 4-bit - 5-bit - 6-bit - 8-bit - GGUF - transformers - safetensors - mistral - text-generation - merge - mergekit - 7b - lazymergekit - mistralai/Mistral-7B-Instruct-v0.2 - lvkaokao/mistral-7b-finetuned-orca-dpo-v2 - license:apache-2.0 - autotrain_compatible - endpoints_compatible - text-generation-inference - region:us model_name: mistral-7b-finetuned-orca-dpo-v2-Mistral-7B-Instruct-v0.2-slerp-GGUF base_model: MaziyarPanahi/mistral-7b-finetuned-orca-dpo-v2-Mistral-7B-Instruct-v0.2-slerp inference: false model_creator: MaziyarPanahi pipeline_tag: text-generation quantized_by: MaziyarPanahi --- # [MaziyarPanahi/mistral-7b-finetuned-orca-dpo-v2-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/mistral-7b-finetuned-orca-dpo-v2-Mistral-7B-Instruct-v0.2-slerp-GGUF) - Model creator: [MaziyarPanahi](https://huggingface.co/MaziyarPanahi) - Original model: [MaziyarPanahi/mistral-7b-finetuned-orca-dpo-v2-Mistral-7B-Instruct-v0.2-slerp](https://huggingface.co/MaziyarPanahi/mistral-7b-finetuned-orca-dpo-v2-Mistral-7B-Instruct-v0.2-slerp) ## Description [MaziyarPanahi/mistral-7b-finetuned-orca-dpo-v2-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/mistral-7b-finetuned-orca-dpo-v2-Mistral-7B-Instruct-v0.2-slerp-GGUF) contains GGUF format model files for [MaziyarPanahi/mistral-7b-finetuned-orca-dpo-v2-Mistral-7B-Instruct-v0.2-slerp](https://huggingface.co/MaziyarPanahi/mistral-7b-finetuned-orca-dpo-v2-Mistral-7B-Instruct-v0.2-slerp). ## How to use Thanks to [TheBloke](https://huggingface.co/TheBloke) for preparing an amazing README on how to use GGUF models: ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. ### Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw ## How to download GGUF files Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: [MaziyarPanahi/mistral-7b-finetuned-orca-dpo-v2-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/mistral-7b-finetuned-orca-dpo-v2-Mistral-7B-Instruct-v0.2-slerp-GGUF) and below it, a specific filename to download, such as: mistral-7b-finetuned-orca-dpo-v2-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download MaziyarPanahi/mistral-7b-finetuned-orca-dpo-v2-Mistral-7B-Instruct-v0.2-slerp-GGUF mistral-7b-finetuned-orca-dpo-v2-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` </details> <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download [MaziyarPanahi/mistral-7b-finetuned-orca-dpo-v2-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/mistral-7b-finetuned-orca-dpo-v2-Mistral-7B-Instruct-v0.2-slerp-GGUF) --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download MaziyarPanahi/mistral-7b-finetuned-orca-dpo-v2-Mistral-7B-Instruct-v0.2-slerp-GGUF mistral-7b-finetuned-orca-dpo-v2-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m mistral-7b-finetuned-orca-dpo-v2-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./mistral-7b-finetuned-orca-dpo-v2-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf", # Download the model file first n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "<\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./mistral-7b-finetuned-orca-dpo-v2-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
hoanghoavienvo/roberta-base-detect-cheapfake-ca1-ca2	hoanghoavienvo	2024-02-07T09:36:29Z	6	0	transformers	[ "transformers", "tensorboard", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/roberta-base", "base_model:finetune:FacebookAI/roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-07T09:32:30Z	--- license: mit base_model: roberta-base tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: roberta-base-detect-cheapfake-ca1-ca2 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. --> # roberta-base-detect-cheapfake-ca1-ca2 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1482 - Accuracy: 0.94 - F1: 0.9450 ## 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: 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 38 \| 0.6724 \| 0.705 \| 0.7807 \| \| No log \| 2.0 \| 76 \| 0.5437 \| 0.925 \| 0.9309 \| \| No log \| 3.0 \| 114 \| 0.1945 \| 0.93 \| 0.9340 \| \| No log \| 4.0 \| 152 \| 0.1559 \| 0.94 \| 0.9444 \| \| No log \| 5.0 \| 190 \| 0.1482 \| 0.94 \| 0.9450 \| ### Framework versions - Transformers 4.36.2 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.15.0
MaziyarPanahi/NyakuraV2.1-m7-Mistral-7B-Instruct-v0.2-slerp-GGUF	MaziyarPanahi	2024-02-07T09:36:23Z	19	0	transformers	[ "transformers", "gguf", "mistral", "quantized", "2-bit", "3-bit", "4-bit", "5-bit", "6-bit", "8-bit", "GGUF", "safetensors", "text-generation", "merge", "mergekit", "7b", "lazymergekit", "mistralai/Mistral-7B-Instruct-v0.2", "Sao10K/NyakuraV2.1-m7", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us", "base_model:MaziyarPanahi/NyakuraV2.1-m7-Mistral-7B-Instruct-v0.2-slerp", "base_model:quantized:MaziyarPanahi/NyakuraV2.1-m7-Mistral-7B-Instruct-v0.2-slerp", "conversational" ]	text-generation	2024-01-24T14:03:24Z	--- license: apache-2.0 tags: - quantized - 2-bit - 3-bit - 4-bit - 5-bit - 6-bit - 8-bit - GGUF - transformers - safetensors - mistral - text-generation - merge - mergekit - 7b - lazymergekit - mistralai/Mistral-7B-Instruct-v0.2 - Sao10K/NyakuraV2.1-m7 - license:apache-2.0 - autotrain_compatible - endpoints_compatible - text-generation-inference - region:us model_name: NyakuraV2.1-m7-Mistral-7B-Instruct-v0.2-slerp-GGUF base_model: MaziyarPanahi/NyakuraV2.1-m7-Mistral-7B-Instruct-v0.2-slerp inference: false model_creator: MaziyarPanahi pipeline_tag: text-generation quantized_by: MaziyarPanahi --- # [MaziyarPanahi/NyakuraV2.1-m7-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/NyakuraV2.1-m7-Mistral-7B-Instruct-v0.2-slerp-GGUF) - Model creator: [MaziyarPanahi](https://huggingface.co/MaziyarPanahi) - Original model: [MaziyarPanahi/NyakuraV2.1-m7-Mistral-7B-Instruct-v0.2-slerp](https://huggingface.co/MaziyarPanahi/NyakuraV2.1-m7-Mistral-7B-Instruct-v0.2-slerp) ## Description [MaziyarPanahi/NyakuraV2.1-m7-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/NyakuraV2.1-m7-Mistral-7B-Instruct-v0.2-slerp-GGUF) contains GGUF format model files for [MaziyarPanahi/NyakuraV2.1-m7-Mistral-7B-Instruct-v0.2-slerp](https://huggingface.co/MaziyarPanahi/NyakuraV2.1-m7-Mistral-7B-Instruct-v0.2-slerp). ## How to use Thanks to [TheBloke](https://huggingface.co/TheBloke) for preparing an amazing README on how to use GGUF models: ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. ### Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw ## How to download GGUF files Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: [MaziyarPanahi/NyakuraV2.1-m7-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/NyakuraV2.1-m7-Mistral-7B-Instruct-v0.2-slerp-GGUF) and below it, a specific filename to download, such as: NyakuraV2.1-m7-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download MaziyarPanahi/NyakuraV2.1-m7-Mistral-7B-Instruct-v0.2-slerp-GGUF NyakuraV2.1-m7-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` </details> <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download [MaziyarPanahi/NyakuraV2.1-m7-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/NyakuraV2.1-m7-Mistral-7B-Instruct-v0.2-slerp-GGUF) --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download MaziyarPanahi/NyakuraV2.1-m7-Mistral-7B-Instruct-v0.2-slerp-GGUF NyakuraV2.1-m7-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m NyakuraV2.1-m7-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./NyakuraV2.1-m7-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf", # Download the model file first n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "<\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./NyakuraV2.1-m7-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
CLMBR/det-noun-lstm-1	CLMBR	2024-02-07T09:28:50Z	1	0	transformers	[ "transformers", "pytorch", "rnn", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2024-02-01T11:59:17Z	--- tags: - generated_from_trainer model-index: - name: det-noun-lstm-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. --> # det-noun-lstm-1 This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.9717 ## 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: 1 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 3052726 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-------:\|:---------------:\| \| 4.8048 \| 0.03 \| 76320 \| 4.7692 \| \| 4.5159 \| 1.03 \| 152640 \| 4.4852 \| \| 4.3691 \| 0.03 \| 228960 \| 4.3476 \| \| 4.2797 \| 1.03 \| 305280 \| 4.2637 \| \| 4.2204 \| 0.03 \| 381600 \| 4.2065 \| \| 4.1733 \| 1.03 \| 457920 \| 4.1648 \| \| 4.1326 \| 0.03 \| 534240 \| 4.1336 \| \| 4.0967 \| 1.03 \| 610560 \| 4.1082 \| \| 4.0679 \| 0.03 \| 686880 \| 4.0879 \| \| 4.0421 \| 1.03 \| 763200 \| 4.0721 \| \| 4.0218 \| 0.03 \| 839520 \| 4.0580 \| \| 4.0062 \| 1.03 \| 915840 \| 4.0475 \| \| 3.9891 \| 0.03 \| 992160 \| 4.0381 \| \| 3.9682 \| 0.03 \| 1068480 \| 4.0299 \| \| 3.9583 \| 1.03 \| 1144800 \| 4.0224 \| \| 3.9536 \| 0.03 \| 1221120 \| 4.0173 \| \| 3.9398 \| 1.03 \| 1297440 \| 4.0119 \| \| 3.9296 \| 0.03 \| 1373760 \| 4.0071 \| \| 3.9182 \| 1.03 \| 1450080 \| 4.0036 \| \| 3.9138 \| 0.03 \| 1526400 \| 4.0002 \| \| 3.9124 \| 1.03 \| 1602720 \| 3.9966 \| \| 3.9072 \| 0.03 \| 1679040 \| 3.9941 \| \| 3.9015 \| 1.03 \| 1755360 \| 3.9915 \| \| 3.8912 \| 0.03 \| 1831680 \| 3.9895 \| \| 3.8851 \| 1.03 \| 1908000 \| 3.9876 \| \| 3.8767 \| 0.03 \| 1984320 \| 3.9853 \| \| 3.8708 \| 0.03 \| 2060640 \| 3.9833 \| \| 3.8676 \| 1.03 \| 2136960 \| 3.9817 \| \| 3.8631 \| 0.03 \| 2213280 \| 3.9802 \| \| 3.8513 \| 1.03 \| 2289600 \| 3.9791 \| \| 3.8494 \| 0.03 \| 2365920 \| 3.9776 \| \| 3.8548 \| 1.03 \| 2442240 \| 3.9767 \| \| 3.8471 \| 0.03 \| 2518560 \| 3.9757 \| \| 3.8443 \| 0.03 \| 2594880 \| 3.9748 \| \| 3.8389 \| 1.03 \| 2671200 \| 3.9741 \| \| 3.8405 \| 0.03 \| 2747520 \| 3.9735 \| \| 3.8435 \| 1.03 \| 2823840 \| 3.9728 \| \| 3.844 \| 0.03 \| 2900160 \| 3.9724 \| \| 3.8434 \| 0.03 \| 2976480 \| 3.9719 \| \| 3.8385 \| 0.02 \| 3052726 \| 3.9717 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.13.3
EnDevSols/tinyllama-3T-64k-JSONExtractor	EnDevSols	2024-02-07T09:27:43Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-07T09:26:58Z	--- 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]
JackCloudman/Senku-70B-Full-exl2-3.5bpw	JackCloudman	2024-02-07T09:27:26Z	7	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:cc-by-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-07T08:04:36Z	--- license: cc-by-2.0 --- Finetune of miqu-70b-sf dequant of miqudev's leak of Mistral-70B (allegedly an early mistral medium). My diffs are available under CC-0, this is a merge with the leaked model, you can use the other repository to save bandwidth. EQ-Bench: 84.89 Will run more benches later.
yeye776/OndeviceAI-base-v2	yeye776	2024-02-07T09:18:42Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:paust/pko-t5-base", "base_model:finetune:paust/pko-t5-base", "license:cc-by-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-07T09:18:11Z	--- license: cc-by-4.0 base_model: paust/pko-t5-base tags: - generated_from_trainer model-index: - name: OndeviceAI-base-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. --> # OndeviceAI-base-v2 This model is a fine-tuned version of [paust/pko-t5-base](https://huggingface.co/paust/pko-t5-base) 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: 0.0007 - 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: 0.06 - num_epochs: 20 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
Artefact2/Gembo-v1-70b-GGUF	Artefact2	2024-02-07T09:11:38Z	20	6	null	[ "gguf", "en", "license:llama2", "endpoints_compatible", "region:us" ]	null	2024-02-07T04:55:10Z	--- license: llama2 language: - en --- These are GGUF quantized versions of [ChuckMcSneed/Gembo-v1-70b](https://huggingface.co/ChuckMcSneed/Gembo-v1-70b). The importance matrix was trained for 100K tokens (200 batches of 512 tokens) using `wiki.train.raw`. The IQ2_XXS and IQ2_XS versions are compatible with llama.cpp, version `147b17a` or later. The IQ3_XXS requires version `f4d7e54` or later. Some model files above 50GB are split into smaller files. To concatenate them, use the `cat` command (on Windows, use PowerShell): `cat foo-Q6_K.gguf.* > foo-Q6_K.gguf`
phamtungthuy/law_model_merged	phamtungthuy	2024-02-07T09:07:12Z	5	0	transformers	[ "transformers", "safetensors", "mpt", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-07T09:05:02Z	--- 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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phamtungthuy/quantized_law_model_merged	phamtungthuy	2024-02-07T09:02:02Z	4	0	transformers	[ "transformers", "safetensors", "mpt", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-02-07T09:01:16Z	--- 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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Wembo/ppo-self-LunarLander-v2	Wembo	2024-02-07T09:01:30Z	0	0	null	[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2024-02-07T08:45:25Z	--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 20.77 +/- 54.16 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 500000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'Wembo/ppo-self-LunarLander-v2' 'batch_size': 512 'minibatch_size': 128} ```
varun-v-rao/roberta-base-bn-adapter-895K-snli-model2	varun-v-rao	2024-02-07T08:56:59Z	0	0	null	[ "tensorboard", "generated_from_trainer", "base_model:FacebookAI/roberta-base", "base_model:finetune:FacebookAI/roberta-base", "license:mit", "region:us" ]	null	2024-02-07T08:09:03Z	--- license: mit base_model: roberta-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: roberta-base-bn-adapter-895K-snli-model2 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. --> # roberta-base-bn-adapter-895K-snli-model2 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7648 - Accuracy: 0.7315 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.4332 \| 1.0 \| 8584 \| 0.3469 \| 0.8699 \| \| 0.4008 \| 2.0 \| 17168 \| 0.3200 \| 0.8780 \| \| 0.3889 \| 3.0 \| 25752 \| 0.3143 \| 0.8805 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
mtgv/MobileVLM_V2-7B	mtgv	2024-02-07T08:55:39Z	106	5	transformers	[ "transformers", "pytorch", "mobilevlm", "text-generation", "MobileVLM V2", "arxiv:2402.03766", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-02-06T09:16:05Z	--- license: apache-2.0 tags: - MobileVLM V2 --- ## Model Summery MobileVLM V2 is a family of significantly improved vision language models upon MobileVLM, which proves that a delicate orchestration of novel architectural design, an improved training scheme tailored for mobile VLMs, and rich high-quality dataset curation can substantially benefit VLMs’ performance. Specifically, MobileVLM V2 1.7B achieves better or on-par performance on standard VLM benchmarks compared with much larger VLMs at the 3B scale. Notably, MobileVLM_V2-3B model outperforms a large variety of VLMs at the 7B+ scale. The MobileVLM_V2-7B was built on [Vicuna-7B-v1.5](https://huggingface.co/lmsys/vicuna-7b-v1.5) to facilitate the off-the-shelf deployment. ## Model Sources - Repository: https://github.com/Meituan-AutoML/MobileVLM - Paper: [MobileVLM V2: Faster and Stronger Baseline for Vision Language Model](https://arxiv.org/abs/2402.03766) ## How to Get Started with the Model Inference examples can be found at [Github](https://github.com/Meituan-AutoML/MobileVLM).
varun-v-rao/opt-1.3b-lora-3.15M-snli-model3	varun-v-rao	2024-02-07T08:47:47Z	4	0	transformers	[ "transformers", "tensorboard", "safetensors", "opt", "text-classification", "generated_from_trainer", "base_model:facebook/opt-1.3b", "base_model:finetune:facebook/opt-1.3b", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-02-07T02:16:53Z	--- license: other base_model: facebook/opt-1.3b tags: - generated_from_trainer metrics: - accuracy model-index: - name: opt-1.3b-lora-3.15M-snli-model3 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. --> # opt-1.3b-lora-3.15M-snli-model3 This model is a fine-tuned version of [facebook/opt-1.3b](https://huggingface.co/facebook/opt-1.3b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6832 - Accuracy: 0.761 ## 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: 128 - eval_batch_size: 128 - seed: 49 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.3553 \| 1.0 \| 4292 \| 0.2816 \| 0.8942 \| \| 0.3227 \| 2.0 \| 8584 \| 0.2643 \| 0.9043 \| \| 0.3151 \| 3.0 \| 12876 \| 0.2574 \| 0.9076 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
mikeee/phi-2-ft-evol-instruct-chinese-gpt4	mikeee	2024-02-07T08:33:08Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-07T08:33:07Z	--- 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. 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muzammil-eds/tinyllama-3T-64k-JSONExtractor-v4	muzammil-eds	2024-02-07T08:22:45Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-07T08:21:55Z	--- 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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Aanshula/layoutlm-funsd-tf	Aanshula	2024-02-07T08:10:23Z	46	0	transformers	[ "transformers", "tf", "tensorboard", "layoutlm", "token-classification", "generated_from_keras_callback", "base_model:microsoft/layoutlm-base-uncased", "base_model:finetune:microsoft/layoutlm-base-uncased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-02-06T05:06:14Z	--- license: mit base_model: microsoft/layoutlm-base-uncased tags: - generated_from_keras_callback model-index: - name: Aanshula/layoutlm-funsd-tf results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # Aanshula/layoutlm-funsd-tf This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3182 - Validation Loss: 0.6807 - Train Overall Precision: 0.7172 - Train Overall Recall: 0.7878 - Train Overall F1: 0.7508 - Train Overall Accuracy: 0.7864 - Epoch: 6 ## 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: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 3e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Train Overall Precision \| Train Overall Recall \| Train Overall F1 \| Train Overall Accuracy \| Epoch \| \|:----------:\|:---------------:\|:-----------------------:\|:--------------------:\|:----------------:\|:----------------------:\|:-----:\| \| 1.7000 \| 1.4167 \| 0.2445 \| 0.2107 \| 0.2264 \| 0.4831 \| 0 \| \| 1.1656 \| 0.8677 \| 0.5749 \| 0.6257 \| 0.5992 \| 0.7251 \| 1 \| \| 0.7704 \| 0.7254 \| 0.6356 \| 0.7160 \| 0.6734 \| 0.7637 \| 2 \| \| 0.5758 \| 0.6690 \| 0.6851 \| 0.7476 \| 0.7150 \| 0.7857 \| 3 \| \| 0.4526 \| 0.6096 \| 0.7085 \| 0.7757 \| 0.7406 \| 0.8046 \| 4 \| \| 0.3614 \| 0.6834 \| 0.7118 \| 0.7657 \| 0.7377 \| 0.7872 \| 5 \| \| 0.3182 \| 0.6807 \| 0.7172 \| 0.7878 \| 0.7508 \| 0.7864 \| 6 \| ### Framework versions - Transformers 4.35.2 - TensorFlow 2.15.0 - Datasets 2.16.1 - Tokenizers 0.15.1
varun-v-rao/roberta-base-bn-adapter-895K-snli-model1	varun-v-rao	2024-02-07T08:09:01Z	0	0	null	[ "tensorboard", "generated_from_trainer", "base_model:FacebookAI/roberta-base", "base_model:finetune:FacebookAI/roberta-base", "license:mit", "region:us" ]	null	2024-02-06T04:35:02Z	--- license: mit base_model: roberta-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: roberta-base-bn-adapter-895K-snli-model1 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. --> # roberta-base-bn-adapter-895K-snli-model1 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7623 - Accuracy: 0.728 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 61 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.4254 \| 1.0 \| 8584 \| 0.3365 \| 0.8722 \| \| 0.4021 \| 2.0 \| 17168 \| 0.3165 \| 0.8790 \| \| 0.3806 \| 3.0 \| 25752 \| 0.3115 \| 0.8817 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
empty-michael/tinystories_1layer_attn_mlp_C10k_k100	empty-michael	2024-02-07T08:05:58Z	9	0	transformers	[ "transformers", "safetensors", "codebook", "generated_from_trainer", "dataset:roneneldan/TinyStories", "base_model:roneneldan/TinyStories-1Layer-21M", "base_model:finetune:roneneldan/TinyStories-1Layer-21M", "model-index", "endpoints_compatible", "region:us" ]	null	2024-02-07T04:43:01Z	--- base_model: roneneldan/TinyStories-1Layer-21M tags: - generated_from_trainer datasets: - roneneldan/TinyStories metrics: - accuracy model-index: - name: tinystories_1layer_attn_mlp_C10k_k100 results: - task: name: Causal Language Modeling type: text-generation dataset: name: roneneldan/TinyStories type: roneneldan/TinyStories metrics: - name: Accuracy type: accuracy value: 0.5429091526514649 --- <!-- 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. --> # tinystories_1layer_attn_mlp_C10k_k100 This model is a fine-tuned version of [roneneldan/TinyStories-1Layer-21M](https://huggingface.co/roneneldan/TinyStories-1Layer-21M) on the roneneldan/TinyStories dataset. It achieves the following results on the evaluation set: - Loss: 1.8957 - Accuracy: 0.5429 - Multicode K: 1 - Dead Code Fraction/layer0: 0.0 - Mse/layer0: 611.1572 - Input Norm/layer0: 31.9975 - Output Norm/layer0: 15.0872 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 6 - total_train_batch_size: 48 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Multicode K \| Dead Code Fraction/layer0 \| Mse/layer0 \| Input Norm/layer0 \| Output Norm/layer0 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:-----------:\|:-------------------------:\|:----------:\|:-----------------:\|:------------------:\| \| 2.5072 \| 0.05 \| 500 \| 2.4764 \| 0.4579 \| 1 \| 0.0 \| 841.1602 \| 31.9977 \| 4.9114 \| \| 2.2285 \| 0.1 \| 1000 \| 2.2265 \| 0.4926 \| 1 \| 0.0 \| 792.3023 \| 31.9980 \| 7.5524 \| \| 2.1472 \| 0.16 \| 1500 \| 2.1584 \| 0.5025 \| 1 \| 0.0 \| 761.8683 \| 31.9980 \| 8.9239 \| \| 2.1144 \| 0.21 \| 2000 \| 2.1128 \| 0.5090 \| 1 \| 0.0 \| 737.1843 \| 31.9979 \| 9.8992 \| \| 2.0847 \| 0.26 \| 2500 \| 2.0791 \| 0.5142 \| 1 \| 0.0 \| 716.9390 \| 31.9979 \| 10.6577 \| \| 2.0439 \| 0.31 \| 3000 \| 2.0482 \| 0.5185 \| 1 \| 0.0 \| 698.7266 \| 31.9979 \| 11.3599 \| \| 2.0263 \| 0.37 \| 3500 \| 2.0253 \| 0.5224 \| 1 \| 0.0 \| 682.2680 \| 31.9979 \| 12.0105 \| \| 1.9906 \| 0.42 \| 4000 \| 2.0066 \| 0.5253 \| 1 \| 0.0 \| 669.1965 \| 31.9979 \| 12.5568 \| \| 1.9852 \| 0.47 \| 4500 \| 1.9898 \| 0.5279 \| 1 \| 0.0 \| 657.5872 \| 31.9979 \| 13.0526 \| \| 1.9687 \| 0.52 \| 5000 \| 1.9757 \| 0.5300 \| 1 \| 0.0 \| 648.2462 \| 31.9979 \| 13.4496 \| \| 1.9672 \| 0.57 \| 5500 \| 1.9620 \| 0.5321 \| 1 \| 0.0 \| 640.0822 \| 31.9978 \| 13.8078 \| \| 1.9441 \| 0.63 \| 6000 \| 1.9513 \| 0.5339 \| 1 \| 0.0 \| 633.8831 \| 31.9978 \| 14.1018 \| \| 1.9408 \| 0.68 \| 6500 \| 1.9397 \| 0.5358 \| 1 \| 0.0 \| 628.0929 \| 31.9977 \| 14.3550 \| \| 1.9256 \| 0.73 \| 7000 \| 1.9302 \| 0.5374 \| 1 \| 0.0 \| 623.2726 \| 31.9977 \| 14.5534 \| \| 1.9204 \| 0.78 \| 7500 \| 1.9225 \| 0.5381 \| 1 \| 0.0 \| 619.4573 \| 31.9977 \| 14.7258 \| \| 1.907 \| 0.84 \| 8000 \| 1.9150 \| 0.5393 \| 1 \| 0.0 \| 616.4379 \| 31.9976 \| 14.8625 \| \| 1.8931 \| 0.89 \| 8500 \| 1.9076 \| 0.5408 \| 1 \| 0.0 \| 613.7874 \| 31.9976 \| 14.9685 \| \| 1.9021 \| 0.94 \| 9000 \| 1.9021 \| 0.5417 \| 1 \| 0.0 \| 612.0126 \| 31.9975 \| 15.0379 \| \| 1.8967 \| 0.99 \| 9500 \| 1.8970 \| 0.5426 \| 1 \| 0.0 \| 610.6121 \| 31.9975 \| 15.0932 \| \| 1.8942 \| 1.04 \| 10000 \| 1.8957 \| 0.5429 \| 1 \| 0.0 \| 611.1572 \| 31.9975 \| 15.0872 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
yeye776/OndeviceAI-base-v1	yeye776	2024-02-07T07:40:41Z	4	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:paust/pko-t5-base", "base_model:finetune:paust/pko-t5-base", "license:cc-by-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-07T07:40:06Z	--- license: cc-by-4.0 base_model: paust/pko-t5-base tags: - generated_from_trainer model-index: - name: OndeviceAI-base-v1 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. --> # OndeviceAI-base-v1 This model is a fine-tuned version of [paust/pko-t5-base](https://huggingface.co/paust/pko-t5-base) 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: 0.0007 - 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: 0.06 - num_epochs: 20 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
omartariq612/quran-lora-whisper-medium-epoch-1	omartariq612	2024-02-07T07:40:02Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-07T07:39:58Z	--- 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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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]
chenhaodev/mistral-7b-ocn-v2	chenhaodev	2024-02-07T07:22:09Z	0	0	peft	[ "peft", "safetensors", "llama-factory", "lora", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:other", "region:us" ]	null	2024-02-07T07:07:17Z	--- license: other library_name: peft tags: - llama-factory - lora - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 model-index: - name: mistral-7b-ocn-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. --> # mistral-7b-ocn-v2 This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the oncc_medqa_instruct dataset. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 1.0 - mixed_precision_training: Native AMP ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.1.1+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1 ### Performance hf (pretrained=mistralai/Mistral-7B-v0.1,parallelize=True,load_in_4bit=True,peft=chenhugging/mistral-7b-ocn-v2), gen_kwargs: (None), limit: 100.0, num_fewshot: None, batch_size: 1 \| Tasks \|Version\|Filter\|n-shot\| Metric \|Value\| \|Stderr\| \|---------------------\|-------\|------\|-----:\|--------\|----:\|---\|-----:\| \|pubmedqa \| 1\|none \| 0\|acc \| 0.98\|± \|0.0141\| \|medmcqa \|Yaml \|none \| 0\|acc \| 0.40\|± \|0.0492\| \|professional_medicine\| 0\|none \| 0\|acc \| 0.69\|± \|0.0465\| \|college_medicine \| 0\|none \| 0\|acc \| 0.53\|± \|0.0502\| \|clinical_knowledge \| 0\|none \| 0\|acc \| 0.59\|± \|0.0494\| \|ocn \|Yaml \|none \| 0\|acc \| 0.80\|± \|0.0402\| \|aocnp \|Yaml \|none \| 0\|acc \| 0.63\|± \|0.0485\|
TooMuchInfo/LeerdoelenGPT	TooMuchInfo	2024-02-07T07:21:36Z	0	0	null	[ "education", "nl", "region:us" ]	null	2024-02-07T07:21:03Z	--- language: - nl tags: - education ---
areegtarek/patientcommunication-8bit	areegtarek	2024-02-07T07:17:24Z	4	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]	text-generation	2024-02-07T07:13:36Z	--- 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]
ergh0/q-FrozenLake-v1-4x4-noSlippery	ergh0	2024-02-07T07:15:43Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-02-07T07:11:23Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="ergh0/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
BanglaLLM/bangla-llama-13b-base-v0.1	BanglaLLM	2024-02-07T07:13:42Z	163	6	transformers	[ "transformers", "pytorch", "llama", "text-generation", "bn", "en", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-07T07:04:13Z	--- language: - bn - en license: llama2 --- # Bangla LLaMA 13B Base v0.1 [pre-trained] Welcome to the inaugural release of the Bangla LLaMA 13B base model – an important step in advancing LLMs for the Bangla language. This model is ready for immediate inference and is also primed for further fine-tuning to cater to your specific NLP tasks. > Please Note: This model, labeled as a foundational Bangla Language Model (LLM), is designed primarily for Causal Language Modeling (LM) purposes. In other words, if you are looking for an instruction following model in Bangla, you may find [BanglaLLM/bangla-llama-13b-instruct-v0.1](https://huggingface.co/BanglaLLM/bangla-llama-13b-instruct-v0.1) more suitable for your needs. ## Model description The Bangla LLaMA models have been enhanced and tailored specifically with an extensive Bangla vocabulary of 16,000 tokens, building upon the foundation set by the original LLaMA-2. - Model type: A 13B parameter model for Causal LM pre-trained on [CulturaX](https://huggingface.co/datasets/uonlp/CulturaX) dataset's Bangla subset. - Language(s): Bangla and English - License: GNU General Public License v3.0 - Source Model: [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) - Training Precision: `float16` - Code: [GitHub](https://github.com/abhinand5/bangla-llama) ## Related Models \| Model \| Type \| Data \| Base Model \| # Params \| Download Links \| \|--------------------------\|-----------------------------\|-------------------\|----------------------\|------\|------------------------------------------------------------------------\| \| Bangla LLaMA 7B Base \| Base model \| 12GB \| LLaMA 7B \| 7B \| [HF Hub](https://huggingface.co/BanglaLLM/bangla-llama-7b-base-v0.1) \| \| Bangla LLaMA 13B Base \| Base model \| 4GB \| LLaMA 13B \| 13B \| [HF Hub](https://huggingface.co/BanglaLLM/bangla-llama-13b-base-v0.1) \| \| Bangla LLaMA 7B Instruct \| Instruction following model \| 145k instructions \| Bangla LLaMA 7B Base \| 7B \| [HF Hub](https://huggingface.co/BanglaLLM/bangla-llama-7b-instruct-v0.1) \| \| Bangla LLaMA 13B Instruct \| Instruction following model \| 145k instructions \| Bangla LLaMA 13B Base \| 13B \| [HF Hub](BanglaLLM/bangla-llama-13b-instruct-v0.1) \| ## Usage Note It's important to note that the models have not undergone detoxification. Therefore, while they possess impressive linguistic capabilities, there is a possibility for them to generate content that could be deemed harmful or offensive. We urge users to exercise discretion and supervise the model's outputs closely, especially in public or sensitive applications. ## Meet the Developers Get to know the creators behind this innovative model and follow their contributions to the field: - [Abdullah Khan Zehady](https://www.linkedin.com/in/abdullah-khan-zehady-915ba024/) ## Citation We hope this model serves as a valuable tool in your NLP toolkit and look forward to seeing the advancements it will enable in the understanding and generation of the Bangla language.
varun-v-rao/bert-large-cased-bn-adapter-3.17M-snli-model3	varun-v-rao	2024-02-07T07:12:22Z	0	0	null	[ "tensorboard", "generated_from_trainer", "base_model:google-bert/bert-large-cased", "base_model:finetune:google-bert/bert-large-cased", "license:apache-2.0", "region:us" ]	null	2024-02-07T04:47:02Z	--- license: apache-2.0 base_model: bert-large-cased tags: - generated_from_trainer metrics: - accuracy model-index: - name: bert-large-cased-bn-adapter-3.17M-snli-model3 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-large-cased-bn-adapter-3.17M-snli-model3 This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7627 - Accuracy: 0.7315 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 61 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.4101 \| 1.0 \| 8584 \| 0.3392 \| 0.8718 \| \| 0.3707 \| 2.0 \| 17168 \| 0.3116 \| 0.8842 \| \| 0.3628 \| 3.0 \| 25752 \| 0.3035 \| 0.8879 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
rhplus0831/maid-yuzu-v5-mix-exl2-6.0bpw-rpcal	rhplus0831	2024-02-07T07:08:45Z	5	0	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "mergekit", "merge", "conversational", "base_model:smelborp/MixtralOrochi8x7B", "base_model:finetune:smelborp/MixtralOrochi8x7B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-07T07:01:47Z	--- base_model: - smelborp/MixtralOrochi8x7B library_name: transformers tags: - mergekit - merge --- # maid-yuzu-v5-mix This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). This model was created because I was curious about whether the 8X7B model created randomly by the user would be merged with other existing 8x7b models. ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * ../maid-yuzu-v5 * [smelborp/MixtralOrochi8x7B](https://huggingface.co/smelborp/MixtralOrochi8x7B) ### Configuration The following YAML configuration was used to produce this model: ```yaml base_model: model: path: ../maid-yuzu-v5 dtype: bfloat16 merge_method: slerp parameters: t: - value: 0.5 slices: - sources: - layer_range: [0, 32] model: model: path: smelborp/MixtralOrochi8x7B - layer_range: [0, 32] model: model: path: ../maid-yuzu-v5 ```
huolongguo10/LLM_detect	huolongguo10	2024-02-07T07:06:11Z	12	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-05T13:19:11Z	--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to detect text that was generated by LLMs. ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: huolongguo10 - Model type: bert - Language(s) (NLP): Chinese - License: [More Information Needed] - Finetuned from model [optional]: bert-base-chinese ### 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. --> ```python from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("huolongguo10/LLM_detect") model = AutoModelForMaskedLM.from_pretrained("huolongguo10/LLM_detect") ``` ## 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. --> ### 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: fp32 <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> ## 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: P100 - Hours used: 4h - Cloud Provider: kaggle ## Technical Specifications [optional] ### Model Architecture and Objective bert ### Compute Infrastructure [More Information Needed] #### Hardware P100 #### Software transformers ## 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]
psyferpunk/mine	psyferpunk	2024-02-07T07:05:01Z	0	0	bertopic	[ "bertopic", "aa", "dataset:HuggingFaceM4/WebSight", "license:mit", "region:us" ]	null	2024-02-07T07:04:05Z	--- license: mit datasets: - HuggingFaceM4/WebSight language: - aa metrics: - accuracy library_name: bertopic ---
humung/koalpaca-polyglot-12.8B-ia3-vlending-v0.1	humung	2024-02-07T06:59:21Z	1	0	peft	[ "peft", "arxiv:1910.09700", "base_model:beomi/KoAlpaca-Polyglot-12.8B", "base_model:adapter:beomi/KoAlpaca-Polyglot-12.8B", "region:us" ]	null	2024-02-07T06:59:19Z	--- library_name: peft base_model: beomi/KoAlpaca-Polyglot-12.8B --- # 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 0.7.2.dev0
Pranav-10/Sentiment_analysis	Pranav-10	2024-02-07T06:53:03Z	61	1	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-07T06:08:21Z	--- license: apache-2.0 --- # Sentiment Analysis Model using DistilBERT This repository hosts a sentiment analysis model fine-tuned on the IMDb movie reviews dataset using DistilBERT architecture. It's designed to classify text inputs into positive or negative sentiment categories. ## Model Description The model is based on the DistilBERT architecture, a smaller, faster, cheaper, and lighter version of BERT. It has been fine-tuned on the IMDb dataset, which consists of 50,000 movie reviews labeled as positive or negative. DistilBERT has been proven to retain most of the performance of BERT while being more efficient. This makes it an excellent choice for sentiment analysis tasks where the model's size and speed are essential. ## How to Use To use the model, you will need to install the `transformers` library from Hugging Face. You can install it using pip: pip install transformers Once installed, you can use the following code to classify text using this model: from transformers import DistilBertTokenizer, DistilBertForSequenceClassification import torch # Load the tokenizer and model from the Hugging Face Hub tokenizer = DistilBertTokenizer.from_pretrained(Pranav-10/Sentimental_Analysis) model = DistilBertForSequenceClassification.from_pretrained(Pranav-10/Sentimental_Analysis) # Example text text = "I loved this movie. The performances were fantastic!" # Tokenize text and convert to tensor inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512) # Predict sentiment with torch.no_grad(): logits = model(**inputs).logits # Convert logits to probabilities using softmax probabilities = torch.softmax(logits, dim=-1) # Output the result print(probabilities) Evaluation Results The model achieved the following performance on the IMDb dataset: Accuracy: 90% Precision: 89% Recall: 91% F1 Score: 90% These results indicate the model's high efficiency in classifying sentiments as positive or negative. Training Procedure The model was trained using the following procedure: Pre-processing: The dataset was pre-processed by converting all reviews to lowercase and tokenizing using the DistilBERT tokenizer. Optimization: We used the Adam optimizer with a learning rate of 2e-5, a batch size of 16, and trained the model for 3 epochs. Hardware: Training was performed on a single NVIDIA GTX 1650 GPU.
EricValen/ppo-LunarLander-v2	EricValen	2024-02-07T06:18:51Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-02-07T06:18:24Z	--- 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: 270.77 +/- 22.88 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 ... ```
danaleee/dog	danaleee	2024-02-07T06:16:25Z	1	0	diffusers	[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:CompVis/stable-diffusion-v1-4", "base_model:adapter:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-02-07T05:48:21Z	--- license: creativeml-openrail-m base_model: CompVis/stable-diffusion-v1-4 instance_prompt: a photo of sks dog tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - danaleee/dog These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) LoRA for the text encoder was enabled: False.
yaneq/jan_zdRM8UdoamtJ6kdZKNKS_SDXL_LoRA_700_9d94_700_1e4	yaneq	2024-02-07T06:10:46Z	1	1	diffusers	[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-02-07T06:10:43Z	--- tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of MDDL man license: openrail++ --- # SDXL LoRA DreamBooth - yaneq/jan_zdRM8UdoamtJ6kdZKNKS_SDXL_LoRA_700_9d94_700_1e4 <Gallery /> ## Model description These are yaneq/jan_zdRM8UdoamtJ6kdZKNKS_SDXL_LoRA_700_9d94_700_1e4 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of MDDL man to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](yaneq/jan_zdRM8UdoamtJ6kdZKNKS_SDXL_LoRA_700_9d94_700_1e4/tree/main) them in the Files & versions tab. ## Training properties - max_train_steps: 700 - learning_rate: 0.0001 - base_model_name: stabilityai/stable-diffusion-xl-base-1.0 - class_name: man - training_images_urls: - https://firebasestorage.googleapis.com/v0/b/axonic-looks.appspot.com/o/models%2FSBGA9KzaKdSZWWzsvHMP%2FSBGA9KzaKdSZWWzsvHMP%2FWF2NGBPUFgu9eyaCYAwB.jpg?alt=media&token=97c1e215-0a96-4fdf-b292-9ee0e497ba72 - https://firebasestorage.googleapis.com/v0/b/axonic-looks.appspot.com/o/models%2FSBGA9KzaKdSZWWzsvHMP%2FSBGA9KzaKdSZWWzsvHMP%2Fcn54hvM4ahi3MzpCQN5D.jpg?alt=media&token=e096f4dc-e7c5-4e14-88fc-a5562d103127 - https://firebasestorage.googleapis.com/v0/b/axonic-looks.appspot.com/o/models%2FSBGA9KzaKdSZWWzsvHMP%2FSBGA9KzaKdSZWWzsvHMP%2Fz8D9WdMIx4mXcsDGAZm4.jpg?alt=media&token=fded9422-eb7c-4757-8c1f-cb436a348579 - https://firebasestorage.googleapis.com/v0/b/axonic-looks.appspot.com/o/models%2FSBGA9KzaKdSZWWzsvHMP%2FSBGA9KzaKdSZWWzsvHMP%2F6JW19SVZPczh5B2DEqKD.jpg?alt=media&token=0e0dc94f-957d-4b51-8979-0216c0849cf6 - https://firebasestorage.googleapis.com/v0/b/axonic-looks.appspot.com/o/models%2FSBGA9KzaKdSZWWzsvHMP%2FSBGA9KzaKdSZWWzsvHMP%2FDAk5k1hGzP9q9y0jpGoO.jpg?alt=media&token=01ed67d1-938a-4f60-bc1a-e1b91412b97e - https://firebasestorage.googleapis.com/v0/b/axonic-looks.appspot.com/o/models%2FSBGA9KzaKdSZWWzsvHMP%2FSBGA9KzaKdSZWWzsvHMP%2F82McawlxnTeA2vBc4bZg.jpg?alt=media&token=f7cfacb2-2186-4005-9211-b7ef762dafad - https://firebasestorage.googleapis.com/v0/b/axonic-looks.appspot.com/o/models%2FSBGA9KzaKdSZWWzsvHMP%2FSBGA9KzaKdSZWWzsvHMP%2FY7nFiafx8co1nK6cnjWJ.jpg?alt=media&token=a1fe8c9a-4d5e-4043-9a82-9304fd430569 - https://firebasestorage.googleapis.com/v0/b/axonic-looks.appspot.com/o/models%2FSBGA9KzaKdSZWWzsvHMP%2FSBGA9KzaKdSZWWzsvHMP%2FVYOVRhojKt30NzjWRXL0.jpg?alt=media&token=5a3a2afb-4b83-4488-92e5-6651f5173cc0 - gradient_accumulation_steps: 3 - GPU: T4 - duration: 5284.340887546539
saraswathi01/a2c-PandaPickAndPlace-v3	saraswathi01	2024-02-07T06:10:16Z	0	0	stable-baselines3	[ "stable-baselines3", "PandaPickAndPlace-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-02-07T06:06:06Z	--- library_name: stable-baselines3 tags: - PandaPickAndPlace-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaPickAndPlace-v3 type: PandaPickAndPlace-v3 metrics: - type: mean_reward value: -50.00 +/- 0.00 name: mean_reward verified: false --- # A2C Agent playing PandaPickAndPlace-v3 This is a trained model of a A2C agent playing PandaPickAndPlace-v3 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 ... ```
VishalMishraTss/deit-base-patch16-224-finetuned-ind-14-imbalanced-pan-10847-train	VishalMishraTss	2024-02-07T06:08:11Z	6	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:facebook/deit-base-patch16-224", "base_model:finetune:facebook/deit-base-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-02-07T05:07:47Z	--- license: apache-2.0 base_model: facebook/deit-base-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy - recall - f1 - precision model-index: - name: deit-base-patch16-224-finetuned-ind-14-imbalanced-pan-10847-train results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.8703170028818443 - name: Recall type: recall value: 0.8703170028818443 - name: F1 type: f1 value: 0.8411548955923809 - name: Precision type: precision value: 0.8252839064351536 --- <!-- 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. --> # deit-base-patch16-224-finetuned-ind-14-imbalanced-pan-10847-train This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.4660 - Accuracy: 0.8703 - Recall: 0.8703 - F1: 0.8412 - Precision: 0.8253 ## 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 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Recall \| F1 \| Precision \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:------:\|:---------:\| \| 0.7292 \| 0.99 \| 43 \| 0.6759 \| 0.7925 \| 0.7925 \| 0.7582 \| 0.7420 \| \| 0.5224 \| 2.0 \| 87 \| 0.5146 \| 0.8501 \| 0.8501 \| 0.8228 \| 0.8057 \| \| 0.5103 \| 2.97 \| 129 \| 0.4916 \| 0.8674 \| 0.8674 \| 0.8391 \| 0.8244 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
ZiHDeng/peft-lora-starcoder1B-Instruction-ny8-ALL	ZiHDeng	2024-02-07T06:07:53Z	5	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:bigcode/starcoderbase-1b", "base_model:adapter:bigcode/starcoderbase-1b", "license:bigcode-openrail-m", "region:us" ]	null	2024-02-07T03:55:10Z	--- license: bigcode-openrail-m library_name: peft tags: - generated_from_trainer base_model: bigcode/starcoderbase-1b model-index: - name: peft-lora-starcoder1B-Instruction-ny8-ALL 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. --> # peft-lora-starcoder1B-Instruction-ny8-ALL This model is a fine-tuned version of [bigcode/starcoderbase-1b](https://huggingface.co/bigcode/starcoderbase-1b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0870 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 30 - training_steps: 2000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.1891 \| 0.05 \| 100 \| 0.1452 \| \| 0.1244 \| 0.1 \| 200 \| 0.1096 \| \| 0.1077 \| 0.15 \| 300 \| 0.1006 \| \| 0.0996 \| 0.2 \| 400 \| 0.0958 \| \| 0.0953 \| 0.25 \| 500 \| 0.0927 \| \| 0.0916 \| 0.3 \| 600 \| 0.0882 \| \| 0.0875 \| 0.35 \| 700 \| 0.0867 \| \| 0.0845 \| 0.4 \| 800 \| 0.0873 \| \| 0.0818 \| 0.45 \| 900 \| 0.0863 \| \| 0.0788 \| 0.5 \| 1000 \| 0.0848 \| \| 0.0781 \| 0.55 \| 1100 \| 0.0844 \| \| 0.0749 \| 0.6 \| 1200 \| 0.0847 \| \| 0.0726 \| 0.65 \| 1300 \| 0.0849 \| \| 0.0688 \| 0.7 \| 1400 \| 0.0867 \| \| 0.0701 \| 0.75 \| 1500 \| 0.0861 \| \| 0.0662 \| 0.8 \| 1600 \| 0.0863 \| \| 0.0658 \| 0.85 \| 1700 \| 0.0867 \| \| 0.0647 \| 0.9 \| 1800 \| 0.0869 \| \| 0.0644 \| 0.95 \| 1900 \| 0.0870 \| \| 0.0657 \| 1.0 \| 2000 \| 0.0870 \| ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0
rombodawg/DeepMagic-Coder-7b	rombodawg	2024-02-07T06:02:22Z	8	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-06T19:58:50Z	--- license: other license_name: deepseek license_link: https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/LICENSE-MODEL --- DeepMagic-Coder-7b (Note: From short testing, the Alt version generated much better code) Alternate version: - https://huggingface.co/rombodawg/DeepMagic-Coder-7b-Alt ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/642cc1c253e76b4c2286c58e/LlbswwXZQoIQziTNEMSMk.jpeg) This is an extremely successful merge of the deepseek-coder-6.7b-instruct and Magicoder-S-DS-6.7B models, bringing an uplift in overall coding performance without any compromise to the models integrity (at least with limited testing). This is the first of my models to use the merge-kits task_arithmetic merging method. The method is detailed bellow, and its clearly very usefull for merging ai models that were fine-tuned from a common base: Task Arithmetic: ``` Computes "task vectors" for each model by subtracting a base model. Merges the task vectors linearly and adds back the base. Works great for models that were fine tuned from a common ancestor. Also a super useful mental framework for several of the more involved merge methods. ``` The original models used in this merge can be found here: - https://huggingface.co/ise-uiuc/Magicoder-S-DS-6.7B - https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct The Merge was created using Mergekit and the paremeters can be found bellow: ```yaml models: - model: deepseek-ai_deepseek-coder-6.7b-instruct parameters: weight: 1 - model: ise-uiuc_Magicoder-S-DS-6.7B parameters: weight: 1 merge_method: task_arithmetic base_model: ise-uiuc_Magicoder-S-DS-6.7B parameters: normalize: true int8_mask: true dtype: float16 ```
ChayanM/Image_Captioner	ChayanM	2024-02-07T05:57:48Z	9	0	transformers	[ "transformers", "safetensors", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-02-04T17:43:12Z	--- tags: - generated_from_trainer metrics: - rouge model-index: - name: Image_Captioner 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. --> # Image_Captioner This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0923 - Rouge1: 25.0369 - Rouge2: 10.1572 - Rougel: 21.5244 - Rougelsum: 24.0775 - Gen Len: 18.9946 ## 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 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 0.253 \| 1.0 \| 836 \| 0.1372 \| 29.3958 \| 12.2981 \| 25.5129 \| 27.9289 \| 19.0 \| \| 0.1361 \| 2.0 \| 1672 \| 0.1151 \| 25.8361 \| 12.2894 \| 23.7346 \| 25.47 \| 19.0 \| \| 0.115 \| 3.0 \| 2508 \| 0.1037 \| 25.1859 \| 11.9032 \| 23.1038 \| 24.8338 \| 19.0 \| \| 0.1027 \| 4.0 \| 3344 \| 0.0942 \| 26.0345 \| 12.0324 \| 23.4843 \| 25.5426 \| 19.0 \| \| 0.0873 \| 5.0 \| 4180 \| 0.0864 \| 26.1657 \| 11.685 \| 23.6563 \| 25.6247 \| 19.0 \| \| 0.0742 \| 6.0 \| 5016 \| 0.0794 \| 24.3621 \| 10.5113 \| 21.7192 \| 23.8253 \| 19.0 \| \| 0.0646 \| 7.0 \| 5852 \| 0.0740 \| 24.711 \| 11.194 \| 22.2089 \| 24.1793 \| 19.0 \| \| 0.0542 \| 8.0 \| 6688 \| 0.0690 \| 25.0339 \| 10.8651 \| 22.171 \| 24.4106 \| 19.0 \| \| 0.046 \| 9.0 \| 7524 \| 0.0650 \| 25.0982 \| 11.8399 \| 22.701 \| 24.623 \| 18.9987 \| \| 0.0386 \| 10.0 \| 8360 \| 0.0623 \| 26.2563 \| 10.4715 \| 22.5319 \| 25.1412 \| 18.9987 \| \| 0.0317 \| 11.0 \| 9196 \| 0.0591 \| 26.4001 \| 11.8031 \| 23.1653 \| 25.2856 \| 18.9919 \| \| 0.0273 \| 12.0 \| 10032 \| 0.0587 \| 25.6521 \| 11.0174 \| 22.7327 \| 24.9068 \| 18.9879 \| \| 0.0231 \| 13.0 \| 10868 \| 0.0583 \| 26.7035 \| 11.2021 \| 23.0121 \| 25.6384 \| 18.9946 \| \| 0.0195 \| 14.0 \| 11704 \| 0.0592 \| 25.5747 \| 10.7424 \| 22.3673 \| 24.6944 \| 19.0 \| \| 0.0167 \| 15.0 \| 12540 \| 0.0608 \| 25.3022 \| 10.163 \| 21.9556 \| 24.3587 \| 18.9596 \| \| 0.0142 \| 16.0 \| 13376 \| 0.0614 \| 25.0496 \| 10.0656 \| 21.7629 \| 24.1094 \| 18.9206 \| \| 0.0119 \| 17.0 \| 14212 \| 0.0618 \| 26.0112 \| 10.2519 \| 22.1926 \| 24.8873 \| 18.8735 \| \| 0.0102 \| 18.0 \| 15048 \| 0.0653 \| 25.6183 \| 10.04 \| 22.1136 \| 24.5255 \| 18.9125 \| \| 0.0086 \| 19.0 \| 15884 \| 0.0671 \| 24.7352 \| 9.6328 \| 21.0675 \| 23.7704 \| 18.8694 \| \| 0.0076 \| 20.0 \| 16720 \| 0.0693 \| 24.9512 \| 9.6635 \| 21.4761 \| 23.9132 \| 18.9112 \| \| 0.0067 \| 21.0 \| 17556 \| 0.0708 \| 24.1732 \| 9.158 \| 20.3408 \| 23.029 \| 18.8358 \| \| 0.0058 \| 22.0 \| 18392 \| 0.0732 \| 24.4503 \| 9.4394 \| 20.8584 \| 23.4242 \| 18.8035 \| \| 0.0048 \| 23.0 \| 19228 \| 0.0738 \| 24.8844 \| 9.9125 \| 21.3509 \| 23.9336 \| 18.8089 \| \| 0.0043 \| 24.0 \| 20064 \| 0.0777 \| 25.5401 \| 10.1857 \| 21.8328 \| 24.4294 \| 18.9058 \| \| 0.0038 \| 25.0 \| 20900 \| 0.0781 \| 24.2235 \| 9.0445 \| 20.4463 \| 23.0001 \| 18.9166 \| \| 0.0033 \| 26.0 \| 21736 \| 0.0801 \| 25.0127 \| 9.8025 \| 21.3116 \| 23.9683 \| 18.7308 \| \| 0.0029 \| 27.0 \| 22572 \| 0.0807 \| 24.5765 \| 9.6283 \| 20.9556 \| 23.4559 \| 18.9166 \| \| 0.0027 \| 28.0 \| 23408 \| 0.0830 \| 24.8389 \| 9.8899 \| 21.4027 \| 23.9416 \| 18.9233 \| \| 0.0024 \| 29.0 \| 24244 \| 0.0833 \| 25.3695 \| 10.162 \| 21.7865 \| 24.3737 \| 18.7106 \| \| 0.0022 \| 30.0 \| 25080 \| 0.0832 \| 24.8804 \| 10.0825 \| 21.4621 \| 24.0326 \| 18.9287 \| \| 0.0021 \| 31.0 \| 25916 \| 0.0853 \| 25.0049 \| 9.7036 \| 21.3664 \| 23.9173 \| 18.9044 \| \| 0.0019 \| 32.0 \| 26752 \| 0.0855 \| 25.0529 \| 9.4994 \| 21.2781 \| 24.0076 \| 18.9125 \| \| 0.002 \| 33.0 \| 27588 \| 0.0852 \| 24.8417 \| 9.9376 \| 21.2526 \| 23.8552 \| 18.9031 \| \| 0.0015 \| 34.0 \| 28424 \| 0.0857 \| 24.6359 \| 9.5179 \| 20.8941 \| 23.4553 \| 18.8937 \| \| 0.0014 \| 35.0 \| 29260 \| 0.0858 \| 25.1156 \| 10.1869 \| 21.5805 \| 23.9664 \| 18.8156 \| \| 0.0013 \| 36.0 \| 30096 \| 0.0871 \| 24.739 \| 9.5548 \| 21.15 \| 23.749 \| 18.9219 \| \| 0.0011 \| 37.0 \| 30932 \| 0.0884 \| 24.774 \| 9.7848 \| 21.2467 \| 23.833 \| 18.9556 \| \| 0.0011 \| 38.0 \| 31768 \| 0.0889 \| 25.2656 \| 9.9796 \| 21.517 \| 24.1836 \| 18.9462 \| \| 0.0011 \| 39.0 \| 32604 \| 0.0895 \| 24.6627 \| 9.3783 \| 20.9288 \| 23.5835 \| 18.9704 \| \| 0.001 \| 40.0 \| 33440 \| 0.0906 \| 25.1326 \| 9.814 \| 21.3593 \| 24.0816 \| 18.9260 \| \| 0.0009 \| 41.0 \| 34276 \| 0.0900 \| 25.6889 \| 10.3712 \| 22.0588 \| 24.695 \| 18.9731 \| \| 0.0008 \| 42.0 \| 35112 \| 0.0911 \| 24.6819 \| 9.8307 \| 21.1335 \| 23.7053 \| 18.9071 \| \| 0.0008 \| 43.0 \| 35948 \| 0.0905 \| 24.4835 \| 9.7292 \| 21.017 \| 23.5027 \| 18.9623 \| \| 0.0007 \| 44.0 \| 36784 \| 0.0910 \| 24.8203 \| 9.5875 \| 21.245 \| 23.7718 \| 18.9825 \| \| 0.0007 \| 45.0 \| 37620 \| 0.0914 \| 25.1212 \| 10.1024 \| 21.6215 \| 24.1061 \| 18.9771 \| \| 0.0006 \| 46.0 \| 38456 \| 0.0914 \| 25.1636 \| 9.8127 \| 21.5343 \| 24.13 \| 18.9475 \| \| 0.0006 \| 47.0 \| 39292 \| 0.0915 \| 24.866 \| 9.8427 \| 21.3531 \| 23.8643 \| 18.9394 \| \| 0.0006 \| 48.0 \| 40128 \| 0.0916 \| 25.064 \| 10.049 \| 21.5198 \| 24.1158 \| 18.9731 \| \| 0.0005 \| 49.0 \| 40964 \| 0.0923 \| 24.8424 \| 9.9718 \| 21.3263 \| 23.9031 \| 18.9933 \| \| 0.0005 \| 50.0 \| 41800 \| 0.0923 \| 25.0369 \| 10.1572 \| 21.5244 \| 24.0775 \| 18.9946 \| ### Framework versions - Transformers 4.37.1 - Pytorch 1.13.1+cu117 - Datasets 2.15.0 - Tokenizers 0.15.1
yeye776/OndeviceAI-large	yeye776	2024-02-07T05:57:09Z	4	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:paust/pko-t5-large", "base_model:finetune:paust/pko-t5-large", "license:cc-by-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-07T05:54:55Z	--- license: cc-by-4.0 base_model: paust/pko-t5-large tags: - generated_from_trainer model-index: - name: OndeviceAI-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. --> # OndeviceAI-large This model is a fine-tuned version of [paust/pko-t5-large](https://huggingface.co/paust/pko-t5-large) 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: 0.0007 - 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: 0.06 - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
shnl/llama2-13b-vinewsqa	shnl	2024-02-07T05:27:13Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:manhtt-079/llama-2-13b", "base_model:adapter:manhtt-079/llama-2-13b", "region:us" ]	null	2024-02-07T05:22:51Z	--- library_name: peft base_model: manhtt-079/llama-2-13b --- # 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] ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.6.2 ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.6.2
cvzion/mistral-dqg-v3	cvzion	2024-02-07T05:21:52Z	0	0	null	[ "safetensors", "autotrain", "text-generation", "conversational", "license:other", "endpoints_compatible", "region:us" ]	text-generation	2024-02-07T04:24:52Z	--- tags: - autotrain - text-generation widget: - text: "I love AutoTrain because " license: other --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
debajyotidasgupta/convnextv2-base-22k-384	debajyotidasgupta	2024-02-07T05:20:08Z	179	0	transformers	[ "transformers", "tensorboard", "safetensors", "convnextv2", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:facebook/convnextv2-base-22k-384", "base_model:finetune:facebook/convnextv2-base-22k-384", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-02-04T15:27:03Z	--- license: apache-2.0 base_model: facebook/convnextv2-base-22k-384 tags: - generated_from_trainer datasets: - imagefolder metrics: - f1 model-index: - name: convnextv2-base-22k-384 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: F1 type: f1 value: 0.9913113141099743 --- <!-- 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. --> # convnextv2-base-22k-384 This model is a fine-tuned version of [facebook/convnextv2-base-22k-384](https://huggingface.co/facebook/convnextv2-base-22k-384) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0069 - F1: 0.9913 ## 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 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.1521 \| 1.0 \| 202 \| 0.0982 \| 0.8278 \| \| 0.0664 \| 2.0 \| 404 \| 0.0626 \| 0.9079 \| \| 0.1053 \| 3.0 \| 606 \| 0.0356 \| 0.9537 \| \| 0.0432 \| 4.0 \| 808 \| 0.0302 \| 0.9703 \| \| 0.0552 \| 5.0 \| 1010 \| 0.0114 \| 0.9827 \| \| 0.0352 \| 6.0 \| 1212 \| 0.0131 \| 0.9824 \| \| 0.0221 \| 7.0 \| 1414 \| 0.0063 \| 0.9943 \| \| 0.0018 \| 8.0 \| 1616 \| 0.0169 \| 0.9824 \| \| 0.0283 \| 9.0 \| 1818 \| 0.0028 \| 0.9971 \| \| 0.0429 \| 10.0 \| 2020 \| 0.0069 \| 0.9913 \| ### Framework versions - Transformers 4.37.2 - Pytorch 1.12.1+cu102 - Datasets 2.16.1 - Tokenizers 0.15.1
tvjoseph/ABSA1	tvjoseph	2024-02-07T05:12:02Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-07T05:11:59Z	--- 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]
ealvaradob/bert-finetuned-phishing	ealvaradob	2024-02-07T05:11:47Z	3,247	13	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "phishing", "BERT", "en", "dataset:ealvaradob/phishing-dataset", "base_model:google-bert/bert-large-uncased", "base_model:finetune:google-bert/bert-large-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-20T18:31:54Z	--- license: apache-2.0 base_model: bert-large-uncased tags: - generated_from_trainer - phishing - BERT metrics: - accuracy - precision - recall model-index: - name: bert-finetuned-phishing results: [] widget: - text: https://www.verif22.com example_title: Phishing URL - text: Dear colleague, An important update about your email has exceeded your storage limit. You will not be able to send or receive all of your messages. We will close all older versions of our Mailbox as of Friday, June 12, 2023. To activate and complete the required information click here (https://ec-ec.squarespace.com). Account must be reactivated today to regenerate new space. Management Team example_title: Phishing Email - text: You have access to FREE Video Streaming in your plan. REGISTER with your email, password and then select the monthly subscription option. https://bit.ly/3vNrU5r example_title: Phishing SMS - text: if(data.selectedIndex > 0){$('#hidCflag').val(data.selectedData.value);};; var sprypassword1 = new Spry.Widget.ValidationPassword("sprypassword1"); var sprytextfield1 = new Spry.Widget.ValidationTextField("sprytextfield1", "email"); example_title: Phishing Script - text: Hi, this model is really accurate :) example_title: Benign message datasets: - ealvaradob/phishing-dataset language: - en pipeline_tag: text-classification --- <!-- 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 ON PHISHING DETECTION This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an [phishing dataset](https://huggingface.co/datasets/ealvaradob/phishing-dataset), capable of detecting phishing in its four most common forms: URLs, Emails, SMS messages and even websites. It achieves the following results on the evaluation set: - Loss: 0.1953 - Accuracy: 0.9717 - Precision: 0.9658 - Recall: 0.9670 - False Positive Rate: 0.0249 ## Model description BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. This model has the following configuration: - 24-layer - 1024 hidden dimension - 16 attention heads - 336M parameters ## Motivation and Purpose Phishing is one of the most frequent and most expensive cyber-attacks according to several security reports. This model aims to efficiently and accurately prevent phishing attacks against individuals and organizations. To achieve it, BERT was trained on a diverse and robust dataset containing: URLs, SMS Messages, Emails and Websites, which allows the model to extend its detection capability beyond the usual and to be used in various contexts. ### 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| False Positive Rate \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:---------:\|:------:\|:-------------------:\| \| 0.1487 \| 1.0 \| 3866 \| 0.1454 \| 0.9596 \| 0.9709 \| 0.9320 \| 0.0203 \| \| 0.0805 \| 2.0 \| 7732 \| 0.1389 \| 0.9691 \| 0.9663 \| 0.9601 \| 0.0243 \| \| 0.0389 \| 3.0 \| 11598 \| 0.1779 \| 0.9683 \| 0.9778 \| 0.9461 \| 0.0156 \| \| 0.0091 \| 4.0 \| 15464 \| 0.1953 \| 0.9717 \| 0.9658 \| 0.9670 \| 0.0249 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.1+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1
FinancialSupport/saiga-70b	FinancialSupport	2024-02-07T05:11:15Z	8	0	null	[ "gguf", "it", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-02-06T22:56:40Z	--- license: apache-2.0 language: - it --- il saiga è uno strano incrocio di antilopi che vive nelle steppe siberiane. Il nome deriva dal fatto che è un parente di fauno/camoscio e un lontano cugino di cerbero (altri modelli open source ita). E' un progetto portato avanti nei weekend con pochi soldi/tempo a disposizione ![image/png](https://cdn-uploads.huggingface.co/production/uploads/648cca46d38113f34bf7cb72/nqYw-P2uPLsNI8FMnLHtN.png)
ybzz/detr-pothole-augment	ybzz	2024-02-07T04:56:57Z	4	0	transformers	[ "transformers", "safetensors", "detr", "object-detection", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	object-detection	2024-02-07T04:56:47Z	--- 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]
fionazhang/mistral-finetune-short	fionazhang	2024-02-07T04:49:37Z	0	0	peft	[ "peft", "safetensors", "mistral", "trl", "sft", "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	2024-01-29T00:07:01Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 model-index: - name: mistral-finetune-short results: [] --- # mistral-finetune-short This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1). It achieves the following results on the evaluation set: - Loss: 2.0377 ## Model description This model is fine-tuned to specialize in generating content related to the environment and sustainability domain. The training involved Supervised Fine-Tuning (SFT), Parameter Efficient Fine-Tuning (PEFT), and Low-Rank Adaptation (LoRA) techniques to optimize model performance. The motivation behind this research is to explore the feasibility and effectiveness of Semantically Sufficient Private Large Language Models (LLMs) for secure, domain-specific knowledge extraction in the context of environment and sustainability. ## Intended uses The model is intended for information retrieval and knowledge extraction tasks within the domain of environment and sustainability. ## Training and evaluation data The training data consists of domain-specific text collected from Wikipedia pages related to environmental topics. This model was trained using the Short dataset. [Model trained with the Long dataset](https://huggingface.co/fionazhang/mistral-finetune-long). \| Dataset \| URLs \| Number of Rows \| Number of Words \| Number of Sentences \| \|-------------\|----------\|--------------------\|----------------------\|--------------------------\| \| Short \| 11 \| 577 \| 51,526 \| 2,150 \| \| Long \| 23 \| 1,431 \| 124,682 \| 5,209 \| Table 1: Summary of Dataset Information ### Environment and Sustainability This model is tailored for the environment and sustainability domain, with a focus on assisting researchers and enterprises, particularly in alignment with the work of the Commonwealth Scientific and Industrial Research Organisation (CSIRO). ### Data Collection Process The training data was collected through a Python program that extracted and cleaned text content from specific Wikipedia pages related to environmental topics. The program utilized various libraries, such as `requests`, `BeautifulSoup`, and `nltk`, for efficient web scraping, HTML parsing, and natural language processing. ## Training procedure ## Fine-tuning The fine-tuning process involved Soft Fine-Tuning, PEFT, and LoRA techniques. Soft Fine-Tuning utilized continuous-valued probabilities as labels, suitable for generation models. PEFT focused on updating a small subset of parameters during fine-tuning to prevent catastrophic forgetting. LoRA, a lightweight training technique, reduced the number of trainable parameters for faster and memory-efficient training. #### Low-Rank Adaptation (LoRA) Parameters - lora_alpha: 16 - lora_dropout: 0.1 - r: 8 #### Training Parameters - num_train_epochs: 2 - per_device_train_batch_size: 3 - per_device_eval_batch_size: 3 - gradient_accumulation_steps: 1 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - learning_rate: 5e-05 - weight_decay: 0.001 - max_grad_norm: 0.3 - max_steps: -1 - warmup_ratio: 0.03 - group_by_length: True - lr_scheduler_type: constant - seed: 42 ### Training results #### Training Loss ![Loss](https://huggingface.co/fionazhang/mistral-finetune-short/blob/main/short-loss-curve.png) Figure 1: Training loss curve of model fionazhang/mistral-finetune-short (logging step = 10) In the training process, the observed training losses exhibit jittery yet overall decreasing trends. The final evaluation loss reaches a satisfactory value of 2.0377, indicating successful learning and adaptation to the nuances of the provided data. ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.0a0+git7bcf7da - Datasets 2.16.1 - Tokenizers 0.15.0
varun-v-rao/t5-large-bn-adapter-6.34M-snli-model1	varun-v-rao	2024-02-07T04:47:48Z	0	0	null	[ "tensorboard", "generated_from_trainer", "base_model:google-t5/t5-large", "base_model:finetune:google-t5/t5-large", "license:apache-2.0", "region:us" ]	null	2024-02-06T21:11:35Z	--- license: apache-2.0 base_model: t5-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: t5-large-bn-adapter-6.34M-snli-model1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-large-bn-adapter-6.34M-snli-model1 This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6034 - Accuracy: 0.8005 ## 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: 32 - eval_batch_size: 32 - seed: 40 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.3118 \| 1.0 \| 17168 \| 0.2381 \| 0.9150 \| \| 0.2742 \| 2.0 \| 34336 \| 0.2299 \| 0.9171 \| \| 0.2725 \| 3.0 \| 51504 \| 0.2277 \| 0.9197 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
varun-v-rao/bert-large-cased-bn-adapter-3.17M-snli-model2	varun-v-rao	2024-02-07T04:46:51Z	0	0	null	[ "tensorboard", "generated_from_trainer", "base_model:google-bert/bert-large-cased", "base_model:finetune:google-bert/bert-large-cased", "license:apache-2.0", "region:us" ]	null	2024-02-07T02:22:08Z	--- license: apache-2.0 base_model: bert-large-cased tags: - generated_from_trainer metrics: - accuracy model-index: - name: bert-large-cased-bn-adapter-3.17M-snli-model2 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-large-cased-bn-adapter-3.17M-snli-model2 This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7747 - Accuracy: 0.731 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.4017 \| 1.0 \| 8584 \| 0.3327 \| 0.8763 \| \| 0.3769 \| 2.0 \| 17168 \| 0.3069 \| 0.8881 \| \| 0.3641 \| 3.0 \| 25752 \| 0.3005 \| 0.8895 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
AsphyXIA/baarat-hin-en-0.1	AsphyXIA	2024-02-07T04:46:11Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-07T04:46:05Z	--- 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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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]
varun-v-rao/t5-base-bn-adapter-1.79M-snli-model3	varun-v-rao	2024-02-07T04:42:15Z	0	0	null	[ "tensorboard", "generated_from_trainer", "base_model:google-t5/t5-base", "base_model:finetune:google-t5/t5-base", "license:apache-2.0", "region:us" ]	null	2024-02-07T02:16:46Z	--- license: apache-2.0 base_model: t5-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: t5-base-bn-adapter-1.79M-snli-model3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-base-bn-adapter-1.79M-snli-model3 This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7044 - Accuracy: 0.7455 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 79 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.4101 \| 1.0 \| 8584 \| 0.3336 \| 0.8763 \| \| 0.3814 \| 2.0 \| 17168 \| 0.3112 \| 0.8858 \| \| 0.3695 \| 3.0 \| 25752 \| 0.3061 \| 0.8883 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
ealvaradob/bert-phishing-text	ealvaradob	2024-02-07T04:37:15Z	7	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "dataset:ealvaradob/phishing-dataset", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-01-28T19:06:47Z	--- license: apache-2.0 datasets: - ealvaradob/phishing-dataset --- <strong><span style="color:red">WARNING ...</span></strong> This is NOT the final BERT model trained for phishing detection. It only corresponds to an evaluation of BERT performance against email and SMS samples. This model has the following performance in email and SMS phishing detection: - Accuracy: 0.990318 - Precision: 0.990170 - Recall: 0.984365 - AUC: 0.999146 👇¡CHECK BERT FINAL MODEL FINETUNED FOR PHISHING DETECTION ON THE FOLLOWING LINK!👇 _https://huggingface.co/ealvaradob/bert-finetuned-phishing_
Opensourced/wormgpt-24	Opensourced	2024-02-07T04:31:50Z	0	6	null	[ "license:apache-2.0", "region:us" ]	null	2024-02-07T04:21:04Z	--- license: apache-2.0 --- from datasets import load_dataset dataset = load_dataset("suriyagunasekar/stackoverflow-python-with-meta-data")
Telugu-LLM-Labs/Telugu-Llama2-7B-v0-Instruct	Telugu-LLM-Labs	2024-02-07T04:24:52Z	173	13	transformers	[ "transformers", "pytorch", "llama", "text-generation", "conversational", "te", "en", "dataset:Telugu-LLM-Labs/yahma_alpaca_cleaned_telugu_filtered_and_romanized", "dataset:Telugu-LLM-Labs/teknium_GPTeacher_general_instruct_telugu_filtered_and_romanized", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-06T12:07:42Z	--- license: llama2 datasets: - Telugu-LLM-Labs/yahma_alpaca_cleaned_telugu_filtered_and_romanized - >- Telugu-LLM-Labs/teknium_GPTeacher_general_instruct_telugu_filtered_and_romanized language: - te - en --- # Telugu-Llama2-7B-v0-Instruct This model is based on [Telugu-Llama2-7B-v0-Base](https://huggingface.co/Telugu-LLM-Labs/Telugu-Llama2-7B-v0-Base) and hase been finetuned on instruction datasets: 1. [yahma_alpaca_cleaned_telugu_filtered_and_romanized](https://huggingface.co/datasets/Telugu-LLM-Labs/yahma_alpaca_cleaned_telugu_filtered_and_romanized) 2. [teknium_GPTeacher_general_instruct_telugu_filtered_and_romanized](https://huggingface.co/datasets/Telugu-LLM-Labs/teknium_GPTeacher_general_instruct_telugu_filtered_and_romanized) # Input Text Format ``` ### Instruction: {instruction} ### Input: {input} ## Response: {response} ``` # Usage ## With Romanized Telugu ```python3 import torch from transformers import AutoTokenizer, AutoModelForCausalLM device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') model_name = "Telugu-LLM-Labs/Telugu-Llama2-7B-v0-Instruct" tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="right") model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16).to(device) instruction = "Krindi samaacharam prakaram google app eppudu release ayyindi?" input ="Google News is a news aggregator service developed by Google. It presents a continuous flow of links to articles organized from thousands of publishers and magazines. Google News is available as an app on Android, iOS, and the Web. Google released a beta version in September 2002 and the official app in January 2006." text = f"""Instruction: {instruction} \nInput: {input} \nResponse:""" encodings = tokenizer(text, padding=True, return_tensors="pt") encodings = encodings.to(device) with torch.inference_mode(): outputs = model.generate(encodings.input_ids, do_sample=False, max_new_tokens=500) output = tokenizer.batch_decode(outputs.detach(), skip_special_tokens=True) ``` ### Sample Output: ``` 1. September 2002 Google released a beta version of Google News. 2. January 2006 Google released the official version of Google News. ``` ## With Native Telugu ```python3 import torch from transformers import AutoTokenizer, AutoModelForCausalLM device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') model_name = "Telugu-LLM-Labs/Telugu-Llama2-7B-v0-Instruct" tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="right") model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16).to(device) instruction = "కింది వచనాన్ని సంగ్రహించండి" input="గూగుల్ వార్తలు అనేది గూగుల్ ద్వారా అభివృద్ధి చేయబడిన వార్తా అగ్రిగేటర్ సేవ. ఇది వేలకొద్దీ ప్రచురణకర్తలు మరియు మ్యాగజైన్‌ల నుండి నిర్వహించబడిన కథనాలకు నిరంతర లింక్‌లను అందిస్తుంది. గూగుల్ వార్తలు Android, iOS మరియు వెబ్‌లో యాప్‌గా అందుబాటులో ఉన్నాయి. గూగుల్ సెప్టెంబరు 2002లో బీటా వెర్షన్‌ను మరియు జనవరి 2006లో అధికారిక యాప్‌ను విడుదల చేసింది." text = f"""Instruction: {instruction} \nInput: {input} \nResponse:""" encodings = tokenizer(text, padding=True, return_tensors="pt") encodings = encodings.to(device) with torch.inference_mode(): outputs = model.generate(encodings.input_ids, do_sample=False, max_new_tokens=500) output = tokenizer.batch_decode(outputs.detach(), skip_special_tokens=True) ``` ### Sample Output: 1. గూగుల్ వార్తలు అనేది గూగుల్ ద్వారా అభివృద్ధి చేయబడిన వార్తా అగ్రిగేటర్ సేవ, వేలకొద్దీ ప్రచురణకర్తలు మరియు మ్యాగజైన్‌ల నుండి నిర్వహించబడిన కథనాలకు నిరంతర లింక్‌లను అందిస్తుంది. 2. గూగుల్ సెప్టెంబరు 2002లో బీటా వెర్షన్ మరియు జనవరి 2006లో అధికారిక యాప్ ను విడుదల చేసింది. # Developers: The model is a collaborative effort by [Ravi Theja](https://twitter.com/ravithejads) and [Ramsri Goutham](https://twitter.com/ramsri_goutham). Feel free to DM either of us if you have any questions. # Note: The model is quite sensitive to parameters and inputs and is not yet ready for production. It remains in the experimental phase, and we recommend using it accordingly.
sneakykilli/Qatar_BERTopic	sneakykilli	2024-02-07T04:18:52Z	3	0	bertopic	[ "bertopic", "text-classification", "region:us" ]	text-classification	2024-02-07T03:52:25Z	--- tags: - bertopic library_name: bertopic pipeline_tag: text-classification --- # Qatar_BERTopic This is a [BERTopic](https://github.com/MaartenGr/BERTopic) model. BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets. ## Usage To use this model, please install BERTopic: ``` pip install -U bertopic ``` You can use the model as follows: ```python from bertopic import BERTopic topic_model = BERTopic.load("sneakykilli/Qatar_BERTopic") topic_model.get_topic_info() ``` ## Topic overview * Number of topics: 22 * Number of training documents: 714 <details> <summary>Click here for an overview of all topics.</summary> \| Topic ID \| Topic Keywords \| Topic Frequency \| Label \| \|----------\|----------------\|-----------------\|-------\| \| -1 \| doha - qatar - airline - airlines - refund \| 5 \| -1_doha_qatar_airline_airlines \| \| 0 \| doha - qatar - airline - airlines - flights \| 211 \| 0_doha_qatar_airline_airlines \| \| 1 \| refund - refunded - refunds - booking - voucher \| 78 \| 1_refund_refunded_refunds_booking \| \| 2 \| doha - qatar - baggage - luggage - airline \| 72 \| 2_doha_qatar_baggage_luggage \| \| 3 \| airline - passengers - flights - attendant - steward \| 49 \| 3_airline_passengers_flights_attendant \| \| 4 \| qatar - airline - airlines - flights - carriers \| 44 \| 4_qatar_airline_airlines_flights \| \| 5 \| baggage - doha - airlines - airline - luggage \| 39 \| 5_baggage_doha_airlines_airline \| \| 6 \| airline - airlines - flights - emirates - flight \| 35 \| 6_airline_airlines_flights_emirates \| \| 7 \| refund - airline - flights - flight - cancel \| 32 \| 7_refund_airline_flights_flight \| \| 8 \| airline - airlines - seats - qatar - seating \| 28 \| 8_airline_airlines_seats_qatar \| \| 9 \| qatar - doha - airlines - flights - emirates \| 18 \| 9_qatar_doha_airlines_flights \| \| 10 \| customer - complaints - service - terrible - horrible \| 17 \| 10_customer_complaints_service_terrible \| \| 11 \| qatar - complaint - doha - complaints - airline \| 15 \| 11_qatar_complaint_doha_complaints \| \| 12 \| avios - qatar - booking - compensation - aviso \| 14 \| 12_avios_qatar_booking_compensation \| \| 13 \| airline - airlines - flight - airplane - horrible \| 9 \| 13_airline_airlines_flight_airplane \| \| 14 \| doha - qatar - flights - cancellation - airlines \| 8 \| 14_doha_qatar_flights_cancellation \| \| 15 \| doha - qatar - qatari - emirates - flight \| 8 \| 15_doha_qatar_qatari_emirates \| \| 16 \| doha - qatar - airlines - bangkok - airport \| 8 \| 16_doha_qatar_airlines_bangkok \| \| 17 \| seats - seating - airline - booked - seat \| 7 \| 17_seats_seating_airline_booked \| \| 18 \| qatar - opodo - airline - refunded - voucher \| 6 \| 18_qatar_opodo_airline_refunded \| \| 19 \| doha - qatar - flight - destinations - airways \| 6 \| 19_doha_qatar_flight_destinations \| \| 20 \| qatar - airlines - disability - flight - wheelchair \| 5 \| 20_qatar_airlines_disability_flight \| </details> ## Training hyperparameters * calculate_probabilities: False * language: None * low_memory: False * min_topic_size: 5 * n_gram_range: (1, 1) * nr_topics: None * seed_topic_list: None * top_n_words: 10 * verbose: False * zeroshot_min_similarity: 0.7 * zeroshot_topic_list: None ## Framework versions * Numpy: 1.24.3 * HDBSCAN: 0.8.33 * UMAP: 0.5.5 * Pandas: 2.0.3 * Scikit-Learn: 1.2.2 * Sentence-transformers: 2.3.1 * Transformers: 4.36.2 * Numba: 0.57.1 * Plotly: 5.16.1 * Python: 3.10.12
sneakykilli/Singapore_BERTopic	sneakykilli	2024-02-07T04:18:48Z	4	0	bertopic	[ "bertopic", "text-classification", "region:us" ]	text-classification	2024-02-07T03:52:40Z	--- tags: - bertopic library_name: bertopic pipeline_tag: text-classification --- # Singapore_BERTopic This is a [BERTopic](https://github.com/MaartenGr/BERTopic) model. BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets. ## Usage To use this model, please install BERTopic: ``` pip install -U bertopic ``` You can use the model as follows: ```python from bertopic import BERTopic topic_model = BERTopic.load("sneakykilli/Singapore_BERTopic") topic_model.get_topic_info() ``` ## Topic overview * Number of topics: 10 * Number of training documents: 160 <details> <summary>Click here for an overview of all topics.</summary> \| Topic ID \| Topic Keywords \| Topic Frequency \| Label \| \|----------\|----------------\|-----------------\|-------\| \| -1 \| airline - airlines - flights - refund - flight \| 6 \| -1_airline_airlines_flights_refund \| \| 0 \| airline - airlines - flights - singapore - meals \| 31 \| 0_airline_airlines_flights_singapore \| \| 1 \| refund - airline - airlines - complaint - singapore \| 43 \| 1_refund_airline_airlines_complaint \| \| 2 \| baggage - luggage - airlines - airline - bags \| 20 \| 2_baggage_luggage_airlines_airline \| \| 3 \| airlines - passengers - seats - flight - cabin \| 14 \| 3_airlines_passengers_seats_flight \| \| 4 \| refund - repayment - sia - customer - complaints \| 11 \| 4_refund_repayment_sia_customer \| \| 5 \| airlines - airline - fees - singapore - flights \| 10 \| 5_airlines_airline_fees_singapore \| \| 6 \| refund - airline - cancellation - booking - cancel \| 9 \| 6_refund_airline_cancellation_booking \| \| 7 \| miles - airlines - airline - mileage - loyalty \| 9 \| 7_miles_airlines_airline_mileage \| \| 8 \| airline - flight - reviews - booking - customer \| 7 \| 8_airline_flight_reviews_booking \| </details> ## Training hyperparameters * calculate_probabilities: False * language: None * low_memory: False * min_topic_size: 5 * n_gram_range: (1, 1) * nr_topics: None * seed_topic_list: None * top_n_words: 10 * verbose: False * zeroshot_min_similarity: 0.7 * zeroshot_topic_list: None ## Framework versions * Numpy: 1.24.3 * HDBSCAN: 0.8.33 * UMAP: 0.5.5 * Pandas: 2.0.3 * Scikit-Learn: 1.2.2 * Sentence-transformers: 2.3.1 * Transformers: 4.36.2 * Numba: 0.57.1 * Plotly: 5.16.1 * Python: 3.10.12
wentingzhao/question-evaluator	wentingzhao	2024-02-07T04:12:53Z	4	1	transformers	[ "transformers", "safetensors", "llama", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-02-05T04:50:08Z	--- 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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chenhaodev/mistral-7b-medmcqa-inst-v1	chenhaodev	2024-02-07T04:06:07Z	7	0	peft	[ "peft", "safetensors", "llama-factory", "lora", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:other", "region:us" ]	null	2024-02-07T03:31:34Z	--- license: other library_name: peft tags: - llama-factory - lora - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 model-index: - name: mistral-7b-medmcqa-inst-v1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistral-7b-medmcqa-inst-v1 This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the medmcqa_instruct dataset. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 20 - num_epochs: 1.0 - mixed_precision_training: Native AMP ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.1.1+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1 ### Performance hf (pretrained=mistralai/Mistral-7B-v0.1,parallelize=True,load_in_4bit=True,peft=chenhugging/mistral-7b-medmcqa-inst-v1), gen_kwargs: (None), limit: 100.0, num_fewshot: None, batch_size: 1 \| Tasks \|Version\|Filter\|n-shot\| Metric \|Value\| \|Stderr\| \|---------------------\|-------\|------\|-----:\|--------\|----:\|---\|-----:\| \|pubmedqa \| 1\|none \| 0\|acc \| 0.98\|± \|0.0141\| \|medmcqa \|Yaml \|none \| 0\|acc \| 0.48\|± \|0.0502\| \|professional_medicine\| 0\|none \| 0\|acc \| 0.61\|± \|0.0490\| \|college_medicine \| 0\|none \| 0\|acc \| 0.57\|± \|0.0498\| \|clinical_knowledge \| 0\|none \| 0\|acc \| 0.65\|± \|0.0479\| \|ocn \|Yaml \|none \| 0\|acc \| 0.68\|± \|0.0469\| \|aocnp \|Yaml \|none \| 0\|acc \| 0.56\|± \|0.0499\| ### Original Performance (mistralai/Mistral-7B-v0.1) hf (pretrained=mistralai/Mistral-7B-v0.1,parallelize=True,load_in_4bit=True), gen_kwargs: (None), limit: 100.0, num_fewshot: None, batch_size: 1 \| Tasks \|Version\|Filter\|n-shot\| Metric \|Value\| \|Stderr\| \|---------------------\|-------\|------\|-----:\|--------\|----:\|---\|-----:\| \|pubmedqa \| 1\|none \| 0\|acc \| 0.98\|± \|0.0141\| \|medmcqa \|Yaml \|none \| 0\|acc \| 0.45\|± \|0.0500\| \|professional_medicine\| 0\|none \| 0\|acc \| 0.64\|± \|0.0482\| \|college_medicine \| 0\|none \| 0\|acc \| 0.65\|± \|0.0479\| \|clinical_knowledge \| 0\|none \| 0\|acc \| 0.68\|± \|0.0469\| \|ocn \|Yaml \|none \| 0\|acc \| 0.62\|± \|0.0488\| \|aocnp \|Yaml \|none \| 0\|acc \| 0.47\|± \|0.0502\|
houdini001/nep-spell-mbart-epoch5	houdini001	2024-02-07T03:55:54Z	4	0	transformers	[ "transformers", "tensorboard", "safetensors", "mbart", "text2text-generation", "generated_from_trainer", "base_model:houdini001/nep-spell-mbart-epoch3", "base_model:finetune:houdini001/nep-spell-mbart-epoch3", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-06T19:18:48Z	--- tags: - generated_from_trainer base_model: houdini001/nep-spell-mbart-epoch3 model-index: - name: nep-spell-mbart-epoch5 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. --> # nep-spell-mbart-epoch5 This model is a fine-tuned version of [houdini001/nep-spell-mbart-epoch3](https://huggingface.co/houdini001/nep-spell-mbart-epoch3) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 ## 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: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 0.0026 \| 0.32 \| 2000 \| 0.0001 \| \| 0.0 \| 0.63 \| 4000 \| 0.0001 \| \| 0.0 \| 0.95 \| 6000 \| 0.0000 \| \| 0.0 \| 1.27 \| 8000 \| 0.0000 \| \| 0.0 \| 1.58 \| 10000 \| 0.0000 \| \| 0.0 \| 1.9 \| 12000 \| 0.0000 \| ### Framework versions - Transformers 4.37.0 - Pytorch 2.1.2 - Datasets 2.1.0 - Tokenizers 0.15.1
frntcx/Reinforce	frntcx	2024-02-07T03:50:28Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2024-02-07T03:50:21Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 348.70 +/- 57.73 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
humung/koalpaca-polyglot-12.8B-lora-vlending-v0.1	humung	2024-02-07T03:49:51Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-07T03:49:41Z	--- 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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weijie210/zephyr-7b-UFB-0	weijie210	2024-02-07T03:49:39Z	6	0	transformers	[ "transformers", "tensorboard", "safetensors", "mistral", "text-generation", "trl", "dpo", "generated_from_trainer", "conversational", "base_model:alignment-handbook/zephyr-7b-sft-full", "base_model:finetune:alignment-handbook/zephyr-7b-sft-full", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-07T01:25:02Z	--- license: apache-2.0 base_model: alignment-handbook/zephyr-7b-sft-full tags: - trl - dpo - generated_from_trainer model-index: - name: zephyr-7b-UFB-0 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. --> # zephyr-7b-UFB-0 This model is a fine-tuned version of [alignment-handbook/zephyr-7b-sft-full](https://huggingface.co/alignment-handbook/zephyr-7b-sft-full) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1492 - Rewards/chosen: -1.5452 - Rewards/rejected: -7.2115 - Rewards/accuracies: 0.8359 - Rewards/margins: 5.6663 - Logps/rejected: -171.0846 - Logps/chosen: -143.6666 - Logits/rejected: -2.3237 - Logits/chosen: -2.3692 ## 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-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 32 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.36.1 - Pytorch 2.0.1+cu117 - Datasets 2.16.1 - Tokenizers 0.15.0
LoneStriker/DeepMagic-Coder-7b-AWQ	LoneStriker	2024-02-07T03:46:40Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "awq", "region:us" ]	text-generation	2024-02-07T03:44:58Z	--- license: other license_name: deepseek license_link: https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/LICENSE-MODEL --- DeepMagic-Coder-7b Alternate version: - https://huggingface.co/rombodawg/DeepMagic-Coder-7b-Alt ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/642cc1c253e76b4c2286c58e/LlbswwXZQoIQziTNEMSMk.jpeg) This is an extremely successful merge of the deepseek-coder-6.7b-instruct and Magicoder-S-DS-6.7B models, bringing an uplift in overall coding performance without any compromise to the models integrity (at least with limited testing). This is the first of my models to use the merge-kits task_arithmetic merging method. The method is detailed bellow, and its clearly very usefull for merging ai models that were fine-tuned from a common base: Task Arithmetic: ``` Computes "task vectors" for each model by subtracting a base model. Merges the task vectors linearly and adds back the base. Works great for models that were fine tuned from a common ancestor. Also a super useful mental framework for several of the more involved merge methods. ``` The original models used in this merge can be found here: - https://huggingface.co/ise-uiuc/Magicoder-S-DS-6.7B - https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct The Merge was created using Mergekit and the paremeters can be found bellow: ```yaml models: - model: deepseek-ai_deepseek-coder-6.7b-instruct parameters: weight: 1 - model: ise-uiuc_Magicoder-S-DS-6.7B parameters: weight: 1 merge_method: task_arithmetic base_model: ise-uiuc_Magicoder-S-DS-6.7B parameters: normalize: true int8_mask: true dtype: float16 ```

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