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ingeol/kosaul_sft_v0.2	ingeol	2024-05-18T15:21:58Z	115	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T13:50:27Z	--- 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]
SKHIA2024/sifkhenioui	SKHIA2024	2024-05-18T15:16:35Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2024-04-20T16:04:52Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: sifkhenioui 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
tjasad/lora_fine_tuned_boolq_googlemt_sloberta	tjasad	2024-05-18T15:14:41Z	3	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:EMBEDDIA/sloberta", "base_model:adapter:EMBEDDIA/sloberta", "license:cc-by-sa-4.0", "region:us" ]	null	2024-05-18T15:14:39Z	--- license: cc-by-sa-4.0 library_name: peft tags: - generated_from_trainer base_model: EMBEDDIA/sloberta metrics: - accuracy - f1 model-index: - name: lora_fine_tuned_boolq_googlemt_sloberta 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. --> # lora_fine_tuned_boolq_googlemt_sloberta This model is a fine-tuned version of [EMBEDDIA/sloberta](https://huggingface.co/EMBEDDIA/sloberta) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6642 - Accuracy: 0.6217 - F1: 0.4767 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 400 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.6841 \| 0.0424 \| 50 \| 0.6647 \| 0.6217 \| 0.4767 \| \| 0.6685 \| 0.0848 \| 100 \| 0.6632 \| 0.6217 \| 0.4767 \| \| 0.6944 \| 0.1272 \| 150 \| 0.6639 \| 0.6217 \| 0.4767 \| \| 0.6581 \| 0.1696 \| 200 \| 0.6632 \| 0.6217 \| 0.4767 \| \| 0.6625 \| 0.2120 \| 250 \| 0.6642 \| 0.6217 \| 0.4767 \| \| 0.6532 \| 0.2545 \| 300 \| 0.6661 \| 0.6217 \| 0.4767 \| \| 0.6741 \| 0.2969 \| 350 \| 0.6645 \| 0.6217 \| 0.4767 \| \| 0.6852 \| 0.3393 \| 400 \| 0.6642 \| 0.6217 \| 0.4767 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
emilykang/Phi_medmcqa_question_generation-microbiology_lora	emilykang	2024-05-18T15:12:58Z	0	0	peft	[ "peft", "safetensors", "phi", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:microsoft/phi-2", "base_model:adapter:microsoft/phi-2", "license:mit", "region:us" ]	null	2024-05-17T17:53:47Z	--- license: mit library_name: peft tags: - trl - sft - generated_from_trainer base_model: microsoft/phi-2 datasets: - generator model-index: - name: Phi_medmcqa_question_generation-microbiology_lora results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Phi_medmcqa_question_generation-microbiology_lora This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1
OsherElhadad/ppo-PandaReachJointsSparse-v3-1000000	OsherElhadad	2024-05-18T15:12:12Z	2	0	stable-baselines3	[ "stable-baselines3", "PandaReachJointsSparse-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-18T15:08:53Z	--- library_name: stable-baselines3 tags: - PandaReachJointsSparse-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachJointsSparse-v3 type: PandaReachJointsSparse-v3 metrics: - type: mean_reward value: -1.60 +/- 0.80 name: mean_reward verified: false --- # PPO Agent playing PandaReachJointsSparse-v3 This is a trained model of a PPO agent playing PandaReachJointsSparse-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 ... ```
SYX/mistral_based_claim_extractor	SYX	2024-05-18T15:11:42Z	827	1	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit", "base_model:adapter:unsloth/mistral-7b-instruct-v0.2-bnb-4bit", "region:us" ]	null	2024-05-18T14:57:30Z	--- library_name: peft base_model: unsloth/mistral-7b-instruct-v0.2-bnb-4bit --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.10.0
lora-library/B-LoRA-drawing2	lora-library	2024-05-18T15:09:49Z	203	4	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "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	2024-05-18T15:09:39Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: A [v27] widget: - text: ' ' output: url: image_0.png --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # 'SDXL B-LoRA - lora-library/B-LoRA-drawing2 <Gallery /> ## Model description These are lora-library/B-LoRA-drawing2 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 [v27]" to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](lora-library/B-LoRA-drawing2/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
lora-library/B-LoRA-working_cartoon	lora-library	2024-05-18T15:09:30Z	185	4	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "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	2024-05-18T15:09:22Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: A [v39] widget: - text: ' ' output: url: image_0.png --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # 'SDXL B-LoRA - lora-library/B-LoRA-working_cartoon <Gallery /> ## Model description These are lora-library/B-LoRA-working_cartoon 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 [v39]" to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](lora-library/B-LoRA-working_cartoon/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
lora-library/B-LoRA-drawing4	lora-library	2024-05-18T15:09:21Z	46	2	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "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	2024-05-18T15:09:08Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: A [v29] widget: - text: ' ' output: url: image_0.png --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # 'SDXL B-LoRA - lora-library/B-LoRA-drawing4 <Gallery /> ## Model description These are lora-library/B-LoRA-drawing4 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 [v29]" to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](lora-library/B-LoRA-drawing4/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
lora-library/B-LoRA-kiss	lora-library	2024-05-18T15:09:07Z	98	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "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	2024-05-18T15:08:50Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: A [v58] widget: - text: ' ' output: url: image_0.png --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # 'SDXL B-LoRA - lora-library/B-LoRA-kiss <Gallery /> ## Model description These are lora-library/B-LoRA-kiss 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 [v58]" to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](lora-library/B-LoRA-kiss/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
lora-library/B-LoRA-crayon_drawing	lora-library	2024-05-18T15:08:50Z	10	1	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "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	2024-05-18T15:08:32Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: A [v48] widget: - text: ' ' output: url: image_0.png --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # 'SDXL B-LoRA - lora-library/B-LoRA-crayon_drawing <Gallery /> ## Model description These are lora-library/B-LoRA-crayon_drawing 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 [v48]" to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](lora-library/B-LoRA-crayon_drawing/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
lora-library/B-LoRA-ink_sketch	lora-library	2024-05-18T15:08:24Z	42	5	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "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	2024-05-18T15:08:18Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: A [v32] widget: - text: ' ' output: url: image_0.png --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # 'SDXL B-LoRA - lora-library/B-LoRA-ink_sketch <Gallery /> ## Model description These are lora-library/B-LoRA-ink_sketch 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 [v32]" to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](lora-library/B-LoRA-ink_sketch/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
lora-library/B-LoRA-house_3d	lora-library	2024-05-18T15:08:18Z	134	1	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "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	2024-05-18T15:08:12Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: A [v49] widget: - text: ' ' output: url: image_0.png --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # 'SDXL B-LoRA - lora-library/B-LoRA-house_3d <Gallery /> ## Model description These are lora-library/B-LoRA-house_3d 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 [v49]" to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](lora-library/B-LoRA-house_3d/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
lora-library/B-LoRA-village_oil	lora-library	2024-05-18T15:08:05Z	22	1	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "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	2024-05-18T15:08:00Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: A [v50] widget: - text: ' ' output: url: image_0.png --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # 'SDXL B-LoRA - lora-library/B-LoRA-village_oil <Gallery /> ## Model description These are lora-library/B-LoRA-village_oil 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 [v50]" to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](lora-library/B-LoRA-village_oil/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
lora-library/B-LoRA-dog2	lora-library	2024-05-18T15:07:52Z	3	1	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "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	2024-05-18T15:07:46Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: A [v42] widget: - text: ' ' output: url: image_0.png --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # 'SDXL B-LoRA - lora-library/B-LoRA-dog2 <Gallery /> ## Model description These are lora-library/B-LoRA-dog2 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 [v42]" to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](lora-library/B-LoRA-dog2/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
lora-library/B-LoRA-cat	lora-library	2024-05-18T15:07:46Z	13	1	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "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	2024-05-18T15:07:40Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: A [v0] widget: - text: ' ' output: url: image_0.png --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # 'SDXL B-LoRA - lora-library/B-LoRA-cat <Gallery /> ## Model description These are lora-library/B-LoRA-cat 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 [v0]" to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](lora-library/B-LoRA-cat/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
lora-library/B-LoRA-metal_bird	lora-library	2024-05-18T15:07:39Z	9	1	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "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	2024-05-18T15:07:33Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: A [v8] widget: - text: ' ' output: url: image_0.png --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # 'SDXL B-LoRA - lora-library/B-LoRA-metal_bird <Gallery /> ## Model description These are lora-library/B-LoRA-metal_bird 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 [v8]" to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](lora-library/B-LoRA-metal_bird/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
lora-library/B-LoRA-fat_bird	lora-library	2024-05-18T15:07:26Z	2	1	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "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	2024-05-18T15:07:20Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: A [v15] widget: - text: ' ' output: url: image_0.png --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # 'SDXL B-LoRA - lora-library/B-LoRA-fat_bird <Gallery /> ## Model description These are lora-library/B-LoRA-fat_bird 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 [v15]" to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](lora-library/B-LoRA-fat_bird/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
lora-library/B-LoRA-dog6	lora-library	2024-05-18T15:07:07Z	134	1	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "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	2024-05-18T15:07:01Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: A [v43] widget: - text: ' ' output: url: image_0.png --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # 'SDXL B-LoRA - lora-library/B-LoRA-dog6 <Gallery /> ## Model description These are lora-library/B-LoRA-dog6 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 [v43]" to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](lora-library/B-LoRA-dog6/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
lora-library/B-LoRA-buddha	lora-library	2024-05-18T15:07:00Z	5	1	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "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	2024-05-18T15:06:55Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: A [v16] widget: - text: ' ' output: url: image_0.png --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # 'SDXL B-LoRA - lora-library/B-LoRA-buddha <Gallery /> ## Model description These are lora-library/B-LoRA-buddha 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 [v16]" to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](lora-library/B-LoRA-buddha/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
lora-library/B-LoRA-teapot	lora-library	2024-05-18T15:06:47Z	7	1	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "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	2024-05-18T15:06:42Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: A [v46] widget: - text: ' ' output: url: image_0.png --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # 'SDXL B-LoRA - lora-library/B-LoRA-teapot <Gallery /> ## Model description These are lora-library/B-LoRA-teapot 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 [v46]" to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](lora-library/B-LoRA-teapot/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
lora-library/B-LoRA-grey_sloth_plushie	lora-library	2024-05-18T15:06:41Z	3	1	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "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	2024-05-18T15:06:34Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: A [v44] widget: - text: ' ' output: url: image_0.png --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # 'SDXL B-LoRA - lora-library/B-LoRA-grey_sloth_plushie <Gallery /> ## Model description These are lora-library/B-LoRA-grey_sloth_plushie 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 [v44]" to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](lora-library/B-LoRA-grey_sloth_plushie/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
lora-library/B-LoRA-statue	lora-library	2024-05-18T15:06:27Z	44	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "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	2024-05-18T15:06:19Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: A [v20] widget: - text: ' ' output: url: image_0.png --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # 'SDXL B-LoRA - lora-library/B-LoRA-statue <Gallery /> ## Model description These are lora-library/B-LoRA-statue 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 [v20]" to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](lora-library/B-LoRA-statue/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
lora-library/B-LoRA-scary_mug	lora-library	2024-05-18T15:06:18Z	4	1	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "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	2024-05-18T15:06:10Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: A [v10] widget: - text: ' ' output: url: image_0.png --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # 'SDXL B-LoRA - lora-library/B-LoRA-scary_mug <Gallery /> ## Model description These are lora-library/B-LoRA-scary_mug 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 [v10]" to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](lora-library/B-LoRA-scary_mug/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
carlesoctav/coba-pth-4	carlesoctav	2024-05-18T15:04:36Z	38	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-18T13:54:47Z	--- 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. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Ransss/flammen24X-mistral-7B-Q8_0-GGUF	Ransss	2024-05-18T15:00:42Z	1	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:KatyTheCutie/LemonadeRP-4.5.3", "base_model:merge:KatyTheCutie/LemonadeRP-4.5.3", "base_model:Nitral-AI/Nyanade_Stunna-Maid-7B", "base_model:merge:Nitral-AI/Nyanade_Stunna-Maid-7B", "base_model:cgato/TheSpice-7b-v0.1.1", "base_model:merge:cgato/TheSpice-7b-v0.1.1", "base_model:flammenai/Mahou-1.1-mistral-7B", "base_model:merge:flammenai/Mahou-1.1-mistral-7B", "base_model:flammenai/flammen24-mistral-7B", "base_model:merge:flammenai/flammen24-mistral-7B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-18T15:00:20Z	--- license: apache-2.0 library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo base_model: - Nitral-AI/Nyanade_Stunna-Maid-7B - flammenai/flammen24-mistral-7B - cgato/TheSpice-7b-v0.1.1 - flammenai/Mahou-1.1-mistral-7B - KatyTheCutie/LemonadeRP-4.5.3 --- # Ransss/flammen24X-mistral-7B-Q8_0-GGUF This model was converted to GGUF format from [`flammenai/flammen24X-mistral-7B`](https://huggingface.co/flammenai/flammen24X-mistral-7B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/flammenai/flammen24X-mistral-7B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo Ransss/flammen24X-mistral-7B-Q8_0-GGUF --model flammen24x-mistral-7b.Q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo Ransss/flammen24X-mistral-7B-Q8_0-GGUF --model flammen24x-mistral-7b.Q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m flammen24x-mistral-7b.Q8_0.gguf -n 128 ```
TroyDoesAI/Tiny-RAG-gguf	TroyDoesAI	2024-05-18T14:56:50Z	7	0	null	[ "gguf", "license:cc-by-nc-nd-4.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-17T22:43:10Z	--- license: cc-by-nc-nd-4.0 --- # Experimenting with Dataset Quality to improve generations, TinyLlama is faster to prototype datasets. Base Model : TinyLlama Overview This model is meant to enhance adherence to provided context (e.g., for RAG applications) and reduce hallucinations, inspired by airoboros context-obedient question answer format. ## Overview The format for a contextual prompt is as follows: ``` Contextual-Request: BEGININPUT BEGINCONTEXT [key0: value0] [key1: value1] ... other metdata ... ENDCONTEXT [insert your text blocks here] ENDINPUT [add as many other blocks, in the exact same format] BEGININSTRUCTION [insert your instruction(s). The model was tuned with single questions, paragraph format, lists, etc.] ENDINSTRUCTION ``` I know it's a bit verbose and annoying, but after much trial and error, using these explicit delimiters helps the model understand where to find the responses and how to associate specific sources with it. - `Contextual-Request:` - denotes the type of request pattern the model is to follow for consistency - `BEGININPUT` - denotes a new input block - `BEGINCONTEXT` - denotes the block of context (metadata key/value pairs) to associate with the current input block - `ENDCONTEXT` - denotes the end of the metadata block for the current input - [text] - Insert whatever text you want for the input block, as many paragraphs as can fit in the context. - `ENDINPUT` - denotes the end of the current input block - [repeat as many input blocks in this format as you want] - `BEGININSTRUCTION` - denotes the start of the list (or one) instruction(s) to respond to for all of the input blocks above. - [instruction(s)] - `ENDINSTRUCTION` - denotes the end of instruction set Here's a trivial, but important example to prove the point: ``` Contextual-Request: BEGININPUT BEGINCONTEXT date: 2021-01-01 url: https://web.site/123 ENDCONTEXT In a shocking turn of events, blueberries are now green, but will be sticking with the same name. ENDINPUT BEGININSTRUCTION What color are bluberries? Source? ENDINSTRUCTION ``` And the expected response: ``` ### Contextual Response: Blueberries are now green. Source: date: 2021-01-01 url: https://web.site/123 ``` ### References in response As shown in the example, the dataset includes many examples of including source details in the response, when the question asks for source/citation/references. Why do this? Well, the R in RAG seems to be the weakest link in the chain. Retrieval accuracy, depending on many factors including the overall dataset size, can be quite low. This accuracy increases when retrieving more documents, but then you have the issue of actually using the retrieved documents in prompts. If you use one prompt per document (or document chunk), you know exactly which document the answer came from, so there's no issue. If, however, you include multiple chunks in a single prompt, it's useful to include the specific reference chunk(s) used to generate the response, rather than naively including references to all of the chunks included in the prompt. For example, suppose I have two documents: ``` url: http://foo.bar/1 Strawberries are tasty. url: http://bar.foo/2 The cat is blue. ``` If the question being asked is `What color is the cat?`, I would only expect the 2nd document to be referenced in the response, as the other link is irrelevant.
Rhma/MistralaDialo5	Rhma	2024-05-18T14:56:05Z	9	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T14:52:20Z	--- 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]
rafaelsandroni/llama-3-8b-Instruct-4bit	rafaelsandroni	2024-05-18T14:55:39Z	78	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:quantized:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-18T14:47:57Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - Developed by: rafaelsandroni - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Aurelia25/Smile_Twitter_Sentiment_Analysis	Aurelia25	2024-05-18T14:55:02Z	92	0	transformers	[ "transformers", "safetensors", "bert", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-05-18T14:46:10Z	This directory includes a few sample datasets to get you started. * `california_housing_data.csv` is California housing data from the 1990 US Census; more information is available at: https://developers.google.com/machine-learning/crash-course/california-housing-data-description `mnist_.csv` is a small sample of the [MNIST database](https://en.wikipedia.org/wiki/MNIST_database), which is described at: http://yann.lecun.com/exdb/mnist/ `anscombe.json` contains a copy of [Anscombe's quartet](https://en.wikipedia.org/wiki/Anscombe%27s_quartet); it was originally described in Anscombe, F. J. (1973). 'Graphs in Statistical Analysis'. American Statistician. 27 (1): 17-21. JSTOR 2682899. and our copy was prepared by the [vega_datasets library](https://github.com/altair-viz/vega_datasets/blob/4f67bdaad10f45e3549984e17e1b3088c731503d/vega_datasets/_data/anscombe.json).
AliSaadatV/virus_pythia_14_1024_2d_representation_MSEPlusCE	AliSaadatV	2024-05-18T14:52:48Z	128	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "generated_from_trainer", "base_model:EleutherAI/pythia-14m", "base_model:finetune:EleutherAI/pythia-14m", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T14:52:47Z	--- base_model: EleutherAI/pythia-14m tags: - generated_from_trainer model-index: - name: virus_pythia_14_1024_2d_representation_MSEPlusCE results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # virus_pythia_14_1024_2d_representation_MSEPlusCE This model is a fine-tuned version of [EleutherAI/pythia-14m](https://huggingface.co/EleutherAI/pythia-14m) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
Edgar404/a2c-PandaPickAndPlace-v3	Edgar404	2024-05-18T14:51:17Z	1	0	stable-baselines3	[ "stable-baselines3", "PandaPickAndPlace-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-18T14:46:40Z	--- 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 ... ```
rnribeiro/FT-ProsusAI-finbert	rnribeiro	2024-05-18T14:51:00Z	108	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:ProsusAI/finbert", "base_model:finetune:ProsusAI/finbert", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-18T13:16:46Z	--- base_model: ProsusAI/finbert tags: - generated_from_trainer metrics: - accuracy model-index: - name: FT-ProsusAI-finbert 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. --> # FT-ProsusAI-finbert This model is a fine-tuned version of [ProsusAI/finbert](https://huggingface.co/ProsusAI/finbert) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3622 - Accuracy: 0.85 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 40 \| 0.3829 \| 0.85 \| \| No log \| 2.0 \| 80 \| 0.3999 \| 0.825 \| \| No log \| 3.0 \| 120 \| 0.3622 \| 0.85 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2+cu118 - Datasets 2.18.0 - Tokenizers 0.15.2
rnribeiro/FT-distilbert-base-uncased	rnribeiro	2024-05-18T14:50:17Z	119	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-18T13:16:45Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: FT-distilbert-base-uncased 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. --> # FT-distilbert-base-uncased This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6614 - Accuracy: 0.65 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 40 \| 0.6806 \| 0.5 \| \| No log \| 2.0 \| 80 \| 0.6614 \| 0.65 \| \| No log \| 3.0 \| 120 \| 0.6672 \| 0.55 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2+cu118 - Datasets 2.18.0 - Tokenizers 0.15.2
rnribeiro/FT-mrm8488-distilroberta-finetuned-financial-news-sentiment-analysis	rnribeiro	2024-05-18T14:50:14Z	111	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis", "base_model:finetune:mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-18T13:16:44Z	--- license: apache-2.0 base_model: mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis tags: - generated_from_trainer metrics: - accuracy model-index: - name: FT-mrm8488-distilroberta-finetuned-financial-news-sentiment-analysis 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. --> # FT-mrm8488-distilroberta-finetuned-financial-news-sentiment-analysis This model is a fine-tuned version of [mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis](https://huggingface.co/mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2034 - Accuracy: 0.95 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 40 \| 0.2034 \| 0.95 \| \| No log \| 2.0 \| 80 \| 0.2108 \| 0.925 \| \| No log \| 3.0 \| 120 \| 0.2077 \| 0.95 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2+cu118 - Datasets 2.18.0 - Tokenizers 0.15.2
Yann2310/Reinforce	Yann2310	2024-05-18T14:49:24Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2024-05-18T14:49:22Z	--- 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: 17.30 +/- 5.37 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
JhonVanced/sin2piusc-whisper-large-v2-10k-ct2	JhonVanced	2024-05-18T14:48:30Z	16	0	ctranslate2	[ "ctranslate2", "audio", "automatic-speech-recognition", "en", "zh", "de", "es", "ru", "ko", "fr", "ja", "pt", "tr", "pl", "ca", "nl", "ar", "sv", "it", "id", "hi", "fi", "vi", "he", "uk", "el", "ms", "cs", "ro", "da", "hu", "ta", "no", "th", "ur", "hr", "bg", "lt", "la", "mi", "ml", "cy", "sk", "te", "fa", "lv", "bn", "sr", "az", "sl", "kn", "et", "mk", "br", "eu", "is", "hy", "ne", "mn", "bs", "kk", "sq", "sw", "gl", "mr", "pa", "si", "km", "sn", "yo", "so", "af", "oc", "ka", "be", "tg", "sd", "gu", "am", "yi", "lo", "uz", "fo", "ht", "ps", "tk", "nn", "mt", "sa", "lb", "my", "bo", "tl", "mg", "as", "tt", "haw", "ln", "ha", "ba", "jw", "su", "yue", "license:mit", "region:us" ]	automatic-speech-recognition	2024-05-08T13:52:11Z	--- language: - en - zh - de - es - ru - ko - fr - ja - pt - tr - pl - ca - nl - ar - sv - it - id - hi - fi - vi - he - uk - el - ms - cs - ro - da - hu - ta - 'no' - th - ur - hr - bg - lt - la - mi - ml - cy - sk - te - fa - lv - bn - sr - az - sl - kn - et - mk - br - eu - is - hy - ne - mn - bs - kk - sq - sw - gl - mr - pa - si - km - sn - yo - so - af - oc - ka - be - tg - sd - gu - am - yi - lo - uz - fo - ht - ps - tk - nn - mt - sa - lb - my - bo - tl - mg - as - tt - haw - ln - ha - ba - jw - su - yue tags: - audio - automatic-speech-recognition license: mit library_name: ctranslate2 --- Convert from: sin2piusc/whisper-large-v2-10k # Whisper large-v2 model for CTranslate2 This repository contains the conversion of [sin2piusc/whisper-large-v2-10k](https://huggingface.co/sin2piusc/whisper-large-v2-10k) to the [CTranslate2](https://github.com/OpenNMT/CTranslate2) model format. This model can be used in CTranslate2 or projects based on CTranslate2 such as [faster-whisper](https://github.com/systran/faster-whisper). ## Example ```python from faster_whisper import WhisperModel model = WhisperModel("large-v2") segments, info = model.transcribe("audio.mp3") for segment in segments: print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text)) ``` ## Conversion details The original model was converted with the following command: ``` ct2-transformers-converter --model sin2piusc/whisper-large-v2-10k --output_dir whisper-large-v2-10k-ct2 \ --copy_files tokenizer.json preprocessor_config.json --quantization float16 ``` Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the [`compute_type` option in CTranslate2](https://opennmt.net/CTranslate2/quantization.html). ## More information For more information about the original model, see its [model card](https://huggingface.co/sin2piusc/whisper-large-v2-10k).
emilykang/Gemma_medmcqa_question_generation-pharmacology_lora	emilykang	2024-05-18T14:48:15Z	0	0	peft	[ "peft", "safetensors", "gemma", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:google/gemma-2b", "base_model:adapter:google/gemma-2b", "license:gemma", "region:us" ]	null	2024-05-18T13:37:46Z	--- license: gemma library_name: peft tags: - trl - sft - generated_from_trainer base_model: google/gemma-2b datasets: - generator model-index: - name: Gemma_medmcqa_question_generation-pharmacology_lora results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Gemma_medmcqa_question_generation-pharmacology_lora This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1
JhonVanced/sin2piusc-whisper-large-v2-10k-ct2-int8_float32	JhonVanced	2024-05-18T14:47:11Z	18	0	ctranslate2	[ "ctranslate2", "audio", "automatic-speech-recognition", "en", "zh", "de", "es", "ru", "ko", "fr", "ja", "pt", "tr", "pl", "ca", "nl", "ar", "sv", "it", "id", "hi", "fi", "vi", "he", "uk", "el", "ms", "cs", "ro", "da", "hu", "ta", "no", "th", "ur", "hr", "bg", "lt", "la", "mi", "ml", "cy", "sk", "te", "fa", "lv", "bn", "sr", "az", "sl", "kn", "et", "mk", "br", "eu", "is", "hy", "ne", "mn", "bs", "kk", "sq", "sw", "gl", "mr", "pa", "si", "km", "sn", "yo", "so", "af", "oc", "ka", "be", "tg", "sd", "gu", "am", "yi", "lo", "uz", "fo", "ht", "ps", "tk", "nn", "mt", "sa", "lb", "my", "bo", "tl", "mg", "as", "tt", "haw", "ln", "ha", "ba", "jw", "su", "yue", "license:mit", "region:us" ]	automatic-speech-recognition	2024-05-18T13:53:58Z	--- language: - en - zh - de - es - ru - ko - fr - ja - pt - tr - pl - ca - nl - ar - sv - it - id - hi - fi - vi - he - uk - el - ms - cs - ro - da - hu - ta - 'no' - th - ur - hr - bg - lt - la - mi - ml - cy - sk - te - fa - lv - bn - sr - az - sl - kn - et - mk - br - eu - is - hy - ne - mn - bs - kk - sq - sw - gl - mr - pa - si - km - sn - yo - so - af - oc - ka - be - tg - sd - gu - am - yi - lo - uz - fo - ht - ps - tk - nn - mt - sa - lb - my - bo - tl - mg - as - tt - haw - ln - ha - ba - jw - su - yue tags: - audio - automatic-speech-recognition license: mit library_name: ctranslate2 --- Convert from: sin2piusc/whisper-large-v2-10k # Whisper large-v2 model for CTranslate2 This repository contains the conversion of [sin2piusc/whisper-large-v2-10k](https://huggingface.co/sin2piusc/whisper-large-v2-10k) to the [CTranslate2](https://github.com/OpenNMT/CTranslate2) model format. This model can be used in CTranslate2 or projects based on CTranslate2 such as [faster-whisper](https://github.com/systran/faster-whisper). ## Example ```python from faster_whisper import WhisperModel model = WhisperModel("large-v2") segments, info = model.transcribe("audio.mp3") for segment in segments: print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text)) ``` ## Conversion details The original model was converted with the following command: ``` ct2-transformers-converter --model sin2piusc/whisper-large-v2-10k --output_dir whisper-large-v2-10k-ct2-int8_float32 \ --copy_files tokenizer.json preprocessor_config.json --quantization int8_float32 ``` Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the [`compute_type` option in CTranslate2](https://opennmt.net/CTranslate2/quantization.html). ## More information For more information about the original model, see its [model card](https://huggingface.co/sin2piusc/whisper-large-v2-10k).
roofdancer/thesis-led-finetuned	roofdancer	2024-05-18T14:47:02Z	7	0	transformers	[ "transformers", "safetensors", "led", "text2text-generation", "generated_from_trainer", "base_model:allenai/led-base-16384", "base_model:finetune:allenai/led-base-16384", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-05T18:42:41Z	--- license: apache-2.0 base_model: allenai/led-base-16384 tags: - generated_from_trainer metrics: - rouge model-index: - name: thesis-led-finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # thesis-led-finetuned This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.1628 - Rouge1: 42.239 - Rouge2: 14.7162 - Rougel: 22.9523 - Rougelsum: 38.2971 - Gen Len: 219.9278 ## 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: 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: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-------:\|:-------:\|:---------:\|:--------:\| \| 2.2358 \| 1.0 \| 7870 \| 2.1628 \| 42.239 \| 14.7162 \| 22.9523 \| 38.2971 \| 219.9278 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
RichardErkhov/guardrail_-_llama-2-7b-guanaco-instruct-sharded-4bits	RichardErkhov	2024-05-18T14:43:43Z	78	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-18T14:38:13Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) llama-2-7b-guanaco-instruct-sharded - bnb 4bits - Model creator: https://huggingface.co/guardrail/ - Original model: https://huggingface.co/guardrail/llama-2-7b-guanaco-instruct-sharded/ Original model description: --- license: apache-2.0 datasets: - timdettmers/openassistant-guanaco pipeline_tag: text-generation --- Model that is fine-tuned in 4-bit precision using QLoRA on [timdettmers/openassistant-guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) and sharded to be used on a free Google Colab instance that can be loaded with 4bits. It can be easily imported using the `AutoModelForCausalLM` class from `transformers`: ``` from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained( "guardrail/llama-2-7b-guanaco-instruct-sharded", load_in_4bit=True) tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) ```
ethan-ky/distilbert-base-uncased-finetuned-emotion	ethan-ky	2024-05-18T14:41:29Z	119	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-17T03:36:33Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9215 - name: F1 type: f1 value: 0.9213719420412787 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2083 - Accuracy: 0.9215 - F1: 0.9214 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8068 \| 1.0 \| 250 \| 0.2897 \| 0.9155 \| 0.9148 \| \| 0.2389 \| 2.0 \| 500 \| 0.2083 \| 0.9215 \| 0.9214 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.3.0 - Datasets 2.19.1 - Tokenizers 0.19.1
saousan/Reinforce-cartpool	saousan	2024-05-18T14:40:35Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2024-05-18T14:40:26Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-cartpool 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
jerryjiao198/Marcoro14-7B-slerp	jerryjiao198	2024-05-18T14:40:28Z	0	0	null	[ "merge", "mergekit", "lazymergekit", "AIDC-ai-business/Marcoroni-7B-v3", "EmbeddedLLM/Mistral-7B-Merge-14-v0.1", "license:apache-2.0", "region:us" ]	null	2024-05-16T02:58:43Z	--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - AIDC-ai-business/Marcoroni-7B-v3 - EmbeddedLLM/Mistral-7B-Merge-14-v0.1 --- # Marcoro14-7B-slerp Marcoro14-7B-slerp is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): * [AIDC-ai-business/Marcoroni-7B-v3](https://huggingface.co/AIDC-ai-business/Marcoroni-7B-v3) * [EmbeddedLLM/Mistral-7B-Merge-14-v0.1](https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0.1) ## 🧩 Configuration ```yaml slices: - sources: - model: AIDC-ai-business/Marcoroni-7B-v3 layer_range: [0, 32] - model: EmbeddedLLM/Mistral-7B-Merge-14-v0.1 layer_range: [0, 32] merge_method: slerp base_model: AIDC-ai-business/Marcoroni-7B-v3 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ```
aldjia/Pixelcopter-PLE-v0	aldjia	2024-05-18T14:39:09Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2024-05-18T14:38:59Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Pixelcopter-PLE-v0 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
Schadic/Reinforce-1	Schadic	2024-05-18T14:38:28Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2024-05-18T14:38:19Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 497.60 +/- 7.20 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
DanYuHF/sd-class-butterflies-32	DanYuHF	2024-05-18T14:37:41Z	44	0	diffusers	[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2024-05-18T14:37:07Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('DanYuHF/sd-class-butterflies-32') image = pipeline().images[0] image ```
emilykang/Phi_medmcqa_question_generation_model	emilykang	2024-05-18T14:36:35Z	128	0	transformers	[ "transformers", "safetensors", "phi", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T14:27:07Z	--- 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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RichardErkhov/macadeliccc_-_laser-dolphin-mixtral-2x7b-dpo-gguf	RichardErkhov	2024-05-18T14:31:42Z	38	0	null	[ "gguf", "arxiv:2312.13558", "endpoints_compatible", "region:us" ]	null	2024-05-18T11:58:04Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) laser-dolphin-mixtral-2x7b-dpo - GGUF - Model creator: https://huggingface.co/macadeliccc/ - Original model: https://huggingface.co/macadeliccc/laser-dolphin-mixtral-2x7b-dpo/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [laser-dolphin-mixtral-2x7b-dpo.Q2_K.gguf](https://huggingface.co/RichardErkhov/macadeliccc_-_laser-dolphin-mixtral-2x7b-dpo-gguf/blob/main/laser-dolphin-mixtral-2x7b-dpo.Q2_K.gguf) \| Q2_K \| 4.43GB \| \| [laser-dolphin-mixtral-2x7b-dpo.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/macadeliccc_-_laser-dolphin-mixtral-2x7b-dpo-gguf/blob/main/laser-dolphin-mixtral-2x7b-dpo.IQ3_XS.gguf) \| IQ3_XS \| 4.94GB \| \| [laser-dolphin-mixtral-2x7b-dpo.IQ3_S.gguf](https://huggingface.co/RichardErkhov/macadeliccc_-_laser-dolphin-mixtral-2x7b-dpo-gguf/blob/main/laser-dolphin-mixtral-2x7b-dpo.IQ3_S.gguf) \| IQ3_S \| 5.22GB \| \| [laser-dolphin-mixtral-2x7b-dpo.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/macadeliccc_-_laser-dolphin-mixtral-2x7b-dpo-gguf/blob/main/laser-dolphin-mixtral-2x7b-dpo.Q3_K_S.gguf) \| Q3_K_S \| 5.2GB \| \| [laser-dolphin-mixtral-2x7b-dpo.IQ3_M.gguf](https://huggingface.co/RichardErkhov/macadeliccc_-_laser-dolphin-mixtral-2x7b-dpo-gguf/blob/main/laser-dolphin-mixtral-2x7b-dpo.IQ3_M.gguf) \| IQ3_M \| 5.34GB \| \| [laser-dolphin-mixtral-2x7b-dpo.Q3_K.gguf](https://huggingface.co/RichardErkhov/macadeliccc_-_laser-dolphin-mixtral-2x7b-dpo-gguf/blob/main/laser-dolphin-mixtral-2x7b-dpo.Q3_K.gguf) \| Q3_K \| 5.78GB \| \| [laser-dolphin-mixtral-2x7b-dpo.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/macadeliccc_-_laser-dolphin-mixtral-2x7b-dpo-gguf/blob/main/laser-dolphin-mixtral-2x7b-dpo.Q3_K_M.gguf) \| Q3_K_M \| 5.78GB \| \| [laser-dolphin-mixtral-2x7b-dpo.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/macadeliccc_-_laser-dolphin-mixtral-2x7b-dpo-gguf/blob/main/laser-dolphin-mixtral-2x7b-dpo.Q3_K_L.gguf) \| Q3_K_L \| 6.27GB \| \| [laser-dolphin-mixtral-2x7b-dpo.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/macadeliccc_-_laser-dolphin-mixtral-2x7b-dpo-gguf/blob/main/laser-dolphin-mixtral-2x7b-dpo.IQ4_XS.gguf) \| IQ4_XS \| 6.5GB \| \| [laser-dolphin-mixtral-2x7b-dpo.Q4_0.gguf](https://huggingface.co/RichardErkhov/macadeliccc_-_laser-dolphin-mixtral-2x7b-dpo-gguf/blob/main/laser-dolphin-mixtral-2x7b-dpo.Q4_0.gguf) \| Q4_0 \| 6.78GB \| \| [laser-dolphin-mixtral-2x7b-dpo.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/macadeliccc_-_laser-dolphin-mixtral-2x7b-dpo-gguf/blob/main/laser-dolphin-mixtral-2x7b-dpo.IQ4_NL.gguf) \| IQ4_NL \| 6.85GB \| \| [laser-dolphin-mixtral-2x7b-dpo.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/macadeliccc_-_laser-dolphin-mixtral-2x7b-dpo-gguf/blob/main/laser-dolphin-mixtral-2x7b-dpo.Q4_K_S.gguf) \| Q4_K_S \| 6.84GB \| \| [laser-dolphin-mixtral-2x7b-dpo.Q4_K.gguf](https://huggingface.co/RichardErkhov/macadeliccc_-_laser-dolphin-mixtral-2x7b-dpo-gguf/blob/main/laser-dolphin-mixtral-2x7b-dpo.Q4_K.gguf) \| Q4_K \| 7.25GB \| \| [laser-dolphin-mixtral-2x7b-dpo.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/macadeliccc_-_laser-dolphin-mixtral-2x7b-dpo-gguf/blob/main/laser-dolphin-mixtral-2x7b-dpo.Q4_K_M.gguf) \| Q4_K_M \| 7.25GB \| \| [laser-dolphin-mixtral-2x7b-dpo.Q4_1.gguf](https://huggingface.co/RichardErkhov/macadeliccc_-_laser-dolphin-mixtral-2x7b-dpo-gguf/blob/main/laser-dolphin-mixtral-2x7b-dpo.Q4_1.gguf) \| Q4_1 \| 7.52GB \| \| [laser-dolphin-mixtral-2x7b-dpo.Q5_0.gguf](https://huggingface.co/RichardErkhov/macadeliccc_-_laser-dolphin-mixtral-2x7b-dpo-gguf/blob/main/laser-dolphin-mixtral-2x7b-dpo.Q5_0.gguf) \| Q5_0 \| 8.26GB \| \| [laser-dolphin-mixtral-2x7b-dpo.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/macadeliccc_-_laser-dolphin-mixtral-2x7b-dpo-gguf/blob/main/laser-dolphin-mixtral-2x7b-dpo.Q5_K_S.gguf) \| Q5_K_S \| 8.26GB \| \| [laser-dolphin-mixtral-2x7b-dpo.Q5_K.gguf](https://huggingface.co/RichardErkhov/macadeliccc_-_laser-dolphin-mixtral-2x7b-dpo-gguf/blob/main/laser-dolphin-mixtral-2x7b-dpo.Q5_K.gguf) \| Q5_K \| 8.51GB \| \| [laser-dolphin-mixtral-2x7b-dpo.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/macadeliccc_-_laser-dolphin-mixtral-2x7b-dpo-gguf/blob/main/laser-dolphin-mixtral-2x7b-dpo.Q5_K_M.gguf) \| Q5_K_M \| 8.51GB \| \| [laser-dolphin-mixtral-2x7b-dpo.Q5_1.gguf](https://huggingface.co/RichardErkhov/macadeliccc_-_laser-dolphin-mixtral-2x7b-dpo-gguf/blob/main/laser-dolphin-mixtral-2x7b-dpo.Q5_1.gguf) \| Q5_1 \| 9.01GB \| \| [laser-dolphin-mixtral-2x7b-dpo.Q6_K.gguf](https://huggingface.co/RichardErkhov/macadeliccc_-_laser-dolphin-mixtral-2x7b-dpo-gguf/blob/main/laser-dolphin-mixtral-2x7b-dpo.Q6_K.gguf) \| Q6_K \| 9.84GB \| \| [laser-dolphin-mixtral-2x7b-dpo.Q8_0.gguf](https://huggingface.co/RichardErkhov/macadeliccc_-_laser-dolphin-mixtral-2x7b-dpo-gguf/blob/main/laser-dolphin-mixtral-2x7b-dpo.Q8_0.gguf) \| Q8_0 \| 12.75GB \| Original model description: --- license: apache-2.0 library_name: transformers model-index: - name: laser-dolphin-mixtral-2x7b-dpo results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 65.96 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/laser-dolphin-mixtral-2x7b-dpo name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 85.8 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/laser-dolphin-mixtral-2x7b-dpo name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 63.17 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/laser-dolphin-mixtral-2x7b-dpo name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 60.76 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/laser-dolphin-mixtral-2x7b-dpo name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 79.01 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/laser-dolphin-mixtral-2x7b-dpo name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 48.29 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/laser-dolphin-mixtral-2x7b-dpo name: Open LLM Leaderboard --- # Laser-Dolphin-Mixtral-2x7b-dpo ![laser_dolphin_image](./dolphin_moe.png) New Version out now! Credit to Fernando Fernandes and Eric Hartford for their project [laserRMT](https://github.com/cognitivecomputations/laserRMT) ## Overview This model is a medium-sized MoE implementation based on [cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser](https://huggingface.co/cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser) + The new version shows ~1 point increase in evaluation performance on average. ## Process + The process is outlined in this [notebook](https://github.com/cognitivecomputations/laserRMT/blob/main/examples/laser-dolphin-mixtral-2x7b.ipynb) + The mergekit_config is in the files. + The models used in the configuration are not lasered, but the final product is. This is an update from the last version. + This process is experimental. Your mileage may vary. ## Future Goals + [ ] Function Calling + [ ] v2 with new base model to improve performance ## Quantizations ### ExLlamav2 _These are the recommended quantizations for users that are running the model on GPU_ Thanks to user [bartowski](https://huggingface.co/bartowski) we now have exllamav2 quantizations in 3.5 through 8 bpw. They are available here: + [bartowski/laser-dolphin-mixtral-2x7b-dpo-exl2](https://huggingface.co/bartowski/laser-dolphin-mixtral-2x7b-dpo-exl2) \| Branch \| Bits \| lm_head bits \| VRAM (4k) \| VRAM (16k) \| VRAM (32k) \| Description \| \| ----- \| ---- \| ------- \| ------ \| ------ \| ------ \| ------------ \| \| [8_0](https://huggingface.co/bartowski/laser-dolphin-mixtral-2x7b-dpo-exl2/tree/8_0) \| 8.0 \| 8.0 \| 13.7 GB \| 15.1 GB \| 17.2 GB \| Maximum quality that ExLlamaV2 can produce, near unquantized performance. \| \| [6_5](https://huggingface.co/bartowski/laser-dolphin-mixtral-2x7b-dpo-exl2/tree/6_5) \| 6.5 \| 8.0 \| 11.5 GB \| 12.9 GB \| 15.0 GB \| Near unquantized performance at vastly reduced size, recommended. \| \| [5_0](https://huggingface.co/bartowski/laser-dolphin-mixtral-2x7b-dpo-exl2/tree/5_0) \| 5.0 \| 6.0 \| 9.3 GB \| 10.7 GB \| 12.8 GB \| Slightly lower quality vs 6.5, great for 12gb cards with 16k context. \| \| [4_25](https://huggingface.co/bartowski/laser-dolphin-mixtral-2x7b-dpo-exl2/tree/4_25) \| 4.25 \| 6.0 \| 8.2 GB \| 9.6 GB \| 11.7 GB \| GPTQ equivalent bits per weight. \| \| [3_5](https://huggingface.co/bartowski/laser-dolphin-mixtral-2x7b-dpo-exl2/tree/3_5) \| 3.5 \| 6.0 \| 7.0 GB \| 8.4 GB \| 10.5 GB \| Lower quality, not recommended. \| His quantizations represent the first ~13B model with GQA support. Check out his repo for more information! ### GGUF Current GGUF [Quantizations](https://huggingface.co/macadeliccc/laser-dolphin-mixtral-2x7b-dpo-GGUF) ### AWQ Current AWQ [Quantizations](https://huggingface.co/macadeliccc/laser-dolphin-mixtral-2x7b-dpo-AWQ) ### TheBloke These Quants will result in unpredicted behavior. New quants are available as I have updated the model* Quatizations provided by [TheBloke](https://huggingface.co/TheBloke/laser-dolphin-mixtral-2x7b-dpo-GGUF) ## HF Spaces + GGUF chat available [here](https://huggingface.co/spaces/macadeliccc/laser-dolphin-mixtral-chat-GGUF) + 4-bit bnb chat available [here](https://huggingface.co/spaces/macadeliccc/laser-dolphin-mixtral-chat) # Ollama ```bash ollama run macadeliccc/laser-dolphin-mixtral-2x7b-dpo ``` ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6455cc8d679315e4ef16fbec/oVwa7Dwkt00tk8_MtlJdR.png) ## Code Example Switch the commented model definition to use in 4-bit. Should work with 9GB and still exceed the single 7B model by 5-6 points roughly ```python from transformers import AutoModelForCausalLM, AutoTokenizer def generate_response(prompt): """ Generate a response from the model based on the input prompt. Args: prompt (str): Prompt for the model. Returns: str: The generated response from the model. """ # Tokenize the input prompt inputs = tokenizer(prompt, return_tensors="pt") # Generate output tokens outputs = model.generate(**inputs, max_new_tokens=256, eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.pad_token_id) # Decode the generated tokens to a string response = tokenizer.decode(outputs[0], skip_special_tokens=True) return response # Load the model and tokenizer model_id = "macadeliccc/laser-dolphin-mixtral-2x7b-dpo" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id, load_in_4bit=True) prompt = "Write a quicksort algorithm in python" # Generate and print responses for each language print("Response:") print(generate_response(prompt), "\n") ``` [colab](https://colab.research.google.com/drive/1cmRhAkDWItV7utHNqNANVZnqDqQNsTUr?usp=sharing) with usage example ## Eval ## EQ Bench <pre>----Benchmark Complete---- 2024-01-31 16:55:37 Time taken: 31.1 mins Prompt Format: ChatML Model: macadeliccc/laser-dolphin-mixtral-2x7b-dpo-GGUF Score (v2): 72.76 Parseable: 171.0 --------------- Batch completed Time taken: 31.2 mins --------------- </pre> evaluation [colab](https://colab.research.google.com/drive/1FpwgsGzCR4tORTxAwUxpN3PcP22En2xk?usp=sharing) ## Summary of previous evaluation \| Model \|AGIEval\|GPT4All\|TruthfulQA\|Bigbench\|Average\| \|---------------------------------------------------------------------------------------------------\|------:\|------:\|---------:\|-------:\|------:\| \|[laser-dolphin-mixtral-2x7b-dpo](https://huggingface.co/macadeliccc/laser-dolphin-mixtral-2x7b-dpo)\| 41.31\| 73.67\| 61.69\| 42.79\| 54.87\| ## Detailed current evaluation \| Model \|AGIEval\|GPT4All\|TruthfulQA\|Bigbench\|Average\| \|---------------------------------------------------------------------------------------------------\|------:\|------:\|---------:\|-------:\|------:\| \|[laser-dolphin-mixtral-2x7b-dpo](https://huggingface.co/macadeliccc/laser-dolphin-mixtral-2x7b-dpo)\| 42.25\| 73.45\| 63.44\| 43.96\| 55.77\| ### AGIEval \| Task \|Version\| Metric \|Value\| \|Stderr\| \|------------------------------\|------:\|--------\|----:\|---\|-----:\| \|agieval_aqua_rat \| 0\|acc \|21.26\|± \| 2.57\| \| \| \|acc_norm\|21.65\|± \| 2.59\| \|agieval_logiqa_en \| 0\|acc \|34.72\|± \| 1.87\| \| \| \|acc_norm\|35.64\|± \| 1.88\| \|agieval_lsat_ar \| 0\|acc \|26.96\|± \| 2.93\| \| \| \|acc_norm\|26.96\|± \| 2.93\| \|agieval_lsat_lr \| 0\|acc \|45.88\|± \| 2.21\| \| \| \|acc_norm\|46.08\|± \| 2.21\| \|agieval_lsat_rc \| 0\|acc \|59.48\|± \| 3.00\| \| \| \|acc_norm\|59.48\|± \| 3.00\| \|agieval_sat_en \| 0\|acc \|73.79\|± \| 3.07\| \| \| \|acc_norm\|73.79\|± \| 3.07\| \|agieval_sat_en_without_passage\| 0\|acc \|42.23\|± \| 3.45\| \| \| \|acc_norm\|41.26\|± \| 3.44\| \|agieval_sat_math \| 0\|acc \|37.27\|± \| 3.27\| \| \| \|acc_norm\|33.18\|± \| 3.18\| Average: 42.25% ### GPT4All \| Task \|Version\| Metric \|Value\| \|Stderr\| \|-------------\|------:\|--------\|----:\|---\|-----:\| \|arc_challenge\| 0\|acc \|58.36\|± \| 1.44\| \| \| \|acc_norm\|58.02\|± \| 1.44\| \|arc_easy \| 0\|acc \|82.20\|± \| 0.78\| \| \| \|acc_norm\|77.40\|± \| 0.86\| \|boolq \| 1\|acc \|87.52\|± \| 0.58\| \|hellaswag \| 0\|acc \|67.50\|± \| 0.47\| \| \| \|acc_norm\|84.43\|± \| 0.36\| \|openbookqa \| 0\|acc \|34.40\|± \| 2.13\| \| \| \|acc_norm\|47.00\|± \| 2.23\| \|piqa \| 0\|acc \|81.61\|± \| 0.90\| \| \| \|acc_norm\|82.59\|± \| 0.88\| \|winogrande \| 0\|acc \|77.19\|± \| 1.18\| Average: 73.45% ### GSM8K \|Task \|Version\| Metric \|Value\| \|Stderr\| \|-----\|------:\|-----------------------------\|-----\|---\|------\| \|gsm8k\| 2\|exact_match,get-answer \| 0.75\| \| \| \| \| \|exact_match_stderr,get-answer\| 0.01\| \| \| \| \| \|alias \|gsm8k\| \| \| ### TruthfulQA \| Task \|Version\|Metric\|Value\| \|Stderr\| \|-------------\|------:\|------\|----:\|---\|-----:\| \|truthfulqa_mc\| 1\|mc1 \|45.90\|± \| 1.74\| \| \| \|mc2 \|63.44\|± \| 1.56\| Average: 63.44% ### Bigbench \| Task \|Version\| Metric \|Value\| \|Stderr\| \|------------------------------------------------\|------:\|---------------------\|----:\|---\|-----:\| \|bigbench_causal_judgement \| 0\|multiple_choice_grade\|58.42\|± \| 3.59\| \|bigbench_date_understanding \| 0\|multiple_choice_grade\|60.70\|± \| 2.55\| \|bigbench_disambiguation_qa \| 0\|multiple_choice_grade\|38.37\|± \| 3.03\| \|bigbench_geometric_shapes \| 0\|multiple_choice_grade\|21.73\|± \| 2.18\| \| \| \|exact_str_match \| 0.00\|± \| 0.00\| \|bigbench_logical_deduction_five_objects \| 0\|multiple_choice_grade\|35.00\|± \| 2.14\| \|bigbench_logical_deduction_seven_objects \| 0\|multiple_choice_grade\|23.57\|± \| 1.61\| \|bigbench_logical_deduction_three_objects \| 0\|multiple_choice_grade\|50.33\|± \| 2.89\| \|bigbench_movie_recommendation \| 0\|multiple_choice_grade\|45.00\|± \| 2.23\| \|bigbench_navigate \| 0\|multiple_choice_grade\|50.00\|± \| 1.58\| \|bigbench_reasoning_about_colored_objects \| 0\|multiple_choice_grade\|60.35\|± \| 1.09\| \|bigbench_ruin_names \| 0\|multiple_choice_grade\|51.12\|± \| 2.36\| \|bigbench_salient_translation_error_detection \| 0\|multiple_choice_grade\|32.26\|± \| 1.48\| \|bigbench_snarks \| 0\|multiple_choice_grade\|67.96\|± \| 3.48\| \|bigbench_sports_understanding \| 0\|multiple_choice_grade\|70.59\|± \| 1.45\| \|bigbench_temporal_sequences \| 0\|multiple_choice_grade\|35.80\|± \| 1.52\| \|bigbench_tracking_shuffled_objects_five_objects \| 0\|multiple_choice_grade\|22.56\|± \| 1.18\| \|bigbench_tracking_shuffled_objects_seven_objects\| 0\|multiple_choice_grade\|17.20\|± \| 0.90\| \|bigbench_tracking_shuffled_objects_three_objects\| 0\|multiple_choice_grade\|50.33\|± \| 2.89\| Average: 43.96% Average score: 55.77% Elapsed time: 02:43:45 ## Citations Fernando Fernandes Neto and Eric Hartford. "Optimizing Large Language Models Using Layer-Selective Rank Reduction and Random Matrix Theory." 2024. ```bibtex @article{sharma2023truth, title={The Truth is in There: Improving Reasoning in Language Models with Layer-Selective Rank Reduction}, author={Sharma, Pratyusha and Ash, Jordan T and Misra, Dipendra}, journal={arXiv preprint arXiv:2312.13558}, year={2023} } ``` ```bibtex @article{gao2021framework, title={A framework for few-shot language model evaluation}, author={Gao, Leo and Tow, Jonathan and Biderman, Stella and Black, Sid and DiPofi, Anthony and Foster, Charles and Golding, Laurence and Hsu, Jeffrey and McDonell, Kyle and Muennighoff, Niklas and others}, journal={Version v0. 0.1. Sept}, year={2021} } ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_macadeliccc__laser-dolphin-mixtral-2x7b-dpo) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|67.16\| \|AI2 Reasoning Challenge (25-Shot)\|65.96\| \|HellaSwag (10-Shot) \|85.80\| \|MMLU (5-Shot) \|63.17\| \|TruthfulQA (0-shot) \|60.76\| \|Winogrande (5-shot) \|79.01\| \|GSM8k (5-shot) \|48.29\|
Ransss/Kuro-Lotus-10.7B-Q8_0-GGUF	Ransss	2024-05-18T14:31:06Z	0	0	null	[ "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:BlueNipples/SnowLotus-v2-10.7B", "base_model:merge:BlueNipples/SnowLotus-v2-10.7B", "base_model:Himitsui/KuroMitsu-11B", "base_model:merge:Himitsui/KuroMitsu-11B", "license:cc-by-nc-4.0", "model-index", "endpoints_compatible", "region:us" ]	null	2024-05-18T14:30:35Z	--- license: cc-by-nc-4.0 tags: - mergekit - merge - llama-cpp - gguf-my-repo base_model: - BlueNipples/SnowLotus-v2-10.7B - Himitsui/KuroMitsu-11B model-index: - name: Kuro-Lotus-10.7B results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 68.69 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/Kuro-Lotus-10.7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 87.51 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/Kuro-Lotus-10.7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 66.64 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/Kuro-Lotus-10.7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 58.27 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/Kuro-Lotus-10.7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 84.21 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/Kuro-Lotus-10.7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 66.11 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/Kuro-Lotus-10.7B name: Open LLM Leaderboard --- # Ransss/Kuro-Lotus-10.7B-Q8_0-GGUF This model was converted to GGUF format from [`saishf/Kuro-Lotus-10.7B`](https://huggingface.co/saishf/Kuro-Lotus-10.7B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/saishf/Kuro-Lotus-10.7B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo Ransss/Kuro-Lotus-10.7B-Q8_0-GGUF --model kuro-lotus-10.7b.Q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo Ransss/Kuro-Lotus-10.7B-Q8_0-GGUF --model kuro-lotus-10.7b.Q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m kuro-lotus-10.7b.Q8_0.gguf -n 128 ```
aertsimon90/Thuner24	aertsimon90	2024-05-18T14:27:55Z	0	0	transformers	[ "transformers", "text-generation", "tr", "en", "de", "it", "ru", "ar", "dataset:open-llm-leaderboard/details_mistralai__Mistral-7B-Instruct-v0.2", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T14:22:24Z	--- license: apache-2.0 pipeline_tag: text-generation datasets: - open-llm-leaderboard/details_mistralai__Mistral-7B-Instruct-v0.2 language: - tr - en - de - it - ru - ar metrics: - character library_name: transformers ---
rafaelsandroni/llama-3-8b-Instruct-16bit	rafaelsandroni	2024-05-18T14:19:48Z	4	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T14:10:07Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - Developed by: rafaelsandroni - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Ja3ffar/tuned-mist	Ja3ffar	2024-05-18T14:19:48Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2", "region:us" ]	null	2024-05-17T22:30:26Z	--- library_name: peft base_model: mistralai/Mistral-7B-Instruct-v0.2 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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EssalhiSara/gpt2-french-corpus	EssalhiSara	2024-05-18T14:15:16Z	130	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T14:15:03Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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PQlet/lora-narutoblip-v1-ablation-r16-a16-module_to_q_to_v	PQlet	2024-05-18T14:14:48Z	1	0	diffusers	[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers-training", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-05-18T14:14:43Z	--- license: creativeml-openrail-m library_name: diffusers tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - diffusers-training - lora base_model: runwayml/stable-diffusion-v1-5 inference: true --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # LoRA text2image fine-tuning - PQlet/lora-narutoblip-v1-ablation-r16-a16-module_to_q_to_v These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the Naruto-BLIP dataset. You can find some example images in the following. ![img_0](./image_a_man_with_glasses_and_a_shirt_on.png) ![img_1](./image_a_group_of_people_sitting_on_the_ground.png) ![img_2](./image_a_man_in_a_green_hoodie_standing_in_front_of_a_mountain.png) ![img_3](./image_a_man_with_a_gun_in_his_hand.png) ![img_4](./image_a_woman_with_red_hair_and_a_cat_on_her_head.png) ![img_5](./image_two_pokemons_sitting_on_top_of_a_cloud.png) ![img_6](./image_a_man_standing_in_front_of_a_bridge.png) ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
charlesdj/CSR_LLaVA_1.5_7b_3Iteration	charlesdj	2024-05-18T14:12:34Z	79	0	transformers	[ "transformers", "safetensors", "llava_llama", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T14:08:17Z	--- license: apache-2.0 ---
rtx07/mental_health_40k	rtx07	2024-05-18T14:11:50Z	89	0	transformers	[ "transformers", "gpt_neox", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T13:33:35Z	--- license: apache-2.0 ---
Dandan0K/Pilot_vox_Ref_italian	Dandan0K	2024-05-18T14:07:44Z	78	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "it", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-18T14:00:14Z	--- language: - it license: apache-2.0 tags: - automatic-speech-recognition - it datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2t_it_vp-100k_s449 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (it)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
alexandro767/stable-diffusion-v1-5-finetuned_5e_r8_v1	alexandro767	2024-05-18T14:03:54Z	29	0	diffusers	[ "diffusers", "safetensors", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-05-18T14:00:56Z	--- library_name: diffusers --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
emilykang/Phi_medmcqa_question_generation-gynaecology_n_obstetrics_lora	emilykang	2024-05-18T14:01:02Z	1	0	peft	[ "peft", "safetensors", "phi", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:microsoft/phi-2", "base_model:adapter:microsoft/phi-2", "license:mit", "region:us" ]	null	2024-05-17T16:40:38Z	--- license: mit library_name: peft tags: - trl - sft - generated_from_trainer base_model: microsoft/phi-2 datasets: - generator model-index: - name: Phi_medmcqa_question_generation-gynaecology_n_obstetrics_lora results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Phi_medmcqa_question_generation-gynaecology_n_obstetrics_lora This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1
selmamalak/organcmnist-swin-base-finetuned	selmamalak	2024-05-18T14:00:55Z	8	0	peft	[ "peft", "safetensors", "generated_from_trainer", "dataset:medmnist-v2", "base_model:microsoft/swin-large-patch4-window7-224-in22k", "base_model:adapter:microsoft/swin-large-patch4-window7-224-in22k", "license:apache-2.0", "region:us" ]	null	2024-05-18T13:03:26Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: microsoft/swin-large-patch4-window7-224-in22k datasets: - medmnist-v2 metrics: - accuracy - precision - recall - f1 model-index: - name: organcmnist-swin-base-finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # organcmnist-swin-base-finetuned This model is a fine-tuned version of [microsoft/swin-large-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-large-patch4-window7-224-in22k) on the medmnist-v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.2582 - Accuracy: 0.9317 - Precision: 0.9295 - Recall: 0.9177 - F1: 0.9229 ## 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: 0.005 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.7563 \| 0.9988 \| 203 \| 0.1859 \| 0.9365 \| 0.9432 \| 0.9127 \| 0.9201 \| \| 0.6145 \| 1.9975 \| 406 \| 0.1260 \| 0.9640 \| 0.9630 \| 0.9608 \| 0.9600 \| \| 0.6476 \| 2.9963 \| 609 \| 0.0926 \| 0.9774 \| 0.9715 \| 0.9754 \| 0.9723 \| \| 0.5719 \| 4.0 \| 813 \| 0.0912 \| 0.9770 \| 0.9749 \| 0.9746 \| 0.9740 \| \| 0.5374 \| 4.9988 \| 1016 \| 0.1281 \| 0.9695 \| 0.9730 \| 0.9690 \| 0.9699 \| \| 0.5615 \| 5.9975 \| 1219 \| 0.1088 \| 0.9791 \| 0.9839 \| 0.9819 \| 0.9825 \| \| 0.4959 \| 6.9963 \| 1422 \| 0.1134 \| 0.9741 \| 0.9812 \| 0.9742 \| 0.9768 \| \| 0.425 \| 8.0 \| 1626 \| 0.1016 \| 0.9808 \| 0.9816 \| 0.9820 \| 0.9815 \| \| 0.3151 \| 8.9988 \| 1829 \| 0.1368 \| 0.9804 \| 0.9843 \| 0.9832 \| 0.9834 \| \| 0.3347 \| 9.9877 \| 2030 \| 0.1156 \| 0.9837 \| 0.9853 \| 0.9864 \| 0.9856 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
stablediffusionapi/analog-madness-v70	stablediffusionapi	2024-05-18T13:59:25Z	29	0	diffusers	[ "diffusers", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-05-18T13:57:23Z	--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # Analog Madness v7.0 API Inference ![generated from modelslab.com](https://pub-3626123a908346a7a8be8d9295f44e26.r2.dev/generations/629076491716040429.png) ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change model_id to "analog-madness-v70" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://docs.modelslab.com) Try model for free: [Generate Images](https://modelslab.com/models/analog-madness-v70) Model link: [View model](https://modelslab.com/models/analog-madness-v70) View all models: [View Models](https://modelslab.com/models) import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "analog-madness-v70", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off DMGG0RBN
Lena2024/CustomModel_disney_sentiment_3	Lena2024	2024-05-18T13:59:06Z	119	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-18T13:58:56Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Lena2024/CustomModel_disney_sentiment_2	Lena2024	2024-05-18T13:58:46Z	119	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-18T13:58:35Z	--- 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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Lena2024/CustomModel_disney_sentiment_1	Lena2024	2024-05-18T13:58:25Z	108	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-18T13:58:13Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
theosun/gemma-2b-it-sharegpt-full	theosun	2024-05-18T13:58:13Z	6	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T13:49:16Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Ishu789/phi-therapist-chat-v1	Ishu789	2024-05-18T13:58:04Z	130	0	transformers	[ "transformers", "safetensors", "phi", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T13:53:28Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
kanlo/videomae-base-finetuned-ucf101-subset	kanlo	2024-05-18T13:55:18Z	63	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	2024-05-17T18:46:13Z	--- license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: videomae-base-finetuned-ucf101-subset 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-finetuned-ucf101-subset 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: 0.5398 - Accuracy: 0.8194 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 148 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| 2.1597 \| 0.2568 \| 38 \| 1.8560 \| 0.4857 \| \| 0.9646 \| 1.2568 \| 76 \| 1.0908 \| 0.6286 \| \| 0.4806 \| 2.2568 \| 114 \| 0.5811 \| 0.7857 \| \| 0.3196 \| 3.2297 \| 148 \| 0.4874 \| 0.8286 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
PaulR79/mistral_finetuned_synthetic	PaulR79	2024-05-18T13:54:52Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-18T13:54:50Z	--- 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]
WbjuSrceu/model8blora	WbjuSrceu	2024-05-18T13:52:54Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-18T13:52:08Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: WbjuSrceu - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
codelorhd/distilbert-base-uncased-finetuned-emotion	codelorhd	2024-05-18T13:52:50Z	118	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotions", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-18T10:34:52Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotions metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotions type: emotions args: split metrics: - name: Accuracy type: accuracy value: 0.9235 - name: F1 type: f1 value: 0.9233442192567661 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotions dataset. It achieves the following results on the evaluation set: - Loss: 0.2338 - Accuracy: 0.9235 - F1: 0.9233 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8681 \| 1.0 \| 250 \| 0.3529 \| 0.895 \| 0.8895 \| \| 0.2675 \| 2.0 \| 500 \| 0.2338 \| 0.9235 \| 0.9233 \| ### Framework versions - Transformers 4.16.2 - Pytorch 2.3.0+cu121 - Datasets 1.16.1 - Tokenizers 0.19.1
HariprasathSB/whispeeerrr	HariprasathSB	2024-05-18T13:52:46Z	87	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:HariprasathSB/whispeerr", "base_model:finetune:HariprasathSB/whispeerr", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-18T13:26:38Z	--- license: apache-2.0 base_model: HariprasathSB/whispeerr tags: - generated_from_trainer model-index: - name: whispeeerrr 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. --> # whispeeerrr This model is a fine-tuned version of [HariprasathSB/whispeerr](https://huggingface.co/HariprasathSB/whispeerr) 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: 0.003 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - training_steps: 100 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.41.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
stablediffusionapi/absolutereality-v181	stablediffusionapi	2024-05-18T13:50:35Z	241	2	diffusers	[ "diffusers", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-05-18T13:48:22Z	--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # AbsoluteReality v1.8.1 API Inference ![generated from modelslab.com](https://pub-3626123a908346a7a8be8d9295f44e26.r2.dev/generations/11161561981716040047.png) ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change model_id to "absolutereality-v181" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://docs.modelslab.com) Try model for free: [Generate Images](https://modelslab.com/models/absolutereality-v181) Model link: [View model](https://modelslab.com/models/absolutereality-v181) View all models: [View Models](https://modelslab.com/models) import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "absolutereality-v181", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off DMGG0RBN
carlesoctav/coba-pth-3	carlesoctav	2024-05-18T13:48:27Z	37	0	transformers	[ "transformers", "safetensors", "bert", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-18T13:48:14Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
carlesoctav/coba-pth-2	carlesoctav	2024-05-18T13:47:19Z	37	0	transformers	[ "transformers", "safetensors", "bert", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-18T13:38:19Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
fzzhang/mistralv1_dora_r4_25e5_e05	fzzhang	2024-05-18T13:47:16Z	1	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	null	2024-05-18T13:47:14Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 model-index: - name: mistralv1_dora_r4_25e5_e05 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistralv1_dora_r4_25e5_e05 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2.5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.38.2 - Pytorch 2.2.1 - Datasets 2.18.0 - Tokenizers 0.15.2
fzzhang/mistralv1_dora_r8_25e5_e05	fzzhang	2024-05-18T13:46:00Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	null	2024-05-18T13:45:58Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 model-index: - name: mistralv1_dora_r8_25e5_e05 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistralv1_dora_r8_25e5_e05 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2.5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.38.2 - Pytorch 2.2.1 - Datasets 2.18.0 - Tokenizers 0.15.2
Edgar-00/Models-RoBERTa-1716034377.745672	Edgar-00	2024-05-18T13:44:26Z	110	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	2024-05-18T12:13:02Z	--- license: mit base_model: roberta-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: Models-RoBERTa-1716034377.745672 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. --> # Models-RoBERTa-1716034377.745672 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8785 - Accuracy: 0.8256 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.5004 \| 1.0 \| 4909 \| 0.4480 \| 0.8386 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
ATTIABATOOL/XRAY	ATTIABATOOL	2024-05-18T13:42:38Z	0	0	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2024-05-18T13:38:48Z	--- license: bigscience-openrail-m ---
stablediffusionapi/cetus-mix-whalefall2	stablediffusionapi	2024-05-18T13:41:56Z	30	0	diffusers	[ "diffusers", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-05-18T13:39:41Z	--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # Cetus-Mix WhaleFall2 API Inference ![generated from modelslab.com](https://pub-3626123a908346a7a8be8d9295f44e26.r2.dev/generations/21278661231716039480.png) ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change model_id to "cetus-mix-whalefall2" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://docs.modelslab.com) Try model for free: [Generate Images](https://modelslab.com/models/cetus-mix-whalefall2) Model link: [View model](https://modelslab.com/models/cetus-mix-whalefall2) View all models: [View Models](https://modelslab.com/models) import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "cetus-mix-whalefall2", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off DMGG0RBN
Ayeesha/Yeesha	Ayeesha	2024-05-18T13:37:47Z	0	1	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-18T13:37:47Z	--- license: apache-2.0 ---
emilykang/Gemma_medmcqa_question_generation-medicine_lora	emilykang	2024-05-18T13:37:43Z	3	0	peft	[ "peft", "safetensors", "gemma", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:google/gemma-2b", "base_model:adapter:google/gemma-2b", "license:gemma", "region:us" ]	null	2024-05-17T17:18:36Z	--- license: gemma library_name: peft tags: - trl - sft - generated_from_trainer base_model: google/gemma-2b datasets: - generator model-index: - name: Gemma_medmcqa_question_generation-medicine_lora results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Gemma_medmcqa_question_generation-medicine_lora This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1
Dandan0K/Pilot_vox_italian	Dandan0K	2024-05-18T13:36:31Z	79	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "it", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-18T13:35:39Z	--- language: - it license: apache-2.0 tags: - automatic-speech-recognition - it datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2t_it_vp-100k_s449 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (it)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
OsherElhadad/ppo-PandaReachJointsSparse-v3-250000	OsherElhadad	2024-05-18T13:35:22Z	0	0	stable-baselines3	[ "stable-baselines3", "PandaReachJointsSparse-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-18T13:32:14Z	--- library_name: stable-baselines3 tags: - PandaReachJointsSparse-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachJointsSparse-v3 type: PandaReachJointsSparse-v3 metrics: - type: mean_reward value: -4.30 +/- 2.49 name: mean_reward verified: false --- # PPO Agent playing PandaReachJointsSparse-v3 This is a trained model of a PPO agent playing PandaReachJointsSparse-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 ... ```
Tobi-Bueck/atc-queue-dbert-1	Tobi-Bueck	2024-05-18T13:33:16Z	108	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-18T13:21:20Z	--- 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]
chocopiee/vietnamese-news-summarization-vistral-7b	chocopiee	2024-05-18T13:31:58Z	1	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:Viet-Mistral/Vistral-7B-Chat", "base_model:adapter:Viet-Mistral/Vistral-7B-Chat", "license:afl-3.0", "region:us" ]	null	2024-05-18T06:46:51Z	--- license: afl-3.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: Viet-Mistral/Vistral-7B-Chat datasets: - generator model-index: - name: vietnamese-news-summarization-vistral-7b results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/tritran/vietnamese-news-summarization/runs/as3mgbsl) # vietnamese-news-summarization-vistral-7b This model is a fine-tuned version of [Viet-Mistral/Vistral-7B-Chat](https://huggingface.co/Viet-Mistral/Vistral-7B-Chat) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 1.2452 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_steps: 0.03 - training_steps: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.3699 \| 0.0060 \| 20 \| 1.3159 \| \| 1.4501 \| 0.0119 \| 40 \| 1.2761 \| \| 1.2554 \| 0.0179 \| 60 \| 1.2583 \| \| 1.1901 \| 0.0239 \| 80 \| 1.2474 \| \| 1.4126 \| 0.0298 \| 100 \| 1.2452 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.41.0 - Pytorch 2.1.2 - Datasets 2.16.0 - Tokenizers 0.19.1
thienann/results-news-dataset	thienann	2024-05-18T13:27:58Z	106	0	transformers	[ "transformers", "tensorboard", "safetensors", "pegasus", "text2text-generation", "generated_from_trainer", "base_model:PoseyATX/GPTxLege_FoxHunter", "base_model:finetune:PoseyATX/GPTxLege_FoxHunter", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-18T12:29:59Z	--- base_model: PoseyATX/GPTxLege_FoxHunter tags: - generated_from_trainer model-index: - name: results-news-dataset 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-news-dataset This model is a fine-tuned version of [PoseyATX/GPTxLege_FoxHunter](https://huggingface.co/PoseyATX/GPTxLege_FoxHunter) on the None dataset. It achieves the following results on the evaluation set: - Loss: 9.4768 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 10.4458 \| 1.0 \| 791 \| 9.6071 \| \| 9.805 \| 2.0 \| 1582 \| 9.4768 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
carlesoctav/coba-pth	carlesoctav	2024-05-18T13:27:43Z	40	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-18T13:27:30Z	--- 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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selmamalak/organsmnist-deit-base-finetuned	selmamalak	2024-05-18T13:24:31Z	1	0	peft	[ "peft", "safetensors", "generated_from_trainer", "dataset:medmnist-v2", "base_model:facebook/deit-base-patch16-224", "base_model:adapter:facebook/deit-base-patch16-224", "license:apache-2.0", "region:us" ]	null	2024-05-18T12:31:56Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: facebook/deit-base-patch16-224 datasets: - medmnist-v2 metrics: - accuracy - precision - recall - f1 model-index: - name: organsmnist-deit-base-finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # organsmnist-deit-base-finetuned This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the medmnist-v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.4815 - Accuracy: 0.8080 - Precision: 0.7703 - Recall: 0.7686 - F1: 0.7650 ## 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: 0.005 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.9804 \| 1.0 \| 218 \| 0.6885 \| 0.7243 \| 0.7883 \| 0.6661 \| 0.6426 \| \| 0.9277 \| 2.0 \| 436 \| 0.3513 \| 0.8503 \| 0.7635 \| 0.7943 \| 0.7680 \| \| 0.8144 \| 3.0 \| 654 \| 0.3614 \| 0.8544 \| 0.8331 \| 0.7961 \| 0.7909 \| \| 0.7344 \| 4.0 \| 872 \| 0.3371 \| 0.8609 \| 0.8327 \| 0.8018 \| 0.7886 \| \| 0.7181 \| 5.0 \| 1090 \| 0.2934 \| 0.8923 \| 0.8060 \| 0.8389 \| 0.8096 \| \| 0.5857 \| 6.0 \| 1308 \| 0.2927 \| 0.8858 \| 0.8493 \| 0.8358 \| 0.8315 \| \| 0.5607 \| 7.0 \| 1526 \| 0.2209 \| 0.9062 \| 0.8658 \| 0.8547 \| 0.8416 \| \| 0.5423 \| 8.0 \| 1744 \| 0.2513 \| 0.9025 \| 0.8545 \| 0.8470 \| 0.8487 \| \| 0.4053 \| 9.0 \| 1962 \| 0.2561 \| 0.9038 \| 0.8543 \| 0.8457 \| 0.8373 \| \| 0.4417 \| 10.0 \| 2180 \| 0.2558 \| 0.8997 \| 0.8463 \| 0.8395 \| 0.8416 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
casque/MIS51	casque	2024-05-18T13:02:53Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-05-18T13:02:22Z	--- license: creativeml-openrail-m ---
Toshifumi/Llama3-Toshi-Ja-claim-classifier_20240518v1	Toshifumi	2024-05-18T12:57:27Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T12:50:34Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: Toshifumi - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
piika919/phi_bnb	piika919	2024-05-18T12:54:49Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-18T12:51:32Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
ChiJuiChen/lab9_whisper-tiny-zh-tw	ChiJuiChen	2024-05-18T12:53:39Z	78	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice_13_0", "base_model:Wellyowo/whisper-tiny-zh-tw", "base_model:finetune:Wellyowo/whisper-tiny-zh-tw", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-09T07:22:56Z	--- license: apache-2.0 base_model: Wellyowo/whisper-tiny-zh-tw tags: - generated_from_trainer datasets: - common_voice_13_0 metrics: - wer model-index: - name: lab9_whisper-tiny-zh-tw results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice_13_0 type: common_voice_13_0 config: zh-TW split: test args: zh-TW metrics: - name: Wer type: wer value: 62.13592233009708 --- <!-- 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. --> # lab9_whisper-tiny-zh-tw This model is a fine-tuned version of [Wellyowo/whisper-tiny-zh-tw](https://huggingface.co/Wellyowo/whisper-tiny-zh-tw) on the common_voice_13_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.6336 - Wer Ortho: 64.0 - Wer: 62.1359 ## 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: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer Ortho \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:---------:\|:-------:\| \| 0.0088 \| 0.6882 \| 500 \| 0.5502 \| 60.0 \| 61.1650 \| \| 0.0051 \| 1.3765 \| 1000 \| 0.5735 \| 65.0 \| 64.0777 \| \| 0.0068 \| 2.0647 \| 1500 \| 0.5820 \| 63.0 \| 63.1068 \| \| 0.0021 \| 2.7529 \| 2000 \| 0.5955 \| 62.0 \| 61.1650 \| \| 0.0039 \| 3.4412 \| 2500 \| 0.5858 \| 62.0 \| 61.1650 \| \| 0.0018 \| 4.1294 \| 3000 \| 0.5981 \| 63.0 \| 61.1650 \| \| 0.0019 \| 4.8176 \| 3500 \| 0.6322 \| 63.0 \| 61.1650 \| \| 0.0102 \| 5.5058 \| 4000 \| 0.6336 \| 64.0 \| 62.1359 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.0+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1
presencesw/mt5-base-snli_contradiction-triplet	presencesw	2024-05-18T12:52:38Z	50	0	transformers	[ "transformers", "safetensors", "mt5", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-18T12:52:01Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (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]
chen1212/Models-RoBERTa-1716033686.153194	chen1212	2024-05-18T12:52:36Z	108	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	2024-05-18T12:05:43Z	--- license: mit base_model: roberta-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: Models-RoBERTa-1716033686.153194 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. --> # Models-RoBERTa-1716033686.153194 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3683 - Accuracy: 0.888 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 0.8 \| 500 \| 0.3078 \| 0.875 \| \| No log \| 1.6 \| 1000 \| 0.3683 \| 0.888 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
JUANDECI/PPO-LunarLander-v2	JUANDECI	2024-05-18T12:51:06Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-18T12:47:50Z	--- 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: 175.95 +/- 65.75 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 ... ```
akbargherbal/gemma_7b_en_to_ar_ft_01	akbargherbal	2024-05-18T12:43:25Z	7	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/gemma-7b-it-bnb-4bit", "base_model:finetune:unsloth/gemma-7b-it-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T12:05:03Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma - trl base_model: unsloth/gemma-7b-it-bnb-4bit --- # Uploaded model - Developed by: akbargherbal - License: apache-2.0 - Finetuned from model : unsloth/gemma-7b-it-bnb-4bit This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
presencesw/mt5-base-snli_neutral-triplet	presencesw	2024-05-18T12:43:05Z	50	0	transformers	[ "transformers", "safetensors", "mt5", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-18T12:42:05Z	--- 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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selmamalak/blood-beit-base-finetuned	selmamalak	2024-05-18T12:38:22Z	2	0	peft	[ "peft", "safetensors", "generated_from_trainer", "dataset:medmnist-v2", "base_model:microsoft/beit-base-patch16-224-pt22k-ft22k", "base_model:adapter:microsoft/beit-base-patch16-224-pt22k-ft22k", "license:apache-2.0", "region:us" ]	null	2024-05-18T12:11:10Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: microsoft/beit-base-patch16-224-pt22k-ft22k datasets: - medmnist-v2 metrics: - accuracy - precision - recall - f1 model-index: - name: blood-beit-base-finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # blood-beit-base-finetuned This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the medmnist-v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.0847 - Accuracy: 0.9737 - Precision: 0.9726 - Recall: 0.9724 - F1: 0.9724 ## 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: 0.005 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.4657 \| 1.0 \| 187 \| 0.2452 \| 0.9095 \| 0.8964 \| 0.9083 \| 0.8973 \| \| 0.4327 \| 2.0 \| 374 \| 0.2111 \| 0.9182 \| 0.9299 \| 0.8921 \| 0.9007 \| \| 0.3977 \| 3.0 \| 561 \| 0.1743 \| 0.9340 \| 0.9229 \| 0.9282 \| 0.9244 \| \| 0.3318 \| 4.0 \| 748 \| 0.1776 \| 0.9352 \| 0.9248 \| 0.9353 \| 0.9285 \| \| 0.3461 \| 5.0 \| 935 \| 0.1703 \| 0.9381 \| 0.9311 \| 0.9344 \| 0.9305 \| \| 0.3309 \| 6.0 \| 1122 \| 0.1956 \| 0.9369 \| 0.9336 \| 0.9397 \| 0.9335 \| \| 0.3088 \| 7.0 \| 1309 \| 0.1179 \| 0.9533 \| 0.9427 \| 0.9525 \| 0.9461 \| \| 0.2129 \| 8.0 \| 1496 \| 0.0992 \| 0.9638 \| 0.9569 \| 0.9674 \| 0.9611 \| \| 0.2049 \| 9.0 \| 1683 \| 0.0847 \| 0.9679 \| 0.9627 \| 0.9683 \| 0.9651 \| \| 0.2007 \| 10.0 \| 1870 \| 0.0785 \| 0.9708 \| 0.9668 \| 0.9737 \| 0.9698 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
Upamanyu098/dreambooth-dog	Upamanyu098	2024-05-18T12:30:36Z	29	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "text-to-image", "dreambooth", "diffusers-training", "stable-diffusion", "stable-diffusion-diffusers", "base_model:runwayml/stable-diffusion-v1-5", "base_model:finetune:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-05-18T12:00:07Z	--- license: creativeml-openrail-m library_name: diffusers tags: - text-to-image - dreambooth - diffusers-training - stable-diffusion - stable-diffusion-diffusers base_model: runwayml/stable-diffusion-v1-5 inference: true instance_prompt: a photo of zxc dog --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # DreamBooth - Upamanyu098/dreambooth-dog This is a dreambooth model derived from runwayml/stable-diffusion-v1-5. The weights were trained on a photo of zxc dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]

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