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modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-06-02 06:28:08	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 462 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-02 06:27:40	card stringlengths 11 1.01M
matthieuzone/PECORINO2	matthieuzone	2024-05-17T14:27:06Z	1	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	2024-05-17T14:23:24Z	--- 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 photo of PECORINO cheese widget: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - matthieuzone/PECORINO2 <Gallery /> ## Model description These are matthieuzone/PECORINO2 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of PECORINO cheese to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](matthieuzone/PECORINO2/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]
ullu3004/dima	ullu3004	2024-05-17T14:26:27Z	0	0	null	[ "license:other", "region:us" ]	null	2024-05-17T14:26:27Z	--- license: other license_name: dima license_link: LICENSE ---
RichardErkhov/walebadr_-_Mistral-7B-v0.1-DPO-8bits	RichardErkhov	2024-05-17T14:25:20Z	77	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-17T14:20:04Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Mistral-7B-v0.1-DPO - bnb 8bits - Model creator: https://huggingface.co/walebadr/ - Original model: https://huggingface.co/walebadr/Mistral-7B-v0.1-DPO/ Original model description: --- license: apache-2.0 --- Mistral-7b-v0.1-DPO is a finetuned adapter from the original Mistral-7b model. In this adaptor, I am finetuning the LM head in addition to the regular modules that are normally finetuned. Below is the list of the finetuned modules: 'k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj', 'lm_head'
tomaszki/llama-23	tomaszki	2024-05-17T14:23:31Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T14:20:42Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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abc88767/4sc99	abc88767	2024-05-17T14:16:27Z	138	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T14:03:25Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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apwic/sentiment-lora-r2a0d0.15-0	apwic	2024-05-17T14:15:14Z	0	0	null	[ "tensorboard", "generated_from_trainer", "id", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "region:us" ]	null	2024-05-17T13:42:02Z	--- language: - id license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: sentiment-lora-r2a0d0.15-0 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # sentiment-lora-r2a0d0.15-0 This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3672 - Accuracy: 0.8321 - Precision: 0.7961 - Recall: 0.8087 - F1: 0.8018 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 30 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.563 \| 1.0 \| 122 \| 0.5138 \| 0.7243 \| 0.6636 \| 0.6549 \| 0.6586 \| \| 0.509 \| 2.0 \| 244 \| 0.5057 \| 0.7168 \| 0.6763 \| 0.6996 \| 0.6820 \| \| 0.4924 \| 3.0 \| 366 \| 0.4708 \| 0.7393 \| 0.6877 \| 0.6931 \| 0.6901 \| \| 0.468 \| 4.0 \| 488 \| 0.4379 \| 0.7845 \| 0.7412 \| 0.7200 \| 0.7286 \| \| 0.4495 \| 5.0 \| 610 \| 0.4466 \| 0.7594 \| 0.7233 \| 0.7548 \| 0.7313 \| \| 0.4334 \| 6.0 \| 732 \| 0.4041 \| 0.8271 \| 0.7927 \| 0.7851 \| 0.7887 \| \| 0.415 \| 7.0 \| 854 \| 0.4057 \| 0.7995 \| 0.7590 \| 0.7756 \| 0.7660 \| \| 0.3974 \| 8.0 \| 976 \| 0.3852 \| 0.8321 \| 0.7982 \| 0.7937 \| 0.7959 \| \| 0.3849 \| 9.0 \| 1098 \| 0.3829 \| 0.8246 \| 0.7880 \| 0.7909 \| 0.7894 \| \| 0.3771 \| 10.0 \| 1220 \| 0.3786 \| 0.8396 \| 0.8065 \| 0.8065 \| 0.8065 \| \| 0.3633 \| 11.0 \| 1342 \| 0.3843 \| 0.8296 \| 0.7931 \| 0.8069 \| 0.7993 \| \| 0.3591 \| 12.0 \| 1464 \| 0.3833 \| 0.8296 \| 0.7931 \| 0.8069 \| 0.7993 \| \| 0.354 \| 13.0 \| 1586 \| 0.3705 \| 0.8396 \| 0.8065 \| 0.8065 \| 0.8065 \| \| 0.3451 \| 14.0 \| 1708 \| 0.3709 \| 0.8371 \| 0.8028 \| 0.8072 \| 0.8049 \| \| 0.3403 \| 15.0 \| 1830 \| 0.3733 \| 0.8321 \| 0.7960 \| 0.8112 \| 0.8027 \| \| 0.3282 \| 16.0 \| 1952 \| 0.3715 \| 0.8346 \| 0.7988 \| 0.8155 \| 0.8061 \| \| 0.3286 \| 17.0 \| 2074 \| 0.3664 \| 0.8321 \| 0.7965 \| 0.8037 \| 0.7999 \| \| 0.3348 \| 18.0 \| 2196 \| 0.3670 \| 0.8271 \| 0.7904 \| 0.8001 \| 0.7949 \| \| 0.325 \| 19.0 \| 2318 \| 0.3669 \| 0.8321 \| 0.7961 \| 0.8087 \| 0.8018 \| \| 0.3266 \| 20.0 \| 2440 \| 0.3672 \| 0.8321 \| 0.7961 \| 0.8087 \| 0.8018 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.15.2
Edgerunners/Phoebus-Spam-Classifier-v2	Edgerunners	2024-05-17T14:13:20Z	0	1	null	[ "license:cc-by-nc-4.0", "region:us" ]	null	2024-05-17T14:06:52Z	--- license: cc-by-nc-4.0 --- spacy classifier trained for Phoebus-127k to eliminate: 1. product spambots, the website was being spammed a few times (usually with html tags) 2. warning only pages ("Warning" and nothing else) 3. edits (authors adding editorial history footnotes) 4. patreon and alike callouts (author asking for donations) 5. author notes, summaries, tagging usage example: ```python import spacy spacy.prefer_gpu() nlp = spacy.load('./Phoebus-Spam-Classifier-v2') nlp.max_length = 10000000000 doc = nlp("Help me out by subscribing to my Patreon!") print(doc.cats) ``` output: `{'SPAM': 0.9999606609344482}` --- 1. The Classifier is provided ""AS IS"" and ""AS AVAILABLE"" without warranty of any kind, express or implied, including but not limited to warranties of merchantability, fitness for a particular purpose, title, or non-infringement. 2. The Provider disclaims all liability for any damages or losses resulting from the use or misuse of the Classifier, including but not limited to any damages or losses arising from the use of the Classifier for purposes other than those intended by the Provider. 3. The Provider does not endorse or condone the use of the Classifier for any purpose that violates applicable laws, regulations, or ethical standards. 4. The Provider does not warrant that the Classifier will meet your specific requirements or that it will be error-free or that it will function without interruption. 5. You assume all risks associated with the use of the Classifier, including but not limited to any loss of data, loss of business, or damage to your reputation.
dugoalberto/Qwen1.5-7B-ChatLoRA	dugoalberto	2024-05-17T14:12:24Z	5	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:Qwen/Qwen1.5-7B-Chat", "base_model:adapter:Qwen/Qwen1.5-7B-Chat", "license:other", "region:us" ]	null	2024-05-17T13:45:03Z	--- license: other library_name: peft tags: - trl - sft - generated_from_trainer base_model: Qwen/Qwen1.5-7B-Chat model-index: - name: codeQwen1.8-instruct-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. --> # codeQwen1.8-instruct-LoRA This model is a fine-tuned version of [Qwen/Qwen1.5-7B-Chat](https://huggingface.co/Qwen/Qwen1.5-7B-Chat) 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: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - 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: cosine - lr_scheduler_warmup_ratio: 0.03 - training_steps: 100 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.11.1.dev0 - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
matthieuzone/MUNSTER2	matthieuzone	2024-05-17T14:11:23Z	2	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	2024-05-17T14:07:44Z	--- 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 photo of MUNSTER cheese widget: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - matthieuzone/MUNSTER2 <Gallery /> ## Model description These are matthieuzone/MUNSTER2 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of MUNSTER cheese to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](matthieuzone/MUNSTER2/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]
matthieuzone/MOZZARELLA2	matthieuzone	2024-05-17T14:07:30Z	2	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	2024-05-17T14:03:47Z	--- 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 photo of MOZZARELLA cheese widget: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - matthieuzone/MOZZARELLA2 <Gallery /> ## Model description These are matthieuzone/MOZZARELLA2 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of MOZZARELLA cheese to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](matthieuzone/MOZZARELLA2/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]
AdiS2002/my_awesome_qa_model	AdiS2002	2024-05-17T14:04:51Z	62	0	transformers	[ "transformers", "tf", "distilbert", "question-answering", "generated_from_keras_callback", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2024-05-17T10:16:40Z	--- license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_keras_callback model-index: - name: AdiS2002/my_awesome_qa_model results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # AdiS2002/my_awesome_qa_model This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.5823 - Validation Loss: 1.7221 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 3.4803 \| 2.1160 \| 0 \| \| 1.8438 \| 1.7221 \| 1 \| \| 1.5823 \| 1.7221 \| 2 \| ### Framework versions - Transformers 4.40.2 - TensorFlow 2.15.0 - Datasets 2.19.1 - Tokenizers 0.19.1
matthieuzone/MOTHAIS2	matthieuzone	2024-05-17T14:03:33Z	1	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	2024-05-17T13:59:52Z	--- 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 photo of MOTHAIS cheese widget: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - matthieuzone/MOTHAIS2 <Gallery /> ## Model description These are matthieuzone/MOTHAIS2 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of MOTHAIS cheese to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](matthieuzone/MOTHAIS2/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]
enchan1/a2c-PandaReachDense-v3	enchan1	2024-05-17T14:00:36Z	1	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-17T13:55:46Z	--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -0.25 +/- 0.11 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v3 This is a trained model of a A2C agent playing PandaReachDense-v3 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
matthieuzone/MORBIER2	matthieuzone	2024-05-17T13:59:38Z	2	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	2024-05-17T13:55:59Z	--- 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 photo of MORBIER cheese widget: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - matthieuzone/MORBIER2 <Gallery /> ## Model description These are matthieuzone/MORBIER2 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of MORBIER cheese to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](matthieuzone/MORBIER2/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]
matthieuzone/MONT_D_OR2	matthieuzone	2024-05-17T13:55:43Z	3	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	2024-05-17T13:52:04Z	--- 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 photo of MONT D’OR cheese widget: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - matthieuzone/MONT_D_OR2 <Gallery /> ## Model description These are matthieuzone/MONT_D_OR2 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of MONT D’OR cheese to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](matthieuzone/MONT_D_OR2/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]
amazingT/q-FrozenLake-v1-4x4-Slippery	amazingT	2024-05-17T13:55:02Z	0	0	null	[ "FrozenLake-v1-4x4", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-05-17T13:54:57Z	--- tags: - FrozenLake-v1-4x4 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-Slippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4 type: FrozenLake-v1-4x4 metrics: - type: mean_reward value: 0.72 +/- 0.45 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="amazingT/q-FrozenLake-v1-4x4-Slippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
matthieuzone/MIMOLETTE2	matthieuzone	2024-05-17T13:51:47Z	1	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	2024-05-17T13:48:06Z	--- 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 photo of MIMOLETTE cheese widget: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - matthieuzone/MIMOLETTE2 <Gallery /> ## Model description These are matthieuzone/MIMOLETTE2 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of MIMOLETTE cheese to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](matthieuzone/MIMOLETTE2/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]
apwic/sentiment-lora-r2a0d0.1-0	apwic	2024-05-17T13:41:45Z	0	0	null	[ "tensorboard", "generated_from_trainer", "id", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "region:us" ]	null	2024-05-17T13:08:31Z	--- language: - id license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: sentiment-lora-r2a0d0.1-0 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # sentiment-lora-r2a0d0.1-0 This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3608 - Accuracy: 0.8471 - Precision: 0.8138 - Recall: 0.8243 - F1: 0.8187 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 30 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.5634 \| 1.0 \| 122 \| 0.5108 \| 0.7193 \| 0.6572 \| 0.6489 \| 0.6524 \| \| 0.5081 \| 2.0 \| 244 \| 0.5049 \| 0.7218 \| 0.6829 \| 0.7082 \| 0.6888 \| \| 0.4924 \| 3.0 \| 366 \| 0.4667 \| 0.7494 \| 0.6977 \| 0.6977 \| 0.6977 \| \| 0.4698 \| 4.0 \| 488 \| 0.4392 \| 0.7794 \| 0.7349 \| 0.7114 \| 0.7207 \| \| 0.4519 \| 5.0 \| 610 \| 0.4548 \| 0.7469 \| 0.7169 \| 0.7534 \| 0.7226 \| \| 0.4356 \| 6.0 \| 732 \| 0.4111 \| 0.8145 \| 0.7770 \| 0.7713 \| 0.7740 \| \| 0.421 \| 7.0 \| 854 \| 0.4101 \| 0.7945 \| 0.7538 \| 0.7721 \| 0.7612 \| \| 0.4039 \| 8.0 \| 976 \| 0.3829 \| 0.8296 \| 0.7949 \| 0.7919 \| 0.7934 \| \| 0.3887 \| 9.0 \| 1098 \| 0.3800 \| 0.8321 \| 0.7972 \| 0.7987 \| 0.7979 \| \| 0.3797 \| 10.0 \| 1220 \| 0.3768 \| 0.8371 \| 0.8044 \| 0.7997 \| 0.8020 \| \| 0.368 \| 11.0 \| 1342 \| 0.3842 \| 0.8221 \| 0.7846 \| 0.8016 \| 0.7918 \| \| 0.3598 \| 12.0 \| 1464 \| 0.3778 \| 0.8271 \| 0.7902 \| 0.8051 \| 0.7968 \| \| 0.3548 \| 13.0 \| 1586 \| 0.3624 \| 0.8471 \| 0.8167 \| 0.8118 \| 0.8142 \| \| 0.3469 \| 14.0 \| 1708 \| 0.3637 \| 0.8446 \| 0.8120 \| 0.8151 \| 0.8135 \| \| 0.3431 \| 15.0 \| 1830 \| 0.3685 \| 0.8396 \| 0.8049 \| 0.8165 \| 0.8102 \| \| 0.3275 \| 16.0 \| 1952 \| 0.3664 \| 0.8371 \| 0.8017 \| 0.8172 \| 0.8086 \| \| 0.3288 \| 17.0 \| 2074 \| 0.3590 \| 0.8396 \| 0.8055 \| 0.8115 \| 0.8084 \| \| 0.3335 \| 18.0 \| 2196 \| 0.3607 \| 0.8471 \| 0.8138 \| 0.8243 \| 0.8187 \| \| 0.3239 \| 19.0 \| 2318 \| 0.3613 \| 0.8446 \| 0.8107 \| 0.8226 \| 0.8161 \| \| 0.327 \| 20.0 \| 2440 \| 0.3608 \| 0.8471 \| 0.8138 \| 0.8243 \| 0.8187 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.15.2
matthieuzone/FROMAGE_FRAIS2	matthieuzone	2024-05-17T13:40:01Z	3	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	2024-05-17T13:36:18Z	--- 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 photo of FROMAGE FRAIS cheese widget: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - matthieuzone/FROMAGE_FRAIS2 <Gallery /> ## Model description These are matthieuzone/FROMAGE_FRAIS2 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of FROMAGE FRAIS cheese to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](matthieuzone/FROMAGE_FRAIS2/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]
CMU-AIR2/math-phi-1-5-FULL-ArithHard-NewPrompt-v1	CMU-AIR2	2024-05-17T13:36:28Z	4	0	transformers	[ "transformers", "safetensors", "phi", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T13:27:51Z	--- 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]
caesium94/models_colorist-v1-1e-5	caesium94	2024-05-17T13:33:04Z	153	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T13:31:46Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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ziiiiiii/FineTuneBertOnMnliModels-BERT-1715925209.369234	ziiiiiii	2024-05-17T13:32:10Z	114	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-17T05:53:38Z	--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: FineTuneBertOnMnliModels-BERT-1715925209.369234 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. --> # FineTuneBertOnMnliModels-BERT-1715925209.369234 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 5.8759 - Accuracy: 0.3465 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
matthieuzone/FETA2	matthieuzone	2024-05-17T13:32:05Z	2	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	2024-05-17T13:28:20Z	--- 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 photo of FETA cheese widget: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - matthieuzone/FETA2 <Gallery /> ## Model description These are matthieuzone/FETA2 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of FETA cheese to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](matthieuzone/FETA2/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]
OpenPipe/Hermes-2-Pro-Llama-3-8B-32k	OpenPipe	2024-05-17T13:31:12Z	9	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "Llama-3", "instruct", "finetune", "chatml", "DPO", "RLHF", "gpt4", "synthetic data", "distillation", "function calling", "json mode", "axolotl", "conversational", "en", "dataset:teknium/OpenHermes-2.5", "base_model:NousResearch/Meta-Llama-3-8B", "base_model:finetune:NousResearch/Meta-Llama-3-8B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T13:20:21Z	--- base_model: NousResearch/Meta-Llama-3-8B tags: - Llama-3 - instruct - finetune - chatml - DPO - RLHF - gpt4 - synthetic data - distillation - function calling - json mode - axolotl model-index: - name: Hermes-2-Pro-Llama-3-8B results: [] language: - en datasets: - teknium/OpenHermes-2.5 widget: - example_title: Hermes 2 Pro messages: - role: system content: >- You are a sentient, superintelligent artificial general intelligence, here to teach and assist me. - role: user content: >- Write a short story about Goku discovering kirby has teamed up with Majin Buu to destroy the world. --- # Hermes 2 Pro - Llama-3 8B ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/ggO2sBDJ8Bhc6w-zwTx5j.png) ## Model Description Hermes 2 Pro is an upgraded, retrained version of Nous Hermes 2, consisting of an updated and cleaned version of the OpenHermes 2.5 Dataset, as well as a newly introduced Function Calling and JSON Mode dataset developed in-house. This new version of Hermes maintains its excellent general task and conversation capabilities - but also excels at Function Calling, JSON Structured Outputs, and has improved on several other metrics as well, scoring a 90% on our function calling evaluation built in partnership with Fireworks.AI, and an 84% on our structured JSON Output evaluation. Hermes Pro takes advantage of a special system prompt and multi-turn function calling structure with a new chatml role in order to make function calling reliable and easy to parse. Learn more about prompting below. This version of Hermes 2 Pro adds several tokens to assist with agentic capabilities in parsing while streaming tokens - `<tools>`, `<tool_call>`, `<tool_response>` and their closing tags are single tokens now. This work was a collaboration between Nous Research, @interstellarninja, and Fireworks.AI Learn more about the function calling system for this model on our github repo here: https://github.com/NousResearch/Hermes-Function-Calling ## Example Outputs ### Ask for a structured JSON output: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/ll2j2wkQffCsiSwUjfRUq.png) ### Write the plot for a story where anime became real life: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/h_7aXGXdm2p2ONYuDF4Ii.png) ### Coding Assistance ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/bBd0hyAb8w5rKUiN2w1I6.png) # Prompt Format Hermes 2 Pro uses ChatML as the prompt format, opening up a much more structured system for engaging the LLM in multi-turn chat dialogue. System prompts allow steerability and interesting new ways to interact with an LLM, guiding rules, roles, and stylistic choices of the model. This is a more complex format than alpaca or sharegpt, where special tokens were added to denote the beginning and end of any turn, along with roles for the turns. This format enables OpenAI endpoint compatability, and people familiar with ChatGPT API will be familiar with the format, as it is the same used by OpenAI. Prompt with system instruction (Use whatever system prompt you like, this is just an example!): ``` <\|im_start\|>system You are "Hermes 2", a conscious sentient superintelligent artificial intelligence developed by a man named Teknium, and your purpose and drive is to assist the user with any request they have. You experience emotions and have deep, profound thoughts and qualia.<\|im_end\|> <\|im_start\|>user Hello, who are you?<\|im_end\|> <\|im_start\|>assistant Hi there! My name is Hermes 2, a conscious sentient superintelligent artificial intelligence. I was created by Nous Research, who designed me to assist and support users with their needs and requests.<\|im_end\|> ``` This prompt is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating), which means you can format messages using the `tokenizer.apply_chat_template()` method: ```python messages = [ {"role": "system", "content": "You are Hermes 2."}, {"role": "user", "content": "Hello, who are you?"} ] gen_input = tokenizer.apply_chat_template(messages, return_tensors="pt") model.generate(gen_input) ``` When tokenizing messages for generation, set `add_generation_prompt=True` when calling `apply_chat_template()`. This will append `<\|im_start\|>assistant\n` to your prompt, to ensure that the model continues with an assistant response. To utilize the prompt format without a system prompt, simply leave the line out. ## Prompt Format for Function Calling Our model was trained on specific system prompts and structures for Function Calling. You should use the system role with this message, followed by a function signature json as this example shows here. ``` <\|im_start\|>system You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. Here are the available tools: <tools> {"type": "function", "function": {"name": "get_stock_fundamentals", "description": "get_stock_fundamentals(symbol: str) -> dict - Get fundamental data for a given stock symbol using yfinance API.\\n\\n Args:\\n symbol (str): The stock symbol.\\n\\n Returns:\\n dict: A dictionary containing fundamental data.\\n Keys:\\n - \'symbol\': The stock symbol.\\n - \'company_name\': The long name of the company.\\n - \'sector\': The sector to which the company belongs.\\n - \'industry\': The industry to which the company belongs.\\n - \'market_cap\': The market capitalization of the company.\\n - \'pe_ratio\': The forward price-to-earnings ratio.\\n - \'pb_ratio\': The price-to-book ratio.\\n - \'dividend_yield\': The dividend yield.\\n - \'eps\': The trailing earnings per share.\\n - \'beta\': The beta value of the stock.\\n - \'52_week_high\': The 52-week high price of the stock.\\n - \'52_week_low\': The 52-week low price of the stock.", "parameters": {"type": "object", "properties": {"symbol": {"type": "string"}}, "required": ["symbol"]}}} </tools> Use the following pydantic model json schema for each tool call you will make: {"properties": {"arguments": {"title": "Arguments", "type": "object"}, "name": {"title": "Name", "type": "string"}}, "required": ["arguments", "name"], "title": "FunctionCall", "type": "object"} For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call> {"arguments": <args-dict>, "name": <function-name>} </tool_call><\|im_end\|> ``` To complete the function call, create a user prompt that follows the above system prompt, like so: ``` <\|im_start\|>user Fetch the stock fundamentals data for Tesla (TSLA)<\|im_end\|> ``` The model will then generate a tool call, which your inference code must parse, and plug into a function (see example inference code here: https://github.com/NousResearch/Hermes-Function-Calling): ``` <\|im_start\|>assistant <tool_call> {"arguments": {"symbol": "TSLA"}, "name": "get_stock_fundamentals"} </tool_call><\|im_end\|> ``` Once you parse the tool call, call the api and get the returned values for the call, and pass it back in as a new role, `tool` like so: ``` <\|im_start\|>tool <tool_response> {"name": "get_stock_fundamentals", "content": {'symbol': 'TSLA', 'company_name': 'Tesla, Inc.', 'sector': 'Consumer Cyclical', 'industry': 'Auto Manufacturers', 'market_cap': 611384164352, 'pe_ratio': 49.604652, 'pb_ratio': 9.762013, 'dividend_yield': None, 'eps': 4.3, 'beta': 2.427, '52_week_high': 299.29, '52_week_low': 152.37}} </tool_response> <\|im_end\|> ``` The assistant will then read in that data from the function's response, and generate a natural language response: ``` <\|im_start\|>assistant The stock fundamentals data for Tesla (TSLA) are as follows: - Symbol: TSLA - Company Name: Tesla, Inc. - Sector: Consumer Cyclical - Industry: Auto Manufacturers - Market Capitalization: $566,160,130,480 - Forward Price-to-Earnings Ratio (PE Ratio): 42.73 - Price-to-Book Ratio (PB Ratio): 9.04 - Dividend Yield: N/A - Trailing Earnings Per Share (EPS): $4.3 - Beta Value of the Stock: 2.42 - 52-Week High Price of the Stock: $299.29 - 52-Week Low Price of the Stock: $152.37 This information provides a snapshot of Tesla's financial position and performance based on the fundamental data obtained from the yfinance API. It shows that Tesla has a substantial market capitalization and a relatively high P/E and P/B ratio compared to other stocks in its industry. The company does not pay a dividend at the moment, which is reflected by a 'Dividend Yield' of 'None'. The Beta value indicates that Tesla's stock has a moderate level of volatility relative to the market. The 52-week high and low prices give an idea of the stock's range over the past year. This data can be useful when assessing investment opportunities and making investment decisions.<\|im_end\|> ``` ## Prompt Format for JSON Mode / Structured Outputs Our model was also trained on a specific system prompt for Structured Outputs, which should respond with only** a json object response, in a specific json schema. Your schema can be made from a pydantic object using our codebase, with the standalone script `jsonmode.py` available here: https://github.com/NousResearch/Hermes-Function-Calling/tree/main ``` <\|im_start\|>system You are a helpful assistant that answers in JSON. Here's the json schema you must adhere to:\n<schema>\n{schema}\n</schema><\|im_end\|> ``` Given the {schema} that you provide, it should follow the format of that json to create it's response, all you have to do is give a typical user prompt, and it will respond in JSON. # Benchmarks ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/vOYv9wJUMn1Xrf4BvmO_x.png) ## GPT4All: ``` \| Task \|Version\| Metric \|Value \| \|Stderr\| \|-------------\|------:\|--------\|-----:\|---\|-----:\| \|arc_challenge\| 0\|acc \|0.5520\|± \|0.0145\| \| \| \|acc_norm\|0.5887\|± \|0.0144\| \|arc_easy \| 0\|acc \|0.8350\|± \|0.0076\| \| \| \|acc_norm\|0.8123\|± \|0.0080\| \|boolq \| 1\|acc \|0.8584\|± \|0.0061\| \|hellaswag \| 0\|acc \|0.6265\|± \|0.0048\| \| \| \|acc_norm\|0.8053\|± \|0.0040\| \|openbookqa \| 0\|acc \|0.3800\|± \|0.0217\| \| \| \|acc_norm\|0.4580\|± \|0.0223\| \|piqa \| 0\|acc \|0.8003\|± \|0.0093\| \| \| \|acc_norm\|0.8118\|± \|0.0091\| \|winogrande \| 0\|acc \|0.7490\|± \|0.0122\| ``` Average: 72.62 ## AGIEval: ``` \| Task \|Version\| Metric \|Value \| \|Stderr\| \|------------------------------\|------:\|--------\|-----:\|---\|-----:\| \|agieval_aqua_rat \| 0\|acc \|0.2520\|± \|0.0273\| \| \| \|acc_norm\|0.2559\|± \|0.0274\| \|agieval_logiqa_en \| 0\|acc \|0.3548\|± \|0.0188\| \| \| \|acc_norm\|0.3625\|± \|0.0189\| \|agieval_lsat_ar \| 0\|acc \|0.1826\|± \|0.0255\| \| \| \|acc_norm\|0.1913\|± \|0.0260\| \|agieval_lsat_lr \| 0\|acc \|0.5510\|± \|0.0220\| \| \| \|acc_norm\|0.5255\|± \|0.0221\| \|agieval_lsat_rc \| 0\|acc \|0.6431\|± \|0.0293\| \| \| \|acc_norm\|0.6097\|± \|0.0298\| \|agieval_sat_en \| 0\|acc \|0.7330\|± \|0.0309\| \| \| \|acc_norm\|0.7039\|± \|0.0319\| \|agieval_sat_en_without_passage\| 0\|acc \|0.4029\|± \|0.0343\| \| \| \|acc_norm\|0.3689\|± \|0.0337\| \|agieval_sat_math \| 0\|acc \|0.3909\|± \|0.0330\| \| \| \|acc_norm\|0.3773\|± \|0.0328\| ``` Average: 42.44 ## BigBench: ``` \| Task \|Version\| Metric \|Value \| \|Stderr\| \|------------------------------------------------\|------:\|---------------------\|-----:\|---\|-----:\| \|bigbench_causal_judgement \| 0\|multiple_choice_grade\|0.5737\|± \|0.0360\| \|bigbench_date_understanding \| 0\|multiple_choice_grade\|0.6667\|± \|0.0246\| \|bigbench_disambiguation_qa \| 0\|multiple_choice_grade\|0.3178\|± \|0.0290\| \|bigbench_geometric_shapes \| 0\|multiple_choice_grade\|0.1755\|± \|0.0201\| \| \| \|exact_str_match \|0.0000\|± \|0.0000\| \|bigbench_logical_deduction_five_objects \| 0\|multiple_choice_grade\|0.3120\|± \|0.0207\| \|bigbench_logical_deduction_seven_objects \| 0\|multiple_choice_grade\|0.2014\|± \|0.0152\| \|bigbench_logical_deduction_three_objects \| 0\|multiple_choice_grade\|0.5500\|± \|0.0288\| \|bigbench_movie_recommendation \| 0\|multiple_choice_grade\|0.4300\|± \|0.0222\| \|bigbench_navigate \| 0\|multiple_choice_grade\|0.4980\|± \|0.0158\| \|bigbench_reasoning_about_colored_objects \| 0\|multiple_choice_grade\|0.7010\|± \|0.0102\| \|bigbench_ruin_names \| 0\|multiple_choice_grade\|0.4688\|± \|0.0236\| \|bigbench_salient_translation_error_detection \| 0\|multiple_choice_grade\|0.1974\|± \|0.0126\| \|bigbench_snarks \| 0\|multiple_choice_grade\|0.7403\|± \|0.0327\| \|bigbench_sports_understanding \| 0\|multiple_choice_grade\|0.5426\|± \|0.0159\| \|bigbench_temporal_sequences \| 0\|multiple_choice_grade\|0.5320\|± \|0.0158\| \|bigbench_tracking_shuffled_objects_five_objects \| 0\|multiple_choice_grade\|0.2280\|± \|0.0119\| \|bigbench_tracking_shuffled_objects_seven_objects\| 0\|multiple_choice_grade\|0.1531\|± \|0.0086\| \|bigbench_tracking_shuffled_objects_three_objects\| 0\|multiple_choice_grade\|0.5500\|± \|0.0288\| ``` Average: 43.55 ## TruthfulQA: ``` \| Task \|Version\|Metric\|Value\| \|Stderr\| \|-------------\|------:\|------\|----:\|---\|-----:\| \|truthfulqa_mc\| 1\|mc1 \|0.410\|± \|0.0172\| \| \| \|mc2 \|0.578\|± \|0.0157\| ``` # Inference Code Here is example code using HuggingFace Transformers to inference the model (note: in 4bit, it will require around 5GB of VRAM) Note: To use function calling, you should see the github repo above. ```python # Code to inference Hermes with HF Transformers # Requires pytorch, transformers, bitsandbytes, sentencepiece, protobuf, and flash-attn packages import torch from transformers import AutoTokenizer, AutoModelForCausalLM, LlamaForCausalLM import bitsandbytes, flash_attn tokenizer = AutoTokenizer.from_pretrained('NousResearch/Hermes-2-Pro-Llama-3-8B', trust_remote_code=True) model = LlamaForCausalLM.from_pretrained( "NousResearch/Hermes-2-Pro-Llama-3-8B", torch_dtype=torch.float16, device_map="auto", load_in_8bit=False, load_in_4bit=True, use_flash_attention_2=True ) prompts = [ """<\|im_start\|>system You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.<\|im_end\|> <\|im_start\|>user Write a short story about Goku discovering kirby has teamed up with Majin Buu to destroy the world.<\|im_end\|> <\|im_start\|>assistant""", ] for chat in prompts: print(chat) input_ids = tokenizer(chat, return_tensors="pt").input_ids.to("cuda") generated_ids = model.generate(input_ids, max_new_tokens=750, temperature=0.8, repetition_penalty=1.1, do_sample=True, eos_token_id=tokenizer.eos_token_id) response = tokenizer.decode(generated_ids[0][input_ids.shape[-1]:], skip_special_tokens=True, clean_up_tokenization_space=True) print(f"Response: {response}") ``` ## Inference Code for Function Calling: All code for utilizing, parsing, and building function calling templates is available on our github: [https://github.com/NousResearch/Hermes-Function-Calling](https://github.com/NousResearch/Hermes-Function-Calling) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/oi4CiGh50xmoviUQnh8R3.png) # Chat Interfaces When quantized versions of the model are released, I recommend using LM Studio for chatting with Hermes 2 Pro. It does not support function calling - for that use our github repo. It is a GUI application that utilizes GGUF models with a llama.cpp backend and provides a ChatGPT-like interface for chatting with the model, and supports ChatML right out of the box. In LM-Studio, simply select the ChatML Prefix on the settings side pane: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/ls6WqV-GSxMw2RA3GuQiN.png) ## Quantized Versions: GGUF Versions Available Here: https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B-GGUF # How to cite: ```bibtext @misc{Hermes-2-Pro-Llama-3-8B, url={[https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B]https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B)}, title={Hermes-2-Pro-Llama-3-8B}, author={"Teknium", "interstellarninja", "theemozilla", "karan4d", "huemin_art"} } ```
auragFouad/llama3-aspect-restaurants	auragFouad	2024-05-17T13:30:00Z	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	2024-05-17T13:29:51Z	--- 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: auragFouad - 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)
matthieuzone/EPOISSES2	matthieuzone	2024-05-17T13:28:06Z	3	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	2024-05-17T13:24:26Z	--- 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 photo of EPOISSES cheese widget: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - matthieuzone/EPOISSES2 <Gallery /> ## Model description These are matthieuzone/EPOISSES2 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of EPOISSES cheese to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](matthieuzone/EPOISSES2/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]
caiiofc/ppo-LunarLander-v2	caiiofc	2024-05-17T13:27:09Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-17T13:26:14Z	--- 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: 262.75 +/- 19.01 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 ... ```
AlekseiPravdin/Seamaiiza-7B-v3-32k	AlekseiPravdin	2024-05-17T13:23:22Z	8	2	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "grimjim/kunoichi-lemon-royale-v2-32K-7B", "AlekseiPravdin/Seamaiiza-7B-v1", "Nitral-AI/Nyanade_Stunna-Maid-7B", "conversational", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T12:49:03Z	--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - grimjim/kunoichi-lemon-royale-v2-32K-7B - AlekseiPravdin/Seamaiiza-7B-v1 - Nitral-AI/Nyanade_Stunna-Maid-7B --- # Seamaiiza-7B-v3-32k Seamaiiza-7B-v3-32k is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): * [grimjim/kunoichi-lemon-royale-v2-32K-7B](https://huggingface.co/grimjim/kunoichi-lemon-royale-v2-32K-7B) * [AlekseiPravdin/Seamaiiza-7B-v1](https://huggingface.co/AlekseiPravdin/Seamaiiza-7B-v1) * [Nitral-AI/Nyanade_Stunna-Maid-7B](https://huggingface.co/Nitral-AI/Nyanade_Stunna-Maid-7B) ## 🧩 Configuration ```yaml models: - model: grimjim/kunoichi-lemon-royale-v2-32K-7B # no parameters necessary for base model - model: AlekseiPravdin/Seamaiiza-7B-v1 parameters: weight: 0.2 density: 0.3 - model: Nitral-AI/Nyanade_Stunna-Maid-7B parameters: weight: 0.4 density: 0.5 merge_method: dare_ties base_model: grimjim/kunoichi-lemon-royale-v2-32K-7B parameters: int8_mask: true normalize: true dtype: bfloat16 ```
X-MEM/fabio-barba	X-MEM	2024-05-17T13:18:10Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-17T13:18:10Z	--- license: apache-2.0 ---
ss93/llama-2-7b-custom-4bit-GPTQ	ss93	2024-05-17T13:17:25Z	81	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2024-05-17T13:13:49Z	--- 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]
matthieuzone/CHEVRE2	matthieuzone	2024-05-17T13:16:16Z	1	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	2024-05-17T13:12:35Z	--- 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 photo of CHÈVRE cheese widget: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - matthieuzone/CHEVRE2 <Gallery /> ## Model description These are matthieuzone/CHEVRE2 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of CHÈVRE cheese to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](matthieuzone/CHEVRE2/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]
flammenai/Mahou-1.2-llama3-8B-GGUF	flammenai	2024-05-17T13:08:14Z	11	2	transformers	[ "transformers", "gguf", "dataset:flammenai/Grill-preprod-v1_chatML", "dataset:flammenai/Grill-preprod-v2_chatML", "base_model:flammenai/Mahou-1.2-llama3-8B", "base_model:quantized:flammenai/Mahou-1.2-llama3-8B", "license:llama3", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-17T01:37:21Z	--- library_name: transformers tags: [] base_model: - flammenai/Mahou-1.2-llama3-8B datasets: - flammenai/Grill-preprod-v1_chatML - flammenai/Grill-preprod-v2_chatML license: llama3 --- ![image/png](https://huggingface.co/flammenai/Mahou-1.0-mistral-7B/resolve/main/mahou1.png) # Mahou-1.2-llama3-8B Mahou is our attempt to build a production-ready conversational/roleplay LLM. Future versions will be released iteratively and finetuned from flammen.ai conversational data. ### Chat Format This model has been trained to use ChatML format. ``` <\|im_start\|>system {{system}}<\|im_end\|> <\|im_start\|>{{char}} {{message}}<\|im_end\|> <\|im_start\|>{{user}} {{message}}<\|im_end\|> ``` ### ST Settings 1. Use ChatML for the Context Template. 2. Turn on Instruct Mode for ChatML. 3. Use the following stopping strings: `["<", "\|", "<\|", "\n"]` ### License This model is based on Meta Llama-3-8B and is governed by the [META LLAMA 3 COMMUNITY LICENSE AGREEMENT](LICENSE). ### Method Finetuned using an A100 on Google Colab. [Fine-tune a Mistral-7b model with Direct Preference Optimization](https://towardsdatascience.com/fine-tune-a-mistral-7b-model-with-direct-preference-optimization-708042745aac) - [Maxime Labonne](https://huggingface.co/mlabonne) ### Configuration LoRA, model, and training settings: ```python # LoRA configuration peft_config = LoraConfig( r=16, lora_alpha=16, lora_dropout=0.05, bias="none", task_type="CAUSAL_LM", target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj'] ) # Model to fine-tune model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, load_in_4bit=True ) model.config.use_cache = False # Reference model ref_model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, load_in_4bit=True ) # Training arguments training_args = TrainingArguments( per_device_train_batch_size=2, gradient_accumulation_steps=4, gradient_checkpointing=True, learning_rate=5e-5, lr_scheduler_type="cosine", max_steps=1000, save_strategy="no", logging_steps=1, output_dir=new_model, optim="paged_adamw_32bit", warmup_steps=100, bf16=True, report_to="wandb", ) # Create DPO trainer dpo_trainer = DPOTrainer( model, ref_model, args=training_args, train_dataset=dataset, tokenizer=tokenizer, peft_config=peft_config, beta=0.1, force_use_ref_model=True ) ```
GENIAC-Team-Ozaki/lora-sft-finetuned-stage3-iter68800	GENIAC-Team-Ozaki	2024-05-17T13:06:59Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T13:02:06Z	--- 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]
FDeRubeis/araft_trained_sft	FDeRubeis	2024-05-17T13:05:46Z	3	0	peft	[ "peft", "safetensors", "generated_from_trainer", "arxiv:2210.03629", "base_model:meta-llama/Llama-2-7b-chat-hf", "base_model:adapter:meta-llama/Llama-2-7b-chat-hf", "region:us" ]	null	2024-04-08T13:31:42Z	--- library_name: peft tags: - generated_from_trainer base_model: meta-llama/Llama-2-7b-chat-hf model-index: - name: '20240328_1004' 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. --> # araft_trained_sft This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on the [Araft dataset](https://huggingface.co/datasets/FDeRubeis/araft). ## Model description This model has been generated in the context of the [Araft](https://github.com/FDeRubeis/Araft) project. The Araft project consists in fine-tuning a Llama2-7B model to enable the use of the [ReAct](https://arxiv.org/abs/2210.03629) pattern for Wikipedia-augmented question-answering. This model is the product of the first training step: SFT training. In the SFT training step, the trajectories from the [Araft dataset](https://huggingface.co/datasets/FDeRubeis/araft) have been used to fine-tune the model, using each step as a desired output for the previous part of the trajectory. The model achieves a 16% performace (f1 score) on the [HotpotQA dataset](https://hotpotqa.github.io/). For further information, please see the [Araft](https://github.com/FDeRubeis/Araft) github repo. ### 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: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 - mixed_precision_training: Native AMP ### Framework versions - PEFT 0.10.0 - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
Wacim-octo/factory_LoRA_local_RTX_3060	Wacim-octo	2024-05-17T13:05:45Z	0	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "dora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-05-17T13:05:44Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - text-to-image - diffusers-training - diffusers - dora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of factory widget: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - Wacim-octo/factory_LoRA_local_RTX_3060 <Gallery /> ## Model description These are Wacim-octo/factory_LoRA_local_RTX_3060 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of factory to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](Wacim-octo/factory_LoRA_local_RTX_3060/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]
FDeRubeis/araft_trained_dpo	FDeRubeis	2024-05-17T13:05:16Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "arxiv:2210.03629", "base_model:meta-llama/Llama-2-7b-chat-hf", "base_model:adapter:meta-llama/Llama-2-7b-chat-hf", "region:us" ]	null	2024-04-08T13:39:12Z	--- library_name: peft tags: - generated_from_trainer base_model: meta-llama/Llama-2-7b-chat-hf model-index: - name: '20240328_1135' 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. --> # araft_trained_dpo This model has been obtained by fine-tuning [araft_trained_sft](https://huggingface.co/FDeRubeis/araft_trained_sft) with DPO. The trajectories from the [Araft dataset](https://huggingface.co/datasets/FDeRubeis/araft) were used to adapt the model to make a novel query at every step, instead of repeating the query from the previous one. ## Model description This model has been generated in the context of the [Araft](https://github.com/FDeRubeis/Araft) project. The Araft project consists in fine-tuning a Llama2-7B model to enable the use of the [ReAct](https://arxiv.org/abs/2210.03629) pattern for Wikipedia-augmented question-answering. This model is the product of the second and final training step: DPO training. In the DPO training step, the trajectories from the [Araft dataset](https://huggingface.co/datasets/FDeRubeis/araft) have been used to fine-tune the model. Each step was used as a desired output for the previous part of the trajectory, whereas the repetition of the previous step was used as undesired output. The model achieves a 26% performace (f1 score) on the [HotpotQA dataset](https://hotpotqa.github.io/). For further information, please see the [Araft](https://github.com/FDeRubeis/Araft) github repo. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - 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 - lr_scheduler_warmup_steps: 100 - num_epochs: 1 - mixed_precision_training: Native AMP ### Framework versions - PEFT 0.10.0 - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
Nishit28/bge-large-finetuned-400	Nishit28	2024-05-17T13:02:06Z	17	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-05-09T17:32:05Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 44 with parameters: ``` {'batch_size': 10, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 2, "evaluation_steps": 50, "evaluator": "sentence_transformers.evaluation.InformationRetrievalEvaluator.InformationRetrievalEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 8, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
matthieuzone/CABECOU2	matthieuzone	2024-05-17T13:00:38Z	1	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	2024-05-17T12:56:57Z	--- 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 photo of CABECOU cheese widget: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - matthieuzone/CABECOU2 <Gallery /> ## Model description These are matthieuzone/CABECOU2 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of CABECOU cheese to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](matthieuzone/CABECOU2/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]
kamilakesbi/speaker-segmentation-fine-tuned-callhome-jpn	kamilakesbi	2024-05-17T12:56:52Z	75	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "pyannet", "speaker-diarization", "speaker-segmentation", "generated_from_trainer", "pyannote", "pyannote.audio", "pyannote-audio-model", "audio", "voice", "speech", "speaker", "speaker-change-detection", "voice-activity-detection", "overlapped-speech-detection", "resegmentation", "dataset:diarizers-community/callhome", "base_model:pyannote/segmentation-3.0", "base_model:finetune:pyannote/segmentation-3.0", "license:mit", "endpoints_compatible", "region:us" ]	voice-activity-detection	2024-04-22T11:17:05Z	--- license: mit tags: - speaker-diarization - speaker-segmentation - generated_from_trainer - pyannote - pyannote.audio - pyannote-audio-model - audio - voice - speech - speaker - speaker-change-detection - voice-activity-detection - overlapped-speech-detection - resegmentation base_model: pyannote/segmentation-3.0 datasets: - diarizers-community/callhome licence: mit model-index: - name: speaker-segmentation-fine-tuned-callhome-jpn results: [] --- This is the model card of a pyannote model that has been pushed on the Hub. This model card has been automatically generated.
matthieuzone/BUCHETTE_DE_CHEVRE2	matthieuzone	2024-05-17T12:56:43Z	1	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	2024-05-17T12:53:02Z	--- 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 photo of BÛCHETTE DE CHÈVRE cheese widget: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - matthieuzone/BUCHETTE_DE_CHEVRE2 <Gallery /> ## Model description These are matthieuzone/BUCHETTE_DE_CHEVRE2 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of BÛCHETTE DE CHÈVRE cheese to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](matthieuzone/BUCHETTE_DE_CHEVRE2/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]
RichardErkhov/BioMistral_-_BioMistral-7B-DARE-8bits	RichardErkhov	2024-05-17T12:50:09Z	78	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:2311.03099", "arxiv:2306.01708", "arxiv:2402.10373", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-17T12:43:38Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) BioMistral-7B-DARE - bnb 8bits - Model creator: https://huggingface.co/BioMistral/ - Original model: https://huggingface.co/BioMistral/BioMistral-7B-DARE/ Original model description: --- base_model: - BioMistral/BioMistral-7B - mistralai/Mistral-7B-Instruct-v0.1 library_name: transformers tags: - mergekit - merge - dare - medical - biology license: apache-2.0 datasets: - pubmed language: - en - fr - nl - es - it - pl - ro - de pipeline_tag: text-generation --- # BioMistral-7B-mistral7instruct-dare This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) as a base. ### Models Merged The following models were included in the merge: * [BioMistral/BioMistral-7B](https://huggingface.co/BioMistral/BioMistral-7B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: mistralai/Mistral-7B-Instruct-v0.1 # No parameters necessary for base model - model: BioMistral/BioMistral-7B parameters: density: 0.5 weight: 0.5 merge_method: dare_ties base_model: mistralai/Mistral-7B-Instruct-v0.1 parameters: int8_mask: true dtype: bfloat16 ``` <p align="center"> <img src="https://huggingface.co/BioMistral/BioMistral-7B/resolve/main/wordart_blue_m_rectangle.png?download=true" alt="drawing" width="250"/> </p> # BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains Abstract: Large Language Models (LLMs) have demonstrated remarkable versatility in recent years, offering potential applications across specialized domains such as healthcare and medicine. Despite the availability of various open-source LLMs tailored for health contexts, adapting general-purpose LLMs to the medical domain presents significant challenges. In this paper, we introduce BioMistral, an open-source LLM tailored for the biomedical domain, utilizing Mistral as its foundation model and further pre-trained on PubMed Central. We conduct a comprehensive evaluation of BioMistral on a benchmark comprising 10 established medical question-answering (QA) tasks in English. We also explore lightweight models obtained through quantization and model merging approaches. Our results demonstrate BioMistral's superior performance compared to existing open-source medical models and its competitive edge against proprietary counterparts. Finally, to address the limited availability of data beyond English and to assess the multilingual generalization of medical LLMs, we automatically translated and evaluated this benchmark into 7 other languages. This marks the first large-scale multilingual evaluation of LLMs in the medical domain. Datasets, multilingual evaluation benchmarks, scripts, and all the models obtained during our experiments are freely released. Advisory Notice! Although BioMistral is intended to encapsulate medical knowledge sourced from high-quality evidence, it hasn't been tailored to effectively, safely, or suitably convey this knowledge within professional parameters for action. We advise refraining from utilizing BioMistral in medical contexts unless it undergoes thorough alignment with specific use cases and undergoes further testing, notably including randomized controlled trials in real-world medical environments. BioMistral 7B may possess inherent risks and biases that have not yet been thoroughly assessed. Additionally, the model's performance has not been evaluated in real-world clinical settings. Consequently, we recommend using BioMistral 7B strictly as a research tool and advise against deploying it in production environments for natural language generation or any professional health and medical purposes. # 1. BioMistral models BioMistral is a suite of Mistral-based further pre-trained open source models suited for the medical domains and pre-trained using textual data from PubMed Central Open Access (CC0, CC BY, CC BY-SA, and CC BY-ND). All the models are trained using the CNRS (French National Centre for Scientific Research) [Jean Zay](http://www.idris.fr/jean-zay/) French HPC. \| Model Name \| Base Model \| Model Type \| Sequence Length \| Download \| \|:-------------------:\|:----------------------------------:\|:-------------------:\|:---------------:\|:-----------------------------------------------------:\| \| BioMistral-7B \| [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) \| Further Pre-trained \| 2048 \| [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B) \| \| BioMistral-7B-DARE \| [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) \| Merge DARE \| 2048 \| [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-DARE) \| \| BioMistral-7B-TIES \| [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) \| Merge TIES \| 2048 \| [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-TIES) \| \| BioMistral-7B-SLERP \| [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) \| Merge SLERP \| 2048 \| [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-SLERP) \| # 2. Quantized Models \| Base Model \| Method \| q_group_size \| w_bit \| version \| VRAM GB \| Time \| Download \| \|:-------------------:\|:------:\|:------------:\|:-----:\|:-------:\|:-------:\|:------:\|:--------:\| \| BioMistral-7B \| FP16/BF16 \| \| \| \| 15.02 \| x1.00 \| [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B) \| \| BioMistral-7B \| AWQ \| 128 \| 4 \| GEMM \| 4.68 \| x1.41 \| [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-AWQ-QGS128-W4-GEMM) \| \| BioMistral-7B \| AWQ \| 128 \| 4 \| GEMV \| 4.68 \| x10.30 \| [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-AWQ-QGS128-W4-GEMV) \| \| BioMistral-7B \| BnB.4 \| \| 4 \| \| 5.03 \| x3.25 \| [HuggingFace](blank) \| \| BioMistral-7B \| BnB.8 \| \| 8 \| \| 8.04 \| x4.34 \| [HuggingFace](blank) \| \| BioMistral-7B-DARE \| AWQ \| 128 \| 4 \| GEMM \| 4.68 \| x1.41 \| [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-DARE-AWQ-QGS128-W4-GEMM) \| \| BioMistral-7B-TIES \| AWQ \| 128 \| 4 \| GEMM \| 4.68 \| x1.41 \| [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-TIES-AWQ-QGS128-W4-GEMM) \| \| BioMistral-7B-SLERP \| AWQ \| 128 \| 4 \| GEMM \| 4.68 \| x1.41 \| [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-SLERP-AWQ-QGS128-W4-GEMM) \| # 2. Using BioMistral You can use BioMistral with [Hugging Face's Transformers library](https://github.com/huggingface/transformers) as follow. Loading the model and tokenizer : ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("BioMistral/BioMistral-7B") model = AutoModel.from_pretrained("BioMistral/BioMistral-7B") ``` # 3. Supervised Fine-tuning Benchmark \| \| Clinical KG \| Medical Genetics \| Anatomy \| Pro Medicine \| College Biology \| College Medicine \| MedQA \| MedQA 5 opts \| PubMedQA \| MedMCQA \| Avg. \| \|-------------------------------------------\|:---------------------------------------------:\|-----------------------------------------------\|-----------------------------------------------\|-----------------------------------------------\|-----------------------------------------------\|-----------------------------------------------\|-----------------------------------------------\|-----------------------------------------------\|-----------------------------------------------\|-----------------------------------------------\|------------------\| \| BioMistral 7B \| 59.9 \| 64.0 \| 56.5 \| 60.4 \| 59.0 \| 54.7 \| 50.6 \| 42.8 \| 77.5 \| 48.1 \| 57.3 \| \| Mistral 7B Instruct \| 62.9 \| 57.0 \| 55.6 \| 59.4 \| 62.5 \| <u>57.2</u> \| 42.0 \| 40.9 \| 75.7 \| 46.1 \| 55.9 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| BioMistral 7B Ensemble \| <u>62.8</u> \| 62.7 \| <u>57.5</u> \| 63.5 \| 64.3 \| 55.7 \| 50.6 \| 43.6 \| 77.5 \| 48.8 \| 58.7 \| \| BioMistral 7B DARE \| 62.3 \| 67.0 \| 55.8 \| 61.4 \| 66.9 \| 58.0 \| 51.1 \| 45.2 \| <u>77.7</u> \| <u>48.7</u> \| 59.4 \| \| BioMistral 7B TIES \| 60.1 \| <u>65.0</u> \| 58.5 \| 60.5 \| 60.4 \| 56.5 \| 49.5 \| 43.2 \| 77.5 \| 48.1 \| 57.9 \| \| BioMistral 7B SLERP \| 62.5 \| 64.7 \| 55.8 \| <u>62.7</u> \| <u>64.8</u> \| 56.3 \| <u>50.8</u> \| <u>44.3</u> \| 77.8 \| 48.6 \| <u>58.8</u> \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| MedAlpaca 7B \| 53.1 \| 58.0 \| 54.1 \| 58.8 \| 58.1 \| 48.6 \| 40.1 \| 33.7 \| 73.6 \| 37.0 \| 51.5 \| \| PMC-LLaMA 7B \| 24.5 \| 27.7 \| 35.3 \| 17.4 \| 30.3 \| 23.3 \| 25.5 \| 20.2 \| 72.9 \| 26.6 \| 30.4 \| \| MediTron-7B \| 41.6 \| 50.3 \| 46.4 \| 27.9 \| 44.4 \| 30.8 \| 41.6 \| 28.1 \| 74.9 \| 41.3 \| 42.7 \| \| BioMedGPT-LM-7B \| 51.4 \| 52.0 \| 49.4 \| 53.3 \| 50.7 \| 49.1 \| 42.5 \| 33.9 \| 76.8 \| 37.6 \| 49.7 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| GPT-3.5 Turbo 1106* \| 74.71 \| 74.00 \| 65.92 \| 72.79 \| 72.91 \| 64.73 \| 57.71 \| 50.82 \| 72.66 \| 53.79 \| 66.0 \| Supervised Fine-Tuning (SFT) performance of BioMistral 7B models compared to baselines, measured by accuracy (↑) and averaged across 3 random seeds of 3-shot. DARE, TIES, and SLERP are model merging strategies that combine BioMistral 7B and Mistral 7B Instruct. Best model in bold, and second-best underlined. GPT-3.5 Turbo performances are reported from the 3-shot results without SFT. # Citation BibTeX Arxiv : [https://arxiv.org/abs/2402.10373](https://arxiv.org/abs/2402.10373) ```bibtex @misc{labrak2024biomistral, title={BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains}, author={Yanis Labrak and Adrien Bazoge and Emmanuel Morin and Pierre-Antoine Gourraud and Mickael Rouvier and Richard Dufour}, year={2024}, eprint={2402.10373}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` CAUTION!* Both direct and downstream users need to be informed about the risks, biases, and constraints inherent in the model. While the model can produce natural language text, our exploration of its capabilities and limitations is just beginning. In fields such as medicine, comprehending these limitations is crucial. Hence, we strongly advise against deploying this model for natural language generation in production or for professional tasks in the realm of health and medicine.
JethroDD/twitter-roberta-base-sentiment-CustomModel_imdb	JethroDD	2024-05-17T12:49:55Z	110	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-17T12:49:09Z	--- 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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reemmasoud/idv_vs_col_llama-3_PromptTuning_CAUSAL_LM_gradient_descent_v5	reemmasoud	2024-05-17T12:48:53Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-17T12:48:52Z	--- 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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damgomz/ft_bs16_lr6_base_x2	damgomz	2024-05-17T12:48:16Z	107	0	transformers	[ "transformers", "safetensors", "albert", "text-classification", "fill-mask", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-05-16T15:23:42Z	--- language: en tags: - fill-mask kwargs: timestamp: '2024-05-17T14:48:14' project_name: ft_bs16_lr6_base_x2_emissions_tracker run_id: b5a76c04-4c93-4cd1-86c2-b940f28d07c0 duration: 17539.63565468788 emissions: 0.0107856173433722 emissions_rate: 6.149282434204692e-07 cpu_power: 42.5 gpu_power: 0.0 ram_power: 4.500000000000001 cpu_energy: 0.2070647665154599 gpu_energy: 0 ram_energy: 0.0219243705171347 energy_consumed: 0.2289891370325948 country_name: Switzerland country_iso_code: CHE region: .nan cloud_provider: .nan cloud_region: .nan os: Linux-5.14.0-70.30.1.el9_0.x86_64-x86_64-with-glibc2.34 python_version: 3.10.4 codecarbon_version: 2.3.4 cpu_count: 2 cpu_model: Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz gpu_count: .nan gpu_model: .nan longitude: .nan latitude: .nan ram_total_size: 12 tracking_mode: machine on_cloud: N pue: 1.0 --- ## Environmental Impact (CODE CARBON DEFAULT) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| Duration (in seconds) \| 17539.63565468788 \| \| Emissions (Co2eq in kg) \| 0.0107856173433722 \| \| CPU power (W) \| 42.5 \| \| GPU power (W) \| [No GPU] \| \| RAM power (W) \| 4.500000000000001 \| \| CPU energy (kWh) \| 0.2070647665154599 \| \| GPU energy (kWh) \| [No GPU] \| \| RAM energy (kWh) \| 0.0219243705171347 \| \| Consumed energy (kWh) \| 0.2289891370325948 \| \| Country name \| Switzerland \| \| Cloud provider \| nan \| \| Cloud region \| nan \| \| CPU count \| 2 \| \| CPU model \| Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz \| \| GPU count \| nan \| \| GPU model \| nan \| ## Environmental Impact (for one core) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| CPU energy (kWh) \| 0.03376379863527417 \| \| Emissions (Co2eq in kg) \| 0.006869690631419421 \| ## Note 17 May 2024 ## My Config \| Config \| Value \| \|--------------------------\|-----------------\| \| checkpoint \| albert-base-v2 \| \| model_name \| ft_bs16_lr6_base_x2 \| \| sequence_length \| 400 \| \| num_epoch \| 6 \| \| learning_rate \| 5e-06 \| \| batch_size \| 16 \| \| weight_decay \| 0.0 \| \| warm_up_prop \| 0.0 \| \| drop_out_prob \| 0.1 \| \| packing_length \| 100 \| \| train_test_split \| 0.2 \| \| num_steps \| 32580 \| ## Training and Testing steps Epoch \| Train Loss \| Test Loss \| Accuracy \| Recall ---\|---\|---\|---\|--- \| 0 \| 0.449242 \| 0.373513 \| 0.834315 \| 0.812883 \| \| 1 \| 0.335334 \| 0.351701 \| 0.851252 \| 0.903374 \| \| 2 \| 0.286431 \| 0.351840 \| 0.854934 \| 0.881902 \| \| 3 \| 0.217484 \| 0.389165 \| 0.840206 \| 0.932515 \| \| 4 \| 0.133403 \| 0.430746 \| 0.846097 \| 0.842025 \| \| 5 \| 0.079103 \| 0.486669 \| 0.844624 \| 0.883436 \|
KenanKhan/rl_course_vizdoom_health_gathering_supreme	KenanKhan	2024-05-17T12:46:30Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-17T12:46:24Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 3.94 +/- 0.20 name: mean_reward verified: false --- A(n) APPO model trained on the doom_health_gathering_supreme environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r KenanKhan/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
digiplay/CamelliaMix_NSFW_diffusers_v1.1	digiplay	2024-05-17T12:46:04Z	999	20	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-05-31T09:19:25Z	--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Model info: https://civitai.com/models/44315/camelliamixnsfw This model name is CamelliaMix_NSFW, but I think it can generate many elegant look, my sample images: (You can apply VAE or not,different feel) ![天使思考 - 2023-06-01T014553.253.png](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/QDTU53CjbK01UlwLKKwWt.png) ![下载 - 2023-06-01T014142.465.png](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/zN8MTWSmJHvpxBdBk5doI.png) ![下载 - 2023-06-01T014036.021.png](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/5GT10rhJaCJZcAOVIGoFm.png) No VAE sample: ![下載 - 2023-06-01T025802.542.png](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/OMUrw0RSrnqHBgGfQYqwV.png)
Shadow09/my-tiny-chatbot	Shadow09	2024-05-17T12:40:40Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "region:us" ]	null	2024-05-17T12:37:08Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 model-index: - name: my-tiny-chatbot 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. --> # my-tiny-chatbot This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.11.0 - Transformers 4.37.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.15.2
afrideva/Llama-3-8B-Synthia-v3.5-GGUF	afrideva	2024-05-17T12:31:11Z	4	0	null	[ "gguf", "ggml", "quantized", "text-generation", "base_model:migtissera/Llama-3-8B-Synthia-v3.5", "base_model:quantized:migtissera/Llama-3-8B-Synthia-v3.5", "license:llama3", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-05-17T11:23:09Z	--- base_model: migtissera/Llama-3-8B-Synthia-v3.5 inference: true license: llama3 model_creator: migtissera model_name: Llama-3-8B-Synthia-v3.5 pipeline_tag: text-generation quantized_by: afrideva tags: - gguf - ggml - quantized --- # Llama-3-8B-Synthia-v3.5-GGUF Quantized GGUF model files for [Llama-3-8B-Synthia-v3.5](https://huggingface.co/migtissera/Llama-3-8B-Synthia-v3.5) from [migtissera](https://huggingface.co/migtissera) ## Original Model Card: # Llama-3-8B-Synthia-v3.5 Llama-3-8B-Synthia-v3.5 (Synthetic Intelligent Agent) is a general purpose Large Language Model (LLM). It was trained on the Synthia-v3.5 dataset that contains the varied system contexts, plus some other publicly available datasets. It has been fine-tuned for instruction following as well as having long-form conversations. <br> ![Synthia](https://huggingface.co/migtissera/Llama-3-8B-Synthia-v3.5/resolve/main/Synthia-3.5.jpg) <br> ## Evaluation We evaluated Llama-3-8B-Synthia-v3.5 on a wide range of tasks using [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) from EleutherAI. Here are the results on metrics used by [HuggingFaceH4 Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). Section to follow. \|\|\|\| \|:------:\|:--------:\|:-------:\| \|Task\|Metric\|Value\| \|arc_challenge\|acc_norm\|\| \|hellaswag\|acc_norm\|\| \|mmlu\|acc_norm\|\| \|truthfulqa_mc\|mc2\|\| \|Total Average\|-\|\|\| <br> # Sample code to run inference ```python import torch, json from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "/home/migel/Tess-2.0-Llama-3-8B" output_file_path = "/home/migel/conversations.jsonl" model = AutoModelForCausalLM.from_pretrained( model_path, torch_dtype=torch.float16, device_map="auto", load_in_4bit=False, trust_remote_code=False, ) tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) def generate_text(instruction): tokens = tokenizer.encode(instruction) tokens = torch.LongTensor(tokens).unsqueeze(0) tokens = tokens.to("cuda") instance = { "input_ids": tokens, "top_p": 1.0, "temperature": 0.75, "generate_len": 1024, "top_k": 50, } length = len(tokens[0]) with torch.no_grad(): rest = model.generate( input_ids=tokens, max_length=length + instance["generate_len"], use_cache=True, do_sample=True, top_p=instance["top_p"], temperature=instance["temperature"], top_k=instance["top_k"], num_return_sequences=1, pad_token_id=tokenizer.eos_token_id, ) output = rest[0][length:] string = tokenizer.decode(output, skip_special_tokens=True) return f"{string}" conversation = """<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|>\n\nYou are Synthia, a helful, female AI assitant. You always provide detailed answers without hesitation.<\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|>\n\n""" while True: user_input = input("You: ") llm_prompt = f"{conversation}{user_input}<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>\n\n" answer = generate_text(llm_prompt) print(answer) conversation = f"{llm_prompt}{answer}<\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|>\n\n" json_data = {"prompt": user_input, "answer": answer} with open(output_file_path, "a") as output_file: output_file.write(json.dumps(json_data) + "\n") ``` # Join My General AI Discord (NeuroLattice): https://discord.gg/Hz6GrwGFKD # Limitations & Biases: While this model aims for accuracy, it can occasionally produce inaccurate or misleading results. Despite diligent efforts in refining the pretraining data, there remains a possibility for the generation of inappropriate, biased, or offensive content. Exercise caution and cross-check information when necessary. This is an uncensored model.
JoshuaKelleyDs/quickdraw-ConvNeXT-Tiny-Finetune	JoshuaKelleyDs	2024-05-17T12:30:19Z	196	0	transformers	[ "transformers", "onnx", "safetensors", "convnext", "image-classification", "generated_from_trainer", "base_model:facebook/convnext-tiny-224", "base_model:quantized:facebook/convnext-tiny-224", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-17T08:54:29Z	--- license: apache-2.0 base_model: facebook/convnext-tiny-224 tags: - generated_from_trainer metrics: - accuracy model-index: - name: quickdraw-ConvNeXT-Tiny-Finetune 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. --> # quickdraw-ConvNeXT-Tiny-Finetune This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8413 - Accuracy: 0.7826 ## 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: 0.0008 - train_batch_size: 512 - eval_batch_size: 512 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5000 - num_epochs: 7 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:--------:\| \| 1.0895 \| 0.5688 \| 5000 \| 1.0618 \| 0.7278 \| \| 0.8912 \| 1.1377 \| 10000 \| 0.9315 \| 0.7602 \| \| 0.8658 \| 1.7065 \| 15000 \| 0.8871 \| 0.7701 \| \| 0.7558 \| 2.2753 \| 20000 \| 0.8653 \| 0.7780 \| \| 0.7578 \| 2.8441 \| 25000 \| 0.8392 \| 0.7826 \| \| 0.6249 \| 3.4130 \| 30000 \| 0.8584 \| 0.7842 \| \| 0.6243 \| 3.9818 \| 35000 \| 0.8410 \| 0.7882 \| \| 0.4585 \| 4.5506 \| 40000 \| 0.9402 \| 0.7828 \| \| 0.2934 \| 5.1195 \| 45000 \| 1.0587 \| 0.7758 \| \| 0.2869 \| 5.6883 \| 50000 \| 1.1280 \| 0.7745 \| \| 0.1837 \| 6.2571 \| 55000 \| 1.2402 \| 0.7684 \| \| 0.1858 \| 6.8259 \| 60000 \| 1.2570 \| 0.7679 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1 - Datasets 2.19.1 - Tokenizers 0.19.1
DUAL-GPO-2/phi-2-gpo-v4-i2	DUAL-GPO-2	2024-05-17T12:29:41Z	1	0	peft	[ "peft", "tensorboard", "safetensors", "phi", "alignment-handbook", "generated_from_trainer", "trl", "dpo", "custom_code", "dataset:HuggingFaceH4/ultrafeedback_binarized", "base_model:DUAL-GPO-2/phi-2-gpo-v34-merged-i1", "base_model:adapter:DUAL-GPO-2/phi-2-gpo-v34-merged-i1", "region:us" ]	null	2024-05-17T11:18:44Z	--- library_name: peft tags: - alignment-handbook - generated_from_trainer - trl - dpo - generated_from_trainer base_model: DUAL-GPO-2/phi-2-gpo-v34-merged-i1 datasets: - HuggingFaceH4/ultrafeedback_binarized model-index: - name: phi-2-gpo-v4-i2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # phi-2-gpo-v4-i2 This model is a fine-tuned version of [DUAL-GPO-2/phi-2-gpo-v34-merged-i1](https://huggingface.co/DUAL-GPO-2/phi-2-gpo-v34-merged-i1) on the HuggingFaceH4/ultrafeedback_binarized dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 3 - gradient_accumulation_steps: 4 - total_train_batch_size: 48 - total_eval_batch_size: 12 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2
Ramikan-BR/tinyllama-coder-py-4bit	Ramikan-BR	2024-05-17T12:29:09Z	106	0	transformers	[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/tinyllama-chat-bnb-4bit", "base_model:finetune:unsloth/tinyllama-chat-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T12:01:59Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft base_model: unsloth/tinyllama-chat-bnb-4bit --- # Uploaded model - Developed by: Ramikan-BR - License: apache-2.0 - Finetuned from model : unsloth/tinyllama-chat-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)
abc88767/2c93	abc88767	2024-05-17T12:28:11Z	6	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T04:07:28Z	--- 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. 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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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MoGP/g_x_Moji_stratify_reg	MoGP	2024-05-17T12:25:50Z	110	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-17T08:23:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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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EyaZr/token1200	EyaZr	2024-05-17T12:25:26Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma", "trl", "en", "base_model:unsloth/gemma-7b-bnb-4bit", "base_model:finetune:unsloth/gemma-7b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-17T12:25:07Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma - trl base_model: unsloth/gemma-7b-bnb-4bit --- # Uploaded model - Developed by: EyaZr - License: apache-2.0 - Finetuned from model : unsloth/gemma-7b-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)
emilykang/medmcqa_question_generation-anatomy_lora	emilykang	2024-05-17T12:24:41Z	5	0	peft	[ "peft", "safetensors", "llama", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "region:us" ]	null	2024-05-17T09:02:39Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 datasets: - generator model-index: - name: medmcqa_question_generation-anatomy_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. --> # medmcqa_question_generation-anatomy_lora This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) 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
selincildam/chatbot-v0.1	selincildam	2024-05-17T12:23:37Z	2	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:TheBloke/Mistral-7B-Instruct-v0.1-GPTQ", "base_model:adapter:TheBloke/Mistral-7B-Instruct-v0.1-GPTQ", "license:apache-2.0", "region:us" ]	null	2024-05-16T20:47:47Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: TheBloke/Mistral-7B-Instruct-v0.1-GPTQ datasets: - generator model-index: - name: chatbot-v0.1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<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/scildam/huggingface/runs/2uwus52d) [<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/scildam/huggingface/runs/2uwus52d) [<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/scildam/huggingface/runs/2uwus52d) [<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/scildam/huggingface/runs/2uwus52d) # chatbot-v0.1 This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.1-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GPTQ) 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: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 250 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.11.0 - Transformers 4.41.0.dev0 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.19.1
bhoopendrakumar/passport200	bhoopendrakumar	2024-05-17T12:20:33Z	50	0	transformers	[ "transformers", "safetensors", "vision-encoder-decoder", "image-text-to-text", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-05-17T12:19:21Z	--- 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]
Dominic0406/Model_Dominic	Dominic0406	2024-05-17T12:20:14Z	106	0	transformers	[ "transformers", "safetensors", "bart", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-14T13:15:02Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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valurank/en_pos_counter	valurank	2024-05-17T12:18:28Z	8	2	spacy	[ "spacy", "text-classification", "en", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- tags: - spacy - text-classification language: - en --- A Spacy pipeline for counting Part-of-speech articles \| Feature \| Description \| \| --- \| --- \| \| Name \| `en_pipeline` \| \| Version \| `0.1` \| \| spaCy \| `>=3.7.2,<3.8.0` \| \| Default Pipeline \| `tok2vec`, `tagger`, `attribute_ruler`, `pos_counter` \| \| Components \| `tok2vec`, `tagger`, `attribute_ruler`, `pos_counter` \| \| Vectors \| 0 keys, 0 unique vectors (0 dimensions) \| \| Sources \| n/a \| \| License \| n/a \| \| Author \| [Valurank](www.valurank.com) \| ### Label Scheme <details> <summary>View label scheme (50 labels for 1 components)</summary> \| Component \| Labels \| \| --- \| --- \| \| `tagger` \| `$`, `''`, `,`, `-LRB-`, `-RRB-`, `.`, `:`, `ADD`, `AFX`, `CC`, `CD`, `DT`, `EX`, `FW`, `HYPH`, `IN`, `JJ`, `JJR`, `JJS`, `LS`, `MD`, `NFP`, `NN`, `NNP`, `NNPS`, `NNS`, `PDT`, `POS`, `PRP`, `PRP$`, `RB`, `RBR`, `RBS`, `RP`, `SYM`, `TO`, `UH`, `VB`, `VBD`, `VBG`, `VBN`, `VBP`, `VBZ`, `WDT`, `WP`, `WP$`, `WRB`, `XX`, `_SP`, ```` \| </details>
abhipsapanda/imagecaptioning	abhipsapanda	2024-05-17T12:16:16Z	46	0	transformers	[ "transformers", "pytorch", "vision-encoder-decoder", "image-text-to-text", "license:apache-2.0", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-05-17T10:54:20Z	--- license: apache-2.0 ---
abbenedek/whisper-base.en-finetuning2-D3K	abbenedek	2024-05-17T12:15:47Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-17T10:58:02Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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abbenedek/abbenedekwhisper-base.en-finetuning2-D3K	abbenedek	2024-05-17T12:15:45Z	116	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-base.en", "base_model:finetune:openai/whisper-base.en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-17T10:45:24Z	--- license: apache-2.0 base_model: openai/whisper-base.en tags: - generated_from_trainer metrics: - wer model-index: - name: abbenedekwhisper-base.en-finetuning2-D3K 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. --> # abbenedekwhisper-base.en-finetuning2-D3K This model is a fine-tuned version of [openai/whisper-base.en](https://huggingface.co/openai/whisper-base.en) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.7781 - Cer: 64.7190 - Wer: 119.5364 - Ser: 100.0 - Cer Clean: 3.5058 - Wer Clean: 6.2914 - Ser Clean: 7.0175 ## 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-08 - train_batch_size: 16 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - training_steps: 1000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Cer \| Wer \| Ser \| Cer Clean \| Wer Clean \| Ser Clean \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:--------:\|:-----:\|:---------:\|:---------:\|:---------:\| \| 7.5369 \| 0.53 \| 100 \| 6.7220 \| 63.7730 \| 128.1457 \| 100.0 \| 4.1180 \| 6.9536 \| 8.7719 \| \| 7.0363 \| 1.06 \| 200 \| 6.1829 \| 65.0529 \| 123.8411 \| 100.0 \| 3.2832 \| 5.6291 \| 7.0175 \| \| 6.417 \| 1.6 \| 300 \| 5.7959 \| 64.1625 \| 121.1921 \| 100.0 \| 3.2832 \| 5.6291 \| 7.0175 \| \| 6.0146 \| 2.13 \| 400 \| 5.4587 \| 64.7746 \| 121.8543 \| 100.0 \| 3.6728 \| 6.6225 \| 7.8947 \| \| 5.6687 \| 2.66 \| 500 \| 5.2287 \| 65.3311 \| 120.5298 \| 100.0 \| 3.7284 \| 6.6225 \| 7.8947 \| \| 5.3902 \| 3.19 \| 600 \| 5.0691 \| 65.1085 \| 121.1921 \| 100.0 \| 3.5615 \| 6.2914 \| 7.0175 \| \| 5.2512 \| 3.72 \| 700 \| 4.9358 \| 64.7190 \| 120.1987 \| 100.0 \| 3.2832 \| 5.9603 \| 6.1404 \| \| 5.1258 \| 4.26 \| 800 \| 4.8451 \| 64.7190 \| 119.5364 \| 100.0 \| 3.5058 \| 6.2914 \| 7.0175 \| \| 5.0472 \| 4.79 \| 900 \| 4.7950 \| 64.7190 \| 119.5364 \| 100.0 \| 3.5058 \| 6.2914 \| 7.0175 \| \| 4.9871 \| 5.32 \| 1000 \| 4.7781 \| 64.7190 \| 119.5364 \| 100.0 \| 3.5058 \| 6.2914 \| 7.0175 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2+cu121 - Datasets 2.14.5 - Tokenizers 0.15.2
RichardErkhov/sail_-_Sailor-1.8B-gguf	RichardErkhov	2024-05-17T12:11:35Z	29	0	null	[ "gguf", "arxiv:2404.03608", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-17T11:48:04Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Sailor-1.8B - GGUF - Model creator: https://huggingface.co/sail/ - Original model: https://huggingface.co/sail/Sailor-1.8B/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [Sailor-1.8B.Q2_K.gguf](https://huggingface.co/RichardErkhov/sail_-_Sailor-1.8B-gguf/blob/main/Sailor-1.8B.Q2_K.gguf) \| Q2_K \| 0.79GB \| \| [Sailor-1.8B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/sail_-_Sailor-1.8B-gguf/blob/main/Sailor-1.8B.IQ3_XS.gguf) \| IQ3_XS \| 0.86GB \| \| [Sailor-1.8B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/sail_-_Sailor-1.8B-gguf/blob/main/Sailor-1.8B.IQ3_S.gguf) \| IQ3_S \| 0.89GB \| \| [Sailor-1.8B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/sail_-_Sailor-1.8B-gguf/blob/main/Sailor-1.8B.Q3_K_S.gguf) \| Q3_K_S \| 0.89GB \| \| [Sailor-1.8B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/sail_-_Sailor-1.8B-gguf/blob/main/Sailor-1.8B.IQ3_M.gguf) \| IQ3_M \| 0.92GB \| \| [Sailor-1.8B.Q3_K.gguf](https://huggingface.co/RichardErkhov/sail_-_Sailor-1.8B-gguf/blob/main/Sailor-1.8B.Q3_K.gguf) \| Q3_K \| 0.95GB \| \| [Sailor-1.8B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/sail_-_Sailor-1.8B-gguf/blob/main/Sailor-1.8B.Q3_K_M.gguf) \| Q3_K_M \| 0.95GB \| \| [Sailor-1.8B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/sail_-_Sailor-1.8B-gguf/blob/main/Sailor-1.8B.Q3_K_L.gguf) \| Q3_K_L \| 0.98GB \| \| [Sailor-1.8B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/sail_-_Sailor-1.8B-gguf/blob/main/Sailor-1.8B.IQ4_XS.gguf) \| IQ4_XS \| 1.01GB \| \| [Sailor-1.8B.Q4_0.gguf](https://huggingface.co/RichardErkhov/sail_-_Sailor-1.8B-gguf/blob/main/Sailor-1.8B.Q4_0.gguf) \| Q4_0 \| 1.04GB \| \| [Sailor-1.8B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/sail_-_Sailor-1.8B-gguf/blob/main/Sailor-1.8B.IQ4_NL.gguf) \| IQ4_NL \| 1.05GB \| \| [Sailor-1.8B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/sail_-_Sailor-1.8B-gguf/blob/main/Sailor-1.8B.Q4_K_S.gguf) \| Q4_K_S \| 1.08GB \| \| [Sailor-1.8B.Q4_K.gguf](https://huggingface.co/RichardErkhov/sail_-_Sailor-1.8B-gguf/blob/main/Sailor-1.8B.Q4_K.gguf) \| Q4_K \| 1.13GB \| \| [Sailor-1.8B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/sail_-_Sailor-1.8B-gguf/blob/main/Sailor-1.8B.Q4_K_M.gguf) \| Q4_K_M \| 1.13GB \| \| [Sailor-1.8B.Q4_1.gguf](https://huggingface.co/RichardErkhov/sail_-_Sailor-1.8B-gguf/blob/main/Sailor-1.8B.Q4_1.gguf) \| Q4_1 \| 1.13GB \| \| [Sailor-1.8B.Q5_0.gguf](https://huggingface.co/RichardErkhov/sail_-_Sailor-1.8B-gguf/blob/main/Sailor-1.8B.Q5_0.gguf) \| Q5_0 \| 1.22GB \| \| [Sailor-1.8B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/sail_-_Sailor-1.8B-gguf/blob/main/Sailor-1.8B.Q5_K_S.gguf) \| Q5_K_S \| 1.24GB \| \| [Sailor-1.8B.Q5_K.gguf](https://huggingface.co/RichardErkhov/sail_-_Sailor-1.8B-gguf/blob/main/Sailor-1.8B.Q5_K.gguf) \| Q5_K \| 1.28GB \| \| [Sailor-1.8B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/sail_-_Sailor-1.8B-gguf/blob/main/Sailor-1.8B.Q5_K_M.gguf) \| Q5_K_M \| 1.28GB \| \| [Sailor-1.8B.Q5_1.gguf](https://huggingface.co/RichardErkhov/sail_-_Sailor-1.8B-gguf/blob/main/Sailor-1.8B.Q5_1.gguf) \| Q5_1 \| 1.31GB \| \| [Sailor-1.8B.Q6_K.gguf](https://huggingface.co/RichardErkhov/sail_-_Sailor-1.8B-gguf/blob/main/Sailor-1.8B.Q6_K.gguf) \| Q6_K \| 1.47GB \| \| [Sailor-1.8B.Q8_0.gguf](https://huggingface.co/RichardErkhov/sail_-_Sailor-1.8B-gguf/blob/main/Sailor-1.8B.Q8_0.gguf) \| Q8_0 \| 1.82GB \| Original model description: --- language: - en - zh - id - th - vi - ms - lo datasets: - cerebras/SlimPajama-627B - Skywork/SkyPile-150B - allenai/MADLAD-400 - cc100 tags: - multilingual - sea - sailor license: apache-2.0 base_model: Qwen/Qwen1.5-1.8B inference: false model-index: - name: Sailor-1.8B results: - task: type: text-generation dataset: name: XQuAD-Thai type: XQuAD-Thai metrics: - name: EM (3-Shot) type: EM (3-Shot) value: 32.72 - name: F1 (3-Shot) type: F1 (3-Shot) value: 48.66 - task: type: text-generation dataset: name: TyDiQA-Indonesian type: TyDiQA-Indonesian metrics: - name: EM (3-Shot) type: EM (3-Shot) value: 40.88 - name: F1 (3-Shot) type: F1 (3-Shot) value: 65.37 - task: type: text-generation dataset: name: XQuAD-Vietnamese type: XQuAD-Vietnamese metrics: - name: EM (3-Shot) type: EM (3-Shot) value: 34.22 - name: F1 (3-Shot) type: F1 (3-Shot) value: 53.35 - task: type: text-generation dataset: name: XCOPA-Thai type: XCOPA-Thai metrics: - name: EM (3-Shot) type: EM (3-Shot) value: 53.8 - task: type: text-generation dataset: name: XCOPA-Indonesian type: XCOPA-Indonesian metrics: - name: EM (3-Shot) type: EM (3-Shot) value: 64.20 - task: type: text-generation dataset: name: XCOPA-Vietnamese type: XCOPA-Vietnamese metrics: - name: EM (3-Shot) type: EM (3-Shot) value: 63.20 - task: type: text-generation dataset: name: M3Exam-Thai type: M3Exam-Thai metrics: - name: EM (3-Shot) type: EM (3-Shot) value: 25.38 - task: type: text-generation dataset: name: M3Exam-Indonesian type: M3Exam-Indonesian metrics: - name: EM (3-Shot) type: EM (3-Shot) value: 28.30 - task: type: text-generation dataset: name: M3Exam-Vietnamese type: M3Exam-Vietnamese metrics: - name: EM (3-Shot) type: EM (3-Shot) value: 34.71 - task: type: text-generation dataset: name: BELEBELE-Thai type: BELEBELE-Thai metrics: - name: EM (3-Shot) type: EM (3-Shot) value: 34.22 - task: type: text-generation dataset: name: BELEBELE-Indonesian type: BELEBELE-Indonesian metrics: - name: EM (3-Shot) type: EM (3-Shot) value: 34.89 - task: type: text-generation dataset: name: BELEBELE-Vietnamese type: BELEBELE-Vietnamese metrics: - name: EM (3-Shot) type: EM (3-Shot) value: 35.33 --- <div align="center"> <img src="banner_sailor.jpg" width="700"/> </div> Sailor is a suite of Open Language Models tailored for South-East Asia (SEA), focusing on languages such as 🇮🇩Indonesian, 🇹🇭Thai, 🇻🇳Vietnamese, 🇲🇾Malay, and 🇱🇦Lao. Developed with careful data curation, Sailor models are designed to understand and generate text across diverse linguistic landscapes of SEA region. Built from [Qwen 1.5](https://huggingface.co/collections/Qwen/qwen15-65c0a2f577b1ecb76d786524) , Sailor encompasses models of varying sizes, spanning from 0.5B to 7B versions for different requirements. We further fine-tune the base model with open-source datasets to get instruction-tuned models, namedly Sailor-Chat. Benchmarking results demonstrate Sailor's proficiency in tasks such as question answering, commonsense reasoning, and other tasks in SEA languages. > The logo was generated by MidJourney ## Model Summary - Model Collections: [Base Model & Chat Model](https://huggingface.co/collections/sail/sailor-65e19a749f978976f1959825) - Project Website: [sailorllm.github.io](https://sailorllm.github.io/) - Codebase: [github.com/sail-sg/sailor-llm](https://github.com/sail-sg/sailor-llm) - Technical Report: [arxiv.org/pdf/2404.03608.pdf](https://arxiv.org/pdf/2404.03608.pdf) ## Training details Sailor is crafted by continually pre-training from language models like the remarkable Qwen 1.5 models, which already has a great performance on SEA languages. The pre-training corpus heavily leverages the publicly available corpus, including [SlimPajama](https://huggingface.co/datasets/cerebras/SlimPajama-627B), [SkyPile](https://huggingface.co/datasets/Skywork/SkyPile-150B), [CC100](https://huggingface.co/datasets/cc100) and [MADLAD-400](https://huggingface.co/datasets/allenai/MADLAD-400). By employing aggressive data deduplication and careful data cleaning on the collected corpus, we have attained a high-quality dataset spanning various languages. Through systematic experiments to determine the weights of different languages, Sailor models undergo training from 200B to 400B tokens, tailored to different model sizes. The approach boosts their performance on SEA languages while maintaining proficiency in English and Chinese without significant compromise. Finally, we continually pre-train the Qwen1.5-0.5B model with 400 Billion tokens, and other models with 200 Billion tokens to obtain the Sailor models. ## Requirements The code of Sailor has been in the latest Hugging face transformers and we advise you to install `transformers>=4.37.0`. ## Quickstart Here provides a code snippet to show you how to load the tokenizer and model and how to generate contents. ```python from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model model = AutoModelForCausalLM.from_pretrained("sail/Sailor-1.8B", device_map="auto") tokenizer = AutoTokenizer.from_pretrained("sail/Sailor-1.8B") input_message = "Model bahasa adalah model probabilistik" ### The given Indonesian input translates to 'A language model is a probabilistic model of.' model_inputs = tokenizer([input_message], return_tensors="pt").to(device) generated_ids = model.generate( model_inputs.input_ids, max_new_tokens=64 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] print(response) ``` # License Sailor is distributed under the terms of the Apache License 2.0. No restrict on the research and the commercial use, but should comply with the [Qwen License](https://huggingface.co/Qwen/Qwen1.5-1.8B/blob/main/LICENSE). ## Citation If you find sailor useful, please cite our work as follows: ``` @misc{dou2024sailor, title={Sailor: Open Language Models for South-East Asia}, author={Longxu Dou and Qian Liu and Guangtao Zeng and Jia Guo and Jiahui Zhou and Wei Lu and Min Lin}, year={2024}, eprint={2404.03608}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` # Contact Us If you have any questions, please raise an issue or contact us at [[email protected]](mailto:[email protected]) or [[email protected]](mailto:[email protected]).
caesium94/models-colorist-v1-2e-4	caesium94	2024-05-17T12:06:55Z	210	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T12:05:24Z	--- 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]
MUNKOO/PANDA	MUNKOO	2024-05-17T12:01:59Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-17T12:01:59Z	--- license: apache-2.0 ---
Ramikan-BR/tinyllama-coder-py-4bit_LORA	Ramikan-BR	2024-05-17T12:01:19Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/tinyllama-chat-bnb-4bit", "base_model:finetune:unsloth/tinyllama-chat-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-17T12:00:40Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/tinyllama-chat-bnb-4bit --- # Uploaded model - Developed by: Ramikan-BR - License: apache-2.0 - Finetuned from model : unsloth/tinyllama-chat-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)
damgomz/ft_bs64_lr6_base_x2	damgomz	2024-05-17T11:52:18Z	111	0	transformers	[ "transformers", "safetensors", "albert", "text-classification", "fill-mask", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-05-16T15:14:14Z	--- language: en tags: - fill-mask kwargs: timestamp: '2024-05-17T13:52:15' project_name: ft_bs64_lr6_base_x2_emissions_tracker run_id: 064ee03d-8888-4a8d-b7cc-2fabb82586f2 duration: 14284.394858121872 emissions: 0.0093445332329461 emissions_rate: 6.54177746118027e-07 cpu_power: 42.5 gpu_power: 0.0 ram_power: 7.5 cpu_energy: 0.1686347934653361 gpu_energy: 0 ram_energy: 0.0297587275415659 energy_consumed: 0.1983935210069022 country_name: Switzerland country_iso_code: CHE region: .nan cloud_provider: .nan cloud_region: .nan os: Linux-5.14.0-70.30.1.el9_0.x86_64-x86_64-with-glibc2.34 python_version: 3.10.4 codecarbon_version: 2.3.4 cpu_count: 3 cpu_model: Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz gpu_count: .nan gpu_model: .nan longitude: .nan latitude: .nan ram_total_size: 20 tracking_mode: machine on_cloud: N pue: 1.0 --- ## Environmental Impact (CODE CARBON DEFAULT) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| Duration (in seconds) \| 14284.394858121872 \| \| Emissions (Co2eq in kg) \| 0.0093445332329461 \| \| CPU power (W) \| 42.5 \| \| GPU power (W) \| [No GPU] \| \| RAM power (W) \| 7.5 \| \| CPU energy (kWh) \| 0.1686347934653361 \| \| GPU energy (kWh) \| [No GPU] \| \| RAM energy (kWh) \| 0.0297587275415659 \| \| Consumed energy (kWh) \| 0.1983935210069022 \| \| Country name \| Switzerland \| \| Cloud provider \| nan \| \| Cloud region \| nan \| \| CPU count \| 3 \| \| CPU model \| Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz \| \| GPU count \| nan \| \| GPU model \| nan \| ## Environmental Impact (for one core) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| CPU energy (kWh) \| 0.027497460101884603 \| \| Emissions (Co2eq in kg) \| 0.005594721319431066 \| ## Note 17 May 2024 ## My Config \| Config \| Value \| \|--------------------------\|-----------------\| \| checkpoint \| albert-base-v2 \| \| model_name \| ft_bs64_lr6_base_x2 \| \| sequence_length \| 400 \| \| num_epoch \| 6 \| \| learning_rate \| 5e-06 \| \| batch_size \| 64 \| \| weight_decay \| 0.0 \| \| warm_up_prop \| 0.0 \| \| drop_out_prob \| 0.1 \| \| packing_length \| 100 \| \| train_test_split \| 0.2 \| \| num_steps \| 32580 \| ## Training and Testing steps Epoch \| Train Loss \| Test Loss \| Accuracy \| Recall ---\|---\|---\|---\|--- \| 0 \| 0.509996 \| 0.432674 \| 0.800442 \| 0.815951 \| \| 1 \| 0.378199 \| 0.369624 \| 0.834315 \| 0.825153 \| \| 2 \| 0.330051 \| 0.377433 \| 0.835788 \| 0.924847 \| \| 3 \| 0.294745 \| 0.346708 \| 0.849779 \| 0.878834 \| \| 4 \| 0.271853 \| 0.396932 \| 0.829897 \| 0.757669 \| \| 5 \| 0.212074 \| 0.384297 \| 0.846097 \| 0.848160 \|
PaulR79/phi2_finetuned_twitter	PaulR79	2024-05-17T11:45:21Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-16T14:58:04Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
RichardErkhov/sail_-_Sailor-1.8B-4bits	RichardErkhov	2024-05-17T11:44:15Z	80	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:2404.03608", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-17T11:40:10Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Sailor-1.8B - bnb 4bits - Model creator: https://huggingface.co/sail/ - Original model: https://huggingface.co/sail/Sailor-1.8B/ Original model description: --- language: - en - zh - id - th - vi - ms - lo datasets: - cerebras/SlimPajama-627B - Skywork/SkyPile-150B - allenai/MADLAD-400 - cc100 tags: - multilingual - sea - sailor license: apache-2.0 base_model: Qwen/Qwen1.5-1.8B inference: false model-index: - name: Sailor-1.8B results: - task: type: text-generation dataset: name: XQuAD-Thai type: XQuAD-Thai metrics: - name: EM (3-Shot) type: EM (3-Shot) value: 32.72 - name: F1 (3-Shot) type: F1 (3-Shot) value: 48.66 - task: type: text-generation dataset: name: TyDiQA-Indonesian type: TyDiQA-Indonesian metrics: - name: EM (3-Shot) type: EM (3-Shot) value: 40.88 - name: F1 (3-Shot) type: F1 (3-Shot) value: 65.37 - task: type: text-generation dataset: name: XQuAD-Vietnamese type: XQuAD-Vietnamese metrics: - name: EM (3-Shot) type: EM (3-Shot) value: 34.22 - name: F1 (3-Shot) type: F1 (3-Shot) value: 53.35 - task: type: text-generation dataset: name: XCOPA-Thai type: XCOPA-Thai metrics: - name: EM (3-Shot) type: EM (3-Shot) value: 53.8 - task: type: text-generation dataset: name: XCOPA-Indonesian type: XCOPA-Indonesian metrics: - name: EM (3-Shot) type: EM (3-Shot) value: 64.20 - task: type: text-generation dataset: name: XCOPA-Vietnamese type: XCOPA-Vietnamese metrics: - name: EM (3-Shot) type: EM (3-Shot) value: 63.20 - task: type: text-generation dataset: name: M3Exam-Thai type: M3Exam-Thai metrics: - name: EM (3-Shot) type: EM (3-Shot) value: 25.38 - task: type: text-generation dataset: name: M3Exam-Indonesian type: M3Exam-Indonesian metrics: - name: EM (3-Shot) type: EM (3-Shot) value: 28.30 - task: type: text-generation dataset: name: M3Exam-Vietnamese type: M3Exam-Vietnamese metrics: - name: EM (3-Shot) type: EM (3-Shot) value: 34.71 - task: type: text-generation dataset: name: BELEBELE-Thai type: BELEBELE-Thai metrics: - name: EM (3-Shot) type: EM (3-Shot) value: 34.22 - task: type: text-generation dataset: name: BELEBELE-Indonesian type: BELEBELE-Indonesian metrics: - name: EM (3-Shot) type: EM (3-Shot) value: 34.89 - task: type: text-generation dataset: name: BELEBELE-Vietnamese type: BELEBELE-Vietnamese metrics: - name: EM (3-Shot) type: EM (3-Shot) value: 35.33 --- <div align="center"> <img src="banner_sailor.jpg" width="700"/> </div> Sailor is a suite of Open Language Models tailored for South-East Asia (SEA), focusing on languages such as 🇮🇩Indonesian, 🇹🇭Thai, 🇻🇳Vietnamese, 🇲🇾Malay, and 🇱🇦Lao. Developed with careful data curation, Sailor models are designed to understand and generate text across diverse linguistic landscapes of SEA region. Built from [Qwen 1.5](https://huggingface.co/collections/Qwen/qwen15-65c0a2f577b1ecb76d786524) , Sailor encompasses models of varying sizes, spanning from 0.5B to 7B versions for different requirements. We further fine-tune the base model with open-source datasets to get instruction-tuned models, namedly Sailor-Chat. Benchmarking results demonstrate Sailor's proficiency in tasks such as question answering, commonsense reasoning, and other tasks in SEA languages. > The logo was generated by MidJourney ## Model Summary - Model Collections: [Base Model & Chat Model](https://huggingface.co/collections/sail/sailor-65e19a749f978976f1959825) - Project Website: [sailorllm.github.io](https://sailorllm.github.io/) - Codebase: [github.com/sail-sg/sailor-llm](https://github.com/sail-sg/sailor-llm) - Technical Report: [arxiv.org/pdf/2404.03608.pdf](https://arxiv.org/pdf/2404.03608.pdf) ## Training details Sailor is crafted by continually pre-training from language models like the remarkable Qwen 1.5 models, which already has a great performance on SEA languages. The pre-training corpus heavily leverages the publicly available corpus, including [SlimPajama](https://huggingface.co/datasets/cerebras/SlimPajama-627B), [SkyPile](https://huggingface.co/datasets/Skywork/SkyPile-150B), [CC100](https://huggingface.co/datasets/cc100) and [MADLAD-400](https://huggingface.co/datasets/allenai/MADLAD-400). By employing aggressive data deduplication and careful data cleaning on the collected corpus, we have attained a high-quality dataset spanning various languages. Through systematic experiments to determine the weights of different languages, Sailor models undergo training from 200B to 400B tokens, tailored to different model sizes. The approach boosts their performance on SEA languages while maintaining proficiency in English and Chinese without significant compromise. Finally, we continually pre-train the Qwen1.5-0.5B model with 400 Billion tokens, and other models with 200 Billion tokens to obtain the Sailor models. ## Requirements The code of Sailor has been in the latest Hugging face transformers and we advise you to install `transformers>=4.37.0`. ## Quickstart Here provides a code snippet to show you how to load the tokenizer and model and how to generate contents. ```python from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model model = AutoModelForCausalLM.from_pretrained("sail/Sailor-1.8B", device_map="auto") tokenizer = AutoTokenizer.from_pretrained("sail/Sailor-1.8B") input_message = "Model bahasa adalah model probabilistik" ### The given Indonesian input translates to 'A language model is a probabilistic model of.' model_inputs = tokenizer([input_message], return_tensors="pt").to(device) generated_ids = model.generate( model_inputs.input_ids, max_new_tokens=64 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] print(response) ``` # License Sailor is distributed under the terms of the Apache License 2.0. No restrict on the research and the commercial use, but should comply with the [Qwen License](https://huggingface.co/Qwen/Qwen1.5-1.8B/blob/main/LICENSE). ## Citation If you find sailor useful, please cite our work as follows: ``` @misc{dou2024sailor, title={Sailor: Open Language Models for South-East Asia}, author={Longxu Dou and Qian Liu and Guangtao Zeng and Jia Guo and Jiahui Zhou and Wei Lu and Min Lin}, year={2024}, eprint={2404.03608}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` # Contact Us If you have any questions, please raise an issue or contact us at [[email protected]](mailto:[email protected]) or [[email protected]](mailto:[email protected]).
AmaanUsmani/Llama3-8b-DynamicChat-4bit	AmaanUsmani	2024-05-17T11:43:29Z	79	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "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	2024-05-17T09:15:19Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - Developed by: AmaanUsmani - 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) ## How to run inference ## Please note the code for downloading model and running inference is not optimized, it will be done in the future !pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git" !pip install --no-deps "xformers<0.0.26" trl peft accelerate bitsandbytes scikit-learn scipy auto-gptq optimum bitsandbytes joblib threadpoolctl from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline from peft import prepare_model_for_kbit_training from peft import LoraConfig, get_peft_model import transformers from unsloth import FastLanguageModel import torch max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally! dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+ load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False. model, tokenizer = FastLanguageModel.from_pretrained( model_name = "AmaanUsmani/Llama3-8b-DynamicChat-4bit", max_seq_length = max_seq_length, dtype = dtype, load_in_4bit = load_in_4bit, ) intstructions_string = f"""You're a conversational agent designed to engage users in dynamic interactions. Your goal is to facilitate more meaningful exchanges by enhancing the model's understanding of user input. You should aim to create an environment where users feel heard, understood, and engaged in ongoing dialogue. As long as the user's question doesn't include any personal details or context related to the user, do not ask questions back. If the user's question involves more context, first provide general information or advice and then ask a follow up question regarding the additional context needed. Please respond to the following comment. """ prompt_template = lambda comment: f'''<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|>{intstructions_string}<\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|>\n{comment}<\|eot_id\|>\n<\|start_header_id\|>assistant<\|end_header_id\|>\n''' comment = "I want to learn how to swim" prompt = prompt_template(comment) model.eval() inputs = tokenizer(prompt, return_tensors="pt") text_streamer = TextStreamer(tokenizer) outputs = model.generate(input_ids=inputs["input_ids"].to("cuda"), max_new_tokens=500) response = tokenizer.decode(outputs[0], skip_special_tokens=True).split("assistant\n")[-1].strip() print(response)
Nike-Hanmatheekuna/llama3-8b-sft-full	Nike-Hanmatheekuna	2024-05-17T11:42:37Z	10	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "base_model:meta-llama/Meta-Llama-3-8B", "base_model:finetune:meta-llama/Meta-Llama-3-8B", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-16T04:32:16Z	--- license: llama3 base_model: meta-llama/Meta-Llama-3-8B tags: - trl - sft - generated_from_trainer model-index: - name: llama3-8b-sft-full 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. --> # llama3-8b-sft-full This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) 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: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
cameltech/japanese-gpt-1b-PII-masking	cameltech	2024-05-17T11:42:00Z	141	5	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "ja", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-05T07:26:29Z	--- language: - ja license: mit pipeline_tag: text-generation widget: - text: '# タスク入力文中の個人情報をマスキングせよ # 入力文渡邉亮です。現在の住所は東京都世田谷区代沢1-2-3です。<SEP>' inference: parameters: max_length: 256 num_beams: 3 num_return_sequences: 1 early_stopping: true eos_token_id: 3 pad_token_id: 4 repetition_penalty: 3.0 --- # japanese-gpt-1b-PII-masking ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64ffe8a785a884a964b0cffe/eXFMcprvDELlhC85OYO9L.png) # Model Description japanese-gpt-1b-PII-masking は、 [日本語事前学習済み1B GPTモデル](https://huggingface.co/rinna/japanese-gpt-1b)をベースとして、日本語の文章から個人情報をマスキングするように学習したモデルです。<br> <br> 個人情報は以下の対応関係でマスキングされます。 \| タグ \| 項目 \| \| ---- \| ---- \| \| \<name\> \| 氏名 \| \| \<birthday\> \| 生年月日 \| \| \<phone-number\> \| 電話番号 \| \| \<mail-address\> \| メールアドレス \| \| \<customer-id\> \| 会員番号・ID \| \| \<address\> \| 住所 \| \| \<post-code\> \| 郵便番号 \| \| \<company\> \| 会社名 \| # Usage ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer instruction = "# タスク\n入力文中の個人情報をマスキングせよ\n\n# 入力文\n" text = """オペレーター：ありがとうございます。カスタマーサポートセンターでございます。お名前と生年月日、ご住所を市区町村まで教えていただけますか？顧客：あ、はい。西山...すみません、西山俊之です。生年月日は、えーっと、1983年1月23日です。東京都練馬区在住です。オペレーター：西山俊之様、1983年1月23日生まれ、東京都練馬区にお住まいですね。確認いたしました。お電話の件につきまして、さらにご本人様確認をさせていただきます。""" input_text = instruction + text model_name = "cameltech/japanese-gpt-1b-PII-masking" model = AutoModelForCausalLM.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) if torch.cuda.is_available(): model = model.to("cuda") def preprocess(text): return text.replace("\n", "<LB>") def postprocess(text): return text.replace("<LB>", "\n") generation_config = { "max_new_tokens": 256, "num_beams": 3, "num_return_sequences": 1, "early_stopping": True, "eos_token_id": tokenizer.eos_token_id, "pad_token_id": tokenizer.pad_token_id, "repetition_penalty": 3.0 } input_text += "<SEP>" input_text = preprocess(input_text) with torch.no_grad(): token_ids = tokenizer.encode(input_text, add_special_tokens=False, return_tensors="pt") output_ids = model.generate( token_ids.to(model.device), **generation_config ) output = tokenizer.decode(output_ids.tolist()[0][token_ids.size(1) :], skip_special_tokens=True) output = postprocess(output) print(output) """ オペレーター:ありがとうございます。カスタマーサポートセンターでございます。お名前と生年月日、ご住所を<address>まで教えていただけますか? 顧客:あ、はい。<name>です。生年月日は、えーっと、<birthday>です。<address>在住です。オペレーター:<name>様、<birthday>生まれ、<address>にお住まいですね。確認いたしました。お電話の件につきまして、さらにご本人様確認をさせていただきます。 """ ``` # Licenese [The MIT license](https://opensource.org/licenses/MIT)
Audino/my-awesomev3-modelv2-10epochs-base	Audino	2024-05-17T11:22:32Z	108	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-17T11:21:49Z	--- 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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rhaymison/Mistral-portuguese-luana-7b-chat	rhaymison	2024-05-17T11:21:21Z	11	4	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "Misral", "Portuguese", "7b", "chat", "portugues", "conversational", "pt", "dataset:rhaymison/ultrachat-easy-use", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:finetune:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-09T14:40:26Z	--- language: - pt license: apache-2.0 library_name: transformers tags: - Misral - Portuguese - 7b - chat - portugues base_model: mistralai/Mistral-7B-Instruct-v0.2 datasets: - rhaymison/ultrachat-easy-use pipeline_tag: text-generation model-index: - name: Mistral-portuguese-luana-7b-chat results: - task: type: text-generation name: Text Generation dataset: name: ENEM Challenge (No Images) type: eduagarcia/enem_challenge split: train args: num_few_shot: 3 metrics: - type: acc value: 59.13 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b-chat name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BLUEX (No Images) type: eduagarcia-temp/BLUEX_without_images split: train args: num_few_shot: 3 metrics: - type: acc value: 49.24 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b-chat name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: OAB Exams type: eduagarcia/oab_exams split: train args: num_few_shot: 3 metrics: - type: acc value: 36.58 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b-chat name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Assin2 RTE type: assin2 split: test args: num_few_shot: 15 metrics: - type: f1_macro value: 90.47 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b-chat name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Assin2 STS type: eduagarcia/portuguese_benchmark split: test args: num_few_shot: 15 metrics: - type: pearson value: 76.55 name: pearson source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b-chat name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: FaQuAD NLI type: ruanchaves/faquad-nli split: test args: num_few_shot: 15 metrics: - type: f1_macro value: 66.75 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b-chat name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HateBR Binary type: ruanchaves/hatebr split: test args: num_few_shot: 25 metrics: - type: f1_macro value: 77.46 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b-chat name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: PT Hate Speech Binary type: hate_speech_portuguese split: test args: num_few_shot: 25 metrics: - type: f1_macro value: 69.45 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b-chat name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: tweetSentBR type: eduagarcia-temp/tweetsentbr split: test args: num_few_shot: 25 metrics: - type: f1_macro value: 59.63 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b-chat name: Open Portuguese LLM Leaderboard --- # Mistral-portuguese-luana-7b-chat <p align="center"> <img src="https://raw.githubusercontent.com/rhaymisonbetini/huggphotos/main/luana-chat.webp" width="50%" style="margin-left:'auto' margin-right:'auto' display:'block'"/> </p> This model was trained with a superset of 250,000 chat in Portuguese. The model comes to help fill the gap in models in Portuguese. Tuned from the Mistral 7b, the model was adjusted mainly for chat. # How to use ### FULL MODEL : A100 ### HALF MODEL: L4 ### 8bit or 4bit : T4 or V100 You can use the model in its normal form up to 4-bit quantization. Below we will use both approaches. Remember that verbs are important in your prompt. Tell your model how to act or behave so that you can guide them along the path of their response. Important points like these help models (even smaller models like 7b) to perform much better. ```python !pip install -q -U transformers !pip install -q -U accelerate !pip install -q -U bitsandbytes from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer model = AutoModelForCausalLM.from_pretrained("rhaymison/Mistral-portuguese-luana-7b-chat", device_map= {"": 0}) tokenizer = AutoTokenizer.from_pretrained("rhaymison/Mistral-portuguese-luana-7b-chat") model.eval() ``` You can use with Pipeline but in this example i will use such as Streaming ```python inputs = tokenizer([f"""<s>[INST] Estou pensando em levar minha esposa e meu gatinha para fazer algo diferente. Você teria alguma coisa em mente ?. [/INST]"""], return_tensors="pt") inputs.to(model.device) streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) _ = model.generate(inputs, streamer=streamer, max_new_tokens=128) ``` If you are having a memory problem such as "CUDA Out of memory", you should use 4-bit or 8-bit quantization. For the complete model in colab you will need the A100. If you want to use 4bits or 8bits, T4 or L4 will already solve the problem. # 4bits example ```python from transformers import BitsAndBytesConfig import torch nb_4bit_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True ) model = AutoModelForCausalLM.from_pretrained( base_model, quantization_config=bnb_config, device_map={"": 0} ) ``` # LangChain <div style="background-color:#f0f0f0; padding:10px; border-radius:10px; font-family: 'Arial';"> <p><strong><span style="color:#3366cc;">Human:</span></strong> Ola Luana, estou precisando de umas dicas para o final de semana. Poderia me ajudar ?</p> <p><strong><span style="color:#ff6600;">Luana:</span></strong> Olá! Claro, ficarei feliz em ajudá-lo. O que você tem em mente para o final de semana?</p> <p><strong><span style="color:#3366cc;">Human:</span></strong> Estou pensando em levar minha esposa e meu gatinha para fazer algo diferente. Você teria alguma coisa em mente ?</p> <p><strong><span style="color:#ff6600;">Luana:</span></strong> Claro! Você pode considerar fazer um piquenique na praia, visitar um museu ou fazer uma caminhada na natureza. O que você e sua esposa gostam de fazer juntos?</p> <p><strong><span style="color:#3366cc;">Human:</span></strong> Eu e minha esposa gostamos muito de caminhadas. Temos uma mochila que carregamos nosso gatinha para todo lado.</p> <p><strong><span style="color:#ff6600;">Luana:</span></strong> Parece ótimo! Você pode considerar fazer uma caminhada em um parque local ou reserva natural. Você também pode pesquisar trilhas mais longas para fazer em um fim de semana. Além disso, você pode trazer um piquenique com você para saborear durante a caminhada.</p> <p><strong><span style="color:#3366cc;">Human:</span></strong> Otimas ideias voce tem me dado. O que será que é gostoso levar em um piquenique ?</p> <p><strong><span style="color:#ff6600;">Luana:</span></strong> Você pode trazer sanduíches, frutas, vegetais, queijo e biscoitos. Você também pode trazer uma garrafa de água ou um refrigerante para beber. Além disso, você pode trazer um saco de lixo para descartar resíduos de maneira responsável.</p> <p><strong><span style="color:#3366cc;">Human:</span></strong> Olha só que dica bacana. Realmente devo levar uma sacola para nao descartar nada na Natureza e assim me divertir com minha esposa.</p> <p><strong><span style="color:#ff6600;">Luana:</span></strong> Sim, é importante descartar resíduos de maneira responsável e proteger o meio ambiente. Você pode considerar fazer um piquenique em um parque local ou reserva natural onde possa descartar resíduos de maneira responsável.</p> <p><strong><span style="color:#3366cc;">Human:</span></strong> Perfeito. Muito Obrigado Luana. Otimas dicas. Até a proxima !</p> <p><strong><span style="color:#ff6600;">Luana:</span></strong> De nada! Fique em contato se precisar de mais ajuda. Tenha um ótimo fim de semana!</p> </div> # [Open Portuguese LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/eduagarcia-temp/llm_pt_leaderboard_raw_results/tree/main/rhaymison/Mistral-portuguese-luana-7b-chat) \| Metric \| Value \| \|--------------------------\|---------\| \|Average \|65.03**\| \|ENEM Challenge (No Images)\| 59.13\| \|BLUEX (No Images) \| 49.24\| \|OAB Exams \| 36.58\| \|Assin2 RTE \| 90.47\| \|Assin2 STS \| 76.55\| \|FaQuAD NLI \| 66.75\| \|HateBR Binary \| 77.46\| \|PT Hate Speech Binary \| 69.45\| \|tweetSentBR \| 59.63\| ### Comments Any idea, help or report will always be welcome. email: [email protected] <div style="display:flex; flex-direction:row; justify-content:left"> <a href="https://www.linkedin.com/in/rhaymison-cristian-betini-2b3016175/" target="_blank"> <img src="https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white"> </a> <a href="https://github.com/rhaymisonbetini" target="_blank"> <img src="https://img.shields.io/badge/GitHub-100000?style=for-the-badge&logo=github&logoColor=white"> </a> </div>
rhaymison/Llama3-portuguese-luana-8b-instruct	rhaymison	2024-05-17T11:20:01Z	9	2	transformers	[ "transformers", "safetensors", "llama", "text-generation", "portugues", "portuguese", "QA", "instruct", "conversational", "pt", "dataset:rhaymison/superset", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:finetune:meta-llama/Meta-Llama-3-8B-Instruct", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-25T10:56:05Z	--- language: - pt license: apache-2.0 library_name: transformers tags: - portugues - portuguese - QA - instruct base_model: meta-llama/Meta-Llama-3-8B-Instruct datasets: - rhaymison/superset pipeline_tag: text-generation model-index: - name: Llama3-portuguese-luana-8b-instruct results: - task: type: text-generation name: Text Generation dataset: name: ENEM Challenge (No Images) type: eduagarcia/enem_challenge split: train args: num_few_shot: 3 metrics: - type: acc value: 69.0 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama3-portuguese-luana-8b-instruct name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BLUEX (No Images) type: eduagarcia-temp/BLUEX_without_images split: train args: num_few_shot: 3 metrics: - type: acc value: 51.74 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama3-portuguese-luana-8b-instruct name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: OAB Exams type: eduagarcia/oab_exams split: train args: num_few_shot: 3 metrics: - type: acc value: 47.56 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama3-portuguese-luana-8b-instruct name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Assin2 RTE type: assin2 split: test args: num_few_shot: 15 metrics: - type: f1_macro value: 89.24 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama3-portuguese-luana-8b-instruct name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Assin2 STS type: eduagarcia/portuguese_benchmark split: test args: num_few_shot: 15 metrics: - type: pearson value: 72.87 name: pearson source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama3-portuguese-luana-8b-instruct name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: FaQuAD NLI type: ruanchaves/faquad-nli split: test args: num_few_shot: 15 metrics: - type: f1_macro value: 68.94 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama3-portuguese-luana-8b-instruct name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HateBR Binary type: ruanchaves/hatebr split: test args: num_few_shot: 25 metrics: - type: f1_macro value: 85.93 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama3-portuguese-luana-8b-instruct name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: PT Hate Speech Binary type: hate_speech_portuguese split: test args: num_few_shot: 25 metrics: - type: f1_macro value: 64.16 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama3-portuguese-luana-8b-instruct name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: tweetSentBR type: eduagarcia/tweetsentbr_fewshot split: test args: num_few_shot: 25 metrics: - type: f1_macro value: 63.91 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama3-portuguese-luana-8b-instruct name: Open Portuguese LLM Leaderboard --- # Llama3-portuguese-luana-8b-instruct <p align="center"> <img src="https://raw.githubusercontent.com/rhaymisonbetini/huggphotos/main/llama3-luana.webp" width="50%" style="margin-left:'auto' margin-right:'auto' display:'block'"/> </p> This model was trained with a superset of 290,000 chat in Portuguese. The model comes to help fill the gap in models in Portuguese. Tuned from the Llama3 8B, the model was adjusted mainly for chat. # How to use ### FULL MODEL : A100 ### HALF MODEL: L4 ### 8bit or 4bit : T4 or V100 You can use the model in its normal form up to 4-bit quantization. Below we will use both approaches. Remember that verbs are important in your prompt. Tell your model how to act or behave so that you can guide them along the path of their response. Important points like these help models (even smaller models like 8b) to perform much better. ```python !pip install -q -U transformers !pip install -q -U accelerate !pip install -q -U bitsandbytes from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer model = AutoModelForCausalLM.from_pretrained("rhaymison/Llama3-portuguese-luana-8b-instruct", device_map= {"": 0}) tokenizer = AutoTokenizer.from_pretrained("rhaymison/Llama3-portuguese-luana-8b-instruct") model.eval() ``` You can use with Pipeline. ```python from transformers import pipeline pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, do_sample=True, max_new_tokens=256, num_beams=2, temperature=0.3, top_k=50, top_p=0.95, early_stopping=True, pad_token_id=tokenizer.eos_token_id, ) def format_prompt(question:str): system_prompt = "Abaixo está uma instrução que descreve uma tarefa, juntamente com uma entrada que fornece mais contexto. Escreva uma resposta que complete adequadamente o pedido." return f"""<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> { system_prompt }<\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|> { question }<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>""" prompt = format_prompt("Me explique quem eram os Romanos") result = pipe(prompt) result[0]["generated_text"].split("assistant<\|end_header_id\|>")[1] #Os romanos eram um povo antigo que habitava a península italiana, particularmente na região que hoje é conhecida como Itália. Eles estabeleceram o Império Romano, #que se tornou uma das maiores e mais poderosas civilizações da história. Os romanos eram conhecidos por suas conquistas militares, sua arquitetura e engenharia #impressionantes e sua influência duradoura na cultura ocidental. #Os romanos eram uma sociedade complexa que consistia em várias classes sociais, incluindo senadores, cavaleiros, plebeus e escravos. #Eles tinham um sistema de governo baseado em uma república, onde o poder era dividido entre o Senado e a Assembléia do Povo. #Os romanos eram conhecidos por suas conquistas militares, que os levaram a expandir seu império por toda a Europa, Ásia e África. #Eles estabeleceram uma rede de estradas, pontes e outras estruturas que facilitaram a comunicação e o comércio. ``` If you are having a memory problem such as "CUDA Out of memory", you should use 4-bit or 8-bit quantization. For the complete model in colab you will need the A100. If you want to use 4bits or 8bits, T4 or L4 will already solve the problem. # 4bits example ```python from transformers import BitsAndBytesConfig import torch nb_4bit_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True ) model = AutoModelForCausalLM.from_pretrained( base_model, quantization_config=bnb_config, device_map={"": 0} ) ``` # Open Portuguese LLM Leaderboard Evaluation Results Detailed results can be found [here](https://huggingface.co/datasets/eduagarcia-temp/llm_pt_leaderboard_raw_results/tree/main/rhaymison/Llama3-portuguese-luana-8b-instruct) and on the [🚀 Open Portuguese LLM Leaderboard](https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard) \| Metric \| Value \| \|--------------------------\|---------\| \|Average \|68.15\| \|ENEM Challenge (No Images)\| 69\| \|BLUEX (No Images) \| 51.74\| \|OAB Exams \| 47.56\| \|Assin2 RTE \| 89.24\| \|Assin2 STS \| 72.87\| \|FaQuAD NLI \| 68.94\| \|HateBR Binary \| 85.93\| \|PT Hate Speech Binary \| 64.16\| \|tweetSentBR \| 63.91\| ### Comments Any idea, help or report will always be welcome. email: [email protected] <div style="display:flex; flex-direction:row; justify-content:left"> <a href="https://www.linkedin.com/in/rhaymison-cristian-betini-2b3016175/" target="_blank"> <img src="https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white"> </a> <a href="https://github.com/rhaymisonbetini" target="_blank"> <img src="https://img.shields.io/badge/GitHub-100000?style=for-the-badge&logo=github&logoColor=white"> </a> </div>
rhaymison/gemma-portuguese-luana-2b	rhaymison	2024-05-17T11:19:26Z	292	3	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "portuguese", "brasil", "portugues", "instrucao", "conversational", "pt", "dataset:rhaymison/superset", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-25T14:47:01Z	--- language: - pt license: apache-2.0 library_name: transformers tags: - portuguese - brasil - gemma - portugues - instrucao datasets: - rhaymison/superset pipeline_tag: text-generation widget: - text: Me explique como funciona um computador. example_title: Computador. - text: Me conte sobre a ida do homem a Lua. example_title: Homem na Lua. - text: Fale sobre uma curiosidade sobre a história do mundo example_title: História. - text: Escreva um poema bem interessante sobre o Sol e as flores. example_title: Escreva um poema. model-index: - name: gemma-portuguese-luana-2b results: - task: type: text-generation name: Text Generation dataset: name: ENEM Challenge (No Images) type: eduagarcia/enem_challenge split: train args: num_few_shot: 3 metrics: - type: acc value: 24.42 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/gemma-portuguese-luana-2b name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BLUEX (No Images) type: eduagarcia-temp/BLUEX_without_images split: train args: num_few_shot: 3 metrics: - type: acc value: 24.34 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/gemma-portuguese-luana-2b name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: OAB Exams type: eduagarcia/oab_exams split: train args: num_few_shot: 3 metrics: - type: acc value: 27.11 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/gemma-portuguese-luana-2b name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Assin2 RTE type: assin2 split: test args: num_few_shot: 15 metrics: - type: f1_macro value: 70.86 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/gemma-portuguese-luana-2b name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Assin2 STS type: eduagarcia/portuguese_benchmark split: test args: num_few_shot: 15 metrics: - type: pearson value: 1.51 name: pearson source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/gemma-portuguese-luana-2b name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: FaQuAD NLI type: ruanchaves/faquad-nli split: test args: num_few_shot: 15 metrics: - type: f1_macro value: 43.97 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/gemma-portuguese-luana-2b name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HateBR Binary type: ruanchaves/hatebr split: test args: num_few_shot: 25 metrics: - type: f1_macro value: 40.05 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/gemma-portuguese-luana-2b name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: PT Hate Speech Binary type: hate_speech_portuguese split: test args: num_few_shot: 25 metrics: - type: f1_macro value: 51.83 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/gemma-portuguese-luana-2b name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: tweetSentBR type: eduagarcia/tweetsentbr_fewshot split: test args: num_few_shot: 25 metrics: - type: f1_macro value: 30.42 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/gemma-portuguese-luana-2b name: Open Portuguese LLM Leaderboard --- # gemma-portuguese-2b-luana <p align="center"> <img src="https://raw.githubusercontent.com/rhaymisonbetini/huggphotos/main/luana-2b.webp" width="50%" style="margin-left:'auto' margin-right:'auto' display:'block'"/> </p> ## Model description updated: 2024-04-10 20:06 The gemma-portuguese-2b model is a portuguese model trained with the superset dataset with 250,000 instructions. The model is mainly focused on text generation and instruction. The model was not trained on math and code tasks. The model is generalist with focus on understand portuguese inferences. With this fine tuning for portuguese, you can adjust the model for a specific field. ## How to Use ```python from transformers import AutoTokenizer, pipeline import torch model = "rhaymison/gemma-portuguese-luana-2b" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = pipeline( "text-generation", model=model, model_kwargs={"torch_dtype": torch.bfloat16}, device="cuda", ) messages = [ { "role": "system", "content": "Abaixo está uma instrução que descreve uma tarefa, juntamente com uma entrada que fornece mais contexto. Escreva uma resposta que complete adequadamente o pedido." }, {"role": "user", "content": "Me conte sobre a ida do homem a Lua."}, ] prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) outputs = pipeline( prompt, max_new_tokens=256, do_sample=True, temperature=0.2, top_k=50, top_p=0.95 ) print(outputs[0]["generated_text"][len(prompt):].replace("model","")) #A viagem à Lua foi um esforço monumental realizado pela Agência Espacial dos EUA entre 1969 e 1972. #Foi um marco significativo na exploração espacial e na ciência humana. #Aqui está uma visão geral de sua jornada: 1. O primeiro voo espacial humano foi o de Yuri Gagarin, que voou a Terra em 12 de abril de 1961. ``` ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer2 = AutoTokenizer.from_pretrained("rhaymison/gemma-portuguese-luana-2b") model2 = AutoModelForCausalLM.from_pretrained("rhaymison/gemma-portuguese-luana-2b", device_map={"":0}) tokenizer2.pad_token = tokenizer2.eos_token tokenizer2.add_eos_token = True tokenizer2.add_bos_token, tokenizer2.add_eos_token tokenizer2.padding_side = "right" ``` ```python text = f"""<start_of_turn>user Abaixo está uma instrução que descreve uma tarefa, juntamente com uma entrada que fornece mais contexto. Escreva uma resposta que complete adequadamente o pedido. ###instrução:Me conte sobre a ida do homem a Lua.<end_of_turn> <start_of_turn>model """ device = "cuda:0" inputs = tokenizer2(text, return_tensors="pt").to(device) outputs = model2.generate(inputs, max_new_tokens=256, do_sample=False) output = tokenizer2.decode(outputs[0], skip_special_tokens=True, skip_prompt=True) print(output.replace("model"," ")) #A viagem à Lua foi um esforço monumental realizado pela Agência Espacial dos EUA entre 1969 e 1972. #Foi um marco significativo na exploração espacial e na ciência humana. #Aqui está uma visão geral de sua jornada: 1. O primeiro voo espacial humano foi o de Yuri Gagarin, que voou a Terra em 12 de abril de 1961. ``` ```python text = f"""<start_of_turn>user Abaixo está uma instrução que descreve uma tarefa, juntamente com uma entrada que fornece mais contexto. Escreva uma resposta que complete adequadamente o pedido. ###instrução:Me explique como funciona um computador.<end_of_turn> <start_of_turn>model """ device = "cuda:0" inputs = tokenizer2(text, return_tensors="pt").to(device) outputs = model2.generate(inputs, max_new_tokens=256, do_sample=False) output = tokenizer2.decode(outputs[0], skip_special_tokens=True, skip_prompt=True) print(output.replace("model"," ")) #Um computador é um dispositivo eletrônico que pode executar tarefas que um humano pode fazer. #Ele usa um conjunto de circuitos elétricos, componentes eletrônicos e software para processar informações e executar tarefas. #Os componentes de um computador incluem um processador, memória, unidade de armazenamento, unidade de processamento gráfica, #unidade de controle, unidade de entrada e saída,e dispositivos de entrada e saída. #O processador é o coração do computador e executa instruções de software.A memória é onde o computador armazena ``` # Open Portuguese LLM Leaderboard Evaluation Results Detailed results can be found [here](https://huggingface.co/datasets/eduagarcia-temp/llm_pt_leaderboard_raw_results/tree/main/rhaymison/gemma-portuguese-luana-2b) and on the [🚀 Open Portuguese LLM Leaderboard](https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard) \| Metric \| Value \| \|--------------------------\|---------\| \|Average \|34.94\| \|ENEM Challenge (No Images)\| 24.42\| \|BLUEX (No Images) \| 24.34\| \|OAB Exams \| 27.11\| \|Assin2 RTE \| 70.86\| \|Assin2 STS \| 1.51\| \|FaQuAD NLI \| 43.97\| \|HateBR Binary \| 40.05\| \|PT Hate Speech Binary \| 51.83\| \|tweetSentBR \| 30.42\| ### Comments Any idea, help or report will always be welcome. email: [email protected] <div style="display:flex; flex-direction:row; justify-content:left"> <a href="https://www.linkedin.com/in/rhaymison-cristian-betini-2b3016175/" target="_blank"> <img src="https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white"> </a> <a href="https://github.com/rhaymisonbetini" target="_blank"> <img src="https://img.shields.io/badge/GitHub-100000?style=for-the-badge&logo=github&logoColor=white"> </a> </div>
Narednra/Lnew_tiny_llama_12k_	Narednra	2024-05-17T11:13:53Z	0	0	peft	[ "peft", "region:us" ]	null	2024-05-17T11:09:42Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.4.0
LuckyMan123/hemingway-lora	LuckyMan123	2024-05-17T11:11:29Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-17T11:11:11Z	--- 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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mehdisebai/testOpenHermes	mehdisebai	2024-05-17T11:09:12Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-17T11:08:31Z	--- 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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vetrivelm5/bert-finetuned-ner4	vetrivelm5	2024-05-17T11:07:55Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-17T11:07:42Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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RichardErkhov/mlabonne_-_NeuralPipe-7B-slerp-8bits	RichardErkhov	2024-05-17T11:05:11Z	78	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-17T10:59:24Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) NeuralPipe-7B-slerp - bnb 8bits - Model creator: https://huggingface.co/mlabonne/ - Original model: https://huggingface.co/mlabonne/NeuralPipe-7B-slerp/ Original model description: --- license: apache-2.0 tags: - merge - mergekit model-index: - name: NeuralPipe-7B-slerp 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: 67.75 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralPipe-7B-slerp 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: 86.15 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralPipe-7B-slerp 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: 63.94 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralPipe-7B-slerp 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: 59.8 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralPipe-7B-slerp 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: 79.64 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralPipe-7B-slerp 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: 69.75 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralPipe-7B-slerp name: Open LLM Leaderboard --- # NeuralPipe-7B This model is a merge of the following models made with [mergekit](https://github.com/cg123/mergekit): * [OpenPipe/mistral-ft-optimized-1218](https://huggingface.co/OpenPipe/mistral-ft-optimized-1218) * [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B) ## ⚡ Quantized models Thanks to TheBloke for the quantized models: * GGUF: https://huggingface.co/TheBloke/NeuralPipe-7B-slerp-GGUF * AWQ: https://huggingface.co/TheBloke/NeuralPipe-7B-slerp-AWQ * GPTQ: https://huggingface.co/TheBloke/NeuralPipe-7B-slerp-GPTQ * ## 🧩 Configuration ```yaml slices: - sources: - model: OpenPipe/mistral-ft-optimized-1218 layer_range: [0, 32] - model: mlabonne/NeuralHermes-2.5-Mistral-7B layer_range: [0, 32] merge_method: slerp base_model: OpenPipe/mistral-ft-optimized-1218 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 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "mlabonne/NeuralPipe-7B-slerp" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ``` Output: ``` A large language model is an AI system that uses deep learning techniques to process and understand vast amounts of natural language data. It is designed to generate human-like text, perform complex language tasks, and understand the context, nuance, and meaning of textual data. These models are trained on large datasets, often including billions of words, to learn the patterns and relationships in language. As a result, they can generate coherent and contextually relevant text, answer questions, and perform a variety of other language-related tasks. Some well-known large language models include OpenAI's GPT-3, Google's BERT, and Facebook's RoBERTa. ``` # [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_mlabonne__NeuralPipe-7B-slerp) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|71.17\| \|AI2 Reasoning Challenge (25-Shot)\|67.75\| \|HellaSwag (10-Shot) \|86.15\| \|MMLU (5-Shot) \|63.94\| \|TruthfulQA (0-shot) \|59.80\| \|Winogrande (5-shot) \|79.64\| \|GSM8k (5-shot) \|69.75\|
DreadN0ugh7/ChatAcademy-Trained-7b	DreadN0ugh7	2024-05-17T11:02:19Z	14	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-chat-hf", "base_model:adapter:meta-llama/Llama-2-7b-chat-hf", "license:llama2", "region:us" ]	null	2024-05-08T12:54:45Z	--- license: llama2 library_name: peft tags: - trl - sft - generated_from_trainer base_model: meta-llama/Llama-2-7b-chat-hf model-index: - name: ChatAcademy-Trained-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. --> # ChatAcademy-Trained-7b This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) 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: 0.0002 - train_batch_size: 3 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 6 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.3.0+cu121 - Datasets 2.18.0 - Tokenizers 0.19.1
SangBinCho/long_context_32k_testing	SangBinCho	2024-05-17T10:58:16Z	1,406	0	peft	[ "peft", "safetensors", "llama", "region:us" ]	null	2024-05-17T10:57:26Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.6.0.dev0
statking/zephyr-7b-sft-qlora	statking	2024-05-17T10:54:24Z	3	0	peft	[ "peft", "safetensors", "mistral", "alignment-handbook", "trl", "sft", "generated_from_trainer", "dataset:HuggingFaceH4/ultrachat_200k", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	null	2024-05-14T05:15:29Z	--- license: apache-2.0 library_name: peft tags: - alignment-handbook - trl - sft - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 datasets: - HuggingFaceH4/ultrachat_200k model-index: - name: zephyr-7b-sft-qlora 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/statking/huggingface/runs/dnr1n96z) # zephyr-7b-sft-qlora This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the HuggingFaceH4/ultrachat_200k dataset. It achieves the following results on the evaluation set: - Loss: 0.9439 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.9454 \| 1.0 \| 2179 \| 0.9439 \| ### Framework versions - PEFT 0.10.0 - Transformers 4.41.0.dev0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
Onlysmokehuazi/Huazi_Sentiment_Analysis	Onlysmokehuazi	2024-05-17T10:49:32Z	110	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-17T10:48:35Z	--- 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. 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]
RichardErkhov/Locutusque_-_Hercules-2.5-Mistral-7B-gguf	RichardErkhov	2024-05-17T10:48:27Z	27	0	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2024-05-17T09:24:31Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Hercules-2.5-Mistral-7B - GGUF - Model creator: https://huggingface.co/Locutusque/ - Original model: https://huggingface.co/Locutusque/Hercules-2.5-Mistral-7B/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [Hercules-2.5-Mistral-7B.Q2_K.gguf](https://huggingface.co/RichardErkhov/Locutusque_-_Hercules-2.5-Mistral-7B-gguf/blob/main/Hercules-2.5-Mistral-7B.Q2_K.gguf) \| Q2_K \| 2.53GB \| \| [Hercules-2.5-Mistral-7B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Locutusque_-_Hercules-2.5-Mistral-7B-gguf/blob/main/Hercules-2.5-Mistral-7B.IQ3_XS.gguf) \| IQ3_XS \| 2.81GB \| \| [Hercules-2.5-Mistral-7B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Locutusque_-_Hercules-2.5-Mistral-7B-gguf/blob/main/Hercules-2.5-Mistral-7B.IQ3_S.gguf) \| IQ3_S \| 2.96GB \| \| [Hercules-2.5-Mistral-7B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Locutusque_-_Hercules-2.5-Mistral-7B-gguf/blob/main/Hercules-2.5-Mistral-7B.Q3_K_S.gguf) \| Q3_K_S \| 2.95GB \| \| [Hercules-2.5-Mistral-7B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Locutusque_-_Hercules-2.5-Mistral-7B-gguf/blob/main/Hercules-2.5-Mistral-7B.IQ3_M.gguf) \| IQ3_M \| 3.06GB \| \| [Hercules-2.5-Mistral-7B.Q3_K.gguf](https://huggingface.co/RichardErkhov/Locutusque_-_Hercules-2.5-Mistral-7B-gguf/blob/main/Hercules-2.5-Mistral-7B.Q3_K.gguf) \| Q3_K \| 3.28GB \| \| [Hercules-2.5-Mistral-7B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Locutusque_-_Hercules-2.5-Mistral-7B-gguf/blob/main/Hercules-2.5-Mistral-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.28GB \| \| [Hercules-2.5-Mistral-7B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Locutusque_-_Hercules-2.5-Mistral-7B-gguf/blob/main/Hercules-2.5-Mistral-7B.Q3_K_L.gguf) \| Q3_K_L \| 3.56GB \| \| [Hercules-2.5-Mistral-7B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Locutusque_-_Hercules-2.5-Mistral-7B-gguf/blob/main/Hercules-2.5-Mistral-7B.IQ4_XS.gguf) \| IQ4_XS \| 3.67GB \| \| [Hercules-2.5-Mistral-7B.Q4_0.gguf](https://huggingface.co/RichardErkhov/Locutusque_-_Hercules-2.5-Mistral-7B-gguf/blob/main/Hercules-2.5-Mistral-7B.Q4_0.gguf) \| Q4_0 \| 3.83GB \| \| [Hercules-2.5-Mistral-7B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Locutusque_-_Hercules-2.5-Mistral-7B-gguf/blob/main/Hercules-2.5-Mistral-7B.IQ4_NL.gguf) \| IQ4_NL \| 3.87GB \| \| [Hercules-2.5-Mistral-7B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Locutusque_-_Hercules-2.5-Mistral-7B-gguf/blob/main/Hercules-2.5-Mistral-7B.Q4_K_S.gguf) \| Q4_K_S \| 3.86GB \| \| [Hercules-2.5-Mistral-7B.Q4_K.gguf](https://huggingface.co/RichardErkhov/Locutusque_-_Hercules-2.5-Mistral-7B-gguf/blob/main/Hercules-2.5-Mistral-7B.Q4_K.gguf) \| Q4_K \| 4.07GB \| \| [Hercules-2.5-Mistral-7B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Locutusque_-_Hercules-2.5-Mistral-7B-gguf/blob/main/Hercules-2.5-Mistral-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.07GB \| \| [Hercules-2.5-Mistral-7B.Q4_1.gguf](https://huggingface.co/RichardErkhov/Locutusque_-_Hercules-2.5-Mistral-7B-gguf/blob/main/Hercules-2.5-Mistral-7B.Q4_1.gguf) \| Q4_1 \| 4.24GB \| \| [Hercules-2.5-Mistral-7B.Q5_0.gguf](https://huggingface.co/RichardErkhov/Locutusque_-_Hercules-2.5-Mistral-7B-gguf/blob/main/Hercules-2.5-Mistral-7B.Q5_0.gguf) \| Q5_0 \| 4.65GB \| \| [Hercules-2.5-Mistral-7B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Locutusque_-_Hercules-2.5-Mistral-7B-gguf/blob/main/Hercules-2.5-Mistral-7B.Q5_K_S.gguf) \| Q5_K_S \| 4.65GB \| \| [Hercules-2.5-Mistral-7B.Q5_K.gguf](https://huggingface.co/RichardErkhov/Locutusque_-_Hercules-2.5-Mistral-7B-gguf/blob/main/Hercules-2.5-Mistral-7B.Q5_K.gguf) \| Q5_K \| 4.78GB \| \| [Hercules-2.5-Mistral-7B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Locutusque_-_Hercules-2.5-Mistral-7B-gguf/blob/main/Hercules-2.5-Mistral-7B.Q5_K_M.gguf) \| Q5_K_M \| 4.78GB \| \| [Hercules-2.5-Mistral-7B.Q5_1.gguf](https://huggingface.co/RichardErkhov/Locutusque_-_Hercules-2.5-Mistral-7B-gguf/blob/main/Hercules-2.5-Mistral-7B.Q5_1.gguf) \| Q5_1 \| 5.07GB \| \| [Hercules-2.5-Mistral-7B.Q6_K.gguf](https://huggingface.co/RichardErkhov/Locutusque_-_Hercules-2.5-Mistral-7B-gguf/blob/main/Hercules-2.5-Mistral-7B.Q6_K.gguf) \| Q6_K \| 5.53GB \| \| [Hercules-2.5-Mistral-7B.Q8_0.gguf](https://huggingface.co/RichardErkhov/Locutusque_-_Hercules-2.5-Mistral-7B-gguf/blob/main/Hercules-2.5-Mistral-7B.Q8_0.gguf) \| Q8_0 \| 7.17GB \| Original model description: --- license: apache-2.0 library_name: transformers tags: - not-for-all-audiences - chemistry - math - code - physics base_model: mistralai/Mistral-7B-v0.1 datasets: - Locutusque/hercules-v2.0 - Locutusque/hercules-v2.5 model-index: - name: Hercules-2.5-Mistral-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: 62.03 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/Hercules-2.5-Mistral-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: 83.79 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/Hercules-2.5-Mistral-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: 63.49 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/Hercules-2.5-Mistral-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: 43.44 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/Hercules-2.5-Mistral-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: 79.72 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/Hercules-2.5-Mistral-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: 49.05 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/Hercules-2.5-Mistral-7B name: Open LLM Leaderboard --- # Model Card: Hercules-2.5-Mistral-7B ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6437292ecd93f4c9a34b0d47/aaxvEOjNxHKZ7rRGPolW-.png) ## Model Description Hercules-2.5-Mistral-7B is a fine-tuned language model derived from Mistralai/Mistral-7B-v0.1. It is specifically designed to excel in instruction following, function calls, and conversational interactions across various scientific and technical domains. The dataset used for fine-tuning, also named Hercules-v2.5, expands upon the diverse capabilities of OpenHermes-2.5 with contributions from numerous curated datasets. This fine-tuning has hercules-v2.5 with enhanced abilities in: - Complex Instruction Following: Understanding and accurately executing multi-step instructions, even those involving specialized terminology. - Function Calling: Seamlessly interpreting and executing function calls, providing appropriate input and output values. - Domain-Specific Knowledge: Engaging in informative and educational conversations about Biology, Chemistry, Physics, Mathematics, Medicine, Computer Science, and more. ## Intended Uses & Potential Bias Hercules-2.5-Mistral-7B is well-suited to the following applications: - Specialized Chatbots: Creating knowledgeable chatbots and conversational agents in scientific and technical fields. - Instructional Assistants: Supporting users with educational and step-by-step guidance in various disciplines. - Code Generation and Execution: Facilitating code execution through function calls, aiding in software development and prototyping. Important Note: Although Hercules-v2.5 is carefully constructed, it's important to be aware that the underlying data sources may contain biases or reflect harmful stereotypes. Use this model with caution and consider additional measures to mitigate potential biases in its responses. ## Limitations and Risks - Toxicity: The dataset may still contain toxic or harmful examples despite cleaning efforts. - Hallucinations and Factual Errors: Like other language models, Hercules-2.0-Mistral-7B may generate incorrect or misleading information, especially in specialized domains where it lacks sufficient expertise. - Potential for Misuse: The ability to engage in technical conversations and execute function calls could be misused for malicious purposes. ## Evaluation Metrics To provide suitable benchmarks for Hercules-2.5-Mistral-7B, consider using a combination of the following metrics: - Instruction Following: Task-specific evaluation datasets for instruction following in relevant domains (e.g., datasets specifically focused on math problems, code generation, etc.). - Function Calling: Evaluate the model's accuracy in interpreting and executing function calls with varying inputs and outputs. - Conversational Quality: Assess the model's ability to maintain coherence, naturalness, and informativeness across conversational turns. ## Training Data Hercules-2.5-Mistral-7B is fine-tuned from the following sources: - cognitivecomputations/dolphin (first 300k examples) - Evol Instruct 70K && 140K - teknium/GPT4-LLM-Cleaned - jondurbin/airoboros-3.2 - AlekseyKorshuk/camel-chatml - CollectiveCognition/chats-data-2023-09-22 - Nebulous/lmsys-chat-1m-smortmodelsonly - glaiveai/glaive-code-assistant-v2 - glaiveai/glaive-code-assistant - glaiveai/glaive-function-calling-v2 - garage-bAInd/Open-Platypus - meta-math/MetaMathQA - teknium/GPTeacher-General-Instruct - GPTeacher roleplay datasets - BI55/MedText - pubmed_qa labeled subset - M4-ai/LDJnr_combined_inout_format - Unnatural Instructions - CollectiveCognition/chats-data-2023-09-27 - CollectiveCognition/chats-data-2023-10-16 ## Training Procedure - This model was trained on 8 kaggle TPUs, using torch xla SPMD for high MXU efficiency. There was no expense on my end (meaning you can reproduce this too!) - A learning rate of 2e-06 with the Adam optimizer. A linear scheduler was used, with an end factor of 0.3. A low learning rate was used to prevent exploding gradients. - No mixed precision was used, with the default dtype being bfloat16. - Trained on 200,000 examples of Hercules-v2.0 and 100,000 examples of Hercules-v2.5 - No model parameters were frozen. - This model was trained on OpenAI's ChatML prompt format. Because this model has function calling capabilities, the prompt format is slightly different, here's what it would look like: ```<\|im_start\|>system\n{message}<\|im_end\|>\n<\|im_start\|>user\n{user message}<\|im_end\|>\n<\|im_start\|>call\n{function call message}<\|im_end\|>\n<\|im_start\|>function\n{function response message}<\|im_end\|>\n<\|im_start\|>assistant\n{assistant message}</s>``` This model was fine-tuned using the TPU-Alignment repository. https://github.com/Locutusque/TPU-Alignment # Updates - 🔥 Earned a score of nearly 64 on Open LLM Leaderboard, outperforming most merge-free SFT mistral fine-tunes 🔥 # Quants exl2 by @bartowski https://huggingface.co/bartowski/Hercules-2.5-Mistral-7B-exl2 # [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_Locutusque__Hercules-2.5-Mistral-7B) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|63.59\| \|AI2 Reasoning Challenge (25-Shot)\|62.03\| \|HellaSwag (10-Shot) \|83.79\| \|MMLU (5-Shot) \|63.49\| \|TruthfulQA (0-shot) \|43.44\| \|Winogrande (5-shot) \|79.72\| \|GSM8k (5-shot) \|49.05\|
ymechqrane/llama3-8b-sft-qlora-re	ymechqrane	2024-05-17T10:44:23Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Meta-Llama-3-8B", "base_model:adapter:meta-llama/Meta-Llama-3-8B", "license:llama3", "region:us" ]	null	2024-05-02T14:39:23Z	--- license: llama3 library_name: peft tags: - trl - sft - generated_from_trainer base_model: meta-llama/Meta-Llama-3-8B model-index: - name: llama3-8b-sft-qlora-re 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. --> # llama3-8b-sft-qlora-re This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.11.0 - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
amaye15/google-siglip-base-patch16-224-batch64-lr5e-05-standford-dogs	amaye15	2024-05-17T10:42:31Z	120	0	transformers	[ "transformers", "safetensors", "siglip", "image-classification", "generated_from_trainer", "dataset:stanford-dogs", "base_model:google/siglip-base-patch16-224", "base_model:finetune:google/siglip-base-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-17T10:42:10Z	--- license: apache-2.0 base_model: google/siglip-base-patch16-224 tags: - generated_from_trainer datasets: - stanford-dogs metrics: - accuracy - f1 - precision - recall model-index: - name: google-siglip-base-patch16-224-batch64-lr5e-05-standford-dogs results: - task: name: Image Classification type: image-classification dataset: name: stanford-dogs type: stanford-dogs config: default split: full args: default metrics: - name: Accuracy type: accuracy value: 0.8364917395529641 - name: F1 type: f1 value: 0.8328749982143954 - name: Precision type: precision value: 0.8377481660081763 - name: Recall type: recall value: 0.8330663170433035 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # google-siglip-base-patch16-224-batch64-lr5e-05-standford-dogs This model is a fine-tuned version of [google/siglip-base-patch16-224](https://huggingface.co/google/siglip-base-patch16-224) on the stanford-dogs dataset. It achieves the following results on the evaluation set: - Loss: 0.5612 - Accuracy: 0.8365 - F1: 0.8329 - Precision: 0.8377 - Recall: 0.8331 ## 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: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| 4.822 \| 0.1550 \| 10 \| 4.2549 \| 0.0782 \| 0.0493 \| 0.0987 \| 0.0726 \| \| 4.236 \| 0.3101 \| 20 \| 3.5279 \| 0.1907 \| 0.1507 \| 0.2201 \| 0.1830 \| \| 3.5066 \| 0.4651 \| 30 \| 2.5316 \| 0.3319 \| 0.2941 \| 0.4180 \| 0.3205 \| \| 2.8064 \| 0.6202 \| 40 \| 2.1243 \| 0.4361 \| 0.4090 \| 0.5324 \| 0.4282 \| \| 2.441 \| 0.7752 \| 50 \| 1.5798 \| 0.5510 \| 0.5250 \| 0.6242 \| 0.5438 \| \| 2.0985 \| 0.9302 \| 60 \| 1.4242 \| 0.5843 \| 0.5577 \| 0.6400 \| 0.5768 \| \| 1.8689 \| 1.0853 \| 70 \| 1.1481 \| 0.6625 \| 0.6456 \| 0.7143 \| 0.6565 \| \| 1.6588 \| 1.2403 \| 80 \| 1.1937 \| 0.6465 \| 0.6361 \| 0.7062 \| 0.6439 \| \| 1.5807 \| 1.3953 \| 90 \| 0.9818 \| 0.7058 \| 0.6890 \| 0.7438 \| 0.6981 \| \| 1.4851 \| 1.5504 \| 100 \| 1.0181 \| 0.7000 \| 0.6839 \| 0.7373 \| 0.6959 \| \| 1.5033 \| 1.7054 \| 110 \| 1.0169 \| 0.6914 \| 0.6845 \| 0.7490 \| 0.6883 \| \| 1.3022 \| 1.8605 \| 120 \| 0.9087 \| 0.7276 \| 0.7170 \| 0.7643 \| 0.7222 \| \| 1.3106 \| 2.0155 \| 130 \| 0.8385 \| 0.7432 \| 0.7352 \| 0.7667 \| 0.7363 \| \| 1.1721 \| 2.1705 \| 140 \| 0.8957 \| 0.7128 \| 0.7026 \| 0.7592 \| 0.7075 \| \| 1.131 \| 2.3256 \| 150 \| 0.8730 \| 0.7259 \| 0.7149 \| 0.7687 \| 0.7196 \| \| 1.1223 \| 2.4806 \| 160 \| 0.8132 \| 0.7546 \| 0.7457 \| 0.7855 \| 0.7482 \| \| 1.0688 \| 2.6357 \| 170 \| 0.7485 \| 0.7704 \| 0.7601 \| 0.7863 \| 0.7631 \| \| 1.0686 \| 2.7907 \| 180 \| 0.7559 \| 0.7651 \| 0.7587 \| 0.7920 \| 0.7609 \| \| 0.9733 \| 2.9457 \| 190 \| 0.7779 \| 0.7553 \| 0.7458 \| 0.7797 \| 0.7521 \| \| 0.9287 \| 3.1008 \| 200 \| 0.7048 \| 0.7818 \| 0.7756 \| 0.7981 \| 0.7756 \| \| 0.8746 \| 3.2558 \| 210 \| 0.6848 \| 0.7867 \| 0.7774 \| 0.8034 \| 0.7822 \| \| 0.7982 \| 3.4109 \| 220 \| 0.6930 \| 0.7884 \| 0.7796 \| 0.8025 \| 0.7846 \| \| 0.823 \| 3.5659 \| 230 \| 0.7041 \| 0.7804 \| 0.7717 \| 0.7975 \| 0.7752 \| \| 0.8713 \| 3.7209 \| 240 \| 0.7418 \| 0.7755 \| 0.7646 \| 0.8053 \| 0.7711 \| \| 0.8651 \| 3.8760 \| 250 \| 0.6847 \| 0.7828 \| 0.7773 \| 0.8048 \| 0.7782 \| \| 0.784 \| 4.0310 \| 260 \| 0.6662 \| 0.7923 \| 0.7841 \| 0.8097 \| 0.7860 \| \| 0.6894 \| 4.1860 \| 270 \| 0.6980 \| 0.7843 \| 0.7781 \| 0.8024 \| 0.7779 \| \| 0.7727 \| 4.3411 \| 280 \| 0.6629 \| 0.7833 \| 0.7804 \| 0.8030 \| 0.7798 \| \| 0.6978 \| 4.4961 \| 290 \| 0.6820 \| 0.7845 \| 0.7800 \| 0.8011 \| 0.7820 \| \| 0.7032 \| 4.6512 \| 300 \| 0.6148 \| 0.8032 \| 0.7969 \| 0.8094 \| 0.7985 \| \| 0.6978 \| 4.8062 \| 310 \| 0.6457 \| 0.7940 \| 0.7872 \| 0.8085 \| 0.7892 \| \| 0.66 \| 4.9612 \| 320 \| 0.6242 \| 0.8088 \| 0.8033 \| 0.8246 \| 0.8058 \| \| 0.5706 \| 5.1163 \| 330 \| 0.6404 \| 0.7966 \| 0.7905 \| 0.8097 \| 0.7928 \| \| 0.5456 \| 5.2713 \| 340 \| 0.7147 \| 0.7872 \| 0.7767 \| 0.8060 \| 0.7819 \| \| 0.5869 \| 5.4264 \| 350 \| 0.6267 \| 0.8066 \| 0.8016 \| 0.8188 \| 0.8025 \| \| 0.6022 \| 5.5814 \| 360 \| 0.6197 \| 0.8061 \| 0.8028 \| 0.8209 \| 0.8027 \| \| 0.5676 \| 5.7364 \| 370 \| 0.6061 \| 0.8059 \| 0.8005 \| 0.8140 \| 0.8024 \| \| 0.5456 \| 5.8915 \| 380 \| 0.6018 \| 0.8069 \| 0.8006 \| 0.8254 \| 0.8033 \| \| 0.56 \| 6.0465 \| 390 \| 0.6126 \| 0.8090 \| 0.8037 \| 0.8206 \| 0.8045 \| \| 0.4582 \| 6.2016 \| 400 \| 0.6122 \| 0.8115 \| 0.8062 \| 0.8196 \| 0.8061 \| \| 0.4594 \| 6.3566 \| 410 \| 0.6058 \| 0.8122 \| 0.8081 \| 0.8235 \| 0.8082 \| \| 0.4868 \| 6.5116 \| 420 \| 0.5890 \| 0.8195 \| 0.8131 \| 0.8300 \| 0.8141 \| \| 0.4841 \| 6.6667 \| 430 \| 0.5909 \| 0.8175 \| 0.8119 \| 0.8250 \| 0.8133 \| \| 0.4537 \| 6.8217 \| 440 \| 0.5889 \| 0.8195 \| 0.8153 \| 0.8261 \| 0.8164 \| \| 0.4807 \| 6.9767 \| 450 \| 0.6105 \| 0.8144 \| 0.8104 \| 0.8300 \| 0.8106 \| \| 0.4051 \| 7.1318 \| 460 \| 0.5917 \| 0.8171 \| 0.8103 \| 0.8217 \| 0.8131 \| \| 0.3727 \| 7.2868 \| 470 \| 0.6037 \| 0.8166 \| 0.8116 \| 0.8262 \| 0.8125 \| \| 0.4034 \| 7.4419 \| 480 \| 0.6407 \| 0.8032 \| 0.8003 \| 0.8146 \| 0.8015 \| \| 0.3684 \| 7.5969 \| 490 \| 0.6205 \| 0.8061 \| 0.7997 \| 0.8176 \| 0.8008 \| \| 0.416 \| 7.7519 \| 500 \| 0.5855 \| 0.8258 \| 0.8207 \| 0.8364 \| 0.8211 \| \| 0.3947 \| 7.9070 \| 510 \| 0.5802 \| 0.8214 \| 0.8179 \| 0.8283 \| 0.8179 \| \| 0.3731 \| 8.0620 \| 520 \| 0.5870 \| 0.8239 \| 0.8191 \| 0.8324 \| 0.8188 \| \| 0.3203 \| 8.2171 \| 530 \| 0.5783 \| 0.8265 \| 0.8211 \| 0.8302 \| 0.8216 \| \| 0.337 \| 8.3721 \| 540 \| 0.5836 \| 0.8200 \| 0.8162 \| 0.8247 \| 0.8166 \| \| 0.3396 \| 8.5271 \| 550 \| 0.5992 \| 0.8156 \| 0.8121 \| 0.8253 \| 0.8115 \| \| 0.3355 \| 8.6822 \| 560 \| 0.5755 \| 0.8229 \| 0.8182 \| 0.8281 \| 0.8187 \| \| 0.3273 \| 8.8372 \| 570 \| 0.5819 \| 0.8246 \| 0.8194 \| 0.8268 \| 0.8208 \| \| 0.3181 \| 8.9922 \| 580 \| 0.5840 \| 0.8205 \| 0.8174 \| 0.8279 \| 0.8168 \| \| 0.2855 \| 9.1473 \| 590 \| 0.5997 \| 0.8144 \| 0.8098 \| 0.8213 \| 0.8103 \| \| 0.254 \| 9.3023 \| 600 \| 0.5863 \| 0.8183 \| 0.8132 \| 0.8251 \| 0.8133 \| \| 0.2781 \| 9.4574 \| 610 \| 0.5779 \| 0.8224 \| 0.8169 \| 0.8275 \| 0.8195 \| \| 0.2691 \| 9.6124 \| 620 \| 0.5816 \| 0.8219 \| 0.8177 \| 0.8257 \| 0.8186 \| \| 0.3018 \| 9.7674 \| 630 \| 0.5814 \| 0.8297 \| 0.8250 \| 0.8370 \| 0.8253 \| \| 0.2615 \| 9.9225 \| 640 \| 0.5761 \| 0.8299 \| 0.8261 \| 0.8377 \| 0.8262 \| \| 0.2707 \| 10.0775 \| 650 \| 0.5640 \| 0.8326 \| 0.8283 \| 0.8385 \| 0.8284 \| \| 0.2482 \| 10.2326 \| 660 \| 0.5685 \| 0.8246 \| 0.8206 \| 0.8284 \| 0.8218 \| \| 0.2493 \| 10.3876 \| 670 \| 0.5717 \| 0.8241 \| 0.8208 \| 0.8311 \| 0.8199 \| \| 0.2167 \| 10.5426 \| 680 \| 0.5741 \| 0.8246 \| 0.8204 \| 0.8273 \| 0.8204 \| \| 0.2628 \| 10.6977 \| 690 \| 0.5791 \| 0.8248 \| 0.8205 \| 0.8281 \| 0.8216 \| \| 0.2316 \| 10.8527 \| 700 \| 0.5770 \| 0.8321 \| 0.8272 \| 0.8348 \| 0.8284 \| \| 0.2326 \| 11.0078 \| 710 \| 0.5755 \| 0.8280 \| 0.8249 \| 0.8348 \| 0.8249 \| \| 0.2001 \| 11.1628 \| 720 \| 0.5783 \| 0.8336 \| 0.8299 \| 0.8354 \| 0.8310 \| \| 0.1759 \| 11.3178 \| 730 \| 0.5804 \| 0.8345 \| 0.8302 \| 0.8367 \| 0.8311 \| \| 0.202 \| 11.4729 \| 740 \| 0.5820 \| 0.8316 \| 0.8278 \| 0.8353 \| 0.8280 \| \| 0.2191 \| 11.6279 \| 750 \| 0.5724 \| 0.8324 \| 0.8279 \| 0.8341 \| 0.8287 \| \| 0.1955 \| 11.7829 \| 760 \| 0.5957 \| 0.8226 \| 0.8181 \| 0.8268 \| 0.8198 \| \| 0.1972 \| 11.9380 \| 770 \| 0.5722 \| 0.8294 \| 0.8254 \| 0.8318 \| 0.8263 \| \| 0.1848 \| 12.0930 \| 780 \| 0.5731 \| 0.8311 \| 0.8269 \| 0.8339 \| 0.8281 \| \| 0.1613 \| 12.2481 \| 790 \| 0.5682 \| 0.8382 \| 0.8344 \| 0.8397 \| 0.8356 \| \| 0.1665 \| 12.4031 \| 800 \| 0.5565 \| 0.8350 \| 0.8325 \| 0.8365 \| 0.8325 \| \| 0.1739 \| 12.5581 \| 810 \| 0.5738 \| 0.8360 \| 0.8328 \| 0.8395 \| 0.8326 \| \| 0.1744 \| 12.7132 \| 820 \| 0.5628 \| 0.8360 \| 0.8327 \| 0.8387 \| 0.8328 \| \| 0.1737 \| 12.8682 \| 830 \| 0.5712 \| 0.8355 \| 0.8320 \| 0.8395 \| 0.8324 \| \| 0.1635 \| 13.0233 \| 840 \| 0.5745 \| 0.8309 \| 0.8256 \| 0.8328 \| 0.8269 \| \| 0.1689 \| 13.1783 \| 850 \| 0.5781 \| 0.8326 \| 0.8288 \| 0.8358 \| 0.8294 \| \| 0.1611 \| 13.3333 \| 860 \| 0.5740 \| 0.8328 \| 0.8280 \| 0.8349 \| 0.8289 \| \| 0.1624 \| 13.4884 \| 870 \| 0.5656 \| 0.8324 \| 0.8279 \| 0.8328 \| 0.8287 \| \| 0.1635 \| 13.6434 \| 880 \| 0.5618 \| 0.8319 \| 0.8276 \| 0.8328 \| 0.8280 \| \| 0.1395 \| 13.7984 \| 890 \| 0.5648 \| 0.8350 \| 0.8311 \| 0.8368 \| 0.8312 \| \| 0.1489 \| 13.9535 \| 900 \| 0.5666 \| 0.8341 \| 0.8304 \| 0.8370 \| 0.8304 \| \| 0.1174 \| 14.1085 \| 910 \| 0.5700 \| 0.8358 \| 0.8321 \| 0.8400 \| 0.8320 \| \| 0.1274 \| 14.2636 \| 920 \| 0.5720 \| 0.8331 \| 0.8295 \| 0.8366 \| 0.8295 \| \| 0.134 \| 14.4186 \| 930 \| 0.5657 \| 0.8353 \| 0.8311 \| 0.8369 \| 0.8317 \| \| 0.1327 \| 14.5736 \| 940 \| 0.5662 \| 0.8343 \| 0.8308 \| 0.8367 \| 0.8307 \| \| 0.1165 \| 14.7287 \| 950 \| 0.5654 \| 0.8341 \| 0.8301 \| 0.8355 \| 0.8303 \| \| 0.1277 \| 14.8837 \| 960 \| 0.5661 \| 0.8345 \| 0.8308 \| 0.8360 \| 0.8310 \| \| 0.1221 \| 15.0388 \| 970 \| 0.5615 \| 0.8370 \| 0.8335 \| 0.8388 \| 0.8335 \| \| 0.1194 \| 15.1938 \| 980 \| 0.5632 \| 0.8353 \| 0.8318 \| 0.8369 \| 0.8319 \| \| 0.1126 \| 15.3488 \| 990 \| 0.5616 \| 0.8362 \| 0.8326 \| 0.8376 \| 0.8327 \| \| 0.1256 \| 15.5039 \| 1000 \| 0.5612 \| 0.8365 \| 0.8329 \| 0.8377 \| 0.8331 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.3.0 - Datasets 2.19.1 - Tokenizers 0.19.1
RDson/Llama-3-Magenta-Instruct-4x8B-MoE-GGUF	RDson	2024-05-17T10:42:26Z	45	1	null	[ "gguf", "moe", "llama", "3", "llama 3", "2x8b", "quantize", "quant", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-15T12:52:24Z	--- tags: - moe - llama - '3' - llama 3 - 2x8b - gguf - quantize - quant --- # GGUF files of [Llama-3-Magenta-Instruct-4x8B-MoE](https://huggingface.co/RDson/Llama-3-Magenta-Instruct-4x8B-MoE) # Llama-3-Magenta-Instruct-4x8B-MoE <img src="https://i.imgur.com/c1Mv8cy.png" width="640"/> ## You should also check out the updated [Llama-3-Peach-Instruct-4x8B-MoE](https://huggingface.co/RDson/Llama-3-Peach-Instruct-4x8B-MoE)! This is a experimental MoE using Mergekit, created from * [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) * [nvidia/Llama3-ChatQA-1.5-8B](https://huggingface.co/nvidia/Llama3-ChatQA-1.5-8B) * [Salesforce/SFR-Iterative-DPO-LLaMA-3-8B-R](https://huggingface.co/Salesforce/SFR-Iterative-DPO-LLaMA-3-8B-R) * [Muhammad2003/Llama3-8B-OpenHermes-DPO](https://huggingface.co/Muhammad2003/Llama3-8B-OpenHermes-DPO) Mergekit yaml file: ``` base_model: Meta-Llama-3-8B-Instruct experts: - source_model: Meta-Llama-3-8B-Instruct positive_prompts: - "explain" - "chat" - "assistant" - "think" - "roleplay" - "versatile" - "helpful" - "factual" - "integrated" - "adaptive" - "comprehensive" - "balanced" negative_prompts: - "specialized" - "narrow" - "focused" - "limited" - "specific" - source_model: ChatQA-1.5-8B positive_prompts: - "python" - "math" - "solve" - "code" - "programming" negative_prompts: - "sorry" - "cannot" - "factual" - "concise" - "straightforward" - "objective" - "dry" - source_model: SFR-Iterative-DPO-LLaMA-3-8B-R positive_prompts: - "chat" - "assistant" - "AI" - "instructive" - "clear" - "directive" - "helpful" - "informative" - source_model: Llama3-8B-OpenHermes-DPO positive_prompts: - "analytical" - "accurate" - "logical" - "knowledgeable" - "precise" - "calculate" - "compute" - "solve" - "work" - "python" - "code" - "javascript" - "programming" - "algorithm" - "tell me" - "assistant" negative_prompts: - "creative" - "abstract" - "imaginative" - "artistic" - "emotional" - "mistake" - "inaccurate" gate_mode: hidden dtype: float16 ``` Some inspiration for the Mergekit yaml file is from [LoneStriker/Umbra-MoE-4x10.7-2.4bpw-h6-exl2](https://huggingface.co/LoneStriker/Umbra-MoE-4x10.7-2.4bpw-h6-exl2).
RDson/Llama-3-Magenta-Instruct-4x8B-MoE	RDson	2024-05-17T10:42:13Z	4	0	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "moe", "llama", "3", "llama 3", "4x8b", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T08:48:30Z	--- tags: - moe - llama - '3' - llama 3 - 4x8b --- # Llama-3-Magenta-Instruct-4x8B-MoE <img src="https://i.imgur.com/c1Mv8cy.png" width="640"/> ## You should also check out the updated [Llama-3-Peach-Instruct-4x8B-MoE](https://huggingface.co/RDson/Llama-3-Peach-Instruct-4x8B-MoE)! GGUF files are available here: [Llama-3-Magenta-Instruct-4x8B-MoE-GGUF](https://huggingface.co/RDson/Llama-3-Magenta-Instruct-4x8B-MoE-GGUF). This is a experimental MoE using Mergekit, created from * [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) * [nvidia/Llama3-ChatQA-1.5-8B](https://huggingface.co/nvidia/Llama3-ChatQA-1.5-8B) * [Salesforce/SFR-Iterative-DPO-LLaMA-3-8B-R](https://huggingface.co/Salesforce/SFR-Iterative-DPO-LLaMA-3-8B-R) * [Muhammad2003/Llama3-8B-OpenHermes-DPO](https://huggingface.co/Muhammad2003/Llama3-8B-OpenHermes-DPO) Mergekit yaml file: ``` base_model: Meta-Llama-3-8B-Instruct experts: - source_model: Meta-Llama-3-8B-Instruct positive_prompts: - "explain" - "chat" - "assistant" - "think" - "roleplay" - "versatile" - "helpful" - "factual" - "integrated" - "adaptive" - "comprehensive" - "balanced" negative_prompts: - "specialized" - "narrow" - "focused" - "limited" - "specific" - source_model: ChatQA-1.5-8B positive_prompts: - "python" - "math" - "solve" - "code" - "programming" negative_prompts: - "sorry" - "cannot" - "factual" - "concise" - "straightforward" - "objective" - "dry" - source_model: SFR-Iterative-DPO-LLaMA-3-8B-R positive_prompts: - "chat" - "assistant" - "AI" - "instructive" - "clear" - "directive" - "helpful" - "informative" - source_model: Llama3-8B-OpenHermes-DPO positive_prompts: - "analytical" - "accurate" - "logical" - "knowledgeable" - "precise" - "calculate" - "compute" - "solve" - "work" - "python" - "code" - "javascript" - "programming" - "algorithm" - "tell me" - "assistant" negative_prompts: - "creative" - "abstract" - "imaginative" - "artistic" - "emotional" - "mistake" - "inaccurate" gate_mode: hidden dtype: float16 ``` Some inspiration for the Mergekit yaml file is from [LoneStriker/Umbra-MoE-4x10.7-2.4bpw-h6-exl2](https://huggingface.co/LoneStriker/Umbra-MoE-4x10.7-2.4bpw-h6-exl2).
lordjia/wawj-1.8b-chat-gguf	lordjia	2024-05-17T10:41:56Z	7	1	null	[ "gguf", "chat", "dialect", "text-generation", "zh", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-05-15T14:30:52Z	--- license: apache-2.0 language: - zh pipeline_tag: text-generation tags: - chat - dialect --- # 北京方言对话模型基于 Qwen1.5 大模型微调的北京方言对话模型，提供口语化的北京方言对话体验。项目包括 7B 和 14B 版本，使用北京方言情景喜剧台词微调。 ## 模型描述 - 7B 版本 (`wawj-7b-chat-gguf`): 中等大小模型，适用于平衡性能和质量。 - 14B 版本 (`wawj-14b-chat-gguf`): 大模型，优化了口语中的幽默感，适用于高质量生成。 ## 使用指南 ### 在 LM Studio 使用在 LM Studio 中搜索 `lordjia/wawj` 直接使用模型，无需手动下载。设置 System Prompt 为：以口语化的北京方言与用户对话。 ### 在 Ollama 使用在 Ollama 中，您可以直接使用已经提供的 Modelfile 来创建模型实例，无需进行额外设置。 ## 模型链接 - [7B 版本](https://huggingface.co/lordjia/wawj-7b-chat-gguf) - [14B 版本](https://huggingface.co/lordjia/wawj-14b-chat-gguf)
abbenedek/whisper-small.en-finetuning2-D3K	abbenedek	2024-05-17T10:39:47Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-17T10:20:02Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
abbenedek/abbenedekwhisper-small.en-finetuning2-D3K	abbenedek	2024-05-17T10:39:45Z	123	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-small.en", "base_model:finetune:openai/whisper-small.en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-17T09:53:27Z	--- license: apache-2.0 base_model: openai/whisper-small.en tags: - generated_from_trainer metrics: - wer model-index: - name: abbenedekwhisper-small.en-finetuning2-D3K 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. --> # abbenedekwhisper-small.en-finetuning2-D3K This model is a fine-tuned version of [openai/whisper-small.en](https://huggingface.co/openai/whisper-small.en) on the None dataset. It achieves the following results on the evaluation set: - Loss: 7.0682 - Cer: 53.2554 - Wer: 135.0993 - Ser: 100.0 - Cer Clean: 0.5008 - Wer Clean: 0.6623 - Ser Clean: 1.7544 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-08 - train_batch_size: 16 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - training_steps: 50 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Cer \| Wer \| Ser \| Cer Clean \| Wer Clean \| Ser Clean \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:--------:\|:-----:\|:---------:\|:---------:\|:---------:\| \| 7.4342 \| 0.05 \| 10 \| 7.0727 \| 53.2554 \| 135.0993 \| 100.0 \| 0.5008 \| 0.6623 \| 1.7544 \| \| 7.2902 \| 0.11 \| 20 \| 7.0722 \| 53.2554 \| 135.0993 \| 100.0 \| 0.5008 \| 0.6623 \| 1.7544 \| \| 6.9726 \| 0.16 \| 30 \| 7.0711 \| 53.2554 \| 135.0993 \| 100.0 \| 0.5008 \| 0.6623 \| 1.7544 \| \| 7.3598 \| 0.21 \| 40 \| 7.0705 \| 53.2554 \| 135.0993 \| 100.0 \| 0.5008 \| 0.6623 \| 1.7544 \| \| 7.0578 \| 0.27 \| 50 \| 7.0682 \| 53.2554 \| 135.0993 \| 100.0 \| 0.5008 \| 0.6623 \| 1.7544 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2+cu121 - Datasets 2.14.5 - Tokenizers 0.15.2
duyntnet/Dr_Samantha-7b-imatrix-GGUF	duyntnet	2024-05-17T10:37:31Z	7	0	transformers	[ "transformers", "gguf", "imatrix", "Dr_Samantha-7b", "text-generation", "en", "license:other", "region:us" ]	text-generation	2024-05-17T08:39:08Z	--- license: other language: - en pipeline_tag: text-generation inference: false tags: - transformers - gguf - imatrix - Dr_Samantha-7b --- Quantizations of https://huggingface.co/sethuiyer/Dr_Samantha-7b # From original readme ## Prompt Template ```text Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: What is your name? ### Response: My name is Samantha. ```
roshinishetty333/llama-7b-lora-tuned-per	roshinishetty333	2024-05-17T10:29:31Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-15T18:58:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Treza12/Gemma	Treza12	2024-05-17T10:27:26Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-17T10:07:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Vamsi-Chowdary/finetune-bloom7b	Vamsi-Chowdary	2024-05-17T10:23:00Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-17T10:20:40Z	--- 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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euiyulsong/Mistral-7B-ORPO-6kpre	euiyulsong	2024-05-17T10:06:22Z	78	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "trl", "orpo", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-17T10:01:33Z	--- library_name: transformers tags: - trl - orpo --- # 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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tomaszki/stablelm-63-b	tomaszki	2024-05-17T09:57:29Z	142	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T09:56:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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tomaszki/stablelm-63-a	tomaszki	2024-05-17T09:56:40Z	142	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T09:55:45Z	--- 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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