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alimenemen/hinxe	alimenemen	2024-06-07T09:07:49Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-07T09:07:49Z	--- license: apache-2.0 ---
aleoaaaa/my_awesome_billsum_model	aleoaaaa	2024-06-07T09:04:43Z	107	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:plguillou/t5-base-fr-sum-cnndm", "base_model:finetune:plguillou/t5-base-fr-sum-cnndm", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-06-06T15:34:46Z	--- base_model: plguillou/t5-base-fr-sum-cnndm tags: - generated_from_trainer model-index: - name: my_awesome_billsum_model 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_awesome_billsum_model This model is a fine-tuned version of [plguillou/t5-base-fr-sum-cnndm](https://huggingface.co/plguillou/t5-base-fr-sum-cnndm) 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: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 29 \| 2.7412 \| 0.167 \| 0.0404 \| 0.1474 \| 0.1471 \| 19.0 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cpu - Datasets 2.19.2 - Tokenizers 0.19.1
shivanikerai/Llama-2-7b-chat-hf-adapter-title-ner-and-title-suggestions-v2.0	shivanikerai	2024-06-07T09:03:31Z	1	0	peft	[ "peft", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-chat-hf", "base_model:adapter:meta-llama/Llama-2-7b-chat-hf", "region:us" ]	null	2024-06-07T09:03:21Z	--- library_name: peft base_model: meta-llama/Llama-2-7b-chat-hf --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.1
foye501/ppo-LunarLander-v2	foye501	2024-06-07T09:01:30Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-06-07T08:59:39Z	--- 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: 264.79 +/- 9.50 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 ... ```
Chijioke-Mgbahurike/hubert-large-ls960-ft-ft	Chijioke-Mgbahurike	2024-06-07T08:59:30Z	20	0	transformers	[ "transformers", "tensorboard", "safetensors", "hubert", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-06-06T10:57:51Z	--- 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. 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]
stablediffusionapi/aam-xl-anime-mix	stablediffusionapi	2024-06-07T08:58:22Z	6	1	diffusers	[ "diffusers", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2024-06-07T06:42:53Z	--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # API Inference ![generated from modelslab.com](https://cdn2.stablediffusionapi.com/generations/bf190b5a-fe19-437c-ba05-82f29cb1f7ad-0.png) ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change model_id to "aam-xl-anime-mix" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://docs.modelslab.com) Try model for free: [Generate Images](https://modelslab.com/models/aam-xl-anime-mix) Model link: [View model](https://modelslab.com/models/aam-xl-anime-mix) View all models: [View Models](https://modelslab.com/models) import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "aam-xl-anime-mix", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off DMGG0RBN
gharshit412/toxic-bert-reddit-finetuned	gharshit412	2024-06-07T08:57:31Z	108	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-06-07T08:53:52Z	--- 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. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Ariffiq99/KUCI_e_care_Albert_Base_Finetuned	Ariffiq99	2024-06-07T08:52:08Z	104	0	transformers	[ "transformers", "tensorboard", "safetensors", "albert", "multiple-choice", "generated_from_trainer", "base_model:Ariffiq99/e_care_albert_base_finetuned", "base_model:finetune:Ariffiq99/e_care_albert_base_finetuned", "license:apache-2.0", "endpoints_compatible", "region:us" ]	multiple-choice	2024-06-07T05:15:58Z	--- license: apache-2.0 base_model: Ariffiq99/e_care_albert_base_finetuned tags: - generated_from_trainer metrics: - f1 model-index: - name: KUCI_e_care_Albert_Base_Finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # KUCI_e_care_Albert_Base_Finetuned This model is a fine-tuned version of [Ariffiq99/e_care_albert_base_finetuned](https://huggingface.co/Ariffiq99/e_care_albert_base_finetuned) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3212 - F1: 0.3683 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 7 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 1.3365 \| 1.0 \| 5196 \| 1.3307 \| 0.3527 \| \| 1.3318 \| 2.0 \| 10392 \| 1.3201 \| 0.3720 \| \| 1.3268 \| 3.0 \| 15588 \| 1.3195 \| 0.3625 \| \| 1.326 \| 4.0 \| 20784 \| 1.3182 \| 0.3594 \| \| 1.3204 \| 5.0 \| 25980 \| 1.3159 \| 0.3668 \| \| 1.3152 \| 6.0 \| 31176 \| 1.3181 \| 0.3638 \| \| 1.3114 \| 7.0 \| 36372 \| 1.3212 \| 0.3683 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1
gowhyyou/Qwen-Qwen1.5-0.5B-1717750067	gowhyyou	2024-06-07T08:48:10Z	149	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-07T08:47:48Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
ioseff/llama2_cs	ioseff	2024-06-07T08:42:17Z	2	0	peft	[ "peft", "safetensors", "text-generation", "conversational", "en", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-chat-hf", "base_model:adapter:meta-llama/Llama-2-7b-chat-hf", "license:apache-2.0", "region:us" ]	text-generation	2024-06-06T06:20:30Z	--- library_name: peft base_model: meta-llama/Llama-2-7b-chat-hf language: - en pipeline_tag: text-generation license: apache-2.0 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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chainup244/Qwen-Qwen1.5-7B-1717749487	chainup244	2024-06-07T08:42:09Z	7	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-07T08:38:13Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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to-be/autotrain-signatures-yolos-tiny-v4	to-be	2024-06-07T08:36:55Z	215	0	transformers	[ "transformers", "tensorboard", "safetensors", "yolos", "object-detection", "autotrain", "vision", "base_model:hustvl/yolos-tiny", "base_model:finetune:hustvl/yolos-tiny", "endpoints_compatible", "region:us" ]	object-detection	2024-06-07T08:29:33Z	--- tags: - autotrain - object-detection - vision base_model: hustvl/yolos-tiny widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace --- # Model Trained Using AutoTrain - Problem type: Object Detection ## Validation Metrics loss: 1.4424972534179688 map: 0.0152 map_50: 0.053 map_75: 0.005 map_small: -1.0 map_medium: 0.018 map_large: 0.0094 mar_1: 0.0473 mar_10: 0.1992 mar_100: 0.3797 mar_small: -1.0 mar_medium: 0.3686 mar_large: 0.4261
AlekseyElygin/mistral-7b-bnb-4bit-LORA	AlekseyElygin	2024-06-07T08:33:40Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-bnb-4bit", "base_model:finetune:unsloth/mistral-7b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-06-07T08:33:29Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/mistral-7b-bnb-4bit --- # Uploaded model - Developed by: AlekseyElygin - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-bnb-4bit This mistral 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)
galocher/patent-7b-v0.3-16b	galocher	2024-06-07T08:33:29Z	13	0	transformers	[ "transformers", "gguf", "mistral", "text-generation-inference", "unsloth", "en", "base_model:unsloth/mistral-7b-v0.3-bnb-4bit", "base_model:quantized:unsloth/mistral-7b-v0.3-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-06-07T08:22:08Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - gguf base_model: unsloth/mistral-7b-v0.3-bnb-4bit --- # Uploaded model - Developed by: galocher - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-v0.3-bnb-4bit This mistral 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)
guishe/nuner-v1_fewnerd_fine_super	guishe	2024-06-07T08:31:28Z	112	0	transformers	[ "transformers", "safetensors", "roberta", "token-classification", "ner", "named-entity-recognition", "en", "dataset:DFKI-SLT/few-nerd", "arxiv:2402.15343", "base_model:numind/NuNER-v1.0", "base_model:finetune:numind/NuNER-v1.0", "license:cc-by-sa-4.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-03-27T22:26:35Z	--- language: en license: cc-by-sa-4.0 tags: - token-classification - ner - named-entity-recognition datasets: - DFKI-SLT/few-nerd metrics: - precision - recall - f1 widget: - text: Concern and scepticism surround Niger uranium mining waste storage plans. Towering mounds dot the desert landscape in northern Niger's Arlit region, but they are heaps of partially radioactive waste left from four decades of operations at one of the world's biggest uranium mines. An ambitious 10-year scheme costing $160 million is underway to secure the waste and avoid risks to health and the environment, but many local people are worried or sceptical. France's nuclear giant Areva, now called Orano, worked the area under a subsidiary, the Akouta Mining Company (Cominak). Cominak closed the site in 2021 after extracting 75,000 tonnes of uranium, much of which went to fuelling the scores of nuclear reactors that provide the backbone of France's electricity supply. Cominak's director general Mahaman Sani Abdoulaye showcased the rehabilitation project to the first French journalists to visit the site since 2010, when seven Areva employees were kidnapped by jihadists. - text: SE Michigan counties allege insulin gouging; Localities file lawsuit against pharmaceutical makers. Four metro Detroit counties filed federal lawsuits Wednesday against some of the nation's biggest pharmaceutical manufacturers and pharmacy benefit managers alleging illegal price fixing for insulin products. Macomb, Monroe, Wayne and Washtenaw counties filed the lawsuits in U.S. District Court in New Jersey against more than a dozen companies, including Lilly, Sanofi Aventis, Novo Nordisk, Express Scripts, Optum Rx and CVS Caremark, per their attorneys. "These are the first such lawsuits that have been filed in the state of Michigan and probably more to come," said attorney Melvin Butch Hollowell of the Miller Law Firm. He described the allegations during a news conference, saying that nationally "the pharmacies and manufacturers get together. They control about 90% of the market each, of the insulin market. They talk to each other secretly. And they jack up the prices through anticompetitive means. And what we've seen is over the past 20 years, when we talk about jacking up the prices, they jack them up 1,500% in the last 20 years. 1,500%." - text: Foreign governments may be spying on your smartphone notifications, senator says. Washington (CNN) — Foreign governments have reportedly attempted to spy on iPhone and Android users through the mobile app notifications they receive on their smartphones - and the US government has forced Apple and Google to keep quiet about it, according to a top US senator. Through legal demands sent to the tech giants, governments have allegedly tried to force Apple and Google to turn over sensitive information that could include the contents of a notification - such as previews of a text message displayed on a lock screen, or an update about app activity, Oregon Democratic Sen. Ron Wyden said in a new report. Wyden's report reflects the latest example of long-running tensions between tech companies and governments over law enforcement demands, which have stretched on for more than a decade. Governments around the world have particularly battled with tech companies over encryption, which provides critical protections to users and businesses while in some cases preventing law enforcement from pursuing investigations into messages sent over the internet. - text: Tech giants ‘could severely disable UK spooks from stopping online harms’. Silicon Valley tech giants’ actions could “severely disable” UK spooks from preventing harm caused by online paedophiles and fraudsters, Suella Braverman has suggested. The Conservative former home secretary named Facebook owner Meta , and Apple, and their use of technologies such as end-to-end encryption as a threat to attempts to tackle digital crimes. She claimed the choice to back these technologies without “safeguards” could “enable and indeed facilitate some of the worst atrocities that our brave men and women in law enforcement agencies deal with every day”, as MPs began considering changes to investigatory powers laws. The Investigatory Powers (Amendment) Bill includes measures to make it easier for agencies to examine and retain bulk datasets, such as publicly available online telephone records, and would allow intelligence agencies to use internet connection records to aid detection of their targets. We know that the terrorists, the serious organised criminals, and fraudsters, and the online paedophiles, all take advantage of the dark web and encrypted spaces - text: Camargo Corrêa asks Toffoli to suspend the fine agreed with Lava Jato. The Camargo Corrêa group has asked Justice Dias Toffoli to suspend the R$1.4 billion fine it agreed to pay in its leniency agreement under Operation Car Wash. The company asked for an extension of the minister's decisions that benefited J&F and Odebrecht. Like the other companies, it claimed that it suffered undue pressure from members of the Federal Public Prosecutor's Office (MPF) to close the deal. Much of the request is based on messages exchanged between prosecutors from the Curitiba task force and former judge Sergio Moro - Camargo Corrêa requested full access to the material, seized in Operation Spoofing, which arrested the hackers who broke into cell phones. The dialogues, according to the group's defense, indicate that the executives did not freely agree to the deal, since they were the targets of lawsuits and pre-trial detentions. pipeline_tag: token-classification inference: parameters: aggregation_strategy: "simple" base_model: numind/NuNER-v1.0 model-index: - name: numind/NuNER-v1.0 fine-tuned on FewNERD-fine-supervised results: - task: type: token-classification name: Named Entity Recognition dataset: name: FewNERD type: DFKI-SLT/few-nerd split: eval metrics: - type: f1 value: 0.6938826894412441 name: F1 - type: precision value: 0.6775065885222044 name: Precision - type: recall value: 0.7110700573834785 name: Recall --- # numind/NuNER-v1.0 fine-tuned on FewNERD-fine-supervised This is a [NuNER](https://arxiv.org/abs/2402.15343) model fine-tuned on the [FewNERD](https://huggingface.co/datasets/DFKI-SLT/few-nerd) dataset that can be used for Named Entity Recognition. NuNER model uses [RoBERTa-base](https://huggingface.co/FacebookAI/roberta-base) as the backbone encoder and it was trained on the [NuNER dataset](https://huggingface.co/datasets/numind/NuNER), which is a large and diverse dataset synthetically labeled by gpt-3.5-turbo-0301 of 1M sentences. This further pre-training phase allowed the generation of high quality token embeddings, a good starting point for fine-tuning on more specialized datasets. ## Model Details The model was fine-tuned as a regular BERT-based model for NER task using HuggingFace Trainer class. ### Model Labels \| Label \| Examples \| \|:-----------------------------------------\|:---------------------------------------------------------------------------------------------------------\| \| art_broadcastprogram \| "Corazones", "The Gale Storm Show : Oh , Susanna", "Street Cents" \| \| art_film \| "Shawshank Redemption", "L'Atlantide", "Bosch" \| \| art_music \| "Hollywood Studio Symphony", "Atkinson , Danko and Ford ( with Brockie and Hilton )", "Champion Lover" \| \| art_other \| "The Today Show", "Venus de Milo", "Aphrodite of Milos" \| \| art_painting \| "Production/Reproduction", "Touit", "Cofiwch Dryweryn" \| \| art_writtenart \| "The Seven Year Itch", "Imelda de ' Lambertazzi", "Time" \| \| building_airport \| "Sheremetyevo International Airport", "Newark Liberty International Airport", "Luton Airport" \| \| building_hospital \| "Yeungnam University Hospital", "Hokkaido University Hospital", "Memorial Sloan-Kettering Cancer Center" \| \| building_hotel \| "The Standard Hotel", "Flamingo Hotel", "Radisson Blu Sea Plaza Hotel" \| \| building_library \| "British Library", "Bayerische Staatsbibliothek", "Berlin State Library" \| \| building_other \| "Henry Ford Museum", "Alpha Recording Studios", "Communiplex" \| \| building_restaurant \| "Carnegie Deli", "Fatburger", "Trumbull" \| \| building_sportsfacility \| "Boston Garden", "Sports Center", "Glenn Warner Soccer Facility" \| \| building_theater \| "Sanders Theatre", "National Paris Opera", "Pittsburgh Civic Light Opera" \| \| event_attack/battle/war/militaryconflict \| "Easter Offensive", "Jurist", "Vietnam War" \| \| event_disaster \| "the 1912 North Mount Lyell Disaster", "1990s North Korean famine", "1693 Sicily earthquake" \| \| event_election \| "Elections to the European Parliament", "March 1898 elections", "1982 Mitcham and Morden by-election" \| \| event_other \| "Union for a Popular Movement", "Masaryk Democratic Movement", "Eastwood Scoring Stage" \| \| event_protest \| "Iranian Constitutional Revolution", "French Revolution", "Russian Revolution" \| \| event_sportsevent \| "World Cup", "National Champions", "Stanley Cup" \| \| location_GPE \| "Croatian", "Mediterranean Basin", "the Republic of Croatia" \| \| location_bodiesofwater \| "Arthur Kill", "Atatürk Dam Lake", "Norfolk coast" \| \| location_island \| "new Samsat district", "Laccadives", "Staten Island" \| \| location_mountain \| "Salamander Glacier", "Miteirya Ridge", "Ruweisat Ridge" \| \| location_other \| "Victoria line", "Northern City Line", "Cartuther" \| \| location_park \| "Painted Desert Community Complex Historic District", "Gramercy Park", "Shenandoah National Park" \| \| location_road/railway/highway/transit \| "NJT", "Newark-Elizabeth Rail Link", "Friern Barnet Road" \| \| organization_company \| "Texas Chicken", "Dixy Chicken", "Church 's Chicken" \| \| organization_education \| "MIT", "Belfast Royal Academy and the Ulster College of Physical Education", "Barnard College" \| \| organization_government/governmentagency \| "Congregazione dei Nobili", "Diet", "Supreme Court" \| \| organization_media/newspaper \| "Clash", "Al Jazeera", "TimeOut Melbourne" \| \| organization_other \| "Defence Sector C", "IAEA", "4th Army" \| \| organization_politicalparty \| "Al Wafa ' Islamic", "Shimpotō", "Kenseitō" \| \| organization_religion \| "UPCUSA", "Christian", "Jewish" \| \| organization_showorganization \| "Lizzy", "Bochumer Symphoniker", "Mr. Mister" \| \| organization_sportsleague \| "China League One", "NHL", "First Division" \| \| organization_sportsteam \| "Arsenal", "Luc Alphand Aventures", "Tottenham" \| \| other_astronomything \| "Algol", "`` Caput Larvae ''", "Zodiac" \| \| other_award \| "Order of the Republic of Guinea and Nigeria", "Grand Commander of the Order of the Niger", "GCON" \| \| other_biologything \| "N-terminal lipid", "Amphiphysin", "BAR" \| \| other_chemicalthing \| "uranium", "carbon dioxide", "sulfur" \| \| other_currency \| "$", "lac crore", "Travancore Rupee" \| \| other_disease \| "bladder cancer", "French Dysentery Epidemic of 1779", "hypothyroidism" \| \| other_educationaldegree \| "BSc ( Hons ) in physics", "Bachelor", "Master" \| \| other_god \| "Raijin", "Fujin", "El" \| \| other_language \| "Breton-speaking", "Latin", "English" \| \| other_law \| "Leahy–Smith America Invents Act ( AIA", "United States Freedom Support Act", "Thirty Years ' Peace" \| \| other_livingthing \| "monkeys", "patchouli", "insects" \| \| other_medical \| "amitriptyline", "Pediatrics", "pediatrician" \| \| person_actor \| "Tchéky Karyo", "Edmund Payne", "Ellaline Terriss" \| \| person_artist/author \| "Hicks", "Gaetano Donizett", "George Axelrod" \| \| person_athlete \| "Tozawa", "Neville", "Jaguar" \| \| person_director \| "Richard Quine", "Bob Swaim", "Frank Darabont" \| \| person_other \| "Campbell", "Holden", "Richard Benson" \| \| person_politician \| "William", "Rivière", "Emeric" \| \| person_scholar \| "Wurdack", "Stalmine", "Stedman" \| \| person_soldier \| "Joachim Ziegler", "Helmuth Weidling", "Krukenberg" \| \| product_airplane \| "Spey-equipped FGR.2s", "EC135T2 CPDS", "Luton" \| \| product_car \| "Phantom", "100EX", "Corvettes - GT1 C6R" \| \| product_food \| "red grape", "yakiniku", "V. labrusca" \| \| product_game \| "Hardcore RPG", "Splinter Cell", "Airforce Delta" \| \| product_other \| "X11", "PDP-1", "Fairbottom Bobs" \| \| product_ship \| "Essex", "Congress", "HMS `` Chinkara ''" \| \| product_software \| "AmiPDF", "Wikipedia", "Apdf" \| \| product_train \| "55022", "Royal Scots Grey", "High Speed Trains" \| \| product_weapon \| "AR-15 's", "ZU-23-2MR Wróbel II", "ZU-23-2M Wróbel" \| ## Uses ### Direct Use for Inference ```python >>> from transformers import pipeline >>> text = """Foreign governments may be spying on your smartphone notifications, senator says. Washington (CNN) — Foreign governments have reportedly attempted to spy on iPhone and Android users through the mobile app notifications they receive on their smartphones - and the US government has forced Apple and Google to keep quiet about it, according to a top US senator. Through legal demands sent to the tech giants, governments have allegedly tried to force Apple and Google to turn over sensitive information that could include the contents of a notification - such as previews of a text message displayed on a lock screen, or an update about app activity, Oregon Democratic Sen. Ron Wyden said in a new report. Wyden's report reflects the latest example of long-running tensions between tech companies and governments over law enforcement demands, which have stretched on for more than a decade. Governments around the world have particularly battled with tech companies over encryption, which provides critical protections to users and businesses while in some cases preventing law enforcement from pursuing investigations into messages sent over the internet.""" >>> classifier = pipeline( "ner", model="guishe/nuner-v1_fewnerd_fine_super", aggregation_strategy="simple", ) >>> classifier(text) [{'entity_group': 'location_GPE', 'score': 0.9424858, 'word': ' Washington', 'start': 82, 'end': 92}, {'entity_group': 'organization_media/newspaper', 'score': 0.83160853, 'word': 'CNN', 'start': 94, 'end': 97}, {'entity_group': 'product_other', 'score': 0.80409557, 'word': ' iPhone', 'start': 157, 'end': 163}, {'entity_group': 'product_other', 'score': 0.7345743, 'word': ' Android', 'start': 168, 'end': 175}, {'entity_group': 'location_GPE', 'score': 0.70951134, 'word': ' US', 'start': 263, 'end': 265}, {'entity_group': 'organization_company', 'score': 0.9712124, 'word': ' Apple', 'start': 288, 'end': 293}, {'entity_group': 'organization_company', 'score': 0.9634242, 'word': ' Google', 'start': 298, 'end': 304}, {'entity_group': 'location_GPE', 'score': 0.9451448, 'word': ' US', 'start': 348, 'end': 350}, {'entity_group': 'organization_company', 'score': 0.96848464, 'word': ' Apple', 'start': 449, 'end': 454}, {'entity_group': 'organization_company', 'score': 0.964712, 'word': ' Google', 'start': 459, 'end': 465}, {'entity_group': 'location_GPE', 'score': 0.7764447, 'word': ' Oregon', 'start': 649, 'end': 655}, {'entity_group': 'organization_politicalparty', 'score': 0.7019166, 'word': ' Democratic', 'start': 656, 'end': 666}, {'entity_group': 'person_politician', 'score': 0.902996, 'word': ' Ron Wyden', 'start': 672, 'end': 681}, {'entity_group': 'person_politician', 'score': 0.82849455, 'word': ' Wyden', 'start': 704, 'end': 709}] ``` ## Training Details ### Training Set Metrics \| Training set \| Min \| Median \| Max \| \|:----------------------\|:----\|:--------\|:----\| \| Sentence length \| 1 \| 24.4945 \| 267 \| \| Entities per sentence \| 0 \| 2.5832 \| 88 \| ### Training Hyperparameters - learning_rate: 3e-5 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - weight_decay: 0.01 - num_epochs: 3 ### Training Results \| Epoch \| Validation Loss \| Validation Precision \| Validation Recall \| Validation F1 \| Validation Accuracy \| \|:------:\|:---------------:\|:--------------------:\|:-----------------:\|:-------------:\|:-------------------:\| \| 1 \| 0.2447 \| 0.6678 \| 0.6924 \| 0.6799 \| 0.9274 \| \| 2 \| 0.2345 \| 0.6779 \| 0.7113 \| 0.6942 \| 0.9303 \| \| 3 \| 0.2321 \| 0.6821 \| 0.7144 \| 0.6979 \| 0.9312 \| ### Framework Versions - Python: 3.10.8 - Transformers: 4.36.0 - PyTorch: 2.0.0+cu117 - Datasets: 2.18.0 - Tokenizers: 0.15.2 ## Citation ### BibTeX ``` @misc{bogdanov2024nuner, title={NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data}, author={Sergei Bogdanov and Alexandre Constantin and Timothée Bernard and Benoit Crabbé and Etienne Bernard}, year={2024}, eprint={2402.15343}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
mradermacher/Qwen2-72B-GGUF	mradermacher	2024-06-07T08:26:10Z	102	1	transformers	[ "transformers", "gguf", "pretrained", "en", "base_model:Qwen/Qwen2-72B", "base_model:quantized:Qwen/Qwen2-72B", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	2024-06-07T02:37:30Z	--- base_model: Qwen/Qwen2-72B language: - en library_name: transformers license: other license_link: https://huggingface.co/Qwen/Qwen2-72B/blob/main/LICENSE license_name: tongyi-qianwen quantized_by: mradermacher tags: - pretrained --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Qwen/Qwen2-72B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen2-72B-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-72B-GGUF/resolve/main/Qwen2-72B.Q2_K.gguf) \| Q2_K \| 29.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-72B-GGUF/resolve/main/Qwen2-72B.IQ3_XS.gguf) \| IQ3_XS \| 32.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-72B-GGUF/resolve/main/Qwen2-72B.IQ3_S.gguf) \| IQ3_S \| 34.6 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-72B-GGUF/resolve/main/Qwen2-72B.Q3_K_S.gguf) \| Q3_K_S \| 34.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-72B-GGUF/resolve/main/Qwen2-72B.IQ3_M.gguf) \| IQ3_M \| 35.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-72B-GGUF/resolve/main/Qwen2-72B.Q3_K_M.gguf) \| Q3_K_M \| 37.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-72B-GGUF/resolve/main/Qwen2-72B.Q3_K_L.gguf) \| Q3_K_L \| 39.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-72B-GGUF/resolve/main/Qwen2-72B.IQ4_XS.gguf) \| IQ4_XS \| 40.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-72B-GGUF/resolve/main/Qwen2-72B.Q4_K_S.gguf) \| Q4_K_S \| 44.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2-72B-GGUF/resolve/main/Qwen2-72B.Q4_K_M.gguf) \| Q4_K_M \| 47.5 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/Qwen2-72B-GGUF/resolve/main/Qwen2-72B.Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Qwen2-72B-GGUF/resolve/main/Qwen2-72B.Q5_K_S.gguf.part2of2) \| Q5_K_S \| 51.5 \| \| \| [PART 1](https://huggingface.co/mradermacher/Qwen2-72B-GGUF/resolve/main/Qwen2-72B.Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Qwen2-72B-GGUF/resolve/main/Qwen2-72B.Q5_K_M.gguf.part2of2) \| Q5_K_M \| 54.5 \| \| \| [PART 1](https://huggingface.co/mradermacher/Qwen2-72B-GGUF/resolve/main/Qwen2-72B.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Qwen2-72B-GGUF/resolve/main/Qwen2-72B.Q6_K.gguf.part2of2) \| Q6_K \| 64.4 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/Qwen2-72B-GGUF/resolve/main/Qwen2-72B.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Qwen2-72B-GGUF/resolve/main/Qwen2-72B.Q8_0.gguf.part2of2) \| Q8_0 \| 77.4 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
PowerInfer/Bamboo-base-v0_1	PowerInfer	2024-06-07T08:23:14Z	26	22	transformers	[ "transformers", "safetensors", "bamboo", "feature-extraction", "custom_code", "en", "dataset:tiiuae/falcon-refinedweb", "dataset:bigcode/starcoderdata", "dataset:open-web-math/open-web-math", "arxiv:2110.01786", "arxiv:2310.04564", "license:apache-2.0", "region:us" ]	feature-extraction	2024-03-22T04:51:04Z	--- license: apache-2.0 language: - en datasets: - tiiuae/falcon-refinedweb - bigcode/starcoderdata - open-web-math/open-web-math --- ## Introducation Sparse computing is increasingly recognized as an important direction to improve the computational efficiency (e.g., inference speed) of large language models (LLM). Recent studies ([Zhang el al., 2021](https://arxiv.org/abs/2110.01786); [Liu et al., 2023](https://openreview.net/pdf?id=wIPIhHd00i); [Mirzadeh et al., 2023](https://arxiv.org/abs/2310.04564)) reveal that LLMs inherently exhibit properties conducive to sparse computation when employing the ReLU activation function. This insight opens up new avenues for inference speed, akin to MoE's selective activation. By dynamically choosing model parameters for computation, we can substantially boost inference speed. However, the widespread adoption of ReLU-based models in the LLM field remains limited. Here we introduce a new 7B ReLU-based LLM, Bamboo (Github link: [https://github.com/SJTU-IPADS/Bamboo](https://github.com/SJTU-IPADS/Bamboo)), which boasts nearly 85% sparsity and performance levels on par with [Mistral-7B](https://huggingface.co/mistralai/Mistral-7B-v0.1). ## Model Architecture To push the model's sparsity, we add a ReLU component after GLU component, called dReLU(double ReLU). So our FFN network works as follows: ```Python class BambooMLP(nn.Module): def __init__(self, config): super().__init__() self.config = config self.hidden_size = config.hidden_size self.intermediate_size = config.intermediate_size self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) self.act_fn = ACT2FN[config.hidden_act] def forward(self, x): return self.down_proj(self.act_fn(self.gate_proj(x)) * self.act_fn(self.up_proj(x))) ``` ## Training Details In this section, we introduce the details of training our model, including types of data used, and hyperparameters. We initialized the model weights to Mistral's model weights and modified the FFN structure to the dReLU structure, then continued pre-training for 200B tokens, divided into two phases: First phase: For the proportion of training corpus, we followed the data mix ratio and sources of the StableLM-3B model ([link](https://stability.wandb.io/stability-llm/stable-lm/reports/StableLM-3B-4E1T--VmlldzoyMjU4?accessToken=u3zujipenkx5g7rtcj9qojjgxpconyjktjkli2po09nffrffdhhchq045vp0wyfo)), conducting a further pre-training with 150B tokens. The following table shows the hyper-paramters we used in our training process. \| Hyper-parameters \| \| \| --------------------- \| ----------- \| \| GPUs \| 64 80G-A800 \| \| Learning Rate Control \| Cosine \| \| Peak Learning Rate \| 5e-5 \| \| Batch Size \| 4M \| \| Weight Decay \| 0.1 \| \| Context Length \| 2k \| Second phase: We further adjusted the training corpus ratio, incorporating more domain-specific datasets (e.g., Math, Coding), and continued training for 50B tokens. \| Hyper-parameters \| \| \| --------------------- \| ----------- \| \| GPUs \| 64 80G-A800 \| \| Learning Rate Control \| Cosine \| \| Peak Learning Rate \| 5e-6 \| \| Batch Size \| 4M \| \| Weight Decay \| 0.01 \| \| Context Length \| 4k \| ## Performance Evaluation Results Our evaluation is based on the framework lm-evaluation-harness and opencompass. The evaluation details are listed as follows: - Huggingface LLM Leaderboard tasks. - Other Popular Benchmarks: We report the average accuracies on Big Bench Hard (BBH) (3-shot), HumanEval. \| \| Average \| MMLU \| Winogrande \| TruthfulQA \| Hellaswag \| GSM8K \| Arc-C \| HumanEval \| BBH \| \| ------- \| ------ \| ---------- \| ---------- \| --------- \| ------ \| ------ \| --------- \| ---- \| ------- \| \| Bamboo \| 57.1 \| 63.89 \| 76.16 \| 44.06 \| 82.17 \| 52.84 \| 62.20 \| 25.6 \| 50.35 \| \| Mistral-v0.1 \| 56.5 \| 62.65 \| 79.24 \| 42.62 \| 83.32 \| 40.18 \| 61.43 \| 26.21 \| 56.35 \| ## Inference Speed Evaluation Results We utilize [PowerInfer](https://github.com/SJTU-IPADS/PowerInfer), a state-of-the-art acceleration framework leveraging activation sparsity. Here we show the inference speed compared with llama.cpp/transformers. ## Limitation & Disclaimer - Bamboo, having undergone training with only 150B tokens, may still exhibit performance gaps in certain tasks. - The Bamboo model has only been trained on English-language datasets, hence its capabilities in other languages are still lacking. - The model may produce unexpected outputs due to its size and probabilistic generation paradigm. ## License The code is licensed under Apache-2.0, while model weights are fully open for academic research and also allow free commercial usage. ## Citation Please kindly cite using the following BibTeX: ``` @misc{bamboo, title={Bamboo: Harmonizing Sparsity and Performance in Large Language Models}, author={Yixin Song, Haotong Xie, Zeyu Mi, Li Ma, Haibo Chen}, year={2024} } ```
ReBatch/Reynaerde-7B-Chat	ReBatch	2024-06-07T08:21:51Z	0	6	peft	[ "peft", "safetensors", "mistral", "alignment-handbook", "dpo", "Dutch", "license:apache-2.0", "region:us" ]	null	2024-06-06T08:54:24Z	--- license: apache-2.0 library_name: peft tags: - alignment-handbook - dpo - Dutch --- <p align="center" style="margin:0;padding:0"> <img src="8.PNG" alt="Reynaerde" width="800" style="margin-left:'auto' margin-right:'auto'/> </p> <div style="margin:auto; text-align:center"> <h1 style="margin-bottom: 0">Reynaerde 7B Chat</h1> <em>A conversational model for Dutch, based on Mistral v0.3 Instruct</em> </div> This model is a fine-tuned version of [ReBatch/Reynaerde-7B-Instruct](https://huggingface.co/ReBatch/Reynaerde-7B-Instruct) on [ReBatch/ultrafeedback_nl](https://huggingface.co/datasets/ReBatch/ultrafeedback_nl). This is a combination of a translation of the [HuggingFaceH4/ultrafeedback_binarized](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized) dataset and the HQ samples from [BramVanroy's translation](https://huggingface.co/datasets/BramVanroy/ultra_feedback_dutch_cleaned). ## Model description This model is a Dutch chat model, originally developed from Mistral 7B v0.3 Instruct and further fine-tuned with QLoRA. It was first fine-tuned with SFT on a chat dataset and then with DPO on a feedback chat dataset. ## Intended uses & limitations This model could still generate wrong, misleading, and potentially even offensive content. Use at your own risk. Use with Mistral's chat template (can be found in the tokenizer). ## Training procedure This model was trained with QLoRa in bfloat16 with Flash Attention 2 on one A100 PCIe, using the DPO script from the [alignment handbook](https://github.com/huggingface/alignment-handbook/) on [RunPod](https://www.runpod.io/). ## Evaluation results The model was evaluated using [scandeval](https://scandeval.com/dutch-nlg/). There are improvements in 4 out of 7 benchmarks compared to the Mistral-7B-v0.3-Instruct model on which it is based. \| Model\| conll_nl \| dutch_social \| scala_nl \| squad_nl \| wiki_lingua_nl \| mmlu_nl \| hellaswag_nl \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------: Reynaerde-7B-Chat \| 56.40 / 38.13 \| 10.83 / 27.67 \| 20.02 / 55.40 \| 53.56 / 65.29 \| 68.13 / 20.85 \| 32.50 / 49.10 \| 31.36 / 47.79 Mistral-7B-v0.3 \| 57.08 / 42.65 \| 14.05 / 39.13 \| 8.08 / 43.07 \| 45.57 / 55.20 \| 62.28 / 16.46 \| 20.39 / 40.03 \| 13.28 / 34.13 Mistral-7B-v0.3-Instruct \| 60.76 / 45.39 \| 13.20 / 34.26 \| 23.23 / 59.26 \| 48.94 / 60.13 \| 66.09 / 18.02 \| 24.95 / 43.67 \| 24.86 / 43.57 ## Naming This model is named after the Middle Dutch epic poem 'Van den vos Reynaerde'. Dating from around 1260, this epic by Flemish author Willem die Madocke maecte is often called 'the pinnacle of Gothic literature in the Netherlands'. The poem tells a version of the Reynard the Fox story, popular in Western Europe during the late Middle Ages ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 3 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 2 - total_train_batch_size: 6 - 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 ### Framework versions - PEFT 0.11.1 - Transformers 4.41.2 - Pytorch 2.2.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1 ### Model Developer The Mistral-7B-v0.3-Instruct model, on which this model is based, was created by [Mistral AI](https://huggingface.co/mistralai). The finetuning was done by [Julien Van den Avenne](https://huggingface.co/vandeju).
KLMFOREVER/microsoft_WizardLM-2-7B-exl2-5bpw	KLMFOREVER	2024-06-07T08:20:56Z	8	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:2304.12244", "arxiv:2306.08568", "arxiv:2308.09583", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-07T08:17:19Z	### exl2 quant (measurement.json included) --- ### original readme below --- --- license: apache-2.0 --- <p style="font-size:20px;" align="center"> 🏠 <a href="https://wizardlm.github.io/WizardLM2" target="_blank">WizardLM-2 Release Blog</a> </p> <p align="center"> 🤗 <a href="https://huggingface.co/collections/microsoft/wizardlm-2-661d403f71e6c8257dbd598a" target="_blank">HF Repo</a> •🐱 <a href="https://github.com/victorsungo/WizardLM/tree/main/WizardLM-2" target="_blank">Github Repo</a> • 🐦 <a href="https://twitter.com/WizardLM_AI" target="_blank">Twitter</a> • 📃 <a href="https://arxiv.org/abs/2304.12244" target="_blank">[WizardLM]</a> • 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> • 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a> <br> </p> <p align="center"> 👋 Join our <a href="https://discord.gg/VZjjHtWrKs" target="_blank">Discord</a> </p> ## News 🔥🔥🔥 [2024/04/15] We introduce and opensource WizardLM-2, our next generation state-of-the-art large language models, which have improved performance on complex chat, multilingual, reasoning and agent. New family includes three cutting-edge models: WizardLM-2 8x22B, WizardLM-2 70B, and WizardLM-2 7B. - WizardLM-2 8x22B is our most advanced model, demonstrates highly competitive performance compared to those leading proprietary works and consistently outperforms all the existing state-of-the-art opensource models. - WizardLM-2 70B reaches top-tier reasoning capabilities and is the first choice in the same size. - WizardLM-2 7B is the fastest and achieves comparable performance with existing 10x larger opensource leading models. For more details of WizardLM-2 please read our [release blog post](https://wizardlm.github.io/WizardLM2) and upcoming paper. ## Model Details * Model name: WizardLM-2 7B * Developed by: WizardLM@Microsoft AI * Base model: [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) * Parameters: 7B * Language(s): Multilingual * Blog: [Introducing WizardLM-2](https://wizardlm.github.io/WizardLM2) * Repository: [https://github.com/nlpxucan/WizardLM](https://github.com/nlpxucan/WizardLM) * Paper: WizardLM-2 (Upcoming) * License: Apache2.0 ## Model Capacities MT-Bench We also adopt the automatic MT-Bench evaluation framework based on GPT-4 proposed by lmsys to assess the performance of models. The WizardLM-2 8x22B even demonstrates highly competitive performance compared to the most advanced proprietary models. Meanwhile, WizardLM-2 7B and WizardLM-2 70B are all the top-performing models among the other leading baselines at 7B to 70B model scales. <p align="center" width="100%"> <a ><img src="https://raw.githubusercontent.com/WizardLM/WizardLM2/main/static/images/mtbench.png" alt="MTBench" style="width: 96%; min-width: 300px; display: block; margin: auto;"></a> </p> Human Preferences Evaluation We carefully collected a complex and challenging set consisting of real-world instructions, which includes main requirements of humanity, such as writing, coding, math, reasoning, agent, and multilingual. We report the win:loss rate without tie: - WizardLM-2 8x22B is just slightly falling behind GPT-4-1106-preview, and significantly stronger than Command R Plus and GPT4-0314. - WizardLM-2 70B is better than GPT4-0613, Mistral-Large, and Qwen1.5-72B-Chat. - WizardLM-2 7B is comparable with Qwen1.5-32B-Chat, and surpasses Qwen1.5-14B-Chat and Starling-LM-7B-beta. <p align="center" width="100%"> <a ><img src="https://raw.githubusercontent.com/WizardLM/WizardLM2/main/static/images/winall.png" alt="Win" style="width: 96%; min-width: 300px; display: block; margin: auto;"></a> </p> ## Method Overview We built a fully AI powered synthetic training system to train WizardLM-2 models, please refer to our [blog](https://wizardlm.github.io/WizardLM2) for more details of this system. <p align="center" width="100%"> <a ><img src="https://raw.githubusercontent.com/WizardLM/WizardLM2/main/static/images/exp_1.png" alt="Method" style="width: 96%; min-width: 300px; display: block; margin: auto;"></a> </p> ## Usage ❗<b>Note for model system prompts usage:</b> <b>WizardLM-2</b> adopts the prompt format from <b>Vicuna</b> and supports multi-turn conversation. The prompt should be as following: ``` A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Hi ASSISTANT: Hello.</s> USER: Who are you? ASSISTANT: I am WizardLM.</s>...... ``` <b> Inference WizardLM-2 Demo Script</b> We provide a WizardLM-2 inference demo [code](https://github.com/nlpxucan/WizardLM/tree/main/demo) on our github.
poori/speecht5_finetune_hw5	poori	2024-06-07T08:15:54Z	105	0	transformers	[ "transformers", "safetensors", "speecht5", "text-to-audio", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	text-to-audio	2024-06-07T08:15:32Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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abdulbanarcle/lora_tpf	abdulbanarcle	2024-06-07T08:10:16Z	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	2024-06-07T08:09:49Z	--- 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: abdulbanarcle - 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)
yodayo-ai/kivotos-xl-2.0	yodayo-ai	2024-06-07T08:06:06Z	12,959	104	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "en", "base_model:cagliostrolab/animagine-xl-3.1", "base_model:finetune:cagliostrolab/animagine-xl-3.1", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2024-06-01T23:41:35Z	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en tags: - text-to-image - stable-diffusion - safetensors - stable-diffusion-xl base_model: cagliostrolab/animagine-xl-3.1 widget: - text: 1girl, green hair, sweater, looking at viewer, upper body, beanie, outdoors, night, turtleneck, masterpiece, best quality, very aesthetic, absurdres parameter: negative_prompt: nsfw, low quality, worst quality, very displeasing, 3d, watermark, signature, ugly, poorly drawn example_title: 1girl - text: 1boy, male focus, green hair, sweater, looking at viewer, upper body, beanie, outdoors, night, turtleneck, masterpiece, best quality, very aesthetic, absurdres parameter: negative_prompt: nsfw, low quality, worst quality, very displeasing, 3d, watermark, signature, ugly, poorly drawn example_title: 1boy --- <style> body { display: flex; align-items: center; justify-content: center; height: 100vh; margin: 0; font-family: Arial, sans-serif; background-color: #f4f4f9; overflow: auto; } .container { display: flex; flex-direction: column; align-items: center; justify-content: center; width: 100%; padding: 20px; } .title-container { display: flex; flex-direction: column; justify-content: center; align-items: center; padding: 1em; border-radius: 10px; } .title { font-size: 3em; font-family: 'Montserrat', sans-serif; text-align: center; font-weight: bold; } .title span { background: -webkit-linear-gradient(45deg, #0077b6, #00b4d8, #90e0ef); -webkit-background-clip: text; -webkit-text-fill-color: transparent; } .gallery { display: grid; grid-template-columns: repeat(5, 1fr); gap: 10px; } .gallery img { width: 100%; height: auto; margin-top: 0px; margin-bottom: 0px; border-radius: 10px; box-shadow: 0 4px 8px rgba(0, 0, 0, 0.2); transition: transform 0.3s; } .gallery img:hover { transform: scale(1.05); } .note { font-size: 1em; opacity: 50%; text-align: center; margin-top: 20px; color: #555; } </style> <div class="container"> <div class="title-container"> <div class="title"><span>Kivotos XL 2.0</span></div> </div> <div class="gallery"> <img src="https://huggingface.co/yodayo-ai/kivotos-xl-2.0/resolve/main/samples/sample-001.png" alt="Image 1"> <img src="https://huggingface.co/yodayo-ai/kivotos-xl-2.0/resolve/main/samples/sample-002.png" alt="Image 2"> <img src="https://huggingface.co/yodayo-ai/kivotos-xl-2.0/resolve/main/samples/sample-003.png" alt="Image 3"> <img src="https://huggingface.co/yodayo-ai/kivotos-xl-2.0/resolve/main/samples/sample-004.png" alt="Image 4"> <img src="https://huggingface.co/yodayo-ai/kivotos-xl-2.0/resolve/main/samples/sample-005.png" alt="Image 5"> <img src="https://huggingface.co/yodayo-ai/kivotos-xl-2.0/resolve/main/samples/sample-006.png" alt="Image 6"> <img src="https://huggingface.co/yodayo-ai/kivotos-xl-2.0/resolve/main/samples/sample-007.png" alt="Image 7"> <img src="https://huggingface.co/yodayo-ai/kivotos-xl-2.0/resolve/main/samples/sample-008.png" alt="Image 8"> <img src="https://huggingface.co/yodayo-ai/kivotos-xl-2.0/resolve/main/samples/sample-009.png" alt="Image 9"> <img src="https://huggingface.co/yodayo-ai/kivotos-xl-2.0/resolve/main/samples/sample-010.png" alt="Image 10"> </div> <div class="note"> Drag and drop each image to <a href="https://huggingface.co/spaces/Linaqruf/pnginfo" target="_blank">this link</a> or use ComfyUI to get the metadata. </div> </div> ## Overview Kivotos XL 2.0 is the latest version of the [Yodayo Kivotos XL](https://yodayo.com/models/ee3c3839-e723-45f5-9151-18b592bc93b9) series, following the previous iteration, [Kivotos XL 1.0](https://yodayo.com/models/ee3c3839-e723-45f5-9151-18b592bc93b9/?modelversion=bf0091c7-4337-4edb-8c34-160d647d249a). This open-source model is built upon Animagine XL V3, a specialized SDXL model designed for generating high-quality anime-style artwork. Kivotos XL V2.0 has undergone additional fine-tuning and optimization to focus specifically on generating images that accurately represent the visual style and aesthetics of the Blue Archive franchise. ## Model Details - Developed by: [Linaqruf](https://github.com/Linaqruf) - Model type: Diffusion-based text-to-image generative model - Model Description: Kivotos XL V2.0, the latest in the Yodayo Kivotos XL series, is an open-source model built on Animagine XL V3. Fine-tuned for high-quality Blue Archive anime-style art generation. - License: [Fair AI Public License 1.0-SD](https://freedevproject.org/faipl-1.0-sd/) - Finetuned from model: [Animagine XL 3.1](https://huggingface.co/cagliostrolab/animagine-xl-3.1) ## Supported Platform 1. Use this model in our platform: [![Open In Spaces](https://img.shields.io/badge/Generate%20in%20Yodayo-141414?style=for-the-badge&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAACAAAAAgCAMAAABEpIrGAAAAIGNIUk0AAHomAACAhAAA+gAAAIDoAAB1MAAA6mAAADqYAAAXcJy6UTwAAAGtUExURf/JZf/KZf/LZf64aftuevx+dv7DZv/HZvyKc/toe/2wa//KZP/MZPt4d/oIjvQUj7uVmPXKa/6va/ohifsFjcpfmtvGe//JZPtme/QOkGOEz87Hg//JY/2mbfoYi/4Hi5lNuoq/rfUOkF2E08THifoZiplOun6/tF6E0sXHiPUOj16F0sXHif6mbfoYivoIjVyG08TJiP/MYv/NZPYNj4Bpw9Cdiv+fbf2eb/2fb/60av2mbPoLjfIRkfcUjfoUi/oUjPkuh+mBgfgai/sJjf4Ii/8Ii/8Hi+8RkoJpw+galf+5aN5pjJ9Ot5lPuplRupxQuYtawIddwvERke/Ib6XAnY+/qpDAqpDCqo+8q42Zs5lcuNInoPcNjvsKjP8GioxXwHzAtf/KY/++Zv+OcP5Lfv4aiP4Ji+4TkrA+rzKZ6JPBp/61avpEgvoQjP0IjN8empdQu0iL3jaz4X2/tevHcvyYcPoOjP4HjPYOj8kto3hmyTid5EW615TCpt/Gef3JZf+8aO5fhKlGslt71jOq5V2+yLPElPDHb/PHbZW9p4TBsM7FhPrIaP///xdsY3gAAAABYktHRI6CBbNvAAAAB3RJTUUH6AIMCis5IjcvIAAAAE96VFh0UmF3IHByb2ZpbGUgdHlwZSBpcHRjAAB4nOPKLChJ5lIAAyMLLmMLEyMTS5MUAxMgRIA0w2QDI7NUIMvY1MjEzMQcxAfLgEigSi4AKJUO4yoibR8AAAEJSURBVDjLY2AYSoCRiQnOZmJixJRnZmFlg7LZOTi5uNEV8PDy8QsIQvQLCYuIiomjKWCS4JOUkpYBM2Xl5BUUZTAVKCmrQBWoyqupY1EgqaGJX4GWtg5EgS5OE3Twm6BESAHCCj2sCvQlDQyNeIDAGJcJJqZm5hYWFpZW1jgU2Nja2QOBg6OTMxYFPLwurm7yIODu4enljqmA0dvH1w8E/AMCg4LdMBUwcIeEhoWFR0RGRcfExsUnJGIoYBCXkUlKTklNS3d1zcjMysZUALQmJzdPPz+uoLCouKRUHIsCnrLyisqq6prauvoGbPIMjI1NzS2tbe0dMlilQQ7t7Oru6cUpDXUpwxAEACsWOLO6J6SrAAAAJXRFWHRkYXRlOmNyZWF0ZQAyMDI0LTAyLTEyVDEwOjQzOjU3KzAwOjAwbykEPgAAACV0RVh0ZGF0ZTptb2RpZnkAMjAyNC0wMi0xMlQxMDo0Mzo1NyswMDowMB50vIIAAAAASUVORK5CYII=)](https://yodayo.com/models/ee3c3839-e723-45f5-9151-18b592bc93b9/?modelversion=f3989e22-5afc-40a1-b435-38eae7760f37) 2. Use it in [`ComfyUI`](https://github.com/comfyanonymous/ComfyUI) or [`Stable Diffusion Webui`](https://github.com/AUTOMATIC1111/stable-diffusion-webui) 3. Use it with 🧨 `diffusers` ## 🧨 Diffusers Installation First install the required libraries: ```bash pip install diffusers transformers accelerate safetensors --upgrade ``` Then run image generation with the following example code: ```python import torch from diffusers import StableDiffusionXLPipeline pipe = StableDiffusionXLPipeline.from_pretrained( "yodayo-ai/kivotos-xl-2.0", torch_dtype=torch.float16, use_safetensors=True, custom_pipeline="lpw_stable_diffusion_xl", add_watermarker=False, variant="fp16" ) pipe.to('cuda') prompt = "1girl, kazusa $blue archive$, blue archive, solo, upper body, v, smile, looking at viewer, outdoors, night, masterpiece, best quality, very aesthetic, absurdres" negative_prompt = "nsfw, (low quality, worst quality:1.2), very displeasing, 3d, watermark, signature, ugly, poorly drawn" image = pipe( prompt, negative_prompt=negative_prompt, width=832, height=1216, guidance_scale=7, num_inference_steps=28 ).images[0] image.save("./cat.png") ``` ## Usage Guidelines ### Tag Ordering For optimal results, it's recommended to follow the structured prompt template because we train the model like this: ``` 1girl/1boy, character name, from which series, by which artists, everything else in any order. ``` ### Special Tags Kivotos XL 2.0 inherits special tags from Animagine XL 3.1 to enhance image generation by steering results toward quality, rating, creation date, and aesthetic. This inheritance ensures that Kivotos XL 2.0 can produce high-quality, relevant, and aesthetically pleasing images. While the model can generate images without these tags, using them helps achieve better results. - Quality tags: masterpiece, best quality, great quality, good quality, normal quality, low quality, worst quality - Rating tags: safe, sensitive, nsfw, explicit - Year tags: newest, recent, mid, early, oldest - Aesthetic tags: very aesthetic, aesthetic, displeasing, very displeasing ### Recommended Settings To guide the model towards generating high-aesthetic images, use the following recommended settings: - Negative prompts: ``` nsfw, (low quality, worst quality:1.2), very displeasing, 3d, watermark, signature, ugly, poorly drawn ``` - Positive prompts: ``` masterpiece, best quality, very aesthetic, absurdres ``` - Classifier-Free Guidance (CFG) Scale: should be around 5 to 7; 10 is fried, >12 is deep-fried. - Sampling steps: should be around 25 to 30; 28 is the sweet spot. - Sampler: Euler Ancestral (Euler a) is highly recommended. - Supported resolutions: ``` 1024 x 1024, 1152 x 896, 896 x 1152, 1216 x 832, 832 x 1216, 1344 x 768, 768 x 1344, 1536 x 640, 640 x 1536 ``` ## Training These are the key hyperparameters used during training: \| Feature \| Pretraining \| Finetuning \| \|-------------------------------\|----------------------------\|---------------------------------\| \| Hardware \| 2x H100 80GB PCIe \| 1x A100 80GB PCIe \| \| Batch Size \| 32 \| 48 \| \| Gradient Accumulation Steps \| 2 \| 1 \| \| Noise Offset \| None \| 0.0357 \| \| Epochs \| 10 \| 10 \| \| UNet Learning Rate \| 5e-6 \| 3.75e-6 \| \| Text Encoder Learning Rate \| 2.5e-6 \| None \| \| Optimizer \| Adafactor \| Adafactor \| \| Optimizer Args \| Scale Parameter: False, Relative Step: False, Warmup Init: False (0.9, 0.99) \| Scale Parameter: False, Relative Step: False, Warmup Init: False \| \| Scheduler \| Constant with Warmups \| Constant with Warmups \| \| Warmup Steps \| 0.05% \| 0.05% \| ## License Kivotos XL 2.0 falls under [Fair AI Public License 1.0-SD](https://freedevproject.org/faipl-1.0-sd/) license, which is compatible with Stable Diffusion models’ license. Key points: 1. Modification Sharing: If you modify Kivotos XL 2.0, you must share both your changes and the original license. 2. Source Code Accessibility: If your modified version is network-accessible, provide a way (like a download link) for others to get the source code. This applies to derived models too. 3. Distribution Terms: Any distribution must be under this license or another with similar rules.
Noveled/xlm-roberta-base-finetuned-panx-all	Noveled	2024-06-07T08:05:34Z	104	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-06-07T07:59:40Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-all results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-all This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1742 - F1: 0.8541 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.3026 \| 1.0 \| 835 \| 0.1851 \| 0.8182 \| \| 0.1575 \| 2.0 \| 1670 \| 0.1712 \| 0.8413 \| \| 0.1031 \| 3.0 \| 2505 \| 0.1742 \| 0.8541 \| ### Framework versions - Transformers 4.33.1 - Pytorch 2.2.0a0+81ea7a4 - Datasets 2.17.1 - Tokenizers 0.13.3
Felladrin/gguf-sharded-Qwen2-0.5B-Instruct	Felladrin	2024-06-07T08:02:21Z	9	0	null	[ "gguf", "base_model:Qwen/Qwen2-0.5B-Instruct", "base_model:quantized:Qwen/Qwen2-0.5B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-06-07T07:58:41Z	--- license: apache-2.0 base_model: Qwen/Qwen2-0.5B-Instruct --- Sharded GGUF version of [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct).
Felladrin/gguf-Qwen2-0.5B-Instruct	Felladrin	2024-06-07T08:01:03Z	21	0	null	[ "gguf", "base_model:Qwen/Qwen2-0.5B-Instruct", "base_model:quantized:Qwen/Qwen2-0.5B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-06-07T07:54:27Z	--- license: apache-2.0 base_model: Qwen/Qwen2-0.5B-Instruct --- GGUF version of [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct).
BAAI/Aquila2-7B	BAAI	2024-06-07T07:59:28Z	571	6	transformers	[ "transformers", "safetensors", "aquila", "text-generation", "conversational", "custom_code", "license:other", "autotrain_compatible", "region:us" ]	text-generation	2023-10-10T01:55:57Z	--- license: other --- ![Aquila_logo](./log.jpeg) <h4 align="center"> <p> <b>English</b> \| <a href="https://huggingface.co/BAAI/Aquila2-7B/blob/main/README_zh.md">简体中文</a> \| <p> </h4> We opensource our Aquila2 series, now including Aquila2, the base language models, namely Aquila2-7B and Aquila2-34B, as well as AquilaChat2, the chat models, namely AquilaChat2-7B and AquilaChat2-34B, as well as the long-text chat models, namely AquilaChat2-7B-16k and AquilaChat2-34B-16k The additional details of the Aquila model will be presented in the official technical report. Please stay tuned for updates on official channels. ## Updates 2024.6.6 We have updated the basic language model Aquila2-7B, which has the following advantages compared to the previous model: * Replaced tokenizer with higher compression ratio: \| Tokenizer \| Size \| Zh \| En \| Code \| Math \| Average \| \|-----------\|-------\|--------------------------\|--------\|-------\|-------\|---------\| \| Aquila2-original \| 100k \| 4.70 \| 4.42 \| 3.20 \| 3.77 \| 4.02 \| \| Qwen1.5 \| 151k \| 4.27 \| 4.51 \| 3.62 \| 3.35 \| 3.94 \| \| Llama3 \| 128k \| 3.45 \| 4.61 \| 3.77 \| 3.88 \| 3.93 \| \| Aquila2-new \| 143k \| 4.60 \| 4.61 \| 3.78 \| 3.88 \| 4.22 \| * The maximum processing length supported by the model has increased from 2048 to 8192 ## Quick Start Aquila2-7B ### 1. Inference Aquila2-7B is a base model that can be used for continuation. ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer from transformers import BitsAndBytesConfig device= "cuda:0" # Model Name model_name = 'BAAI/Aquila2-7B' # load model and tokenizer quantization_config=BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16, ) model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, trust_remote_code=True, # quantization_config=quantization_config # Uncomment this one for 4-bit quantization ) tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True) model.eval() model.to(device) # Example text = "The meaning of life is" tokens = tokenizer.encode_plus(text)['input_ids'] tokens = torch.tensor(tokens)[None,].to(device) with torch.no_grad(): out = model.generate(tokens, do_sample=False, max_length=128, eos_token_id=tokenizer.eos_token_id)[0] out = tokenizer.decode(out.cpu().numpy().tolist()) print(out) ``` ## License Aquila2 series open-source model is licensed under [ BAAI Aquila Model Licence Agreement](https://huggingface.co/BAAI/Aquila2-7B/blob/main/BAAI-Aquila-Model-License%20-Agreement.pdf)
amc5/Reinforce-CartPole-v1	amc5	2024-06-07T07:59:15Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2024-06-07T07:59:05Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
M2LabOrg/ppo-LunarLander-v2	M2LabOrg	2024-06-07T07:58:31Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-06-07T07:58:10Z	--- 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: 264.80 +/- 19.92 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 ... ```
fhamborg/newsframes-econ	fhamborg	2024-06-07T07:56:14Z	7	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "setfit", "text-classification", "license:apache-2.0", "region:us" ]	text-classification	2023-10-20T09:30:33Z	--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # NewsFrames classifier This is one of a series of classifiers devised for automatically identifying universal framing dimensions. A paper on the underlying training dataset and the framing dimensions in particular is currently being written. This page will be updated once the paper is finished. ## Acknowledgements This work would not have been possible without the contributions by [Tilman Hornung](t1h0), Kim Heinser, and our team of student research assistants.
fhamborg/newsframes-gov	fhamborg	2024-06-07T07:56:06Z	5	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "setfit", "text-classification", "license:apache-2.0", "region:us" ]	text-classification	2023-10-20T09:30:50Z	--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # NewsFrames classifier This is one of a series of classifiers devised for automatically identifying universal framing dimensions. A paper on the underlying training dataset and the framing dimensions in particular is currently being written. This page will be updated once the paper is finished. ## Acknowledgements This work would not have been possible without the contributions by [Tilman Hornung](t1h0), Kim Heinser, and our team of student research assistants.
fhamborg/newsframes-aff-bin	fhamborg	2024-06-07T07:55:56Z	4	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "setfit", "text-classification", "license:apache-2.0", "region:us" ]	text-classification	2023-12-04T15:38:16Z	--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # NewsFrames classifier This is one of a series of classifiers devised for automatically identifying universal framing dimensions. A paper on the underlying training dataset and the framing dimensions in particular is currently being written. This page will be updated once the paper is finished. ## Acknowledgements This work would not have been possible without the contributions by [Tilman Hornung](t1h0), Kim Heinser, and our team of student research assistants.
fhamborg/newsframes-econ-bin	fhamborg	2024-06-07T07:55:34Z	4	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "setfit", "text-classification", "license:apache-2.0", "region:us" ]	text-classification	2023-12-04T15:40:46Z	--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # NewsFrames classifier This is one of a series of classifiers devised for automatically identifying universal framing dimensions. A paper on the underlying training dataset and the framing dimensions in particular is currently being written. This page will be updated once the paper is finished. ## Acknowledgements This work would not have been possible without the contributions by [Tilman Hornung](t1h0), Kim Heinser, and our team of student research assistants.
ikmalalfaozi/donut_cord	ikmalalfaozi	2024-06-07T07:55:30Z	29	0	transformers	[ "transformers", "safetensors", "vision-encoder-decoder", "image-text-to-text", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-06-06T21:23:24Z	--- 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. 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fhamborg/newsframes-gov-bin	fhamborg	2024-06-07T07:55:24Z	5	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "setfit", "text-classification", "license:apache-2.0", "region:us" ]	text-classification	2023-12-04T15:41:05Z	--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # NewsFrames classifier This is one of a series of classifiers devised for automatically identifying universal framing dimensions. A paper on the underlying training dataset and the framing dimensions in particular is currently being written. This page will be updated once the paper is finished. ## Acknowledgements This work would not have been possible without the contributions by [Tilman Hornung](t1h0), Kim Heinser, and our team of student research assistants.
fhamborg/newsframes-aff3	fhamborg	2024-06-07T07:55:13Z	3	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "setfit", "text-classification", "license:apache-2.0", "region:us" ]	text-classification	2023-12-07T12:16:35Z	--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # NewsFrames classifier This is one of a series of classifiers devised for automatically identifying universal framing dimensions. A paper on the underlying training dataset and the framing dimensions in particular is currently being written. This page will be updated once the paper is finished. ## Acknowledgements This work would not have been possible without the contributions by [Tilman Hornung](t1h0), Kim Heinser, and our team of student research assistants.
DownwardSpiral33/gpt2-imdb-pos-roberta16-256_0_07-gamma-2024.06.07.07.04	DownwardSpiral33	2024-06-07T07:55:06Z	148	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-07T07:54:31Z	--- 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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rinki24/distilbert-base-uncased-lora-text-classification_try1	rinki24	2024-06-07T07:54:31Z	0	0	peft	[ "peft", "arxiv:1910.09700", "base_model:distilbert/distilbert-base-uncased", "base_model:adapter:distilbert/distilbert-base-uncased", "region:us" ]	null	2024-06-07T07:54:27Z	--- library_name: peft base_model: distilbert-base-uncased --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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jpgacrama/xlm-roberta-base-finetuned-panx-de	jpgacrama	2024-06-07T07:52:34Z	106	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-06-01T10:15:24Z	--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8615279672578444 --- <!-- 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. --> # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1367 - F1: 0.8615 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2571 \| 1.0 \| 525 \| 0.1688 \| 0.8107 \| \| 0.1305 \| 2.0 \| 1050 \| 0.1406 \| 0.8526 \| \| 0.0812 \| 3.0 \| 1575 \| 0.1367 \| 0.8615 \| ### Framework versions - Transformers 4.16.2 - Pytorch 2.1.2 - Datasets 1.16.1 - Tokenizers 0.19.1
NewsLLM/llama-3-8b-NewsLLM-phase2final-clean	NewsLLM	2024-06-07T07:42:25Z	10	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-07T06:59:15Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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]
YongjieNiu/prior_dora-xl-cup	YongjieNiu	2024-06-07T07:39:27Z	3	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "license:openrail++", "region:us" ]	text-to-image	2024-06-07T07:01:45Z	--- 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: SDXL_model instance_prompt: a photo of sks cup widget: - text: a photo of sks cup by the sea output: url: image_0.png - text: a photo of sks cup by the sea output: url: image_1.png - text: a photo of sks cup by the sea output: url: image_2.png - text: a photo of sks cup by the sea output: url: image_3.png --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - YongjieNiu/prior_dora-xl-cup <Gallery /> ## Model description These are YongjieNiu/prior_dora-xl-cup LoRA adaption weights for SDXL_model. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: VAE. ## Trigger words You should use a photo of sks cup to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](YongjieNiu/prior_dora-xl-cup/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]
dyliu/vist_ft	dyliu	2024-06-07T07:36:07Z	3	0	peft	[ "peft", "generated_from_trainer", "base_model:HuggingFaceM4/idefics2-8b", "base_model:adapter:HuggingFaceM4/idefics2-8b", "license:apache-2.0", "region:us" ]	null	2024-06-07T07:33:43Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: HuggingFaceM4/idefics2-8b model-index: - name: vist_ft 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. --> # vist_ft This model is a fine-tuned version of [HuggingFaceM4/idefics2-8b](https://huggingface.co/HuggingFaceM4/idefics2-8b) 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: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 16 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.11.1 - Transformers 4.41.2 - Pytorch 2.3.1 - Datasets 2.19.2 - Tokenizers 0.19.1
vilm/vinallama-7b-chat	vilm	2024-06-07T07:34:52Z	684	22	transformers	[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "conversational", "vi", "arxiv:2312.11011", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-12-12T16:58:23Z	--- language: - vi license: llama2 --- # VinaLLaMA - State-of-the-art Vietnamese LLMs ![image](https://i.ibb.co/W0dq12n/vinallama.png) Read our [Paper](https://huggingface.co/papers/2312.11011) ### Prompt Format (ChatML): ``` <\|im_start\|>system Bạn là một trợ lí AI hữu ích. Hãy trả lời người dùng một cách chính xác. <\|im_end\|> <\|im_start\|>user Hello world!<\|im_end\|> <\|im_start\|>assistant ```
chainup244/Qwen-Qwen1.5-1.8B-1717745215	chainup244	2024-06-07T07:28:48Z	148	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-07T07:26:58Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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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langwnwk/topic_classification	langwnwk	2024-06-07T07:24:03Z	107	1	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "dataset:yahoo_answers_topics", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-06-07T07:23:40Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - yahoo_answers_topics metrics: - accuracy model-index: - name: topic_classification results: - task: name: Text Classification type: text-classification dataset: name: yahoo_answers_topics type: yahoo_answers_topics config: yahoo_answers_topics split: test args: yahoo_answers_topics metrics: - name: Accuracy type: accuracy value: 0.7125166666666667 --- <!-- 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. --> # topic_classification This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the yahoo_answers_topics dataset. It achieves the following results on the evaluation set: - Loss: 0.9119 - Accuracy: 0.7125 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 30000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:--------:\| \| 1.0187 \| 0.0286 \| 5000 \| 1.0647 \| 0.6695 \| \| 0.9944 \| 0.0571 \| 10000 \| 1.0281 \| 0.6782 \| \| 0.9641 \| 0.0857 \| 15000 \| 0.9694 \| 0.6969 \| \| 0.8833 \| 0.1143 \| 20000 \| 0.9426 \| 0.7045 \| \| 0.9416 \| 0.1429 \| 25000 \| 0.9239 \| 0.7093 \| \| 0.932 \| 0.1714 \| 30000 \| 0.9119 \| 0.7125 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.1.2 - Datasets 2.19.2 - Tokenizers 0.19.1
bromhir/whisper-small-ft-nl	bromhir	2024-06-07T07:19:13Z	93	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "nl", "dataset:mozilla-foundation/common_voice_11_0", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-06-06T08:38:11Z	--- language: - nl license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 model-index: - name: Whisper Small nl 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. --> # Whisper Small nl This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 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: 1e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 1000 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1
sebdg/scm_phi3_q5_k_m_v3	sebdg	2024-06-07T07:18:37Z	6	0	transformers	[ "transformers", "gguf", "mistral", "text-generation-inference", "unsloth", "en", "base_model:unsloth/Phi-3-mini-4k-instruct", "base_model:quantized:unsloth/Phi-3-mini-4k-instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-06-07T07:16:22Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - gguf base_model: unsloth/Phi-3-mini-4k-instruct --- # Uploaded model - Developed by: sebdg - License: apache-2.0 - Finetuned from model : unsloth/Phi-3-mini-4k-instruct This mistral 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)
titantomorrow/q-Taxi-v3	titantomorrow	2024-06-07T07:17:56Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-06-07T07:05:32Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="titantomorrow/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
percymamedy/bart-cnn-samsum-finetuned	percymamedy	2024-06-07T07:15:32Z	112	0	transformers	[ "transformers", "tensorboard", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "dataset:samsum", "base_model:facebook/bart-large-cnn", "base_model:finetune:facebook/bart-large-cnn", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-06-07T07:14:33Z	--- license: mit base_model: facebook/bart-large-cnn tags: - generated_from_trainer datasets: - samsum model-index: - name: bart-cnn-samsum-finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-cnn-samsum-finetuned This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 0.1344 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.0887 \| 1.0 \| 37 \| 0.1344 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1
yjkim104906/Meta-Llama-3-8B-ft-Instruct	yjkim104906	2024-06-07T07:14:46Z	19	0	transformers	[ "transformers", "safetensors", "gguf", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-05T04:45:13Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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]
LarryAIDraw/penance	LarryAIDraw	2024-06-07T07:12:34Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-06-07T07:07:58Z	--- license: creativeml-openrail-m --- https://civitai.com/models/464515/penance-skin-arknights-lora
LarryAIDraw/arkBlemishine_XL-Pony_LoRA-C3Lier_16-16-8-8_AdamW_Un3e-4_Te1_5e-4_10batch	LarryAIDraw	2024-06-07T07:12:25Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-06-07T07:07:27Z	--- license: creativeml-openrail-m --- https://civitai.com/models/481123/request-blemishine-arknights-sdxl-pony-diffusion
chohtet/llama3_8b_instruct_lora_ft2	chohtet	2024-06-07T07:11:13Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-06-07T07:11:05Z	--- 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: chohtet - 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)
kuanhoong/gemma-2b-mt-German-to-English-1	kuanhoong	2024-06-07T06:58:28Z	149	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-07T06:53:58Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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stablediffusionapi/sd-xl-v10-vae-fix	stablediffusionapi	2024-06-07T06:54:05Z	34	1	diffusers	[ "diffusers", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2024-06-07T06:51:37Z	--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # SD XL v1.0 VAE fix API Inference ![generated from modelslab.com](https://pub-3626123a908346a7a8be8d9295f44e26.r2.dev/generations/2129442971717742925.png) ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change model_id to "sd-xl-v10-vae-fix" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://docs.modelslab.com) Try model for free: [Generate Images](https://modelslab.com/models/sd-xl-v10-vae-fix) Model link: [View model](https://modelslab.com/models/sd-xl-v10-vae-fix) View all models: [View Models](https://modelslab.com/models) import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "sd-xl-v10-vae-fix", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off DMGG0RBN
LoneStriker/Qwen2-72B-Instruct-2.25bpw-h6-exl2	LoneStriker	2024-06-07T06:53:10Z	5	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "chat", "conversational", "en", "arxiv:2309.00071", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-06-07T06:43:45Z	--- license: other license_name: tongyi-qianwen license_link: https://huggingface.co/Qwen/Qwen2-72B-Instruct/blob/main/LICENSE language: - en pipeline_tag: text-generation tags: - chat --- # Qwen2-72B-Instruct ## Introduction Qwen2 is the new series of Qwen large language models. For Qwen2, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters, including a Mixture-of-Experts model. This repo contains the instruction-tuned 72B Qwen2 model. Compared with the state-of-the-art opensource language models, including the previous released Qwen1.5, Qwen2 has generally surpassed most opensource models and demonstrated competitiveness against proprietary models across a series of benchmarks targeting for language understanding, language generation, multilingual capability, coding, mathematics, reasoning, etc. Qwen2-72B-Instruct supports a context length of up to 131,072 tokens, enabling the processing of extensive inputs. Please refer to [this section](#processing-long-texts) for detailed instructions on how to deploy Qwen2 for handling long texts. For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2/), [GitHub](https://github.com/QwenLM/Qwen2), and [Documentation](https://qwen.readthedocs.io/en/latest/). <br> ## Model Details Qwen2 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes. ## Training details We pretrained the models with a large amount of data, and we post-trained the models with both supervised finetuning and direct preference optimization. ## Requirements The code of Qwen2 has been in the latest Hugging face transformers and we advise you to install `transformers>=4.37.0`, or you might encounter the following error: ``` KeyError: 'qwen2' ``` ## Quickstart Here provides a code snippet with `apply_chat_template` 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 onto model = AutoModelForCausalLM.from_pretrained( "Qwen/Qwen2-72B-Instruct", torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-72B-Instruct") prompt = "Give me a short introduction to large language model." messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(device) generated_ids = model.generate( model_inputs.input_ids, max_new_tokens=512 ) 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] ``` ### Processing Long Texts To handle extensive inputs exceeding 32,768 tokens, we utilize [YARN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts. For deployment, we recommend using vLLM. You can enable the long-context capabilities by following these steps: 1. Install vLLM: You can install vLLM by running the following command. ```bash pip install "vllm>=0.4.3" ``` Or you can install vLLM from [source](https://github.com/vllm-project/vllm/). 2. Configure Model Settings: After downloading the model weights, modify the `config.json` file by including the below snippet: ```json { "architectures": [ "Qwen2ForCausalLM" ], // ... "vocab_size": 152064, // adding the following snippets "rope_scaling": { "factor": 4.0, "original_max_position_embeddings": 32768, "type": "yarn" } } ``` This snippet enable YARN to support longer contexts. 3. Model Deployment: Utilize vLLM to deploy your model. For instance, you can set up an openAI-like server using the command: ```bash python -m vllm.entrypoints.openai.api_server --served-model-name Qwen2-72B-Instruct --model path/to/weights ``` Then you can access the Chat API by: ```bash curl http://localhost:8000/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "Qwen2-72B-Instruct", "messages": [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Your Long Input Here."} ] }' ``` For further usage instructions of vLLM, please refer to our [Github](https://github.com/QwenLM/Qwen2). Note: Presently, vLLM only supports static YARN, which means the scaling factor remains constant regardless of input length, potentially impacting performance on shorter texts. We advise adding the `rope_scaling` configuration only when processing long contexts is required. ## Evaluation We briefly compare Qwen2-72B-Instruct with similar-sized instruction-tuned LLMs, including our previous Qwen1.5-72B-Chat. The results are shown as follows: \| Datasets \| Llama-3-70B-Instruct \| Qwen1.5-72B-Chat \| Qwen2-72B-Instruct \| \| :--- \| :---: \| :---: \| :---: \| \| _English_ \| \| \| \| \| MMLU \| 82.0 \| 75.6 \| 82.3 \| \| MMLU-Pro \| 56.2 \| 51.7 \| 64.4 \| \| GPQA \| 41.9 \| 39.4 \| 42.4 \| \| TheroemQA \| 42.5 \| 28.8 \| 44.4 \| \| MT-Bench \| 8.95 \| 8.61 \| 9.12 \| \| Arena-Hard \| 41.1 \| 36.1 \| 48.1 \| \| IFEval (Prompt Strict-Acc.) \| 77.3 \| 55.8 \| 77.6 \| \| _Coding_ \| \| \| \| \| HumanEval \| 81.7 \| 71.3 \| 86.0 \| \| MBPP \| 82.3 \| 71.9 \| 80.2 \| \| MultiPL-E \| 63.4 \| 48.1 \| 69.2 \| \| EvalPlus \| 75.2 \| 66.9 \| 79.0 \| \| LiveCodeBench \| 29.3 \| 17.9 \| 35.7 \| \| _Mathematics_ \| \| \| \| \| GSM8K \| 93.0 \| 82.7 \| 91.1 \| \| MATH \| 50.4 \| 42.5 \| 59.7 \| \| _Chinese_ \| \| \| \| \| C-Eval \| 61.6 \| 76.1 \| 83.8 \| \| AlignBench \| 7.42 \| 7.28 \| 8.27 \| ## Citation If you find our work helpful, feel free to give us a cite. ``` @article{qwen2, title={Qwen2 Technical Report}, year={2024} } ```
Gkumi/naya-model	Gkumi	2024-06-07T06:53:07Z	63	0	transformers	[ "transformers", "tf", "distilbert", "token-classification", "de", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-06-07T06:34:03Z	--- language: - de license: apache-2.0 base_model: distilbert-base-uncased metrics: - precision - recall - f1 - accuracy model-index: - name: Gkumi/naya-model 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. --> # Gkumi/naya-model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - precision: 0.9260 - recall: 0.9306 - f1: 0.9283 - accuracy: 0.9657 ## 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: - num_train_epochs: 5 - train_batch_size: 16 - eval_batch_size: 32 - learning_rate: 2e-05 - weight_decay_rate: 0.01 - num_warmup_steps: 0 - fp16: True ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.19.1
sebdg/scm_llama3_7b_q5_k_m	sebdg	2024-06-07T06:52:41Z	8	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:unsloth/llama-3-8b-Instruct", "base_model:quantized:unsloth/llama-3-8b-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-06-07T06:48:16Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf base_model: unsloth/llama-3-8b-Instruct --- # Uploaded model - Developed by: sebdg - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct 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)
mfarrington/DeviceBERT-tokenizer	mfarrington	2024-06-07T06:49:25Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-05T04:55:39Z	--- 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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denru/L3-MS-Astoria-70b-4_0bpw-h6-exl2-pippa	denru	2024-06-07T06:49:24Z	7	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "merge", "conversational", "base_model:NeverSleep/Llama-3-Lumimaid-70B-v0.1-alt", "base_model:merge:NeverSleep/Llama-3-Lumimaid-70B-v0.1-alt", "base_model:abacusai/Llama-3-Giraffe-70B", "base_model:merge:abacusai/Llama-3-Giraffe-70B", "base_model:failspy/llama-3-70B-Instruct-abliterated", "base_model:merge:failspy/llama-3-70B-Instruct-abliterated", "base_model:migtissera/Tess-2.0-Llama-3-70B-v0.2", "base_model:merge:migtissera/Tess-2.0-Llama-3-70B-v0.2", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "exl2", "region:us" ]	text-generation	2024-06-07T06:44:28Z	--- base_model: - failspy/llama-3-70B-Instruct-abliterated - migtissera/Tess-2.0-Llama-3-70B-v0.2 - NeverSleep/Llama-3-Lumimaid-70B-v0.1-alt - abacusai/Llama-3-Giraffe-70B library_name: transformers tags: - merge license: llama3 --- <!DOCTYPE html> <style> body { font-family: 'Quicksand', sans-serif; background: linear-gradient(135deg, #2E3440 0%, #1A202C 100%); color: #D8DEE9; margin: 0; padding: 0; font-size: 16px; } .container { width: 80% auto; max-width: 1080px auto; margin: 20px auto; background-color: rgba(255, 255, 255, 0.02); padding: 20px; border-radius: 12px; box-shadow: 0 4px 10px rgba(0, 0, 0, 0.2); backdrop-filter: blur(10px); border: 1px solid rgba(255, 255, 255, 0.1); } .header h1 { font-size: 28px; color: #ECEFF4; margin: 0 0 20px 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); } .update-section { margin-top: 30px; } .update-section h2 { font-size: 24px; color: #88C0D0; } .update-section p { font-size: 16px; line-height: 1.6; color: #ECEFF4; } .info img { width: 100%; border-radius: 10px; margin-bottom: 15px; } a { color: #88C0D0; text-decoration: none; } a:hover { color: #A3BE8C; } .button { display: inline-block; background-color: #5E81AC; color: #E5E9F0; padding: 10px 20px; border-radius: 5px; cursor: pointer; text-decoration: none; } .button:hover { background-color: #81A1C1; } pre { background-color: #2E3440; padding: 10px; border-radius: 5px; overflow-x: auto; } code { font-family: 'Courier New', monospace; color: #D8DEE9; } </style> <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>L3-MS-Astoria-70b Data Card</title> <link href="https://fonts.googleapis.com/css2?family=Quicksand:wght@400;500;600&display=swap" rel="stylesheet"> </head> <body> <div class="container"> <div class="header"> <h1>L3-MS-Astoria-70b</h1> </div> <div class="info"> <img src="https://cdn-uploads.huggingface.co/production/uploads/64545af5ec40bbbd01242ca6/HU5Zz7mb4X0wK3cZM2M9E.png"> <p>Now that the cute anime girl has your attention.</p> <p><strong>Creator:</strong> <a href="https://huggingface.co/Steelskull" target="_blank">SteelSkull</a></p> <h1>About L3-MS-Astoria-70b:</h1> <p>L3 = Llama-3 <p/> <p>MS = Model Stock <p/> <p>This is my first foray into 70b models, so this is more or less an experiment, please let me know your thoughts on the model and where their can be improvements.<br> L3-MS-Astoria-70b combines the strengths of multiple models to deliver a well-rounded, capable assistant. It is aimed at performing general tasks, storytelling, roleplay, and more mature content.<br> The model stock merging method attempts to make the model remain focused, tailored, and high-quality. <h2>Quants:</h2> <p>(Thanks to <a href="https://huggingface.co/mradermacher">@Mradermacher!</a>, please send them likes and follows!)</p> <p><a href="https://huggingface.co/mradermacher/L3-MS-Astoria-70b-GGUF">L3-MS-Astoria-70b-GGUF (GGUFs)</a></p> <p></p> <h3>Config:</h3> <pre><code>MODEL_NAME = "L3-MS-Astoria-70b" yaml_config = """ base_model: failspy/llama-3-70B-Instruct-abliterated merge_method: model_stock dtype: bfloat16 models: - model: migtissera/Tess-2.0-Llama-3-70B-v0.2 - model: abacusai/Llama-3-Giraffe-70B - model: NeverSleep/Llama-3-Lumimaid-70B-v0.1-alt """ </code></pre> <h4>Source Model Details:</h4> <p><strong>migtissera/Tess-2.0-Llama-3-70B-v0.2:</strong><br> Tess, short for Tesoro (Treasure in Italian), is a general purpose Large Language Model series. Tess-2.0-Llama-3-70B-v0.2 was trained on the meta-llama/Meta-Llama-3-70B base. The change between v0.1 and this version, v0.2 is that v0.2 has undergone an additional step of uncensoring. </p> <p><strong>abacusai/Llama-3-Giraffe-70B:</strong><br> General model trained on 1b tokens, up to 128k ctx </p> <p><strong>NeverSleep/Llama-3-Lumimaid-70B-v0.1-alt:</strong><br> Llama3 trained on our RP datasets, NeverSleep tried to have a balance between the ERP and the RP, not too horny, but just enough.<br> NeverSleep also added some non-RP dataset, making the model less dumb overall. It should look like a 40%/60% ratio for Non-RP/RP+ERP data. </p> <p><strong>Base model failspy/llama-3-70B-Instruct-abliterated:</strong><br> This is meta-llama/Llama-3-70B-Instruct with orthogonalized bfloat16 safetensor weights, generated with the methodology that was described in the preview paper/blog post: 'Refusal in LLMs is mediated by a single direction' which I encourage you to read to understand more.<br> TL;DR: this model has had certain weights manipulated to "inhibit" the model's ability to express refusal. It is not in anyway _guaranteed_ that it won't refuse you, understand your request, it may still lecture you about ethics/safety, etc. It is tuned in all other respects the same as the original 70B instruct model was, just with the strongest refusal direction orthogonalized out. </p> </div> </div> </body> </html>
mradermacher/MythoMix-L2-13b-GGUF	mradermacher	2024-06-07T06:46:19Z	41	0	transformers	[ "transformers", "gguf", "en", "base_model:Gryphe/MythoMix-L2-13b", "base_model:quantized:Gryphe/MythoMix-L2-13b", "license:other", "endpoints_compatible", "region:us" ]	null	2024-06-06T04:27:35Z	--- base_model: Gryphe/MythoMix-L2-13b language: - en library_name: transformers license: other quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Gryphe/MythoMix-L2-13b <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/MythoMix-L2-13b-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/MythoMix-L2-13b-GGUF/resolve/main/MythoMix-L2-13b.Q2_K.gguf) \| Q2_K \| 5.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/MythoMix-L2-13b-GGUF/resolve/main/MythoMix-L2-13b.IQ3_XS.gguf) \| IQ3_XS \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MythoMix-L2-13b-GGUF/resolve/main/MythoMix-L2-13b.IQ3_S.gguf) \| IQ3_S \| 5.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/MythoMix-L2-13b-GGUF/resolve/main/MythoMix-L2-13b.Q3_K_S.gguf) \| Q3_K_S \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/MythoMix-L2-13b-GGUF/resolve/main/MythoMix-L2-13b.IQ3_M.gguf) \| IQ3_M \| 6.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/MythoMix-L2-13b-GGUF/resolve/main/MythoMix-L2-13b.Q3_K_M.gguf) \| Q3_K_M \| 6.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MythoMix-L2-13b-GGUF/resolve/main/MythoMix-L2-13b.Q3_K_L.gguf) \| Q3_K_L \| 7.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/MythoMix-L2-13b-GGUF/resolve/main/MythoMix-L2-13b.IQ4_XS.gguf) \| IQ4_XS \| 7.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/MythoMix-L2-13b-GGUF/resolve/main/MythoMix-L2-13b.Q4_K_S.gguf) \| Q4_K_S \| 7.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MythoMix-L2-13b-GGUF/resolve/main/MythoMix-L2-13b.Q4_K_M.gguf) \| Q4_K_M \| 8.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MythoMix-L2-13b-GGUF/resolve/main/MythoMix-L2-13b.Q5_K_S.gguf) \| Q5_K_S \| 9.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/MythoMix-L2-13b-GGUF/resolve/main/MythoMix-L2-13b.Q5_K_M.gguf) \| Q5_K_M \| 9.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MythoMix-L2-13b-GGUF/resolve/main/MythoMix-L2-13b.Q6_K.gguf) \| Q6_K \| 10.8 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/MythoMix-L2-13b-GGUF/resolve/main/MythoMix-L2-13b.Q8_0.gguf) \| Q8_0 \| 13.9 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
LoneStriker/Qwen2-72B-Instruct-5.0bpw-h6-exl2	LoneStriker	2024-06-07T06:42:53Z	9	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "chat", "conversational", "en", "arxiv:2309.00071", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "5-bit", "exl2", "region:us" ]	text-generation	2024-06-07T03:44:11Z	--- license: other license_name: tongyi-qianwen license_link: https://huggingface.co/Qwen/Qwen2-72B-Instruct/blob/main/LICENSE language: - en pipeline_tag: text-generation tags: - chat --- # Qwen2-72B-Instruct ## Introduction Qwen2 is the new series of Qwen large language models. For Qwen2, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters, including a Mixture-of-Experts model. This repo contains the instruction-tuned 72B Qwen2 model. Compared with the state-of-the-art opensource language models, including the previous released Qwen1.5, Qwen2 has generally surpassed most opensource models and demonstrated competitiveness against proprietary models across a series of benchmarks targeting for language understanding, language generation, multilingual capability, coding, mathematics, reasoning, etc. Qwen2-72B-Instruct supports a context length of up to 131,072 tokens, enabling the processing of extensive inputs. Please refer to [this section](#processing-long-texts) for detailed instructions on how to deploy Qwen2 for handling long texts. For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2/), [GitHub](https://github.com/QwenLM/Qwen2), and [Documentation](https://qwen.readthedocs.io/en/latest/). <br> ## Model Details Qwen2 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes. ## Training details We pretrained the models with a large amount of data, and we post-trained the models with both supervised finetuning and direct preference optimization. ## Requirements The code of Qwen2 has been in the latest Hugging face transformers and we advise you to install `transformers>=4.37.0`, or you might encounter the following error: ``` KeyError: 'qwen2' ``` ## Quickstart Here provides a code snippet with `apply_chat_template` 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 onto model = AutoModelForCausalLM.from_pretrained( "Qwen/Qwen2-72B-Instruct", torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-72B-Instruct") prompt = "Give me a short introduction to large language model." messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(device) generated_ids = model.generate( model_inputs.input_ids, max_new_tokens=512 ) 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] ``` ### Processing Long Texts To handle extensive inputs exceeding 32,768 tokens, we utilize [YARN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts. For deployment, we recommend using vLLM. You can enable the long-context capabilities by following these steps: 1. Install vLLM: You can install vLLM by running the following command. ```bash pip install "vllm>=0.4.3" ``` Or you can install vLLM from [source](https://github.com/vllm-project/vllm/). 2. Configure Model Settings: After downloading the model weights, modify the `config.json` file by including the below snippet: ```json { "architectures": [ "Qwen2ForCausalLM" ], // ... "vocab_size": 152064, // adding the following snippets "rope_scaling": { "factor": 4.0, "original_max_position_embeddings": 32768, "type": "yarn" } } ``` This snippet enable YARN to support longer contexts. 3. Model Deployment: Utilize vLLM to deploy your model. For instance, you can set up an openAI-like server using the command: ```bash python -m vllm.entrypoints.openai.api_server --served-model-name Qwen2-72B-Instruct --model path/to/weights ``` Then you can access the Chat API by: ```bash curl http://localhost:8000/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "Qwen2-72B-Instruct", "messages": [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Your Long Input Here."} ] }' ``` For further usage instructions of vLLM, please refer to our [Github](https://github.com/QwenLM/Qwen2). Note: Presently, vLLM only supports static YARN, which means the scaling factor remains constant regardless of input length, potentially impacting performance on shorter texts. We advise adding the `rope_scaling` configuration only when processing long contexts is required. ## Evaluation We briefly compare Qwen2-72B-Instruct with similar-sized instruction-tuned LLMs, including our previous Qwen1.5-72B-Chat. The results are shown as follows: \| Datasets \| Llama-3-70B-Instruct \| Qwen1.5-72B-Chat \| Qwen2-72B-Instruct \| \| :--- \| :---: \| :---: \| :---: \| \| _English_ \| \| \| \| \| MMLU \| 82.0 \| 75.6 \| 82.3 \| \| MMLU-Pro \| 56.2 \| 51.7 \| 64.4 \| \| GPQA \| 41.9 \| 39.4 \| 42.4 \| \| TheroemQA \| 42.5 \| 28.8 \| 44.4 \| \| MT-Bench \| 8.95 \| 8.61 \| 9.12 \| \| Arena-Hard \| 41.1 \| 36.1 \| 48.1 \| \| IFEval (Prompt Strict-Acc.) \| 77.3 \| 55.8 \| 77.6 \| \| _Coding_ \| \| \| \| \| HumanEval \| 81.7 \| 71.3 \| 86.0 \| \| MBPP \| 82.3 \| 71.9 \| 80.2 \| \| MultiPL-E \| 63.4 \| 48.1 \| 69.2 \| \| EvalPlus \| 75.2 \| 66.9 \| 79.0 \| \| LiveCodeBench \| 29.3 \| 17.9 \| 35.7 \| \| _Mathematics_ \| \| \| \| \| GSM8K \| 93.0 \| 82.7 \| 91.1 \| \| MATH \| 50.4 \| 42.5 \| 59.7 \| \| _Chinese_ \| \| \| \| \| C-Eval \| 61.6 \| 76.1 \| 83.8 \| \| AlignBench \| 7.42 \| 7.28 \| 8.27 \| ## Citation If you find our work helpful, feel free to give us a cite. ``` @article{qwen2, title={Qwen2 Technical Report}, year={2024} } ```
QingchuanMa/ppo-Huggy	QingchuanMa	2024-06-07T06:37:11Z	1	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2024-06-07T06:34:10Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: QingchuanMa/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
impuneetg/Meta-Llama-3-8B-hinglish	impuneetg	2024-06-07T06:36:13Z	7	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-07T06:34:17Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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(2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
bullerwins/Qwen2-72B-Instruct_exl2_6.0bpw	bullerwins	2024-06-07T06:35:23Z	10	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "chat", "conversational", "en", "arxiv:2309.00071", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "6-bit", "exl2", "region:us" ]	text-generation	2024-06-06T20:21:49Z	--- license: other license_name: tongyi-qianwen license_link: https://huggingface.co/Qwen/Qwen2-72B-Instruct/blob/main/LICENSE language: - en pipeline_tag: text-generation tags: - chat --- Quantized version in exl2 format using [Exllama2 0.1.4](https://github.com/turboderp/exllamav2) # Qwen2-72B-Instruct ## Introduction Qwen2 is the new series of Qwen large language models. For Qwen2, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters, including a Mixture-of-Experts model. This repo contains the instruction-tuned 72B Qwen2 model. Compared with the state-of-the-art opensource language models, including the previous released Qwen1.5, Qwen2 has generally surpassed most opensource models and demonstrated competitiveness against proprietary models across a series of benchmarks targeting for language understanding, language generation, multilingual capability, coding, mathematics, reasoning, etc. Qwen2-72B-Instruct supports a context length of up to 131,072 tokens, enabling the processing of extensive inputs. Please refer to [this section](#processing-long-texts) for detailed instructions on how to deploy Qwen2 for handling long texts. For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2/), [GitHub](https://github.com/QwenLM/Qwen2), and [Documentation](https://qwen.readthedocs.io/en/latest/). <br> ## Model Details Qwen2 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes. ## Training details We pretrained the models with a large amount of data, and we post-trained the models with both supervised finetuning and direct preference optimization. ## Requirements The code of Qwen2 has been in the latest Hugging face transformers and we advise you to install `transformers>=4.37.0`, or you might encounter the following error: ``` KeyError: 'qwen2' ``` ## Quickstart Here provides a code snippet with `apply_chat_template` 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 onto model = AutoModelForCausalLM.from_pretrained( "Qwen/Qwen2-72B-Instruct", torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-72B-Instruct") prompt = "Give me a short introduction to large language model." messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(device) generated_ids = model.generate( model_inputs.input_ids, max_new_tokens=512 ) 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] ``` ### Processing Long Texts To handle extensive inputs exceeding 32,768 tokens, we utilize [YARN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts. For deployment, we recommend using vLLM. You can enable the long-context capabilities by following these steps: 1. Install vLLM: You can install vLLM by running the following command. ```bash pip install "vllm>=0.4.3" ``` Or you can install vLLM from [source](https://github.com/vllm-project/vllm/). 2. Configure Model Settings: After downloading the model weights, modify the `config.json` file by including the below snippet: ```json { "architectures": [ "Qwen2ForCausalLM" ], // ... "vocab_size": 152064, // adding the following snippets "rope_scaling": { "factor": 4.0, "original_max_position_embeddings": 32768, "type": "yarn" } } ``` This snippet enable YARN to support longer contexts. 3. Model Deployment: Utilize vLLM to deploy your model. For instance, you can set up an openAI-like server using the command: ```bash python -m vllm.entrypoints.openai.api_server --served-model-name Qwen2-72B-Instruct --model path/to/weights ``` Then you can access the Chat API by: ```bash curl http://localhost:8000/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "Qwen2-72B-Instruct", "messages": [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Your Long Input Here."} ] }' ``` For further usage instructions of vLLM, please refer to our [Github](https://github.com/QwenLM/Qwen2). Note: Presently, vLLM only supports static YARN, which means the scaling factor remains constant regardless of input length, potentially impacting performance on shorter texts. We advise adding the `rope_scaling` configuration only when processing long contexts is required. ## Evaluation We briefly compare Qwen2-72B-Instruct with similar-sized instruction-tuned LLMs, including our previous Qwen1.5-72B-Chat. The results are shown as follows: \| Datasets \| Llama-3-70B-Instruct \| Qwen1.5-72B-Chat \| Qwen2-72B-Instruct \| \| :--- \| :---: \| :---: \| :---: \| \| _English_ \| \| \| \| \| MMLU \| 82.0 \| 75.6 \| 82.3 \| \| MMLU-Pro \| 56.2 \| 51.7 \| 64.4 \| \| GPQA \| 41.9 \| 39.4 \| 42.4 \| \| TheroemQA \| 42.5 \| 28.8 \| 44.4 \| \| MT-Bench \| 8.95 \| 8.61 \| 9.12 \| \| Arena-Hard \| 41.1 \| 36.1 \| 48.1 \| \| IFEval (Prompt Strict-Acc.) \| 77.3 \| 55.8 \| 77.6 \| \| _Coding_ \| \| \| \| \| HumanEval \| 81.7 \| 71.3 \| 86.0 \| \| MBPP \| 82.3 \| 71.9 \| 80.2 \| \| MultiPL-E \| 63.4 \| 48.1 \| 69.2 \| \| EvalPlus \| 75.2 \| 66.9 \| 79.0 \| \| LiveCodeBench \| 29.3 \| 17.9 \| 35.7 \| \| _Mathematics_ \| \| \| \| \| GSM8K \| 93.0 \| 82.7 \| 91.1 \| \| MATH \| 50.4 \| 42.5 \| 59.7 \| \| _Chinese_ \| \| \| \| \| C-Eval \| 61.6 \| 76.1 \| 83.8 \| \| AlignBench \| 7.42 \| 7.28 \| 8.27 \| ## Citation If you find our work helpful, feel free to give us a cite. ``` @article{qwen2, title={Qwen2 Technical Report}, year={2024} } ```
Gkumi/tensorflow-DistilBERT	Gkumi	2024-06-07T06:33:03Z	64	0	transformers	[ "transformers", "tf", "distilbert", "token-classification", "generated_from_keras_callback", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-06-07T03:45:14Z	--- license: apache-2.0 tags: - generated_from_keras_callback base_model: distilbert-base-uncased model-index: - name: tensorflow-DistilBERT 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. --> # tensorflow-DistilBERT This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 10875, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results ### Framework versions - Transformers 4.40.0 - TensorFlow 2.15.0 - Datasets 2.18.0 - Tokenizers 0.19.1
LoneStriker/Qwen2-72B-Instruct-4.0bpw-h6-exl2	LoneStriker	2024-06-07T06:24:09Z	10	1	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "chat", "conversational", "en", "arxiv:2309.00071", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "exl2", "region:us" ]	text-generation	2024-06-07T02:39:48Z	--- license: other license_name: tongyi-qianwen license_link: https://huggingface.co/Qwen/Qwen2-72B-Instruct/blob/main/LICENSE language: - en pipeline_tag: text-generation tags: - chat --- # Qwen2-72B-Instruct ## Introduction Qwen2 is the new series of Qwen large language models. For Qwen2, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters, including a Mixture-of-Experts model. This repo contains the instruction-tuned 72B Qwen2 model. Compared with the state-of-the-art opensource language models, including the previous released Qwen1.5, Qwen2 has generally surpassed most opensource models and demonstrated competitiveness against proprietary models across a series of benchmarks targeting for language understanding, language generation, multilingual capability, coding, mathematics, reasoning, etc. Qwen2-72B-Instruct supports a context length of up to 131,072 tokens, enabling the processing of extensive inputs. Please refer to [this section](#processing-long-texts) for detailed instructions on how to deploy Qwen2 for handling long texts. For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2/), [GitHub](https://github.com/QwenLM/Qwen2), and [Documentation](https://qwen.readthedocs.io/en/latest/). <br> ## Model Details Qwen2 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes. ## Training details We pretrained the models with a large amount of data, and we post-trained the models with both supervised finetuning and direct preference optimization. ## Requirements The code of Qwen2 has been in the latest Hugging face transformers and we advise you to install `transformers>=4.37.0`, or you might encounter the following error: ``` KeyError: 'qwen2' ``` ## Quickstart Here provides a code snippet with `apply_chat_template` 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 onto model = AutoModelForCausalLM.from_pretrained( "Qwen/Qwen2-72B-Instruct", torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-72B-Instruct") prompt = "Give me a short introduction to large language model." messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(device) generated_ids = model.generate( model_inputs.input_ids, max_new_tokens=512 ) 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] ``` ### Processing Long Texts To handle extensive inputs exceeding 32,768 tokens, we utilize [YARN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts. For deployment, we recommend using vLLM. You can enable the long-context capabilities by following these steps: 1. Install vLLM: You can install vLLM by running the following command. ```bash pip install "vllm>=0.4.3" ``` Or you can install vLLM from [source](https://github.com/vllm-project/vllm/). 2. Configure Model Settings: After downloading the model weights, modify the `config.json` file by including the below snippet: ```json { "architectures": [ "Qwen2ForCausalLM" ], // ... "vocab_size": 152064, // adding the following snippets "rope_scaling": { "factor": 4.0, "original_max_position_embeddings": 32768, "type": "yarn" } } ``` This snippet enable YARN to support longer contexts. 3. Model Deployment: Utilize vLLM to deploy your model. For instance, you can set up an openAI-like server using the command: ```bash python -m vllm.entrypoints.openai.api_server --served-model-name Qwen2-72B-Instruct --model path/to/weights ``` Then you can access the Chat API by: ```bash curl http://localhost:8000/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "Qwen2-72B-Instruct", "messages": [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Your Long Input Here."} ] }' ``` For further usage instructions of vLLM, please refer to our [Github](https://github.com/QwenLM/Qwen2). Note: Presently, vLLM only supports static YARN, which means the scaling factor remains constant regardless of input length, potentially impacting performance on shorter texts. We advise adding the `rope_scaling` configuration only when processing long contexts is required. ## Evaluation We briefly compare Qwen2-72B-Instruct with similar-sized instruction-tuned LLMs, including our previous Qwen1.5-72B-Chat. The results are shown as follows: \| Datasets \| Llama-3-70B-Instruct \| Qwen1.5-72B-Chat \| Qwen2-72B-Instruct \| \| :--- \| :---: \| :---: \| :---: \| \| _English_ \| \| \| \| \| MMLU \| 82.0 \| 75.6 \| 82.3 \| \| MMLU-Pro \| 56.2 \| 51.7 \| 64.4 \| \| GPQA \| 41.9 \| 39.4 \| 42.4 \| \| TheroemQA \| 42.5 \| 28.8 \| 44.4 \| \| MT-Bench \| 8.95 \| 8.61 \| 9.12 \| \| Arena-Hard \| 41.1 \| 36.1 \| 48.1 \| \| IFEval (Prompt Strict-Acc.) \| 77.3 \| 55.8 \| 77.6 \| \| _Coding_ \| \| \| \| \| HumanEval \| 81.7 \| 71.3 \| 86.0 \| \| MBPP \| 82.3 \| 71.9 \| 80.2 \| \| MultiPL-E \| 63.4 \| 48.1 \| 69.2 \| \| EvalPlus \| 75.2 \| 66.9 \| 79.0 \| \| LiveCodeBench \| 29.3 \| 17.9 \| 35.7 \| \| _Mathematics_ \| \| \| \| \| GSM8K \| 93.0 \| 82.7 \| 91.1 \| \| MATH \| 50.4 \| 42.5 \| 59.7 \| \| _Chinese_ \| \| \| \| \| C-Eval \| 61.6 \| 76.1 \| 83.8 \| \| AlignBench \| 7.42 \| 7.28 \| 8.27 \| ## Citation If you find our work helpful, feel free to give us a cite. ``` @article{qwen2, title={Qwen2 Technical Report}, year={2024} } ```
HyperdustProtocol/HyperAuto_llama3_v2	HyperdustProtocol	2024-06-07T06:22:23Z	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	2024-06-07T06:22:08Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: HyperdustProtocol - 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)
KimByeongSu/gpt-neo-125m-cs-finetuning-3000-2	KimByeongSu	2024-06-07T06:19:09Z	105	0	transformers	[ "transformers", "tensorboard", "safetensors", "gpt_neo", "text-generation", "generated_from_trainer", "base_model:EleutherAI/gpt-neo-125m", "base_model:finetune:EleutherAI/gpt-neo-125m", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-06-07T06:17:14Z	--- license: mit base_model: EleutherAI/gpt-neo-125m tags: - generated_from_trainer model-index: - name: gpt-neo-125m-cs-finetuning-3000-2 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. --> # gpt-neo-125m-cs-finetuning-3000-2 This model is a fine-tuned version of [EleutherAI/gpt-neo-125m](https://huggingface.co/EleutherAI/gpt-neo-125m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.4683 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 39 \| 3.5432 \| \| No log \| 2.0 \| 78 \| 3.4828 \| \| No log \| 3.0 \| 117 \| 3.4683 \| ### Framework versions - Transformers 4.36.2 - Pytorch 1.13.1+cu117 - Datasets 2.14.6 - Tokenizers 0.15.0
phongtintruong/misjava-api-060724	phongtintruong	2024-06-07T06:14:10Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-07T06:13:19Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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belztjti/chat	belztjti	2024-06-07T06:13:29Z	5	0	transformers	[ "transformers", "safetensors", "chatglm", "feature-extraction", "glm", "thudm", "custom_code", "zh", "en", "arxiv:2210.02414", "license:other", "region:us" ]	feature-extraction	2024-06-06T18:47:05Z	--- license: other license_name: glm-4 license_link: https://huggingface.co/THUDM/glm-4-9b-chat/blob/main/LICENSE language: - zh - en tags: - glm - chatglm - thudm inference: false --- # GLM-4-9B-Chat GLM-4-9B 是智谱 AI 推出的最新一代预训练模型 GLM-4 系列中的开源版本。在语义、数学、推理、代码和知识等多方面的数据集测评中，GLM-4-9B 及其人类偏好对齐的版本 GLM-4-9B-Chat 均表现出较高的性能。除了能进行多轮对话，GLM-4-9B-Chat 还具备网页浏览、代码执行、自定义工具调用（Function Call）和长文本推理（支持最大 128K 上下文）等高级功能。本代模型增加了多语言支持，支持包括日语，韩语，德语在内的 26 种语言。我们还推出了支持 1M 上下文长度（约 200 万中文字符）的模型。 ## 评测结果我们在一些经典任务上对 GLM-4-9B-Chat 模型进行了评测,并得到了如下的结果: \| Model \| AlignBench-v2 \| MT-Bench \| IFEval \| MMLU \| C-Eval \| GSM8K \| MATH \| HumanEval \| NCB \| \|:--------------------\|:-------------:\|:--------:\|:------:\|:----:\|:------:\|:-----:\|:----:\|:---------:\|:----:\| \| Llama-3-8B-Instruct \| 5.12 \| 8.00 \| 68.58 \| 68.4 \| 51.3 \| 79.6 \| 30.0 \| 62.2 \| 24.7 \| \| ChatGLM3-6B \| 3.97 \| 5.50 \| 28.1 \| 66.4 \| 69.0 \| 72.3 \| 25.7 \| 58.5 \| 11.3 \| \| GLM-4-9B-Chat \| 6.61 \| 8.35 \| 69.0 \| 72.4 \| 75.6 \| 79.6 \| 50.6 \| 71.8 \| 32.2 \| ### 长文本在 1M 的上下文长度下进行[大海捞针实验](https://github.com/LargeWorldModel/LWM/blob/main/scripts/eval_needle.py)，结果如下： ![needle](https://raw.githubusercontent.com/THUDM/GLM-4/main/resources/eval_needle.jpeg) 在 LongBench-Chat 上对长文本能力进行了进一步评测，结果如下: ![leaderboard](https://raw.githubusercontent.com/THUDM/GLM-4/main/resources/longbench.png) ### 多语言能力在六个多语言数据集上对 GLM-4-9B-Chat 和 Llama-3-8B-Instruct 进行了测试，测试结果及数据集对应选取语言如下表 \| Dataset \| Llama-3-8B-Instruct \| GLM-4-9B-Chat \| Languages \|:------------\|:-------------------:\|:-------------:\|:----------------------------------------------------------------------------------------------:\| \| M-MMLU \| 49.6 \| 56.6 \| all \| FLORES \| 25.0 \| 28.8 \| ru, es, de, fr, it, pt, pl, ja, nl, ar, tr, cs, vi, fa, hu, el, ro, sv, uk, fi, ko, da, bg, no \| MGSM \| 54.0 \| 65.3 \| zh, en, bn, de, es, fr, ja, ru, sw, te, th \| XWinograd \| 61.7 \| 73.1 \| zh, en, fr, jp, ru, pt \| XStoryCloze \| 84.7 \| 90.7 \| zh, en, ar, es, eu, hi, id, my, ru, sw, te \| XCOPA \| 73.3 \| 80.1 \| zh, et, ht, id, it, qu, sw, ta, th, tr, vi ### 工具调用能力我们在 [Berkeley Function Calling Leaderboard](https://github.com/ShishirPatil/gorilla/tree/main/berkeley-function-call-leaderboard)上进行了测试并得到了以下结果： \| Model \| Overall Acc. \| AST Summary \| Exec Summary \| Relevance \| \|:-----------------------\|:------------:\|:-----------:\|:------------:\|:---------:\| \| Llama-3-8B-Instruct \| 58.88 \| 59.25 \| 70.01 \| 45.83 \| \| gpt-4-turbo-2024-04-09 \| 81.24 \| 82.14 \| 78.61 \| 88.75 \| \| ChatGLM3-6B \| 57.88 \| 62.18 \| 69.78 \| 5.42 \| \| GLM-4-9B-Chat \| 81.00 \| 80.26 \| 84.40 \| 87.92 \| 本仓库是 GLM-4-9B-Chat 的模型仓库，支持`128K`上下文长度。 ## 运行模型使用 transformers 后端进行推理: ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" tokenizer = AutoTokenizer.from_pretrained("THUDM/glm-4-9b-chat",trust_remote_code=True) query = "你好" inputs = tokenizer.apply_chat_template([{"role": "user", "content": query}], add_generation_prompt=True, tokenize=True, return_tensors="pt", return_dict=True ) inputs = inputs.to(device) model = AutoModelForCausalLM.from_pretrained( "THUDM/glm-4-9b-chat", torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, trust_remote_code=True ).to(device).eval() gen_kwargs = {"max_length": 2500, "do_sample": True, "top_k": 1} with torch.no_grad(): outputs = model.generate(inputs, gen_kwargs) outputs = outputs[:, inputs['input_ids'].shape[1]:] print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` 使用 VLLM后端进行推理: ```python from transformers import AutoTokenizer from vllm import LLM, SamplingParams # GLM-4-9B-Chat-1M # max_model_len, tp_size = 1048576, 4 # GLM-4-9B-Chat from transformers import AutoTokenizer from vllm import LLM, SamplingParams # 如果遇见 OOM 现象，建议减少max_model_len，或者增加tp_size max_model_len, tp_size = 131072, 1 model_name = "THUDM/glm-4-9b-chat" prompt = [{"role": "user", "content": "你好"}] tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) llm = LLM( model=model_name, tensor_parallel_size=tp_size, max_model_len=max_model_len, trust_remote_code=True, enforce_eager=True, # GLM-4-9B-Chat-1M 如果遇见 OOM 现象，建议开启下述参数 # enable_chunked_prefill=True, # max_num_batched_tokens=8192 ) stop_token_ids = [151329, 151336, 151338] sampling_params = SamplingParams(temperature=0.95, max_tokens=1024, stop_token_ids=stop_token_ids) inputs = tokenizer.apply_chat_template(prompt, tokenize=False, add_generation_prompt=True) outputs = llm.generate(prompts=inputs, sampling_params=sampling_params) print(outputs[0].outputs[0].text) ``` ## 协议 GLM-4 模型的权重的使用则需要遵循 [LICENSE](LICENSE)。 Rhe use of the GLM-4 model weights needs to comply with the [LICENSE](LICENSE). ## 引用如果你觉得我们的工作有帮助的话，请考虑引用下列论文。 ``` @article{zeng2022glm, title={Glm-130b: An open bilingual pre-trained model}, author={Zeng, Aohan and Liu, Xiao and Du, Zhengxiao and Wang, Zihan and Lai, Hanyu and Ding, Ming and Yang, Zhuoyi and Xu, Yifan and Zheng, Wendi and Xia, Xiao and others}, journal={arXiv preprint arXiv:2210.02414}, year={2022} } ``` ``` @inproceedings{du2022glm, title={GLM: General Language Model Pretraining with Autoregressive Blank Infilling}, author={Du, Zhengxiao and Qian, Yujie and Liu, Xiao and Ding, Ming and Qiu, Jiezhong and Yang, Zhilin and Tang, Jie}, booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, pages={320--335}, year={2022} } ```
Jemimmah/gemma-ft	Jemimmah	2024-06-07T06:10:50Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-07T06:10:50Z	--- license: apache-2.0 ---
andricValdez/xlm-roberta-base-finetuned-autext24	andricValdez	2024-06-07T06:09:32Z	5	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-06-07T03:24:05Z	--- license: mit base_model: FacebookAI/xlm-roberta-base tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: xlm-roberta-base-finetuned-autext24 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. --> # xlm-roberta-base-finetuned-autext24 This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3836 - Accuracy: 0.9517 - F1: 0.9515 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 4785 \| 0.2788 \| 0.9180 \| 0.9172 \| \| 0.1496 \| 2.0 \| 9570 \| 0.4590 \| 0.9123 \| 0.9113 \| \| 0.1496 \| 3.0 \| 14355 \| 0.3858 \| 0.9373 \| 0.9369 \| \| 0.0482 \| 4.0 \| 19140 \| 0.3224 \| 0.9546 \| 0.9545 \| \| 0.0482 \| 5.0 \| 23925 \| 0.3836 \| 0.9517 \| 0.9515 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1
sunilghanchi/llama-3-8b-finetune	sunilghanchi	2024-06-07T06:08:54Z	2	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "unsloth", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-07T05:40:13Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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de-coder/UlizaLlama_Q4_K_M-gguf	de-coder	2024-06-07T06:08:19Z	8	0	transformers	[ "transformers", "gguf", "llama", "text-generation", "art", "sw", "en", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-06-07T05:11:07Z	--- license: mit language: - sw - en tags: - art --- # UlizaLlama_Q4_K_M-gguf 4-bit Quantized Bilingual Language Model ## Overview UlizaLlama_Q4_K_M-gguf is a 4-bit quantized version of the UlizaLlama model, a 7B parameter language model fine-tuned for Swahili and English. This quantized model offers the same bilingual capabilities as the original UlizaLlama but with significantly reduced model size and improved inference speed, making it ideal for deployment in resource-constrained environments. ### Key Features - Bilingual Proficiency: Excels in both Swahili and English, with a focus on instructional tasks. - 4-bit Quantization: Utilizes the QQUF (Quantized QUarter Float) format for a 75% reduction in model size. - Efficient Inference: Faster processing and lower memory footprint compared to the full-precision model. - Versatile Applications: Suitable for question-answering, chat assistants, and various domain-specific tasks. ## Model Details - Original Model: UlizaLlama (7B parameters) - Base Model: Jacaranda/kiswallama-pretrained (derived from Meta/Llama2) - Quantization Method: 4-bit QQUF - Languages: Swahili and English - License: CC BY-NC-SA 4.0 DEED ## Installation To use UlizaLlama-QQUF, you'll need a library that supports 4-bit quantized models. We recommend using the `bitsandbytes` library: ```bash !pip install ctransformers ``` ## Usage Here's a simple example of how to load and use de-coder/UlizaLlama_Q4_K_M-gguf ```python from ctransformers import AutoModelForCausalLM # Load the model llm = AutoModelForCausalLM.from_pretrained( "de-coder/UlizaLlama_Q4_K_M-gguf", model_file="Q4_K_M.gguf", lib="avx2" # or "basic" if avx2 isn't supported ) # Generate text prompt = "Niambie kuhusu historia ya Kilimanjaro." print(llm(prompt)) ``` ## Performance and Trade-offs UlizaLlama-QQUF offers substantial improvements in model size and inference speed. However, there might be a slight degradation in performance compared to the full-precision model. We encourage users to benchmark the model on their specific tasks to understand these trade-offs. ## Use Cases 1. Chatbots for healthcare, agriculture, education, and more. 2. Language learning applications. 3. Information services in Swahili-speaking regions. 4. Edge devices and mobile applications. ## Citation and Acknowledgments If you use UlizaLlama_Q4_K_M-gguf in your work, please cite: ```bibtex @misc{UlizaLlama_Q4_K_M-gguf, title={UlizaLlama_Q4_K_M-gguf: A Bilingual Language Model for Swahili and English}, author={Kelvin Githu(de-coder)}, year={2024}, publisher={Kelvin Githu}, howpublished={\url{https://huggingface.co/de-coder/UlizaLlama_Q4_K_M-gguf}}, } ```
ilanaliouchouche/gte-base-lazy-teacher	ilanaliouchouche	2024-06-07T06:08:18Z	138	3	transformers	[ "transformers", "safetensors", "new", "text-classification", "education", "custom_code", "en", "autotrain_compatible", "region:us" ]	text-classification	2024-05-11T21:40:50Z	--- library_name: transformers tags: - education language: - en pipeline_tag: text-classification --- ## Model Details ### Model Description This model was trained for an app called [LazyTeacher](https://github.com/mlengineershub/LazyTeacher). The objective is to train a model so that it automatically predicts the grade that a professor would have given to a student. - Developed by: [Ilan Aliouchouche](https://github.com/ilanaliouchouche) & [Ilyes Djerfaf](https://github.com/ilyesdjerfaf) - Language(s): English - Finetuned from model: [Alibaba-NLP/gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5)
OEvortex/HelpingAI-PixelCraft	OEvortex	2024-06-07T06:04:25Z	10	5	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "dalle-3", "dalle", "deepvision", "template:sd-lora", "HelpingAI", "HelpingAI-PixelCraft", "en", "base_model:fluently/Fluently-XL-Final", "base_model:adapter:fluently/Fluently-XL-Final", "license:mit", "region:us" ]	text-to-image	2024-01-02T12:53:25Z	--- tags: - text-to-image - stable-diffusion - lora - dalle-3 - dalle - deepvision - diffusers - template:sd-lora - HelpingAI - HelpingAI-PixelCraft widget: - text: >- a close up of a fire breathing pokemon figure, digital art, trending on polycount, real life charmander, sparks flying, photo-realistic unreal engine, pokemon in the wild output: url: images/00002441-10291230.jpeg - text: astronaut riding a llama on Mars output: url: images/c96a4147-b14d-4e71-8c08-e04c31c8be18.jpg - text: >- cube cutout of an isometric programmer bedroom, 3d art, muted colors, soft lighting, high detail, concept art, behance, ray tracing output: url: images/b7ad0f38-5d2a-48cd-b7d4-b94be1d23c40.jpg base_model: fluently/Fluently-XL-Final instance_prompt: <lora:Dall-e_3_0.3-v2-000003> license: mit language: - en pipeline_tag: text-to-image library_name: diffusers --- # HelpingAI-PixelCraft --- # Subscribe to my YouTube channel [Subscribe](https://youtube.com/@OEvortex) <Gallery />
scott0926/testing	scott0926	2024-06-07T06:04:25Z	2	0	transformers	[ "transformers", "llama", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-06-07T03:47:51Z	--- license: apache-2.0 ---
AkylaiBva/my_whisper	AkylaiBva	2024-06-07T06:01:45Z	13	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-medium", "base_model:finetune:openai/whisper-medium", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-06-06T06:49:36Z	--- license: apache-2.0 base_model: openai/whisper-medium tags: - generated_from_trainer metrics: - wer model-index: - name: my_whisper 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_whisper This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 - Wer: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 3 - training_steps: 15 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:----:\| \| 1.2537 \| 5.0 \| 5 \| 1.2684 \| 62.5 \| \| 0.2765 \| 10.0 \| 10 \| 0.0001 \| 0.0 \| \| 0.0001 \| 15.0 \| 15 \| 0.0000 \| 0.0 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1
Shiv1143/model_LoRA	Shiv1143	2024-06-07T05:58:09Z	4	0	diffusers	[ "diffusers", "tensorboard", "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	2024-06-06T18:44:43Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of TOK model 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 - Shiv1143/model_LoRA <Gallery /> ## Model description These are Shiv1143/model_LoRA 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 TOK model to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](Shiv1143/model_LoRA/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]
John6666/yaminabe-pony-v6-sdxl	John6666	2024-06-07T05:55:12Z	40	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2024-06-07T05:46:01Z	--- license: other tags: - text-to-image - stable-diffusion - stable-diffusion-xl --- Original model is [here](https://civitai.com/models/409856/yaminabepony?modelVersionId=555395).
gowhyyou/Qwen-Qwen1.5-0.5B-1717739273	gowhyyou	2024-06-07T05:48:14Z	146	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-07T05:47:54Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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halink0803/google-gemma-7b-1717738779	halink0803	2024-06-07T05:48:12Z	14	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-07T05:39:41Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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nanelimon/bert-base-turkish-offensive	nanelimon	2024-06-07T05:46:11Z	152	1	transformers	[ "transformers", "safetensors", "bert", "text-classification", "tr", "dataset:nanelimon/insult-dataset", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-23T08:13:05Z	--- license: mit datasets: - nanelimon/insult-dataset language: - tr pipeline_tag: text-classification --- # About the model This model is designed for text classification, specifically for identifying offensive content in Turkish text. The model classifies text into five categories: INSULT, OTHER, PROFANITY, RACIST, and SEXIST. ## Model Metrics \| \| INSULT \| OTHER \| PROFANITY \| RACIST \| SEXIST \| \| ------ \| ------ \| ------ \| ------ \| ------ \| ------ \| \| Precision \| 0.901 \| 0.924 \| 0.978 \| 1.000 \| 0.980 \| \| Recall \| 0.920 \| 0.980 \| 0.900 \| 0.980 \| 1.000 \| \| F1 Score \| 0.910 \| 0.9514 \| 0.937 \| 0.989 \| 0.990 \| - F-Score: 0.9559690799177005 - Recall: 0.9559999999999998 - Precision: 0.9570284225256961 - Accuracy: 0.956 ## Training Information - Device : macOS 14.5 23F79 arm64 \| GPU: Apple M2 Max \| Memory: 5840MiB / 32768MiB - Training completed in 0:22:54 (hh:mm:ss) - Optimizer: AdamW - learning_rate: 2e-5 - eps: 1e-8 - epochs: 10 - Batch size: 64 ## Dependency ```sh pip install torch torchvision torchaudio pip install tf-keras pip install transformers pip install tensorflow ``` ## Example ```sh from transformers import AutoTokenizer, TFAutoModelForSequenceClassification, TextClassificationPipeline # Load the tokenizer and model model_name = "nanelimon/bert-base-turkish-offensive" tokenizer = AutoTokenizer.from_pretrained(model_name) model = TFAutoModelForSequenceClassification.from_pretrained(model_name) # Create the pipeline pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True, top_k=2) # Test the pipeline print(pipe('Bu bir denemedir hadi sende dene!')) ``` Result; ```sh [[{'label': 'OTHER', 'score': 1.000}, {'label': 'INSULT', 'score': 0.000}]] ``` - label= It shows which class the sent Turkish text belongs to according to the model. - score= It shows the compliance rate of the Turkish text sent to the label found. ## Authors - Seyma SARIGIL: [email protected] ## License gpl-3.0 Free Software, Hell Yeah!
richardkelly/google-gemma-7b-1717710399	richardkelly	2024-06-07T05:43:45Z	7	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-06T21:46:42Z	--- 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. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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srikar-v05/Mistral-Medical-Chat	srikar-v05	2024-06-07T05:37:52Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-bnb-4bit", "base_model:finetune:unsloth/mistral-7b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-06-07T05:37:40Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/mistral-7b-bnb-4bit --- # Uploaded model - Developed by: srikar-v05 - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-bnb-4bit This mistral 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)
la-min/BLINKpedia-chat	la-min	2024-06-07T05:36:52Z	153	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-07T05:02:36Z	--- library_name: transformers tags: - trl - sft --- # BLINKpedia Model ![BLINKpedia](https://huggingface.co/la-min/BLINKpedia/resolve/main/BLINKpedia.png) This model is designed to generate text content related to BLACKPINK, a globally renowned K-pop girl group. It leverages state-of-the-art natural language processing techniques to produce coherent and contextually relevant text based on input prompts. ## Model Details - Model Name: BLINKpedia - Finetuned From Model: [unsloth/tinyllama](https://huggingface.co/unsloth/tinyllama) - Model Type: Text Generation - Training Data: Curated datasets containing information about BLACKPINK, including lyrics, interviews, news articles, and fan content. - Framework: Hugging Face Transformers ## Features - Context-Aware Generation: Generates text that is coherent and contextually relevant to the given prompt. - Customizable Prompts: Users can input various prompts related to BLACKPINK to generate different types of content, such as news articles, social media posts, fan fiction, and more. ## Usage To use the BLACKPINK Text Generation model, you can load it using the Hugging Face Transformers library. Here’s an example of how to use the model in Python: ```python from transformers import pipeline # Load the model generator = pipeline('text-generation', model='la-min/BLINKpedia') # Define your prompt prompt = "Blackpink is the highest-charting female Korean" # Generate text generated_text = generator(prompt, max_length=100, num_return_sequences=1) # Print the generated text print(generated_text[0]['generated_text']) ``` ## Example Outputs Generated Text: ```python Blackpink is the highest-charting female Korean act on the Billboard 200, with their debut album Born Pink (2018) debuting at number one on the Circle Album Chart and the group's second album Born ``` ## Fine-Tuning You can fine-tune this model with additional data to better suit specific needs or to improve its performance on particular types of content. Refer to the Hugging Face documentation for guidance on fine-tuning models. ## Contributing If you'd like to contribute to the development of this model, please reach out or submit a pull request. Contributions can include improvements to the model, new training data, or enhancements to the documentation. ## Contributors - [La Min Ko Ko](https://www.linkedin.com/in/la-min-ko-ko-907827205/) - [Kyu Kyu Swe](https://www.linkedin.com/in/kyu-kyu-swe-533718171/)
linachengq/corgy_CHIIKAWA_LoRA	linachengq	2024-06-07T05:29:23Z	1	0	diffusers	[ "diffusers", "tensorboard", "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	2024-06-06T15:43:34Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - diffusers-training - diffusers - dora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers - 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 PKM 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 - linachengq/corgy_CHIIKAWA_LoRA <Gallery /> ## Model description These are linachengq/corgy_CHIIKAWA_LoRA 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 PKM to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](linachengq/corgy_CHIIKAWA_LoRA/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]
djsull/sentence-simcse-roberta-base	djsull	2024-06-07T05:28:07Z	20	0	sentence-transformers	[ "sentence-transformers", "safetensors", "roberta", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-06-07T05:27:48Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # djsull/sentence-simcse-roberta-base This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 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('djsull/sentence-simcse-roberta-base') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('djsull/sentence-simcse-roberta-base') model = AutoModel.from_pretrained('djsull/sentence-simcse-roberta-base') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_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=djsull/sentence-simcse-roberta-base) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
ansilmbabl/vit-base-patch16-224-in21k-cards-june-06-cropping-filtered-test	ansilmbabl	2024-06-07T05:25:07Z	222	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-06-06T13:04:49Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer model-index: - name: vit-base-patch16-224-in21k-cards-june-06-cropping-filtered-test 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. --> # vit-base-patch16-224-in21k-cards-june-06-cropping-filtered-test This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 1.6758 - eval_accuracy: 0.3141 - eval_runtime: 71.2335 - eval_samples_per_second: 140.383 - eval_steps_per_second: 0.562 - epoch: 5.9981 - step: 779 ## 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: 640 - eval_batch_size: 256 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 5120 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Framework versions - Transformers 4.41.2 - Pytorch 2.0.1+cu117 - Datasets 2.19.1 - Tokenizers 0.19.1
tsavage68/UTI2_M2_1000steps_1e5rate_01beta_CSFTDPO	tsavage68	2024-06-07T05:24:36Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "trl", "dpo", "generated_from_trainer", "conversational", "base_model:tsavage68/UTI_M2_1000steps_1e7rate_SFT", "base_model:finetune:tsavage68/UTI_M2_1000steps_1e7rate_SFT", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-07T05:19:00Z	--- license: apache-2.0 base_model: tsavage68/UTI_M2_1000steps_1e7rate_SFT tags: - trl - dpo - generated_from_trainer model-index: - name: UTI2_M2_1000steps_1e5rate_01beta_CSFTDPO results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # UTI2_M2_1000steps_1e5rate_01beta_CSFTDPO This model is a fine-tuned version of [tsavage68/UTI_M2_1000steps_1e7rate_SFT](https://huggingface.co/tsavage68/UTI_M2_1000steps_1e7rate_SFT) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.9857 - Rewards/chosen: -5.2195 - Rewards/rejected: -3.4974 - Rewards/accuracies: 0.0400 - Rewards/margins: -1.7222 - Logps/rejected: -74.3298 - Logps/chosen: -72.1167 - Logits/rejected: 1.1725 - Logits/chosen: 1.1724 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.8718 \| 0.3333 \| 25 \| 0.8484 \| -2.9302 \| -14.2948 \| 0.8400 \| 11.3646 \| -182.3043 \| -49.2239 \| -2.7108 \| -2.7153 \| \| 1.8764 \| 0.6667 \| 50 \| 2.0267 \| -3.9079 \| -2.1496 \| 0.0500 \| -1.7583 \| -60.8517 \| -59.0005 \| -0.0119 \| -0.0121 \| \| 2.368 \| 1.0 \| 75 \| 2.1981 \| -4.0485 \| -2.1677 \| 0.1300 \| -1.8808 \| -61.0330 \| -60.4067 \| -0.7578 \| -0.7578 \| \| 1.802 \| 1.3333 \| 100 \| 2.2809 \| -4.0920 \| -2.1613 \| 0.1600 \| -1.9306 \| -60.9696 \| -60.8411 \| -0.8665 \| -0.8665 \| \| 1.8302 \| 1.6667 \| 125 \| 2.1253 \| -4.1468 \| -2.3140 \| 0.1000 \| -1.8328 \| -62.4957 \| -61.3891 \| -0.6683 \| -0.6683 \| \| 2.109 \| 2.0 \| 150 \| 2.0797 \| -4.2257 \| -2.4259 \| 0.1000 \| -1.7999 \| -63.6147 \| -62.1788 \| -0.5669 \| -0.5669 \| \| 1.7801 \| 2.3333 \| 175 \| 2.0029 \| -4.2312 \| -2.4934 \| 0.0500 \| -1.7378 \| -64.2898 \| -62.2331 \| -0.6146 \| -0.6146 \| \| 2.0161 \| 2.6667 \| 200 \| 2.1079 \| -4.1571 \| -2.3364 \| 0.1000 \| -1.8207 \| -62.7205 \| -61.4927 \| -0.6148 \| -0.6148 \| \| 2.1333 \| 3.0 \| 225 \| 2.0488 \| -4.3309 \| -2.5546 \| 0.0700 \| -1.7763 \| -64.9022 \| -63.2307 \| -0.4279 \| -0.4279 \| \| 1.9667 \| 3.3333 \| 250 \| 2.0994 \| -4.1512 \| -2.3367 \| 0.0900 \| -1.8144 \| -62.7236 \| -61.4335 \| -0.6099 \| -0.6099 \| \| 1.975 \| 3.6667 \| 275 \| 2.0435 \| -4.3243 \| -2.5523 \| 0.0600 \| -1.7720 \| -64.8788 \| -63.1645 \| -0.4185 \| -0.4184 \| \| 1.8051 \| 4.0 \| 300 \| 1.9829 \| -4.3085 \| -2.5886 \| 0.0400 \| -1.7199 \| -65.2420 \| -63.0064 \| -0.4027 \| -0.4027 \| \| 1.953 \| 4.3333 \| 325 \| 2.0072 \| -4.3371 \| -2.5954 \| 0.0500 \| -1.7417 \| -65.3105 \| -63.2929 \| -0.4070 \| -0.4070 \| \| 2.2799 \| 4.6667 \| 350 \| 2.1923 \| -7.3999 \| -5.5246 \| 0.1300 \| -1.8754 \| -94.6021 \| -93.9210 \| -3.4528 \| -3.4531 \| \| 1.921 \| 5.0 \| 375 \| 2.2218 \| -5.5567 \| -3.6593 \| 0.1300 \| -1.8974 \| -75.9492 \| -75.4888 \| -1.5346 \| -1.5339 \| \| 1.8429 \| 5.3333 \| 400 \| 1.9854 \| -7.6870 \| -5.9651 \| 0.0400 \| -1.7218 \| -99.0076 \| -96.7912 \| -3.1616 \| -3.1613 \| \| 1.8022 \| 5.6667 \| 425 \| 1.9533 \| -4.2767 \| -2.5861 \| 0.0200 \| -1.6907 \| -65.2171 \| -62.6890 \| 0.9412 \| 0.9412 \| \| 2.3129 \| 6.0 \| 450 \| 1.9431 \| -4.4284 \| -2.7482 \| 0.0200 \| -1.6803 \| -66.8379 \| -64.2059 \| 0.4988 \| 0.4988 \| \| 1.906 \| 6.3333 \| 475 \| 2.0904 \| -7.0674 \| -5.2585 \| 0.0900 \| -1.8088 \| -91.9414 \| -90.5951 \| -3.6276 \| -3.6276 \| \| 1.6599 \| 6.6667 \| 500 \| 2.3257 \| -4.5302 \| -2.5743 \| 0.1600 \| -1.9559 \| -65.0988 \| -65.2237 \| 0.2828 \| 0.2828 \| \| 2.1192 \| 7.0 \| 525 \| 2.4249 \| -4.6675 \| -2.6590 \| 0.1900 \| -2.0086 \| -65.9460 \| -66.5970 \| 0.4401 \| 0.4401 \| \| 1.734 \| 7.3333 \| 550 \| 2.4649 \| -4.6820 \| -2.6533 \| 0.2100 \| -2.0287 \| -65.8892 \| -66.7413 \| 0.4168 \| 0.4168 \| \| 2.0797 \| 7.6667 \| 575 \| 1.9457 \| -5.0708 \| -3.3879 \| 0.0200 \| -1.6829 \| -73.2348 \| -70.6292 \| 1.0740 \| 1.0740 \| \| 1.9905 \| 8.0 \| 600 \| 1.8612 \| -5.3637 \| -3.7940 \| 0.0600 \| -1.5697 \| -77.2963 \| -73.5585 \| 1.4106 \| 1.4106 \| \| 1.9525 \| 8.3333 \| 625 \| 1.9808 \| -5.1006 \| -3.3827 \| 0.0400 \| -1.7179 \| -73.1830 \| -70.9278 \| 1.1564 \| 1.1564 \| \| 2.0246 \| 8.6667 \| 650 \| 2.0176 \| -5.0560 \| -3.3053 \| 0.0500 \| -1.7507 \| -72.4090 \| -70.4813 \| 1.0910 \| 1.0910 \| \| 1.9163 \| 9.0 \| 675 \| 1.9146 \| -5.2114 \| -3.5636 \| 0.0600 \| -1.6478 \| -74.9921 \| -72.0358 \| 1.2619 \| 1.2618 \| \| 1.9831 \| 9.3333 \| 700 \| 2.1370 \| -4.9749 \| -3.1338 \| 0.1100 \| -1.8411 \| -70.6938 \| -69.6701 \| 0.9305 \| 0.9305 \| \| 2.1009 \| 9.6667 \| 725 \| 2.0270 \| -5.0976 \| -3.3389 \| 0.0500 \| -1.7587 \| -72.7453 \| -70.8974 \| 1.0811 \| 1.0810 \| \| 1.8532 \| 10.0 \| 750 \| 1.9858 \| -5.1569 \| -3.4344 \| 0.0400 \| -1.7226 \| -73.6998 \| -71.4908 \| 1.1467 \| 1.1467 \| \| 1.8101 \| 10.3333 \| 775 \| 1.9913 \| -5.1561 \| -3.4284 \| 0.0400 \| -1.7277 \| -73.6404 \| -71.4823 \| 1.1431 \| 1.1431 \| \| 1.7788 \| 10.6667 \| 800 \| 1.9572 \| -5.2409 \| -3.5461 \| 0.0200 \| -1.6948 \| -74.8174 \| -72.3310 \| 1.2172 \| 1.2171 \| \| 1.9172 \| 11.0 \| 825 \| 1.9851 \| -5.1923 \| -3.4705 \| 0.0400 \| -1.7218 \| -74.0612 \| -71.8445 \| 1.1654 \| 1.1654 \| \| 1.9927 \| 11.3333 \| 850 \| 1.9926 \| -5.1865 \| -3.4579 \| 0.0400 \| -1.7287 \| -73.9347 \| -71.7869 \| 1.1538 \| 1.1538 \| \| 1.7894 \| 11.6667 \| 875 \| 1.9762 \| -5.2363 \| -3.5228 \| 0.0300 \| -1.7135 \| -74.5845 \| -72.2844 \| 1.1749 \| 1.1749 \| \| 1.7495 \| 12.0 \| 900 \| 1.9855 \| -5.2126 \| -3.4905 \| 0.0400 \| -1.7220 \| -74.2616 \| -72.0471 \| 1.1714 \| 1.1713 \| \| 1.8748 \| 12.3333 \| 925 \| 1.9857 \| -5.2150 \| -3.4928 \| 0.0400 \| -1.7222 \| -74.2844 \| -72.0716 \| 1.1714 \| 1.1713 \| \| 1.8576 \| 12.6667 \| 950 \| 1.9853 \| -5.2202 \| -3.4983 \| 0.0400 \| -1.7218 \| -74.3394 \| -72.1231 \| 1.1732 \| 1.1732 \| \| 1.9874 \| 13.0 \| 975 \| 1.9855 \| -5.2193 \| -3.4973 \| 0.0400 \| -1.7219 \| -74.3294 \| -72.1140 \| 1.1725 \| 1.1724 \| \| 1.8102 \| 13.3333 \| 1000 \| 1.9857 \| -5.2195 \| -3.4974 \| 0.0400 \| -1.7222 \| -74.3298 \| -72.1167 \| 1.1725 \| 1.1724 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.0.0+cu117 - Datasets 2.19.2 - Tokenizers 0.19.1
ka05ar/Banglat5_Dx1	ka05ar	2024-06-07T05:23:22Z	115	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-06-07T05:20:56Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
sebdg/scm_phi3_q8_v3	sebdg	2024-06-07T05:22:31Z	4	1	transformers	[ "transformers", "gguf", "mistral", "text-generation-inference", "unsloth", "en", "base_model:unsloth/Phi-3-mini-4k-instruct", "base_model:quantized:unsloth/Phi-3-mini-4k-instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-06-07T05:19:30Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - gguf base_model: unsloth/Phi-3-mini-4k-instruct --- # Uploaded model - Developed by: sebdg - License: apache-2.0 - Finetuned from model : unsloth/Phi-3-mini-4k-instruct This mistral 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)
tsavage68/UTI2_M2_75steps_1e7rate_01beta_CSFTDPO	tsavage68	2024-06-07T05:18:21Z	7	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "trl", "dpo", "generated_from_trainer", "conversational", "base_model:tsavage68/UTI_M2_1000steps_1e7rate_SFT", "base_model:finetune:tsavage68/UTI_M2_1000steps_1e7rate_SFT", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-07T05:14:18Z	--- license: apache-2.0 base_model: tsavage68/UTI_M2_1000steps_1e7rate_SFT tags: - trl - dpo - generated_from_trainer model-index: - name: UTI2_M2_75steps_1e7rate_01beta_CSFTDPO results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # UTI2_M2_75steps_1e7rate_01beta_CSFTDPO This model is a fine-tuned version of [tsavage68/UTI_M2_1000steps_1e7rate_SFT](https://huggingface.co/tsavage68/UTI_M2_1000steps_1e7rate_SFT) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1276 - Rewards/chosen: 0.0336 - Rewards/rejected: -4.2997 - Rewards/accuracies: 0.8800 - Rewards/margins: 4.3333 - Logps/rejected: -82.3535 - Logps/chosen: -19.5858 - Logits/rejected: -2.5678 - Logits/chosen: -2.5670 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-07 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 75 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.6904 \| 0.3333 \| 25 \| 0.6532 \| 0.0086 \| -0.0741 \| 0.8500 \| 0.0827 \| -40.0972 \| -19.8359 \| -2.6814 \| -2.6788 \| \| 0.4057 \| 0.6667 \| 50 \| 0.3414 \| 0.0792 \| -0.9298 \| 0.8800 \| 1.0089 \| -48.6537 \| -19.1297 \| -2.6601 \| -2.6575 \| \| 0.0995 \| 1.0 \| 75 \| 0.1276 \| 0.0336 \| -4.2997 \| 0.8800 \| 4.3333 \| -82.3535 \| -19.5858 \| -2.5678 \| -2.5670 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.0.0+cu117 - Datasets 2.19.2 - Tokenizers 0.19.1
sharon769/769	sharon769	2024-06-07T05:09:54Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-07T05:09:54Z	--- license: apache-2.0 ---
gaianet/Qwen2-1.5B-Instruct-GGUF	gaianet	2024-06-07T04:54:05Z	68	0	transformers	[ "transformers", "gguf", "qwen2", "text-generation", "chat", "en", "base_model:Qwen/Qwen2-1.5B-Instruct", "base_model:quantized:Qwen/Qwen2-1.5B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-06-07T04:44:55Z	--- base_model: Qwen/Qwen2-1.5B-Instruct license: apache-2.0 model_creator: Qwen model_name: Qwen2-1.5B-Instruct quantized_by: Second State Inc. language: - en pipeline_tag: text-generation tags: - chat --- ![](https://github.com/GaiaNet-AI/.github/assets/45785633/d6976adc-f97d-4f86-a648-0f2f5c8e7eee) # Qwen2-1.5B-Instruct-GGUF ## Original Model [Qwen/Qwen2-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2-1.5B-Instruct) ## Run with Gaianet Prompt template prompt template: `chatml` Context size chat_ctx_size: `32000` Run with GaiaNet - Quick start: https://docs.gaianet.ai/node-guide/quick-start - Customize your node: https://docs.gaianet.ai/node-guide/customize
chainup244/google-gemma-7b-1717735533	chainup244	2024-06-07T04:48:44Z	7	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-07T04:45:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
jurieyel/77cdm-llama3-sqlcoder-8b-4bit	jurieyel	2024-06-07T04:48:37Z	0	1	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:defog/llama-3-sqlcoder-8b", "base_model:finetune:defog/llama-3-sqlcoder-8b", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-27T08:14:05Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: defog/llama-3-sqlcoder-8b --- # Uploaded model - Developed by: jurieyel - License: apache-2.0 - Finetuned from model : defog/llama-3-sqlcoder-8b 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)
srikar-v05/Gemma-2b-Medical-Chat	srikar-v05	2024-06-07T04:45:32Z	104	0	transformers	[ "transformers", "pytorch", "safetensors", "gemma", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/gemma-2b-it-bnb-4bit", "base_model:finetune:unsloth/gemma-2b-it-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-06-07T04:42:00Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma - trl - sft base_model: unsloth/gemma-2b-it-bnb-4bit --- # Uploaded model - Developed by: srikar-v05 - License: apache-2.0 - Finetuned from model : unsloth/gemma-2b-it-bnb-4bit This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
sinsheng/bart-cnn-samsum-finetuned	sinsheng	2024-06-07T04:42:05Z	112	0	transformers	[ "transformers", "tensorboard", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "dataset:samsum", "base_model:facebook/bart-large-cnn", "base_model:finetune:facebook/bart-large-cnn", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-06-07T04:41:07Z	--- license: mit base_model: facebook/bart-large-cnn tags: - generated_from_trainer datasets: - samsum model-index: - name: bart-cnn-samsum-finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-cnn-samsum-finetuned This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 0.1344 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.0887 \| 1.0 \| 37 \| 0.1344 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1
sebdg/scm_phi3_q4_k_m	sebdg	2024-06-07T04:41:53Z	6	0	transformers	[ "transformers", "gguf", "mistral", "text-generation-inference", "unsloth", "en", "base_model:unsloth/Phi-3-mini-4k-instruct", "base_model:quantized:unsloth/Phi-3-mini-4k-instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-06-07T04:40:05Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - gguf base_model: unsloth/Phi-3-mini-4k-instruct --- # Uploaded model - Developed by: sebdg - License: apache-2.0 - Finetuned from model : unsloth/Phi-3-mini-4k-instruct This mistral 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)

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