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train · 844k rows

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Rhma/MistralaDialo5	Rhma	2024-05-18T14:56:05Z	9	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T14:52:20Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Aurelia25/Smile_Twitter_Sentiment_Analysis	Aurelia25	2024-05-18T14:55:02Z	92	0	transformers	[ "transformers", "safetensors", "bert", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-05-18T14:46:10Z	This directory includes a few sample datasets to get you started. * `california_housing_data.csv` is California housing data from the 1990 US Census; more information is available at: https://developers.google.com/machine-learning/crash-course/california-housing-data-description `mnist_.csv` is a small sample of the [MNIST database](https://en.wikipedia.org/wiki/MNIST_database), which is described at: http://yann.lecun.com/exdb/mnist/ `anscombe.json` contains a copy of [Anscombe's quartet](https://en.wikipedia.org/wiki/Anscombe%27s_quartet); it was originally described in Anscombe, F. J. (1973). 'Graphs in Statistical Analysis'. American Statistician. 27 (1): 17-21. JSTOR 2682899. and our copy was prepared by the [vega_datasets library](https://github.com/altair-viz/vega_datasets/blob/4f67bdaad10f45e3549984e17e1b3088c731503d/vega_datasets/_data/anscombe.json).
Dandan0K/Pilot_vox_Ref_french	Dandan0K	2024-05-18T14:55:00Z	79	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "fr", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-18T13:52:46Z	--- language: - fr license: apache-2.0 tags: - automatic-speech-recognition - fr datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2t_fr_vp-100k_s973 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
kapliff89/bert-finetuned-ner	kapliff89	2024-05-18T14:54:35Z	110	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "dataset:conll2003", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-05-18T14:47:58Z	--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: validation args: conll2003 metrics: - name: Precision type: precision value: 0.9373134328358209 - name: Recall type: recall value: 0.9511948838774823 - name: F1 type: f1 value: 0.9442031406615435 - name: Accuracy type: accuracy value: 0.9867398598928593 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0603 - Precision: 0.9373 - Recall: 0.9512 - F1: 0.9442 - Accuracy: 0.9867 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.0761 \| 1.0 \| 1756 \| 0.0671 \| 0.9029 \| 0.9330 \| 0.9177 \| 0.9812 \| \| 0.0349 \| 2.0 \| 3512 \| 0.0644 \| 0.9315 \| 0.9473 \| 0.9393 \| 0.9860 \| \| 0.0209 \| 3.0 \| 5268 \| 0.0603 \| 0.9373 \| 0.9512 \| 0.9442 \| 0.9867 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.3.0+cu118 - Datasets 2.19.1 - Tokenizers 0.19.1
SamirLahouar/Reinforce-unit4	SamirLahouar	2024-05-18T14:53:09Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2024-05-18T14:52:59Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-unit4 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
AliSaadatV/virus_pythia_14_1024_2d_representation_MSEPlusCE	AliSaadatV	2024-05-18T14:52:48Z	128	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "generated_from_trainer", "base_model:EleutherAI/pythia-14m", "base_model:finetune:EleutherAI/pythia-14m", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T14:52:47Z	--- base_model: EleutherAI/pythia-14m tags: - generated_from_trainer model-index: - name: virus_pythia_14_1024_2d_representation_MSEPlusCE results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # virus_pythia_14_1024_2d_representation_MSEPlusCE This model is a fine-tuned version of [EleutherAI/pythia-14m](https://huggingface.co/EleutherAI/pythia-14m) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
RichardErkhov/guardrail_-_llama-2-7b-guanaco-instruct-sharded-8bits	RichardErkhov	2024-05-18T14:51:38Z	78	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-18T14:44:25Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) llama-2-7b-guanaco-instruct-sharded - bnb 8bits - Model creator: https://huggingface.co/guardrail/ - Original model: https://huggingface.co/guardrail/llama-2-7b-guanaco-instruct-sharded/ Original model description: --- license: apache-2.0 datasets: - timdettmers/openassistant-guanaco pipeline_tag: text-generation --- Model that is fine-tuned in 4-bit precision using QLoRA on [timdettmers/openassistant-guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) and sharded to be used on a free Google Colab instance that can be loaded with 4bits. It can be easily imported using the `AutoModelForCausalLM` class from `transformers`: ``` from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained( "guardrail/llama-2-7b-guanaco-instruct-sharded", load_in_4bit=True) tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) ```
Edgar404/a2c-PandaPickAndPlace-v3	Edgar404	2024-05-18T14:51:17Z	1	0	stable-baselines3	[ "stable-baselines3", "PandaPickAndPlace-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-18T14:46:40Z	--- library_name: stable-baselines3 tags: - PandaPickAndPlace-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaPickAndPlace-v3 type: PandaPickAndPlace-v3 metrics: - type: mean_reward value: -50.00 +/- 0.00 name: mean_reward verified: false --- # A2C Agent playing PandaPickAndPlace-v3 This is a trained model of a A2C agent playing PandaPickAndPlace-v3 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
DownwardSpiral33/gpt2-imdb-pos-v2-003	DownwardSpiral33	2024-05-18T14:50:46Z	130	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T14:50:29Z	--- 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]
rnribeiro/FT-distilbert-base-uncased	rnribeiro	2024-05-18T14:50:17Z	119	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-18T13:16:45Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: FT-distilbert-base-uncased results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # FT-distilbert-base-uncased This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6614 - Accuracy: 0.65 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 40 \| 0.6806 \| 0.5 \| \| No log \| 2.0 \| 80 \| 0.6614 \| 0.65 \| \| No log \| 3.0 \| 120 \| 0.6672 \| 0.55 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2+cu118 - Datasets 2.18.0 - Tokenizers 0.15.2
rnribeiro/FT-mrm8488-distilroberta-finetuned-financial-news-sentiment-analysis	rnribeiro	2024-05-18T14:50:14Z	111	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis", "base_model:finetune:mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-18T13:16:44Z	--- license: apache-2.0 base_model: mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis tags: - generated_from_trainer metrics: - accuracy model-index: - name: FT-mrm8488-distilroberta-finetuned-financial-news-sentiment-analysis results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # FT-mrm8488-distilroberta-finetuned-financial-news-sentiment-analysis This model is a fine-tuned version of [mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis](https://huggingface.co/mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2034 - Accuracy: 0.95 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 40 \| 0.2034 \| 0.95 \| \| No log \| 2.0 \| 80 \| 0.2108 \| 0.925 \| \| No log \| 3.0 \| 120 \| 0.2077 \| 0.95 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2+cu118 - Datasets 2.18.0 - Tokenizers 0.15.2
Yann2310/Reinforce	Yann2310	2024-05-18T14:49:24Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2024-05-18T14:49:22Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 17.30 +/- 5.37 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
JhonVanced/sin2piusc-whisper-large-v2-10k-ct2	JhonVanced	2024-05-18T14:48:30Z	16	0	ctranslate2	[ "ctranslate2", "audio", "automatic-speech-recognition", "en", "zh", "de", "es", "ru", "ko", "fr", "ja", "pt", "tr", "pl", "ca", "nl", "ar", "sv", "it", "id", "hi", "fi", "vi", "he", "uk", "el", "ms", "cs", "ro", "da", "hu", "ta", "no", "th", "ur", "hr", "bg", "lt", "la", "mi", "ml", "cy", "sk", "te", "fa", "lv", "bn", "sr", "az", "sl", "kn", "et", "mk", "br", "eu", "is", "hy", "ne", "mn", "bs", "kk", "sq", "sw", "gl", "mr", "pa", "si", "km", "sn", "yo", "so", "af", "oc", "ka", "be", "tg", "sd", "gu", "am", "yi", "lo", "uz", "fo", "ht", "ps", "tk", "nn", "mt", "sa", "lb", "my", "bo", "tl", "mg", "as", "tt", "haw", "ln", "ha", "ba", "jw", "su", "yue", "license:mit", "region:us" ]	automatic-speech-recognition	2024-05-08T13:52:11Z	--- language: - en - zh - de - es - ru - ko - fr - ja - pt - tr - pl - ca - nl - ar - sv - it - id - hi - fi - vi - he - uk - el - ms - cs - ro - da - hu - ta - 'no' - th - ur - hr - bg - lt - la - mi - ml - cy - sk - te - fa - lv - bn - sr - az - sl - kn - et - mk - br - eu - is - hy - ne - mn - bs - kk - sq - sw - gl - mr - pa - si - km - sn - yo - so - af - oc - ka - be - tg - sd - gu - am - yi - lo - uz - fo - ht - ps - tk - nn - mt - sa - lb - my - bo - tl - mg - as - tt - haw - ln - ha - ba - jw - su - yue tags: - audio - automatic-speech-recognition license: mit library_name: ctranslate2 --- Convert from: sin2piusc/whisper-large-v2-10k # Whisper large-v2 model for CTranslate2 This repository contains the conversion of [sin2piusc/whisper-large-v2-10k](https://huggingface.co/sin2piusc/whisper-large-v2-10k) to the [CTranslate2](https://github.com/OpenNMT/CTranslate2) model format. This model can be used in CTranslate2 or projects based on CTranslate2 such as [faster-whisper](https://github.com/systran/faster-whisper). ## Example ```python from faster_whisper import WhisperModel model = WhisperModel("large-v2") segments, info = model.transcribe("audio.mp3") for segment in segments: print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text)) ``` ## Conversion details The original model was converted with the following command: ``` ct2-transformers-converter --model sin2piusc/whisper-large-v2-10k --output_dir whisper-large-v2-10k-ct2 \ --copy_files tokenizer.json preprocessor_config.json --quantization float16 ``` Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the [`compute_type` option in CTranslate2](https://opennmt.net/CTranslate2/quantization.html). ## More information For more information about the original model, see its [model card](https://huggingface.co/sin2piusc/whisper-large-v2-10k).
JhonVanced/sin2piusc-whisper-large-v2-10k-ct2-int8_float32	JhonVanced	2024-05-18T14:47:11Z	18	0	ctranslate2	[ "ctranslate2", "audio", "automatic-speech-recognition", "en", "zh", "de", "es", "ru", "ko", "fr", "ja", "pt", "tr", "pl", "ca", "nl", "ar", "sv", "it", "id", "hi", "fi", "vi", "he", "uk", "el", "ms", "cs", "ro", "da", "hu", "ta", "no", "th", "ur", "hr", "bg", "lt", "la", "mi", "ml", "cy", "sk", "te", "fa", "lv", "bn", "sr", "az", "sl", "kn", "et", "mk", "br", "eu", "is", "hy", "ne", "mn", "bs", "kk", "sq", "sw", "gl", "mr", "pa", "si", "km", "sn", "yo", "so", "af", "oc", "ka", "be", "tg", "sd", "gu", "am", "yi", "lo", "uz", "fo", "ht", "ps", "tk", "nn", "mt", "sa", "lb", "my", "bo", "tl", "mg", "as", "tt", "haw", "ln", "ha", "ba", "jw", "su", "yue", "license:mit", "region:us" ]	automatic-speech-recognition	2024-05-18T13:53:58Z	--- language: - en - zh - de - es - ru - ko - fr - ja - pt - tr - pl - ca - nl - ar - sv - it - id - hi - fi - vi - he - uk - el - ms - cs - ro - da - hu - ta - 'no' - th - ur - hr - bg - lt - la - mi - ml - cy - sk - te - fa - lv - bn - sr - az - sl - kn - et - mk - br - eu - is - hy - ne - mn - bs - kk - sq - sw - gl - mr - pa - si - km - sn - yo - so - af - oc - ka - be - tg - sd - gu - am - yi - lo - uz - fo - ht - ps - tk - nn - mt - sa - lb - my - bo - tl - mg - as - tt - haw - ln - ha - ba - jw - su - yue tags: - audio - automatic-speech-recognition license: mit library_name: ctranslate2 --- Convert from: sin2piusc/whisper-large-v2-10k # Whisper large-v2 model for CTranslate2 This repository contains the conversion of [sin2piusc/whisper-large-v2-10k](https://huggingface.co/sin2piusc/whisper-large-v2-10k) to the [CTranslate2](https://github.com/OpenNMT/CTranslate2) model format. This model can be used in CTranslate2 or projects based on CTranslate2 such as [faster-whisper](https://github.com/systran/faster-whisper). ## Example ```python from faster_whisper import WhisperModel model = WhisperModel("large-v2") segments, info = model.transcribe("audio.mp3") for segment in segments: print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text)) ``` ## Conversion details The original model was converted with the following command: ``` ct2-transformers-converter --model sin2piusc/whisper-large-v2-10k --output_dir whisper-large-v2-10k-ct2-int8_float32 \ --copy_files tokenizer.json preprocessor_config.json --quantization int8_float32 ``` Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the [`compute_type` option in CTranslate2](https://opennmt.net/CTranslate2/quantization.html). ## More information For more information about the original model, see its [model card](https://huggingface.co/sin2piusc/whisper-large-v2-10k).
Astowny/Reinforce-cartpool	Astowny	2024-05-18T14:45:23Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2024-05-18T14:45:15Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-cartpool results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 451.10 +/- 146.70 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
ethan-ky/distilbert-base-uncased-finetuned-emotion	ethan-ky	2024-05-18T14:41:29Z	119	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-17T03:36:33Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9215 - name: F1 type: f1 value: 0.9213719420412787 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2083 - Accuracy: 0.9215 - F1: 0.9214 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8068 \| 1.0 \| 250 \| 0.2897 \| 0.9155 \| 0.9148 \| \| 0.2389 \| 2.0 \| 500 \| 0.2083 \| 0.9215 \| 0.9214 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.3.0 - Datasets 2.19.1 - Tokenizers 0.19.1
saousan/Reinforce-cartpool	saousan	2024-05-18T14:40:35Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2024-05-18T14:40:26Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-cartpool results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
jerryjiao198/Marcoro14-7B-slerp	jerryjiao198	2024-05-18T14:40:28Z	0	0	null	[ "merge", "mergekit", "lazymergekit", "AIDC-ai-business/Marcoroni-7B-v3", "EmbeddedLLM/Mistral-7B-Merge-14-v0.1", "license:apache-2.0", "region:us" ]	null	2024-05-16T02:58:43Z	--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - AIDC-ai-business/Marcoroni-7B-v3 - EmbeddedLLM/Mistral-7B-Merge-14-v0.1 --- # Marcoro14-7B-slerp Marcoro14-7B-slerp is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): * [AIDC-ai-business/Marcoroni-7B-v3](https://huggingface.co/AIDC-ai-business/Marcoroni-7B-v3) * [EmbeddedLLM/Mistral-7B-Merge-14-v0.1](https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0.1) ## 🧩 Configuration ```yaml slices: - sources: - model: AIDC-ai-business/Marcoroni-7B-v3 layer_range: [0, 32] - model: EmbeddedLLM/Mistral-7B-Merge-14-v0.1 layer_range: [0, 32] merge_method: slerp base_model: AIDC-ai-business/Marcoroni-7B-v3 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ```
Schadic/Reinforce-1	Schadic	2024-05-18T14:38:28Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2024-05-18T14:38:19Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 497.60 +/- 7.20 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
emilykang/Phi_medmcqa_question_generation_model	emilykang	2024-05-18T14:36:35Z	128	0	transformers	[ "transformers", "safetensors", "phi", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T14:27:07Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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Ransss/Fimbulvetr-10.7B-v1-Q8_0-GGUF	Ransss	2024-05-18T14:36:04Z	6	1	null	[ "gguf", "llama-cpp", "gguf-my-repo", "en", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	null	2024-05-18T14:35:35Z	--- language: - en license: cc-by-nc-4.0 tags: - llama-cpp - gguf-my-repo --- # Ransss/Fimbulvetr-10.7B-v1-Q8_0-GGUF This model was converted to GGUF format from [`Sao10K/Fimbulvetr-10.7B-v1`](https://huggingface.co/Sao10K/Fimbulvetr-10.7B-v1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Sao10K/Fimbulvetr-10.7B-v1) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo Ransss/Fimbulvetr-10.7B-v1-Q8_0-GGUF --model fimbulvetr-10.7b-v1.Q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo Ransss/Fimbulvetr-10.7B-v1-Q8_0-GGUF --model fimbulvetr-10.7b-v1.Q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m fimbulvetr-10.7b-v1.Q8_0.gguf -n 128 ```
RichardErkhov/macadeliccc_-_laser-dolphin-mixtral-2x7b-dpo-gguf	RichardErkhov	2024-05-18T14:31:42Z	38	0	null	[ "gguf", "arxiv:2312.13558", "endpoints_compatible", "region:us" ]	null	2024-05-18T11:58:04Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) laser-dolphin-mixtral-2x7b-dpo - GGUF - Model creator: https://huggingface.co/macadeliccc/ - Original model: https://huggingface.co/macadeliccc/laser-dolphin-mixtral-2x7b-dpo/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [laser-dolphin-mixtral-2x7b-dpo.Q2_K.gguf](https://huggingface.co/RichardErkhov/macadeliccc_-_laser-dolphin-mixtral-2x7b-dpo-gguf/blob/main/laser-dolphin-mixtral-2x7b-dpo.Q2_K.gguf) \| Q2_K \| 4.43GB \| \| [laser-dolphin-mixtral-2x7b-dpo.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/macadeliccc_-_laser-dolphin-mixtral-2x7b-dpo-gguf/blob/main/laser-dolphin-mixtral-2x7b-dpo.IQ3_XS.gguf) \| IQ3_XS \| 4.94GB \| \| [laser-dolphin-mixtral-2x7b-dpo.IQ3_S.gguf](https://huggingface.co/RichardErkhov/macadeliccc_-_laser-dolphin-mixtral-2x7b-dpo-gguf/blob/main/laser-dolphin-mixtral-2x7b-dpo.IQ3_S.gguf) \| IQ3_S \| 5.22GB \| \| [laser-dolphin-mixtral-2x7b-dpo.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/macadeliccc_-_laser-dolphin-mixtral-2x7b-dpo-gguf/blob/main/laser-dolphin-mixtral-2x7b-dpo.Q3_K_S.gguf) \| Q3_K_S \| 5.2GB \| \| [laser-dolphin-mixtral-2x7b-dpo.IQ3_M.gguf](https://huggingface.co/RichardErkhov/macadeliccc_-_laser-dolphin-mixtral-2x7b-dpo-gguf/blob/main/laser-dolphin-mixtral-2x7b-dpo.IQ3_M.gguf) \| IQ3_M \| 5.34GB \| \| [laser-dolphin-mixtral-2x7b-dpo.Q3_K.gguf](https://huggingface.co/RichardErkhov/macadeliccc_-_laser-dolphin-mixtral-2x7b-dpo-gguf/blob/main/laser-dolphin-mixtral-2x7b-dpo.Q3_K.gguf) \| Q3_K \| 5.78GB \| \| [laser-dolphin-mixtral-2x7b-dpo.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/macadeliccc_-_laser-dolphin-mixtral-2x7b-dpo-gguf/blob/main/laser-dolphin-mixtral-2x7b-dpo.Q3_K_M.gguf) \| Q3_K_M \| 5.78GB \| \| [laser-dolphin-mixtral-2x7b-dpo.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/macadeliccc_-_laser-dolphin-mixtral-2x7b-dpo-gguf/blob/main/laser-dolphin-mixtral-2x7b-dpo.Q3_K_L.gguf) \| Q3_K_L \| 6.27GB \| \| [laser-dolphin-mixtral-2x7b-dpo.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/macadeliccc_-_laser-dolphin-mixtral-2x7b-dpo-gguf/blob/main/laser-dolphin-mixtral-2x7b-dpo.IQ4_XS.gguf) \| IQ4_XS \| 6.5GB \| \| [laser-dolphin-mixtral-2x7b-dpo.Q4_0.gguf](https://huggingface.co/RichardErkhov/macadeliccc_-_laser-dolphin-mixtral-2x7b-dpo-gguf/blob/main/laser-dolphin-mixtral-2x7b-dpo.Q4_0.gguf) \| Q4_0 \| 6.78GB \| \| [laser-dolphin-mixtral-2x7b-dpo.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/macadeliccc_-_laser-dolphin-mixtral-2x7b-dpo-gguf/blob/main/laser-dolphin-mixtral-2x7b-dpo.IQ4_NL.gguf) \| IQ4_NL \| 6.85GB \| \| [laser-dolphin-mixtral-2x7b-dpo.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/macadeliccc_-_laser-dolphin-mixtral-2x7b-dpo-gguf/blob/main/laser-dolphin-mixtral-2x7b-dpo.Q4_K_S.gguf) \| Q4_K_S \| 6.84GB \| \| [laser-dolphin-mixtral-2x7b-dpo.Q4_K.gguf](https://huggingface.co/RichardErkhov/macadeliccc_-_laser-dolphin-mixtral-2x7b-dpo-gguf/blob/main/laser-dolphin-mixtral-2x7b-dpo.Q4_K.gguf) \| Q4_K \| 7.25GB \| \| [laser-dolphin-mixtral-2x7b-dpo.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/macadeliccc_-_laser-dolphin-mixtral-2x7b-dpo-gguf/blob/main/laser-dolphin-mixtral-2x7b-dpo.Q4_K_M.gguf) \| Q4_K_M \| 7.25GB \| \| [laser-dolphin-mixtral-2x7b-dpo.Q4_1.gguf](https://huggingface.co/RichardErkhov/macadeliccc_-_laser-dolphin-mixtral-2x7b-dpo-gguf/blob/main/laser-dolphin-mixtral-2x7b-dpo.Q4_1.gguf) \| Q4_1 \| 7.52GB \| \| [laser-dolphin-mixtral-2x7b-dpo.Q5_0.gguf](https://huggingface.co/RichardErkhov/macadeliccc_-_laser-dolphin-mixtral-2x7b-dpo-gguf/blob/main/laser-dolphin-mixtral-2x7b-dpo.Q5_0.gguf) \| Q5_0 \| 8.26GB \| \| [laser-dolphin-mixtral-2x7b-dpo.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/macadeliccc_-_laser-dolphin-mixtral-2x7b-dpo-gguf/blob/main/laser-dolphin-mixtral-2x7b-dpo.Q5_K_S.gguf) \| Q5_K_S \| 8.26GB \| \| [laser-dolphin-mixtral-2x7b-dpo.Q5_K.gguf](https://huggingface.co/RichardErkhov/macadeliccc_-_laser-dolphin-mixtral-2x7b-dpo-gguf/blob/main/laser-dolphin-mixtral-2x7b-dpo.Q5_K.gguf) \| Q5_K \| 8.51GB \| \| [laser-dolphin-mixtral-2x7b-dpo.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/macadeliccc_-_laser-dolphin-mixtral-2x7b-dpo-gguf/blob/main/laser-dolphin-mixtral-2x7b-dpo.Q5_K_M.gguf) \| Q5_K_M \| 8.51GB \| \| [laser-dolphin-mixtral-2x7b-dpo.Q5_1.gguf](https://huggingface.co/RichardErkhov/macadeliccc_-_laser-dolphin-mixtral-2x7b-dpo-gguf/blob/main/laser-dolphin-mixtral-2x7b-dpo.Q5_1.gguf) \| Q5_1 \| 9.01GB \| \| [laser-dolphin-mixtral-2x7b-dpo.Q6_K.gguf](https://huggingface.co/RichardErkhov/macadeliccc_-_laser-dolphin-mixtral-2x7b-dpo-gguf/blob/main/laser-dolphin-mixtral-2x7b-dpo.Q6_K.gguf) \| Q6_K \| 9.84GB \| \| [laser-dolphin-mixtral-2x7b-dpo.Q8_0.gguf](https://huggingface.co/RichardErkhov/macadeliccc_-_laser-dolphin-mixtral-2x7b-dpo-gguf/blob/main/laser-dolphin-mixtral-2x7b-dpo.Q8_0.gguf) \| Q8_0 \| 12.75GB \| Original model description: --- license: apache-2.0 library_name: transformers model-index: - name: laser-dolphin-mixtral-2x7b-dpo results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 65.96 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/laser-dolphin-mixtral-2x7b-dpo name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 85.8 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/laser-dolphin-mixtral-2x7b-dpo name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 63.17 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/laser-dolphin-mixtral-2x7b-dpo name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 60.76 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/laser-dolphin-mixtral-2x7b-dpo name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 79.01 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/laser-dolphin-mixtral-2x7b-dpo name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 48.29 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/laser-dolphin-mixtral-2x7b-dpo name: Open LLM Leaderboard --- # Laser-Dolphin-Mixtral-2x7b-dpo ![laser_dolphin_image](./dolphin_moe.png) New Version out now! Credit to Fernando Fernandes and Eric Hartford for their project [laserRMT](https://github.com/cognitivecomputations/laserRMT) ## Overview This model is a medium-sized MoE implementation based on [cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser](https://huggingface.co/cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser) + The new version shows ~1 point increase in evaluation performance on average. ## Process + The process is outlined in this [notebook](https://github.com/cognitivecomputations/laserRMT/blob/main/examples/laser-dolphin-mixtral-2x7b.ipynb) + The mergekit_config is in the files. + The models used in the configuration are not lasered, but the final product is. This is an update from the last version. + This process is experimental. Your mileage may vary. ## Future Goals + [ ] Function Calling + [ ] v2 with new base model to improve performance ## Quantizations ### ExLlamav2 _These are the recommended quantizations for users that are running the model on GPU_ Thanks to user [bartowski](https://huggingface.co/bartowski) we now have exllamav2 quantizations in 3.5 through 8 bpw. They are available here: + [bartowski/laser-dolphin-mixtral-2x7b-dpo-exl2](https://huggingface.co/bartowski/laser-dolphin-mixtral-2x7b-dpo-exl2) \| Branch \| Bits \| lm_head bits \| VRAM (4k) \| VRAM (16k) \| VRAM (32k) \| Description \| \| ----- \| ---- \| ------- \| ------ \| ------ \| ------ \| ------------ \| \| [8_0](https://huggingface.co/bartowski/laser-dolphin-mixtral-2x7b-dpo-exl2/tree/8_0) \| 8.0 \| 8.0 \| 13.7 GB \| 15.1 GB \| 17.2 GB \| Maximum quality that ExLlamaV2 can produce, near unquantized performance. \| \| [6_5](https://huggingface.co/bartowski/laser-dolphin-mixtral-2x7b-dpo-exl2/tree/6_5) \| 6.5 \| 8.0 \| 11.5 GB \| 12.9 GB \| 15.0 GB \| Near unquantized performance at vastly reduced size, recommended. \| \| [5_0](https://huggingface.co/bartowski/laser-dolphin-mixtral-2x7b-dpo-exl2/tree/5_0) \| 5.0 \| 6.0 \| 9.3 GB \| 10.7 GB \| 12.8 GB \| Slightly lower quality vs 6.5, great for 12gb cards with 16k context. \| \| [4_25](https://huggingface.co/bartowski/laser-dolphin-mixtral-2x7b-dpo-exl2/tree/4_25) \| 4.25 \| 6.0 \| 8.2 GB \| 9.6 GB \| 11.7 GB \| GPTQ equivalent bits per weight. \| \| [3_5](https://huggingface.co/bartowski/laser-dolphin-mixtral-2x7b-dpo-exl2/tree/3_5) \| 3.5 \| 6.0 \| 7.0 GB \| 8.4 GB \| 10.5 GB \| Lower quality, not recommended. \| His quantizations represent the first ~13B model with GQA support. Check out his repo for more information! ### GGUF Current GGUF [Quantizations](https://huggingface.co/macadeliccc/laser-dolphin-mixtral-2x7b-dpo-GGUF) ### AWQ Current AWQ [Quantizations](https://huggingface.co/macadeliccc/laser-dolphin-mixtral-2x7b-dpo-AWQ) ### TheBloke These Quants will result in unpredicted behavior. New quants are available as I have updated the model* Quatizations provided by [TheBloke](https://huggingface.co/TheBloke/laser-dolphin-mixtral-2x7b-dpo-GGUF) ## HF Spaces + GGUF chat available [here](https://huggingface.co/spaces/macadeliccc/laser-dolphin-mixtral-chat-GGUF) + 4-bit bnb chat available [here](https://huggingface.co/spaces/macadeliccc/laser-dolphin-mixtral-chat) # Ollama ```bash ollama run macadeliccc/laser-dolphin-mixtral-2x7b-dpo ``` ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6455cc8d679315e4ef16fbec/oVwa7Dwkt00tk8_MtlJdR.png) ## Code Example Switch the commented model definition to use in 4-bit. Should work with 9GB and still exceed the single 7B model by 5-6 points roughly ```python from transformers import AutoModelForCausalLM, AutoTokenizer def generate_response(prompt): """ Generate a response from the model based on the input prompt. Args: prompt (str): Prompt for the model. Returns: str: The generated response from the model. """ # Tokenize the input prompt inputs = tokenizer(prompt, return_tensors="pt") # Generate output tokens outputs = model.generate(**inputs, max_new_tokens=256, eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.pad_token_id) # Decode the generated tokens to a string response = tokenizer.decode(outputs[0], skip_special_tokens=True) return response # Load the model and tokenizer model_id = "macadeliccc/laser-dolphin-mixtral-2x7b-dpo" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id, load_in_4bit=True) prompt = "Write a quicksort algorithm in python" # Generate and print responses for each language print("Response:") print(generate_response(prompt), "\n") ``` [colab](https://colab.research.google.com/drive/1cmRhAkDWItV7utHNqNANVZnqDqQNsTUr?usp=sharing) with usage example ## Eval ## EQ Bench <pre>----Benchmark Complete---- 2024-01-31 16:55:37 Time taken: 31.1 mins Prompt Format: ChatML Model: macadeliccc/laser-dolphin-mixtral-2x7b-dpo-GGUF Score (v2): 72.76 Parseable: 171.0 --------------- Batch completed Time taken: 31.2 mins --------------- </pre> evaluation [colab](https://colab.research.google.com/drive/1FpwgsGzCR4tORTxAwUxpN3PcP22En2xk?usp=sharing) ## Summary of previous evaluation \| Model \|AGIEval\|GPT4All\|TruthfulQA\|Bigbench\|Average\| \|---------------------------------------------------------------------------------------------------\|------:\|------:\|---------:\|-------:\|------:\| \|[laser-dolphin-mixtral-2x7b-dpo](https://huggingface.co/macadeliccc/laser-dolphin-mixtral-2x7b-dpo)\| 41.31\| 73.67\| 61.69\| 42.79\| 54.87\| ## Detailed current evaluation \| Model \|AGIEval\|GPT4All\|TruthfulQA\|Bigbench\|Average\| \|---------------------------------------------------------------------------------------------------\|------:\|------:\|---------:\|-------:\|------:\| \|[laser-dolphin-mixtral-2x7b-dpo](https://huggingface.co/macadeliccc/laser-dolphin-mixtral-2x7b-dpo)\| 42.25\| 73.45\| 63.44\| 43.96\| 55.77\| ### AGIEval \| Task \|Version\| Metric \|Value\| \|Stderr\| \|------------------------------\|------:\|--------\|----:\|---\|-----:\| \|agieval_aqua_rat \| 0\|acc \|21.26\|± \| 2.57\| \| \| \|acc_norm\|21.65\|± \| 2.59\| \|agieval_logiqa_en \| 0\|acc \|34.72\|± \| 1.87\| \| \| \|acc_norm\|35.64\|± \| 1.88\| \|agieval_lsat_ar \| 0\|acc \|26.96\|± \| 2.93\| \| \| \|acc_norm\|26.96\|± \| 2.93\| \|agieval_lsat_lr \| 0\|acc \|45.88\|± \| 2.21\| \| \| \|acc_norm\|46.08\|± \| 2.21\| \|agieval_lsat_rc \| 0\|acc \|59.48\|± \| 3.00\| \| \| \|acc_norm\|59.48\|± \| 3.00\| \|agieval_sat_en \| 0\|acc \|73.79\|± \| 3.07\| \| \| \|acc_norm\|73.79\|± \| 3.07\| \|agieval_sat_en_without_passage\| 0\|acc \|42.23\|± \| 3.45\| \| \| \|acc_norm\|41.26\|± \| 3.44\| \|agieval_sat_math \| 0\|acc \|37.27\|± \| 3.27\| \| \| \|acc_norm\|33.18\|± \| 3.18\| Average: 42.25% ### GPT4All \| Task \|Version\| Metric \|Value\| \|Stderr\| \|-------------\|------:\|--------\|----:\|---\|-----:\| \|arc_challenge\| 0\|acc \|58.36\|± \| 1.44\| \| \| \|acc_norm\|58.02\|± \| 1.44\| \|arc_easy \| 0\|acc \|82.20\|± \| 0.78\| \| \| \|acc_norm\|77.40\|± \| 0.86\| \|boolq \| 1\|acc \|87.52\|± \| 0.58\| \|hellaswag \| 0\|acc \|67.50\|± \| 0.47\| \| \| \|acc_norm\|84.43\|± \| 0.36\| \|openbookqa \| 0\|acc \|34.40\|± \| 2.13\| \| \| \|acc_norm\|47.00\|± \| 2.23\| \|piqa \| 0\|acc \|81.61\|± \| 0.90\| \| \| \|acc_norm\|82.59\|± \| 0.88\| \|winogrande \| 0\|acc \|77.19\|± \| 1.18\| Average: 73.45% ### GSM8K \|Task \|Version\| Metric \|Value\| \|Stderr\| \|-----\|------:\|-----------------------------\|-----\|---\|------\| \|gsm8k\| 2\|exact_match,get-answer \| 0.75\| \| \| \| \| \|exact_match_stderr,get-answer\| 0.01\| \| \| \| \| \|alias \|gsm8k\| \| \| ### TruthfulQA \| Task \|Version\|Metric\|Value\| \|Stderr\| \|-------------\|------:\|------\|----:\|---\|-----:\| \|truthfulqa_mc\| 1\|mc1 \|45.90\|± \| 1.74\| \| \| \|mc2 \|63.44\|± \| 1.56\| Average: 63.44% ### Bigbench \| Task \|Version\| Metric \|Value\| \|Stderr\| \|------------------------------------------------\|------:\|---------------------\|----:\|---\|-----:\| \|bigbench_causal_judgement \| 0\|multiple_choice_grade\|58.42\|± \| 3.59\| \|bigbench_date_understanding \| 0\|multiple_choice_grade\|60.70\|± \| 2.55\| \|bigbench_disambiguation_qa \| 0\|multiple_choice_grade\|38.37\|± \| 3.03\| \|bigbench_geometric_shapes \| 0\|multiple_choice_grade\|21.73\|± \| 2.18\| \| \| \|exact_str_match \| 0.00\|± \| 0.00\| \|bigbench_logical_deduction_five_objects \| 0\|multiple_choice_grade\|35.00\|± \| 2.14\| \|bigbench_logical_deduction_seven_objects \| 0\|multiple_choice_grade\|23.57\|± \| 1.61\| \|bigbench_logical_deduction_three_objects \| 0\|multiple_choice_grade\|50.33\|± \| 2.89\| \|bigbench_movie_recommendation \| 0\|multiple_choice_grade\|45.00\|± \| 2.23\| \|bigbench_navigate \| 0\|multiple_choice_grade\|50.00\|± \| 1.58\| \|bigbench_reasoning_about_colored_objects \| 0\|multiple_choice_grade\|60.35\|± \| 1.09\| \|bigbench_ruin_names \| 0\|multiple_choice_grade\|51.12\|± \| 2.36\| \|bigbench_salient_translation_error_detection \| 0\|multiple_choice_grade\|32.26\|± \| 1.48\| \|bigbench_snarks \| 0\|multiple_choice_grade\|67.96\|± \| 3.48\| \|bigbench_sports_understanding \| 0\|multiple_choice_grade\|70.59\|± \| 1.45\| \|bigbench_temporal_sequences \| 0\|multiple_choice_grade\|35.80\|± \| 1.52\| \|bigbench_tracking_shuffled_objects_five_objects \| 0\|multiple_choice_grade\|22.56\|± \| 1.18\| \|bigbench_tracking_shuffled_objects_seven_objects\| 0\|multiple_choice_grade\|17.20\|± \| 0.90\| \|bigbench_tracking_shuffled_objects_three_objects\| 0\|multiple_choice_grade\|50.33\|± \| 2.89\| Average: 43.96% Average score: 55.77% Elapsed time: 02:43:45 ## Citations Fernando Fernandes Neto and Eric Hartford. "Optimizing Large Language Models Using Layer-Selective Rank Reduction and Random Matrix Theory." 2024. ```bibtex @article{sharma2023truth, title={The Truth is in There: Improving Reasoning in Language Models with Layer-Selective Rank Reduction}, author={Sharma, Pratyusha and Ash, Jordan T and Misra, Dipendra}, journal={arXiv preprint arXiv:2312.13558}, year={2023} } ``` ```bibtex @article{gao2021framework, title={A framework for few-shot language model evaluation}, author={Gao, Leo and Tow, Jonathan and Biderman, Stella and Black, Sid and DiPofi, Anthony and Foster, Charles and Golding, Laurence and Hsu, Jeffrey and McDonell, Kyle and Muennighoff, Niklas and others}, journal={Version v0. 0.1. Sept}, year={2021} } ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_macadeliccc__laser-dolphin-mixtral-2x7b-dpo) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|67.16\| \|AI2 Reasoning Challenge (25-Shot)\|65.96\| \|HellaSwag (10-Shot) \|85.80\| \|MMLU (5-Shot) \|63.17\| \|TruthfulQA (0-shot) \|60.76\| \|Winogrande (5-shot) \|79.01\| \|GSM8k (5-shot) \|48.29\|
Ransss/Kuro-Lotus-10.7B-Q8_0-GGUF	Ransss	2024-05-18T14:31:06Z	0	0	null	[ "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:BlueNipples/SnowLotus-v2-10.7B", "base_model:merge:BlueNipples/SnowLotus-v2-10.7B", "base_model:Himitsui/KuroMitsu-11B", "base_model:merge:Himitsui/KuroMitsu-11B", "license:cc-by-nc-4.0", "model-index", "endpoints_compatible", "region:us" ]	null	2024-05-18T14:30:35Z	--- license: cc-by-nc-4.0 tags: - mergekit - merge - llama-cpp - gguf-my-repo base_model: - BlueNipples/SnowLotus-v2-10.7B - Himitsui/KuroMitsu-11B model-index: - name: Kuro-Lotus-10.7B results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 68.69 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/Kuro-Lotus-10.7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 87.51 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/Kuro-Lotus-10.7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 66.64 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/Kuro-Lotus-10.7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 58.27 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/Kuro-Lotus-10.7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 84.21 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/Kuro-Lotus-10.7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 66.11 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/Kuro-Lotus-10.7B name: Open LLM Leaderboard --- # Ransss/Kuro-Lotus-10.7B-Q8_0-GGUF This model was converted to GGUF format from [`saishf/Kuro-Lotus-10.7B`](https://huggingface.co/saishf/Kuro-Lotus-10.7B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/saishf/Kuro-Lotus-10.7B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo Ransss/Kuro-Lotus-10.7B-Q8_0-GGUF --model kuro-lotus-10.7b.Q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo Ransss/Kuro-Lotus-10.7B-Q8_0-GGUF --model kuro-lotus-10.7b.Q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m kuro-lotus-10.7b.Q8_0.gguf -n 128 ```
ucla-nb-project/bart-finetuned	ucla-nb-project	2024-05-18T14:29:52Z	16	0	transformers	[ "transformers", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "dataset:datasets/all_binary_and_xe_ey_fae_counterfactual", "base_model:facebook/bart-base", "base_model:finetune:facebook/bart-base", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-18T10:12:35Z	--- base_model: facebook/bart-base tags: - generated_from_trainer datasets: - datasets/all_binary_and_xe_ey_fae_counterfactual metrics: - accuracy model-index: - name: bart-base-finetuned-xe_ey_fae results: - task: name: Masked Language Modeling type: fill-mask dataset: name: datasets/all_binary_and_xe_ey_fae_counterfactual type: datasets/all_binary_and_xe_ey_fae_counterfactual metrics: - name: Accuracy type: accuracy value: 0.7180178883360112 --- <!-- 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-base-finetuned-xe_ey_fae This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the datasets/all_binary_and_xe_ey_fae_counterfactual dataset. It achieves the following results on the evaluation set: - Loss: 1.3945 - Accuracy: 0.7180 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 100 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 5.4226 \| 0.06 \| 500 \| 3.8138 \| 0.3628 \| \| 4.0408 \| 0.12 \| 1000 \| 3.0576 \| 0.4630 \| \| 3.4979 \| 0.18 \| 1500 \| 2.7016 \| 0.5133 \| \| 3.1691 \| 0.24 \| 2000 \| 2.4880 \| 0.5431 \| \| 2.9564 \| 0.3 \| 2500 \| 2.3309 \| 0.5644 \| \| 2.8078 \| 0.35 \| 3000 \| 2.2320 \| 0.5792 \| \| 2.6741 \| 0.41 \| 3500 \| 2.1506 \| 0.5924 \| \| 2.5323 \| 0.47 \| 4000 \| 1.9846 \| 0.6176 \| \| 2.3678 \| 0.53 \| 4500 \| 1.8813 \| 0.6375 \| \| 2.25 \| 0.59 \| 5000 \| 1.8100 \| 0.6497 \| \| 2.1795 \| 0.65 \| 5500 \| 1.7632 \| 0.6579 \| \| 2.1203 \| 0.71 \| 6000 \| 1.7238 \| 0.6646 \| \| 2.0764 \| 0.77 \| 6500 \| 1.6856 \| 0.6713 \| \| 2.026 \| 0.83 \| 7000 \| 1.6569 \| 0.6760 \| \| 1.9942 \| 0.89 \| 7500 \| 1.6309 \| 0.6803 \| \| 1.9665 \| 0.95 \| 8000 \| 1.6122 \| 0.6836 \| \| 1.9395 \| 1.0 \| 8500 \| 1.5913 \| 0.6866 \| \| 1.9155 \| 1.06 \| 9000 \| 1.5758 \| 0.6895 \| \| 1.8828 \| 1.12 \| 9500 \| 1.5607 \| 0.6918 \| \| 1.8721 \| 1.18 \| 10000 \| 1.5422 \| 0.6948 \| \| 1.8474 \| 1.24 \| 10500 \| 1.5320 \| 0.6964 \| \| 1.8293 \| 1.3 \| 11000 \| 1.5214 \| 0.6978 \| \| 1.8129 \| 1.36 \| 11500 \| 1.5102 \| 0.6998 \| \| 1.8148 \| 1.42 \| 12000 \| 1.5010 \| 0.7013 \| \| 1.7903 \| 1.48 \| 12500 \| 1.4844 \| 0.7038 \| \| 1.7815 \| 1.54 \| 13000 \| 1.4823 \| 0.7039 \| \| 1.7637 \| 1.6 \| 13500 \| 1.4746 \| 0.7052 \| \| 1.7623 \| 1.66 \| 14000 \| 1.4701 \| 0.7061 \| \| 1.7402 \| 1.71 \| 14500 \| 1.4598 \| 0.7076 \| \| 1.7376 \| 1.77 \| 15000 \| 1.4519 \| 0.7090 \| \| 1.7287 \| 1.83 \| 15500 \| 1.4501 \| 0.7101 \| \| 1.7273 \| 1.89 \| 16000 \| 1.4409 \| 0.7107 \| \| 1.7119 \| 1.95 \| 16500 \| 1.4314 \| 0.7125 \| \| 1.7098 \| 2.01 \| 17000 \| 1.4269 \| 0.7129 \| \| 1.6978 \| 2.07 \| 17500 \| 1.4275 \| 0.7132 \| \| 1.698 \| 2.13 \| 18000 \| 1.4218 \| 0.7140 \| \| 1.6837 \| 2.19 \| 18500 \| 1.4151 \| 0.7147 \| \| 1.6908 \| 2.25 \| 19000 \| 1.4137 \| 0.7149 \| \| 1.6902 \| 2.31 \| 19500 \| 1.4085 \| 0.7161 \| \| 1.6741 \| 2.36 \| 20000 \| 1.4121 \| 0.7154 \| \| 1.6823 \| 2.42 \| 20500 \| 1.4037 \| 0.7165 \| \| 1.6692 \| 2.48 \| 21000 \| 1.4039 \| 0.7164 \| \| 1.6669 \| 2.54 \| 21500 \| 1.4015 \| 0.7172 \| \| 1.6613 \| 2.6 \| 22000 \| 1.3979 \| 0.7179 \| \| 1.664 \| 2.66 \| 22500 \| 1.3960 \| 0.7180 \| \| 1.6615 \| 2.72 \| 23000 \| 1.4012 \| 0.7172 \| \| 1.6627 \| 2.78 \| 23500 \| 1.3974 \| 0.7178 \| \| 1.6489 \| 2.84 \| 24000 \| 1.3948 \| 0.7182 \| \| 1.6429 \| 2.9 \| 24500 \| 1.3921 \| 0.7184 \| \| 1.6477 \| 2.96 \| 25000 \| 1.3910 \| 0.7182 \| ### Framework versions - Transformers 4.36.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2
TIGER-Lab/Mantis-llava-7b	TIGER-Lab	2024-05-18T14:29:50Z	9	15	transformers	[ "transformers", "safetensors", "llava", "image-text-to-text", "Mantis", "VLM", "LMM", "Multimodal LLM", "en", "base_model:llava-hf/llava-1.5-7b-hf", "base_model:finetune:llava-hf/llava-1.5-7b-hf", "license:apache-2.0", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-04-13T19:19:14Z	--- tags: - Mantis - VLM - LMM - Multimodal LLM - llava base_model: llava-hf/llava-1.5-7b-hf model-index: - name: Mantis-llava-7b results: [] license: apache-2.0 language: - en --- # Mantis: Interleaved Multi-Image Instruction Tuning (Deprecated) Mantis is a multimodal conversational AI model that can chat with users about images and text. It's optimized for multi-image reasoning, where interleaved text and images can be used to generate responses. Note that this is an older version of Mantis, please refer to our newest version at [mantis-Siglip-llama3](https://huggingface.co/TIGER-Lab/Mantis-8B-siglip-llama3). The newer version improves significantly over both multi-image and single-image tasks. Mantis is trained on the newly curated dataset Mantis-Instruct, a large-scale multi-image QA dataset that covers various multi-image reasoning tasks. \|[Demo](https://huggingface.co/spaces/TIGER-Lab/Mantis) \| [Github](https://github.com/TIGER-AI-Lab/Mantis) \| [Models](https://huggingface.co/collections/TIGER-Lab/mantis-6619b0834594c878cdb1d6e4) \| ![Mantis](https://raw.githubusercontent.com/TIGER-AI-Lab/Mantis/main/docs/assets/images/overall_barchart.jpeg) ## Inference You can install Mantis's GitHub codes as a Python package ```bash pip install git+https://github.com/TIGER-AI-Lab/Mantis.git ``` then run inference with codes here: [examples/run_mantis.py](https://github.com/TIGER-AI-Lab/Mantis/blob/main/examples/run_mantis_hf.py) ```python from mantis.models.mllava import chat_mllava from PIL import Image import torch image1 = "image1.jpg" image2 = "image2.jpg" images = [Image.open(image1), Image.open(image2)] # load processor and model from mantis.models.mllava import MLlavaProcessor, LlavaForConditionalGeneration processor = MLlavaProcessor.from_pretrained("TIGER-Lab/Mantis-bakllava-7b") model = LlavaForConditionalGeneration.from_pretrained("TIGER-Lab/Mantis-bakllava-7b", device_map="auto", torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2") # chat text = "<image> <image> What's the difference between these two images? Please describe as much as you can." response, history = chat_mllava(text, images, model, processor) print("USER: ", text) print("ASSISTANT: ", response) # The image on the right has a larger number of wallets displayed compared to the image on the left. The wallets in the right image are arranged in a grid pattern, while the wallets in the left image are displayed in a more scattered manner. The wallets in the right image have various colors, including red, purple, and brown, while the wallets in the left image are primarily brown. text = "How many items are there in image 1 and image 2 respectively?" response, history = chat_mllava(text, images, model, processor, history=history) print("USER: ", text) print("ASSISTANT: ", response) # There are two items in image 1 and four items in image 2. ``` Or, you can run the model without relying on the mantis codes, using pure hugging face transformers. See [examples/run_mantis_hf.py](https://github.com/TIGER-AI-Lab/Mantis/blob/main/examples/run_mantis_hf.py) for details. ## Training Training codes will be released soon.
OsherElhadad/ppo-PandaReachJointsSparse-v3-750000	OsherElhadad	2024-05-18T14:28:46Z	0	0	stable-baselines3	[ "stable-baselines3", "PandaReachJointsSparse-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-18T14:25:37Z	--- library_name: stable-baselines3 tags: - PandaReachJointsSparse-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachJointsSparse-v3 type: PandaReachJointsSparse-v3 metrics: - type: mean_reward value: -2.20 +/- 1.60 name: mean_reward verified: false --- # PPO Agent playing PandaReachJointsSparse-v3 This is a trained model of a PPO agent playing PandaReachJointsSparse-v3 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
beimu/model	beimu	2024-05-18T14:23:17Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-18T14:23:17Z	--- license: apache-2.0 ---
AliSaadatV/virus_pythia_14_1024_2d_representation_GaussianPlusCE	AliSaadatV	2024-05-18T14:22:18Z	130	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "generated_from_trainer", "base_model:EleutherAI/pythia-14m", "base_model:finetune:EleutherAI/pythia-14m", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T14:22:16Z	--- base_model: EleutherAI/pythia-14m tags: - generated_from_trainer model-index: - name: virus_pythia_14_1024_2d_representation_GaussianPlusCE results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # virus_pythia_14_1024_2d_representation_GaussianPlusCE This model is a fine-tuned version of [EleutherAI/pythia-14m](https://huggingface.co/EleutherAI/pythia-14m) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
rafaelsandroni/llama-3-8b-Instruct-16bit	rafaelsandroni	2024-05-18T14:19:48Z	4	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T14:10:07Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - Developed by: rafaelsandroni - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
charlesdj/CSR_LLaVA_1.5_7b_3Iteration	charlesdj	2024-05-18T14:12:34Z	79	0	transformers	[ "transformers", "safetensors", "llava_llama", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T14:08:17Z	--- license: apache-2.0 ---
selmamalak/organsmnist-swin-base-finetuned	selmamalak	2024-05-18T14:12:24Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "dataset:medmnist-v2", "base_model:microsoft/swin-large-patch4-window7-224-in22k", "base_model:adapter:microsoft/swin-large-patch4-window7-224-in22k", "license:apache-2.0", "region:us" ]	null	2024-05-18T12:44:13Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: microsoft/swin-large-patch4-window7-224-in22k datasets: - medmnist-v2 metrics: - accuracy - precision - recall - f1 model-index: - name: organsmnist-swin-base-finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # organsmnist-swin-base-finetuned This model is a fine-tuned version of [microsoft/swin-large-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-large-patch4-window7-224-in22k) on the medmnist-v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.4655 - Accuracy: 0.8230 - Precision: 0.7898 - Recall: 0.7786 - F1: 0.7831 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.005 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.918 \| 1.0 \| 218 \| 0.4249 \| 0.8356 \| 0.8139 \| 0.7795 \| 0.7529 \| \| 0.889 \| 2.0 \| 436 \| 0.4593 \| 0.8263 \| 0.7916 \| 0.7695 \| 0.7668 \| \| 0.8566 \| 3.0 \| 654 \| 0.3293 \| 0.8748 \| 0.8505 \| 0.8205 \| 0.8088 \| \| 0.7781 \| 4.0 \| 872 \| 0.3455 \| 0.8679 \| 0.8313 \| 0.8088 \| 0.7921 \| \| 0.7241 \| 5.0 \| 1090 \| 0.3565 \| 0.8691 \| 0.8758 \| 0.8110 \| 0.7902 \| \| 0.6568 \| 6.0 \| 1308 \| 0.3337 \| 0.8809 \| 0.8458 \| 0.8295 \| 0.8081 \| \| 0.5643 \| 7.0 \| 1526 \| 0.2581 \| 0.8972 \| 0.8392 \| 0.8377 \| 0.8332 \| \| 0.5834 \| 8.0 \| 1744 \| 0.2706 \| 0.8956 \| 0.8402 \| 0.8360 \| 0.8319 \| \| 0.4771 \| 9.0 \| 1962 \| 0.2721 \| 0.9001 \| 0.8524 \| 0.8445 \| 0.8364 \| \| 0.5102 \| 10.0 \| 2180 \| 0.2898 \| 0.9009 \| 0.8460 \| 0.8410 \| 0.8407 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
rtx07/mental_health_40k	rtx07	2024-05-18T14:11:50Z	89	0	transformers	[ "transformers", "gpt_neox", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T13:33:35Z	--- license: apache-2.0 ---
Dandan0K/Pilot_vox_Ref_italian	Dandan0K	2024-05-18T14:07:44Z	78	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "it", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-18T14:00:14Z	--- language: - it license: apache-2.0 tags: - automatic-speech-recognition - it datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2t_it_vp-100k_s449 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (it)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
alexandro767/stable-diffusion-v1-5-finetuned_5e_r8_v1	alexandro767	2024-05-18T14:03:54Z	29	0	diffusers	[ "diffusers", "safetensors", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-05-18T14:00:56Z	--- library_name: diffusers --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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emilykang/Phi_medmcqa_question_generation-gynaecology_n_obstetrics_lora	emilykang	2024-05-18T14:01:02Z	1	0	peft	[ "peft", "safetensors", "phi", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:microsoft/phi-2", "base_model:adapter:microsoft/phi-2", "license:mit", "region:us" ]	null	2024-05-17T16:40:38Z	--- license: mit library_name: peft tags: - trl - sft - generated_from_trainer base_model: microsoft/phi-2 datasets: - generator model-index: - name: Phi_medmcqa_question_generation-gynaecology_n_obstetrics_lora results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Phi_medmcqa_question_generation-gynaecology_n_obstetrics_lora This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1
selmamalak/organcmnist-swin-base-finetuned	selmamalak	2024-05-18T14:00:55Z	8	0	peft	[ "peft", "safetensors", "generated_from_trainer", "dataset:medmnist-v2", "base_model:microsoft/swin-large-patch4-window7-224-in22k", "base_model:adapter:microsoft/swin-large-patch4-window7-224-in22k", "license:apache-2.0", "region:us" ]	null	2024-05-18T13:03:26Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: microsoft/swin-large-patch4-window7-224-in22k datasets: - medmnist-v2 metrics: - accuracy - precision - recall - f1 model-index: - name: organcmnist-swin-base-finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # organcmnist-swin-base-finetuned This model is a fine-tuned version of [microsoft/swin-large-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-large-patch4-window7-224-in22k) on the medmnist-v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.2582 - Accuracy: 0.9317 - Precision: 0.9295 - Recall: 0.9177 - F1: 0.9229 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.005 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.7563 \| 0.9988 \| 203 \| 0.1859 \| 0.9365 \| 0.9432 \| 0.9127 \| 0.9201 \| \| 0.6145 \| 1.9975 \| 406 \| 0.1260 \| 0.9640 \| 0.9630 \| 0.9608 \| 0.9600 \| \| 0.6476 \| 2.9963 \| 609 \| 0.0926 \| 0.9774 \| 0.9715 \| 0.9754 \| 0.9723 \| \| 0.5719 \| 4.0 \| 813 \| 0.0912 \| 0.9770 \| 0.9749 \| 0.9746 \| 0.9740 \| \| 0.5374 \| 4.9988 \| 1016 \| 0.1281 \| 0.9695 \| 0.9730 \| 0.9690 \| 0.9699 \| \| 0.5615 \| 5.9975 \| 1219 \| 0.1088 \| 0.9791 \| 0.9839 \| 0.9819 \| 0.9825 \| \| 0.4959 \| 6.9963 \| 1422 \| 0.1134 \| 0.9741 \| 0.9812 \| 0.9742 \| 0.9768 \| \| 0.425 \| 8.0 \| 1626 \| 0.1016 \| 0.9808 \| 0.9816 \| 0.9820 \| 0.9815 \| \| 0.3151 \| 8.9988 \| 1829 \| 0.1368 \| 0.9804 \| 0.9843 \| 0.9832 \| 0.9834 \| \| 0.3347 \| 9.9877 \| 2030 \| 0.1156 \| 0.9837 \| 0.9853 \| 0.9864 \| 0.9856 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
stablediffusionapi/analog-madness-v70	stablediffusionapi	2024-05-18T13:59:25Z	29	0	diffusers	[ "diffusers", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-05-18T13:57:23Z	--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # Analog Madness v7.0 API Inference ![generated from modelslab.com](https://pub-3626123a908346a7a8be8d9295f44e26.r2.dev/generations/629076491716040429.png) ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change model_id to "analog-madness-v70" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://docs.modelslab.com) Try model for free: [Generate Images](https://modelslab.com/models/analog-madness-v70) Model link: [View model](https://modelslab.com/models/analog-madness-v70) View all models: [View Models](https://modelslab.com/models) import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "analog-madness-v70", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off DMGG0RBN
Lena2024/CustomModel_disney_sentiment_3	Lena2024	2024-05-18T13:59:06Z	119	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-18T13:58:56Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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theosun/gemma-2b-it-sharegpt-full	theosun	2024-05-18T13:58:13Z	6	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T13:49:16Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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Ishu789/phi-therapist-chat-v1	Ishu789	2024-05-18T13:58:04Z	130	0	transformers	[ "transformers", "safetensors", "phi", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T13:53:28Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
OsherElhadad/ppo-PandaReachJointsSparse-v3-500000	OsherElhadad	2024-05-18T13:58:00Z	1	0	stable-baselines3	[ "stable-baselines3", "PandaReachJointsSparse-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-18T13:54:47Z	--- library_name: stable-baselines3 tags: - PandaReachJointsSparse-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachJointsSparse-v3 type: PandaReachJointsSparse-v3 metrics: - type: mean_reward value: -1.30 +/- 1.00 name: mean_reward verified: false --- # PPO Agent playing PandaReachJointsSparse-v3 This is a trained model of a PPO agent playing PandaReachJointsSparse-v3 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
kanlo/videomae-base-finetuned-ucf101-subset	kanlo	2024-05-18T13:55:18Z	63	0	transformers	[ "transformers", "tensorboard", "safetensors", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-base", "base_model:finetune:MCG-NJU/videomae-base", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	2024-05-17T18:46:13Z	--- license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: videomae-base-finetuned-ucf101-subset results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # videomae-base-finetuned-ucf101-subset This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5398 - Accuracy: 0.8194 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 148 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| 2.1597 \| 0.2568 \| 38 \| 1.8560 \| 0.4857 \| \| 0.9646 \| 1.2568 \| 76 \| 1.0908 \| 0.6286 \| \| 0.4806 \| 2.2568 \| 114 \| 0.5811 \| 0.7857 \| \| 0.3196 \| 3.2297 \| 148 \| 0.4874 \| 0.8286 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
PaulR79/mistral_finetuned_synthetic	PaulR79	2024-05-18T13:54:52Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-18T13:54:50Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
WbjuSrceu/model8blora	WbjuSrceu	2024-05-18T13:52:54Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-18T13:52:08Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: WbjuSrceu - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
HariprasathSB/whispeeerrr	HariprasathSB	2024-05-18T13:52:46Z	87	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:HariprasathSB/whispeerr", "base_model:finetune:HariprasathSB/whispeerr", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-18T13:26:38Z	--- license: apache-2.0 base_model: HariprasathSB/whispeerr tags: - generated_from_trainer model-index: - name: whispeeerrr results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whispeeerrr This model is a fine-tuned version of [HariprasathSB/whispeerr](https://huggingface.co/HariprasathSB/whispeerr) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.003 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - training_steps: 100 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.41.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
WbjuSrceu/lorav2-lama8b-model	WbjuSrceu	2024-05-18T13:51:58Z	8	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T13:42:37Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: WbjuSrceu - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
stablediffusionapi/absolutereality-v181	stablediffusionapi	2024-05-18T13:50:35Z	241	2	diffusers	[ "diffusers", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-05-18T13:48:22Z	--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # AbsoluteReality v1.8.1 API Inference ![generated from modelslab.com](https://pub-3626123a908346a7a8be8d9295f44e26.r2.dev/generations/11161561981716040047.png) ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change model_id to "absolutereality-v181" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://docs.modelslab.com) Try model for free: [Generate Images](https://modelslab.com/models/absolutereality-v181) Model link: [View model](https://modelslab.com/models/absolutereality-v181) View all models: [View Models](https://modelslab.com/models) import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "absolutereality-v181", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off DMGG0RBN
stablediffusionapi/cetus-mix-v4	stablediffusionapi	2024-05-18T13:47:33Z	29	0	diffusers	[ "diffusers", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-05-18T13:45:38Z	--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # Cetus-Mix v4 API Inference ![generated from modelslab.com](https://pub-3626123a908346a7a8be8d9295f44e26.r2.dev/generations/12849060001716039847.png) ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change model_id to "cetus-mix-v4" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://docs.modelslab.com) Try model for free: [Generate Images](https://modelslab.com/models/cetus-mix-v4) Model link: [View model](https://modelslab.com/models/cetus-mix-v4) View all models: [View Models](https://modelslab.com/models) import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "cetus-mix-v4", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off DMGG0RBN
carlesoctav/coba-pth-2	carlesoctav	2024-05-18T13:47:19Z	37	0	transformers	[ "transformers", "safetensors", "bert", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-18T13:38:19Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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/yesmix-v40	stablediffusionapi	2024-05-18T13:46:34Z	29	1	diffusers	[ "diffusers", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-05-18T13:44:18Z	--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # YesMix v4.0 API Inference ![generated from modelslab.com](https://pub-3626123a908346a7a8be8d9295f44e26.r2.dev/generations/8589197591716038433.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 "yesmix-v40" 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/yesmix-v40) Model link: [View model](https://modelslab.com/models/yesmix-v40) 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": "yesmix-v40", "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
fzzhang/mistralv1_dora_r8_25e5_e05	fzzhang	2024-05-18T13:46:00Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	null	2024-05-18T13:45:58Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 model-index: - name: mistralv1_dora_r8_25e5_e05 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistralv1_dora_r8_25e5_e05 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2.5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.38.2 - Pytorch 2.2.1 - Datasets 2.18.0 - Tokenizers 0.15.2
basakdemirok/bert-base-turkish-cased-off_detect_v0_seed42	basakdemirok	2024-05-18T13:44:12Z	62	0	transformers	[ "transformers", "tf", "tensorboard", "bert", "text-classification", "generated_from_keras_callback", "base_model:dbmdz/bert-base-turkish-cased", "base_model:finetune:dbmdz/bert-base-turkish-cased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-18T13:26:10Z	--- license: mit base_model: dbmdz/bert-base-turkish-cased tags: - generated_from_keras_callback model-index: - name: basakdemirok/bert-base-turkish-cased-off_detect_v0_seed42 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. --> # basakdemirok/bert-base-turkish-cased-off_detect_v0_seed42 This model is a fine-tuned version of [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0407 - Validation Loss: 0.4312 - Train F1: 0.7031 - Epoch: 3 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 7488, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train F1 \| Epoch \| \|:----------:\|:---------------:\|:--------:\|:-----:\| \| 0.3093 \| 0.2640 \| 0.6927 \| 0 \| \| 0.1910 \| 0.2941 \| 0.7072 \| 1 \| \| 0.0932 \| 0.3690 \| 0.6971 \| 2 \| \| 0.0407 \| 0.4312 \| 0.7031 \| 3 \| ### Framework versions - Transformers 4.31.0 - TensorFlow 2.13.1 - Datasets 2.4.0 - Tokenizers 0.13.3
ATTIABATOOL/XRAY	ATTIABATOOL	2024-05-18T13:42:38Z	0	0	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2024-05-18T13:38:48Z	--- license: bigscience-openrail-m ---
PaulR79/gemma_finetuned_synthetic	PaulR79	2024-05-18T13:37:50Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-18T13:37:42Z	--- 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. 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]
bartowski/kunoichi-lemon-royale-v2-32K-7B-exl2	bartowski	2024-05-18T13:37:26Z	0	0	transformers	[ "transformers", "mergekit", "merge", "text-generation", "base_model:grimjim/Mistral-7B-Instruct-demi-merge-v0.2-7B", "base_model:merge:grimjim/Mistral-7B-Instruct-demi-merge-v0.2-7B", "base_model:grimjim/kunoichi-lemon-royale-7B", "base_model:merge:grimjim/kunoichi-lemon-royale-7B", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T13:37:25Z	--- base_model: - grimjim/Mistral-7B-Instruct-demi-merge-v0.2-7B - grimjim/kunoichi-lemon-royale-7B library_name: transformers tags: - mergekit - merge license: cc-by-nc-4.0 pipeline_tag: text-generation quantized_by: bartowski --- ## Exllama v2 Quantizations of kunoichi-lemon-royale-v2-32K-7B Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.21">turboderp's ExLlamaV2 v0.0.21</a> for quantization. <b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b> Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions. Original model: https://huggingface.co/grimjim/kunoichi-lemon-royale-v2-32K-7B ## Prompt format ``` <s> [INST] {prompt} [/INST]</s> ``` Note that this model does not support a System prompt. ## Available sizes \| Branch \| Bits \| lm_head bits \| VRAM (4k) \| VRAM (16k) \| VRAM (32k) \| Description \| \| ----- \| ---- \| ------- \| ------ \| ------ \| ------ \| ------------ \| \| [8_0](https://huggingface.co/bartowski/kunoichi-lemon-royale-v2-32K-7B-exl2/tree/8_0) \| 8.0 \| 8.0 \| 8.4 GB \| 9.8 GB \| 11.8 GB \| Maximum quality that ExLlamaV2 can produce, near unquantized performance. \| \| [6_5](https://huggingface.co/bartowski/kunoichi-lemon-royale-v2-32K-7B-exl2/tree/6_5) \| 6.5 \| 8.0 \| 7.2 GB \| 8.6 GB \| 10.6 GB \| Very similar to 8.0, good tradeoff of size vs performance, recommended. \| \| [5_0](https://huggingface.co/bartowski/kunoichi-lemon-royale-v2-32K-7B-exl2/tree/5_0) \| 5.0 \| 6.0 \| 6.0 GB \| 7.4 GB \| 9.4 GB \| Slightly lower quality vs 6.5, but usable on 8GB cards. \| \| [4_25](https://huggingface.co/bartowski/kunoichi-lemon-royale-v2-32K-7B-exl2/tree/4_25) \| 4.25 \| 6.0 \| 5.3 GB \| 6.7 GB \| 8.7 GB \| GPTQ equivalent bits per weight, slightly higher quality. \| \| [3_5](https://huggingface.co/bartowski/kunoichi-lemon-royale-v2-32K-7B-exl2/tree/3_5) \| 3.5 \| 6.0 \| 4.7 GB \| 6.1 GB \| 8.1 GB \| Lower quality, only use if you have to. \| ## Download instructions With git: ```shell git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/kunoichi-lemon-royale-v2-32K-7B-exl2 kunoichi-lemon-royale-v2-32K-7B-exl2-6_5 ``` With huggingface hub (credit to TheBloke for instructions): ```shell pip3 install huggingface-hub ``` To download a specific branch, use the `--revision` parameter. For example, to download the 6.5 bpw branch: Linux: ```shell huggingface-cli download bartowski/kunoichi-lemon-royale-v2-32K-7B-exl2 --revision 6_5 --local-dir kunoichi-lemon-royale-v2-32K-7B-exl2-6_5 --local-dir-use-symlinks False ``` Windows (which apparently doesn't like _ in folders sometimes?): ```shell huggingface-cli download bartowski/kunoichi-lemon-royale-v2-32K-7B-exl2 --revision 6_5 --local-dir kunoichi-lemon-royale-v2-32K-7B-exl2-6.5 --local-dir-use-symlinks False ``` Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
stablediffusionapi/epicphotogasm-ultimate-fi	stablediffusionapi	2024-05-18T13:36:58Z	29	0	diffusers	[ "diffusers", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-05-18T13:34:41Z	--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # epiCPhotoGasm Ultimate Fidelity API Inference ![generated from modelslab.com](https://pub-3626123a908346a7a8be8d9295f44e26.r2.dev/generations/1508582791716039188.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 "epicphotogasm-ultimate-fi" 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/epicphotogasm-ultimate-fi) Model link: [View model](https://modelslab.com/models/epicphotogasm-ultimate-fi) 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": "epicphotogasm-ultimate-fi", "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
AliSaadatV/virus_pythia_14_1024_2d_representation	AliSaadatV	2024-05-18T13:33:27Z	129	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "generated_from_trainer", "base_model:EleutherAI/pythia-14m", "base_model:finetune:EleutherAI/pythia-14m", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T12:50:03Z	--- base_model: EleutherAI/pythia-14m tags: - generated_from_trainer model-index: - name: virus_pythia_14_1024_2d_representation results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # virus_pythia_14_1024_2d_representation This model is a fine-tuned version of [EleutherAI/pythia-14m](https://huggingface.co/EleutherAI/pythia-14m) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
selmamalak/organsmnist-vit-base-finetuned	selmamalak	2024-05-18T13:28:53Z	1	0	peft	[ "peft", "safetensors", "generated_from_trainer", "dataset:medmnist-v2", "base_model:facebook/deit-base-patch16-224", "base_model:adapter:facebook/deit-base-patch16-224", "license:apache-2.0", "region:us" ]	null	2024-05-18T12:24:41Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: facebook/deit-base-patch16-224 datasets: - medmnist-v2 metrics: - accuracy - precision - recall - f1 model-index: - name: organsmnist-vit-base-finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # organsmnist-vit-base-finetuned This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the medmnist-v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.2964 - Accuracy: 0.8993 - Precision: 0.8443 - Recall: 0.8396 - F1: 0.8394 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.005 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.9084 \| 1.0 \| 218 \| 0.7151 \| 0.7288 \| 0.6998 \| 0.6620 \| 0.6412 \| \| 0.89 \| 2.0 \| 436 \| 0.3658 \| 0.8540 \| 0.7873 \| 0.7898 \| 0.7660 \| \| 0.7851 \| 3.0 \| 654 \| 0.3514 \| 0.8438 \| 0.8110 \| 0.7674 \| 0.7741 \| \| 0.7144 \| 4.0 \| 872 \| 0.3632 \| 0.8670 \| 0.8415 \| 0.8133 \| 0.7980 \| \| 0.7383 \| 5.0 \| 1090 \| 0.3680 \| 0.8581 \| 0.7769 \| 0.8029 \| 0.7786 \| \| 0.6065 \| 6.0 \| 1308 \| 0.2824 \| 0.8870 \| 0.8481 \| 0.8328 \| 0.8305 \| \| 0.521 \| 7.0 \| 1526 \| 0.2769 \| 0.8940 \| 0.8439 \| 0.8404 \| 0.8297 \| \| 0.5305 \| 8.0 \| 1744 \| 0.2611 \| 0.9001 \| 0.8517 \| 0.8463 \| 0.8447 \| \| 0.4522 \| 9.0 \| 1962 \| 0.2742 \| 0.9058 \| 0.8594 \| 0.8517 \| 0.8411 \| \| 0.4445 \| 10.0 \| 2180 \| 0.2964 \| 0.8993 \| 0.8443 \| 0.8396 \| 0.8394 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
Mitrofazotron/mistral_10k_snli_gpt	Mitrofazotron	2024-05-18T13:28:32Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-18T13:28:23Z	--- 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]
thienann/results-news-dataset	thienann	2024-05-18T13:27:58Z	106	0	transformers	[ "transformers", "tensorboard", "safetensors", "pegasus", "text2text-generation", "generated_from_trainer", "base_model:PoseyATX/GPTxLege_FoxHunter", "base_model:finetune:PoseyATX/GPTxLege_FoxHunter", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-18T12:29:59Z	--- base_model: PoseyATX/GPTxLege_FoxHunter tags: - generated_from_trainer model-index: - name: results-news-dataset results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # results-news-dataset This model is a fine-tuned version of [PoseyATX/GPTxLege_FoxHunter](https://huggingface.co/PoseyATX/GPTxLege_FoxHunter) on the None dataset. It achieves the following results on the evaluation set: - Loss: 9.4768 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 10.4458 \| 1.0 \| 791 \| 9.6071 \| \| 9.805 \| 2.0 \| 1582 \| 9.4768 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
carlesoctav/coba-pth	carlesoctav	2024-05-18T13:27:43Z	40	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-18T13:27:30Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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selmamalak/organsmnist-deit-base-finetuned	selmamalak	2024-05-18T13:24:31Z	1	0	peft	[ "peft", "safetensors", "generated_from_trainer", "dataset:medmnist-v2", "base_model:facebook/deit-base-patch16-224", "base_model:adapter:facebook/deit-base-patch16-224", "license:apache-2.0", "region:us" ]	null	2024-05-18T12:31:56Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: facebook/deit-base-patch16-224 datasets: - medmnist-v2 metrics: - accuracy - precision - recall - f1 model-index: - name: organsmnist-deit-base-finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # organsmnist-deit-base-finetuned This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the medmnist-v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.4815 - Accuracy: 0.8080 - Precision: 0.7703 - Recall: 0.7686 - F1: 0.7650 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.005 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.9804 \| 1.0 \| 218 \| 0.6885 \| 0.7243 \| 0.7883 \| 0.6661 \| 0.6426 \| \| 0.9277 \| 2.0 \| 436 \| 0.3513 \| 0.8503 \| 0.7635 \| 0.7943 \| 0.7680 \| \| 0.8144 \| 3.0 \| 654 \| 0.3614 \| 0.8544 \| 0.8331 \| 0.7961 \| 0.7909 \| \| 0.7344 \| 4.0 \| 872 \| 0.3371 \| 0.8609 \| 0.8327 \| 0.8018 \| 0.7886 \| \| 0.7181 \| 5.0 \| 1090 \| 0.2934 \| 0.8923 \| 0.8060 \| 0.8389 \| 0.8096 \| \| 0.5857 \| 6.0 \| 1308 \| 0.2927 \| 0.8858 \| 0.8493 \| 0.8358 \| 0.8315 \| \| 0.5607 \| 7.0 \| 1526 \| 0.2209 \| 0.9062 \| 0.8658 \| 0.8547 \| 0.8416 \| \| 0.5423 \| 8.0 \| 1744 \| 0.2513 \| 0.9025 \| 0.8545 \| 0.8470 \| 0.8487 \| \| 0.4053 \| 9.0 \| 1962 \| 0.2561 \| 0.9038 \| 0.8543 \| 0.8457 \| 0.8373 \| \| 0.4417 \| 10.0 \| 2180 \| 0.2558 \| 0.8997 \| 0.8463 \| 0.8395 \| 0.8416 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
HariprasathSB/whispeerr	HariprasathSB	2024-05-18T13:22:33Z	94	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:HariprasathSB/whispeer", "base_model:finetune:HariprasathSB/whispeer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-18T13:06:48Z	--- license: apache-2.0 base_model: HariprasathSB/whispeer tags: - generated_from_trainer model-index: - name: whispeerr 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. --> # whispeerr This model is a fine-tuned version of [HariprasathSB/whispeer](https://huggingface.co/HariprasathSB/whispeer) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.003 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - training_steps: 100 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.41.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
Edgar404/a2c-PandaReachDense-v3	Edgar404	2024-05-18T13:09:40Z	0	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-18T13:05:12Z	--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -0.33 +/- 0.08 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v3 This is a trained model of a A2C agent playing PandaReachDense-v3 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
basakdemirok/bert-base-turkish-cased-off_detect_v03_seed42	basakdemirok	2024-05-18T13:09:27Z	62	0	transformers	[ "transformers", "tf", "tensorboard", "bert", "text-classification", "generated_from_keras_callback", "base_model:dbmdz/bert-base-turkish-cased", "base_model:finetune:dbmdz/bert-base-turkish-cased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-18T12:37:49Z	--- license: mit base_model: dbmdz/bert-base-turkish-cased tags: - generated_from_keras_callback model-index: - name: basakdemirok/bert-base-turkish-cased-off_detect_v03_seed42 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. --> # basakdemirok/bert-base-turkish-cased-off_detect_v03_seed42 This model is a fine-tuned version of [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0094 - Validation Loss: 0.6556 - Train F1: 0.7023 - Epoch: 3 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 14988, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train F1 \| Epoch \| \|:----------:\|:---------------:\|:--------:\|:-----:\| \| 0.2628 \| 0.2933 \| 0.6989 \| 0 \| \| 0.0985 \| 0.4294 \| 0.6954 \| 1 \| \| 0.0247 \| 0.5613 \| 0.6909 \| 2 \| \| 0.0094 \| 0.6556 \| 0.7023 \| 3 \| ### Framework versions - Transformers 4.31.0 - TensorFlow 2.13.1 - Datasets 2.4.0 - Tokenizers 0.13.3
HariprasathSB/whispeer	HariprasathSB	2024-05-18T13:02:16Z	94	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:vasista22/whisper-tamil-medium", "base_model:finetune:vasista22/whisper-tamil-medium", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-18T12:47:54Z	--- license: apache-2.0 base_model: vasista22/whisper-tamil-medium tags: - generated_from_trainer model-index: - name: whispeer 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. --> # whispeer This model is a fine-tuned version of [vasista22/whisper-tamil-medium](https://huggingface.co/vasista22/whisper-tamil-medium) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.003 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - training_steps: 100 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.41.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
piika919/phi_bnb	piika919	2024-05-18T12:54:49Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-18T12:51:32Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
ChiJuiChen/lab9_whisper-tiny-zh-tw	ChiJuiChen	2024-05-18T12:53:39Z	78	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice_13_0", "base_model:Wellyowo/whisper-tiny-zh-tw", "base_model:finetune:Wellyowo/whisper-tiny-zh-tw", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-09T07:22:56Z	--- license: apache-2.0 base_model: Wellyowo/whisper-tiny-zh-tw tags: - generated_from_trainer datasets: - common_voice_13_0 metrics: - wer model-index: - name: lab9_whisper-tiny-zh-tw results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice_13_0 type: common_voice_13_0 config: zh-TW split: test args: zh-TW metrics: - name: Wer type: wer value: 62.13592233009708 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # lab9_whisper-tiny-zh-tw This model is a fine-tuned version of [Wellyowo/whisper-tiny-zh-tw](https://huggingface.co/Wellyowo/whisper-tiny-zh-tw) on the common_voice_13_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.6336 - Wer Ortho: 64.0 - Wer: 62.1359 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer Ortho \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:---------:\|:-------:\| \| 0.0088 \| 0.6882 \| 500 \| 0.5502 \| 60.0 \| 61.1650 \| \| 0.0051 \| 1.3765 \| 1000 \| 0.5735 \| 65.0 \| 64.0777 \| \| 0.0068 \| 2.0647 \| 1500 \| 0.5820 \| 63.0 \| 63.1068 \| \| 0.0021 \| 2.7529 \| 2000 \| 0.5955 \| 62.0 \| 61.1650 \| \| 0.0039 \| 3.4412 \| 2500 \| 0.5858 \| 62.0 \| 61.1650 \| \| 0.0018 \| 4.1294 \| 3000 \| 0.5981 \| 63.0 \| 61.1650 \| \| 0.0019 \| 4.8176 \| 3500 \| 0.6322 \| 63.0 \| 61.1650 \| \| 0.0102 \| 5.5058 \| 4000 \| 0.6336 \| 64.0 \| 62.1359 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.0+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1
OwOpeepeepoopoo/NoSoup4U_1	OwOpeepeepoopoo	2024-05-18T12:52:38Z	93	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T23:21:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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chen1212/Models-RoBERTa-1716033686.153194	chen1212	2024-05-18T12:52:36Z	108	0	transformers	[ "transformers", "tensorboard", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/roberta-base", "base_model:finetune:FacebookAI/roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-18T12:05:43Z	--- license: mit base_model: roberta-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: Models-RoBERTa-1716033686.153194 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Models-RoBERTa-1716033686.153194 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3683 - Accuracy: 0.888 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 0.8 \| 500 \| 0.3078 \| 0.875 \| \| No log \| 1.6 \| 1000 \| 0.3683 \| 0.888 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
nvdenisov2002/llama-longLoRA-v5-8k-all-samples-3-epochs	nvdenisov2002	2024-05-18T12:50:41Z	0	0	peft	[ "peft", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]	null	2024-05-18T12:50:17Z	--- library_name: peft base_model: meta-llama/Llama-2-7b-hf --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - 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 Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.10.0
emilykang/Phi_medmcqa_question_generation-social_n_preventive_medicine_lora	emilykang	2024-05-18T12:46:58Z	0	0	peft	[ "peft", "safetensors", "phi", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:microsoft/phi-2", "base_model:adapter:microsoft/phi-2", "license:mit", "region:us" ]	null	2024-05-17T15:31:33Z	--- license: mit library_name: peft tags: - trl - sft - generated_from_trainer base_model: microsoft/phi-2 datasets: - generator model-index: - name: Phi_medmcqa_question_generation-social_n_preventive_medicine_lora results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Phi_medmcqa_question_generation-social_n_preventive_medicine_lora This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1
presencesw/mt5-base-snli_neutral-triplet	presencesw	2024-05-18T12:43:05Z	50	0	transformers	[ "transformers", "safetensors", "mt5", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-18T12:42:05Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
RichardErkhov/beomi_-_gemma-mling-7b-4bits	RichardErkhov	2024-05-18T12:42:03Z	78	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-18T12:37:41Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) gemma-mling-7b - bnb 4bits - Model creator: https://huggingface.co/beomi/ - Original model: https://huggingface.co/beomi/gemma-mling-7b/ Original model description: --- language: - ko - en - zh - ja license: other library_name: transformers license_name: gemma-terms-of-use license_link: https://ai.google.dev/gemma/terms pipeline_tag: text-generation tags: - pytorch --- # Gemma-Mling: Multilingual Gemma > Update @ 2024.04.15: First release of Gemma-Mling 7B model Original Gemma Model Page: [Gemma](https://ai.google.dev/gemma/docs) This model card corresponds to the 7B base version of the Gemma-Mling model, continual pretrained on mainly Korean/English/Chinese/Japanese + 500 multilingual corpus. Resources and Technical Documentation: * [Original Google's Gemma-7B](https://huggingface.co/google/gemma-7b) * [Training Code @ Github: Gemma-EasyLM](https://github.com/Beomi/Gemma-EasyLM) Terms of Use: [Terms](https://www.kaggle.com/models/google/gemma/license/consent) Citation ```bibtex @misc {gemma_mling_7b, author = { {Junbum Lee, Taekyoon Choi} }, title = { gemma-mling-7b }, year = 2024, url = { https://huggingface.co/beomi/gemma-mling-7b }, publisher = { Hugging Face } } ``` Model Developers: Junbum Lee (Beomi) & Taekyoon Choi (Taekyoon) ## Model Information ### Usage Below we share some code snippets on how to get quickly started with running the model. First make sure to `pip install -U transformers`, then copy the snippet from the section that is relevant for your usecase. #### Running the model on a CPU ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("beomi/gemma-mling-7b") model = AutoModelForCausalLM.from_pretrained("beomi/gemma-mling-7b") input_text = "머신러닝과 딥러닝의 차이는" input_ids = tokenizer(input_text, return_tensors="pt") outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) ``` #### Running the model on a single / multi GPU ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("beomi/gemma-mling-7b") model = AutoModelForCausalLM.from_pretrained("beomi/gemma-mling-7b", device_map="auto") input_text = "머신러닝과 딥러닝의 차이는" input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) ``` ### Inputs and outputs * Input: Text string, such as a question, a prompt, or a document to be summarized. * Output: Generated Multilingual-language text in response to the input, such as an answer to a question, or a summary of a document. ## Implementation Information Details about the model internals. ### Software Training was done using [beomi/Gemma-EasyLM](https://github.com/Beomi/Gemma-EasyLM). ### Dataset We trained a mixture of multiple language datasets and trained until 100B. The released model is the best performance model based on our Evaluation below from model checkpoints. For Korean and English datasets, we utilized sampled llama2ko training dataset which combined 1:1 ratio in each language. \| Dataset \| Jsonl (GB) \| Sampled \| \|--------------------------\|------------\|---------\| \| range3/cc100-ja \| 96.39 \| No \| \| Skywork/SkyPile-150B \| 100.57 \| Yes \| \| llama2ko dataset (ko/en) \| 108.5 \| Yes \| \| cis-lmu/Glot500 \| 181.24 \| No \| \| Total \| 486.7 \| . \| ## Training Progress - Report Link: https://api.wandb.ai/links/tgchoi/6lt0ce3s ## Evaluation Model evaluation metrics and results. ### Evaluation Scripts - For Knowledge / KoBest / XCOPA / XWinograd - [EleutherAI/lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) v0.4.2 ```bash !git clone https://github.com/EleutherAI/lm-evaluation-harness.git !cd lm-evaluation-harness && pip install -r requirements.txt && pip install -e . !lm_eval --model hf \ --model_args pretrained=beomi/gemma-mling-7b,dtype="float16" \ --tasks "haerae,kobest,kmmlu_direct,cmmlu,ceval-valid,mmlu,xwinograd,xcopa \ --num_fewshot "0,5,5,5,5,5,0,5" \ --device cuda ``` - For JP Eval Harness - [Stability-AI/lm-evaluation-harness (`jp-stable` branch)](https://github.com/Stability-AI/lm-evaluation-harness/tree/jp-stable) ```bash !git clone -b jp-stable https://github.com/Stability-AI/lm-evaluation-harness.git !cd lm-evaluation-harness && pip install -e ".[ja]" !pip install 'fugashi[unidic]' && python -m unidic download !cd lm-evaluation-harness && python main.py \ --model hf-causal \ --model_args pretrained=beomi/gemma-mling-7b,torch_dtype='auto'" --tasks "jcommonsenseqa-1.1-0.3,jnli-1.3-0.3,marc_ja-1.1-0.3,jsquad-1.1-0.3,jaqket_v2-0.2-0.3,xlsum_ja,mgsm" --num_fewshot "3,3,3,2,1,1,5" ``` ### Benchmark Results \| Category \| Metric \| Shots \| Score \| \|----------------------------------\|----------------------\|------------\|--------\| \| Default Metric \| ACC \| \| \| \| Knowledge (5-shot) \| MMLU \| \| 61.76 \| \| \| KMMLU (Exact Match) \| \| 42.75 \| \| \| CMLU \| \| 50.93 \| \| \| JMLU \| \| \| \| \| C-EVAL \| \| 50.07 \| \| \| HAERAE \| 0-shot \| 63.89 \| \| KoBest (5-shot) \| BoolQ \| \| 85.47 \| \| \| COPA \| \| 83.5 \| \| \| Hellaswag (acc-norm) \| \| 63.2 \| \| \| Sentineg \| \| 97.98 \| \| \| WiC \| \| 70.95 \| \| XCOPA (5-shot) \| IT \| \| 72.8 \| \| \| ID \| \| 76.4 \| \| \| TH \| \| 60.2 \| \| \| TR \| \| 65.6 \| \| \| VI \| \| 77.2 \| \| \| ZH \| \| 80.2 \| \| JP Eval Harness (Prompt ver 0.3) \| JcommonsenseQA \| 3-shot \| 85.97 \| \| \| JNLI \| 3-shot \| 39.11 \| \| \| Marc_ja \| 3-shot \| 96.48 \| \| \| JSquad (Exact Match) \| 2-shot \| 70.69 \| \| \| Jaqket (Exact Match) \| 1-shot \| 81.53 \| \| \| MGSM \| 5-shot \| 28.8 \| \| XWinograd (0-shot) \| EN \| \| 89.03 \| \| \| FR \| \| 72.29 \| \| \| JP \| \| 82.69 \| \| \| PT \| \| 73.38 \| \| \| RU \| \| 68.57 \| \| \| ZH \| \| 79.17 \| ## Usage and Limitations These models have certain limitations that users should be aware of. ### Intended Usage Open Large Language Models (LLMs) have a wide range of applications across various industries and domains. The following list of potential uses is not comprehensive. The purpose of this list is to provide contextual information about the possible use-cases that the model creators considered as part of model training and development. * Content Creation and Communication * Text Generation: These models can be used to generate creative text formats such as poems, scripts, code, marketing copy, and email drafts. * Research and Education * Natural Language Processing (NLP) Research: These models can serve as a foundation for researchers to experiment with NLP techniques, develop algorithms, and contribute to the advancement of the field. * Language Learning Tools: Support interactive language learning experiences, aiding in grammar correction or providing writing practice. * Knowledge Exploration: Assist researchers in exploring large bodies of text by generating summaries or answering questions about specific topics. ### Limitations * Training Data * The quality and diversity of the training data significantly influence the model's capabilities. Biases or gaps in the training data can lead to limitations in the model's responses. * The scope of the training dataset determines the subject areas the model can handle effectively. * Context and Task Complexity * LLMs are better at tasks that can be framed with clear prompts and instructions. Open-ended or highly complex tasks might be challenging. * A model's performance can be influenced by the amount of context provided (longer context generally leads to better outputs, up to a certain point). * Language Ambiguity and Nuance * Natural language is inherently complex. LLMs might struggle to grasp subtle nuances, sarcasm, or figurative language. * Factual Accuracy * LLMs generate responses based on information they learned from their training datasets, but they are not knowledge bases. They may generate incorrect or outdated factual statements. * Common Sense * LLMs rely on statistical patterns in language. They might lack the ability to apply common sense reasoning in certain situations. ### Ethical Considerations and Risks The development of large language models (LLMs) raises several ethical concerns. In creating an open model, we have carefully considered the following: * Bias and Fairness * LLMs trained on large-scale, real-world text data can reflect socio-cultural biases embedded in the training material. These models underwent careful scrutiny, input data pre-processing described and posterior evaluations reported in this card. * Misinformation and Misuse * LLMs can be misused to generate text that is false, misleading, or harmful. * Guidelines are provided for responsible use with the model, see the [Responsible Generative AI Toolkit](http://ai.google.dev/gemma/responsible). * Transparency and Accountability: * This model card summarizes details on the models' architecture, capabilities, limitations, and evaluation processes. * A responsibly developed open model offers the opportunity to share innovation by making LLM technology accessible to developers and researchers across the AI ecosystem. Risks identified and mitigations: * Perpetuation of biases: It's encouraged to perform continuous monitoring (using evaluation metrics, human review) and the exploration of de-biasing techniques during model training, fine-tuning, and other use cases. * Generation of harmful content: Mechanisms and guidelines for content safety are essential. Developers are encouraged to exercise caution and implement appropriate content safety safeguards based on their specific product policies and application use cases. * Misuse for malicious purposes: Technical limitations and developer and end-user education can help mitigate against malicious applications of LLMs. Educational resources and reporting mechanisms for users to flag misuse are provided. Prohibited uses of Gemma models are outlined in the [Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy). * Privacy violations: Models were trained on data filtered for removal of PII (Personally Identifiable Information). Developers are encouraged to adhere to privacy regulations with privacy-preserving techniques. ## Acknowledgement The training is supported by [TPU Research Cloud](https://sites.research.google/trc/) program.
theosun/gemma-2b-it-sharegpt	theosun	2024-05-18T12:38:32Z	7	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T09:38:43Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf	RichardErkhov	2024-05-18T12:33:06Z	32	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-18T01:49:29Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Mixtral-8x7B-MoE-RP-Story - GGUF - Model creator: https://huggingface.co/Undi95/ - Original model: https://huggingface.co/Undi95/Mixtral-8x7B-MoE-RP-Story/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [Mixtral-8x7B-MoE-RP-Story.Q2_K.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q2_K.gguf) \| Q2_K \| 16.12GB \| \| [Mixtral-8x7B-MoE-RP-Story.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.IQ3_XS.gguf) \| IQ3_XS \| 18.02GB \| \| [Mixtral-8x7B-MoE-RP-Story.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.IQ3_S.gguf) \| IQ3_S \| 19.03GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q3_K_S.gguf) \| Q3_K_S \| 19.03GB \| \| [Mixtral-8x7B-MoE-RP-Story.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.IQ3_M.gguf) \| IQ3_M \| 19.96GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q3_K.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q3_K.gguf) \| Q3_K \| 21.0GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q3_K_M.gguf) \| Q3_K_M \| 21.0GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q3_K_L.gguf) \| Q3_K_L \| 22.51GB \| \| [Mixtral-8x7B-MoE-RP-Story.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.IQ4_XS.gguf) \| IQ4_XS \| 23.63GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q4_0.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q4_0.gguf) \| Q4_0 \| 24.63GB \| \| [Mixtral-8x7B-MoE-RP-Story.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.IQ4_NL.gguf) \| IQ4_NL \| 24.91GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q4_K_S.gguf) \| Q4_K_S \| 24.91GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q4_K.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q4_K.gguf) \| Q4_K \| 26.49GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q4_K_M.gguf) \| Q4_K_M \| 26.49GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q4_1.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q4_1.gguf) \| Q4_1 \| 27.32GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q5_0.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q5_0.gguf) \| Q5_0 \| 30.02GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q5_K_S.gguf) \| Q5_K_S \| 30.02GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q5_K.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q5_K.gguf) \| Q5_K \| 30.95GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q5_K_M.gguf) \| Q5_K_M \| 30.95GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q5_1.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q5_1.gguf) \| Q5_1 \| 32.71GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q6_K.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q6_K.gguf) \| Q6_K \| 35.74GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q8_0.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/tree/main/) \| Q8_0 \| 46.22GB \| Original model description: --- license: cc-by-nc-4.0 tags: - not-for-all-audiences - nsfw --- Mixtral-8x7B-MoE-RP-Story is a model made primarely for chatting, RP (Roleplay) and storywriting. 2 RP model, 2 chat model, 1 occult model, 1 storywritting model, 1 mathematic model and 1 DPO model was used for a MoE. Bagel was the base. The DPO chat model is here to help get more human reply. This is my first try at doing this, so don't hesitate to give feedback! WARNING: ALL THE "K" GGUF QUANT OF MIXTRAL MODELS SEEMS TO BE [BROKEN](https://cdn-uploads.huggingface.co/production/uploads/63ab1241ad514ca8d1430003/TvjEP14ps7ZUgJ-0-mhIX.png), PREFER Q4_0, Q5_0 or Q8_0! <!-- description start --> ## Description This repo contains fp16 files of Mixtral-8x7B-MoE-RP-Story. <!-- description end --> <!-- description start --> ## Models used The list of model used and their activator/theme can be found [here](https://huggingface.co/Undi95/Mixtral-8x7B-MoE-RP-Story/blob/main/config.yaml) <!-- description end --> <!-- prompt-template start --> ## Prompt template: Custom Using Bagel as a base let us a lot of different prompting system theorically, you can see all the prompting available [here](https://huggingface.co/jondurbin/bagel-7b-v0.1#prompt-formatting). If you want to support me, you can [here](https://ko-fi.com/undiai).
Angy309/noti	Angy309	2024-05-18T12:29:51Z	110	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:dccuchile/bert-base-spanish-wwm-cased", "base_model:finetune:dccuchile/bert-base-spanish-wwm-cased", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-18T11:18:41Z	--- tags: - generated_from_trainer base_model: dccuchile/bert-base-spanish-wwm-cased metrics: - accuracy model-index: - name: noti results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # noti This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3911 - Accuracy: 0.5 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.5517 \| 0.5 \| 5 \| 1.5409 \| 0.25 \| \| 1.5245 \| 1.0 \| 10 \| 1.3911 \| 0.5 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
alexandro767/stable-diffusion-v1-5-finetuned_5e_r2_v1	alexandro767	2024-05-18T12:29:08Z	29	0	diffusers	[ "diffusers", "safetensors", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-05-18T12:26:20Z	--- library_name: diffusers --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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]
ruslandev/llama-3-70b-tagengo-GGUF	ruslandev	2024-05-18T12:20:03Z	33	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "llama", "trl", "en", "dataset:lightblue/tagengo-gpt4", "base_model:unsloth/llama-3-70b-bnb-4bit", "base_model:quantized:unsloth/llama-3-70b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-17T06:42:40Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-70b-bnb-4bit datasets: - lightblue/tagengo-gpt4 --- # Uploaded model - Developed by: ruslandev - License: apache-2.0 - Finetuned from model : unsloth/llama-3-70b-bnb-4bit This model is finetuned on the Tagengo dataset. Please note - this model has been created for educational purposes and it needs further training/fine tuning. # How to use The easiest way to use this model on your own computer is to use the GGUF version of this model ([ruslandev/llama-3-70b-tagengo-GGUF](https://huggingface.co/ruslandev/llama-3-70b-tagengo-GGUF)) using a program such as [llama.cpp](https://github.com/ggerganov/llama.cpp). If you want to use this model directly with the Huggingface Transformers stack, I recommend using my framework [gptchain](https://github.com/RuslanPeresy/gptchain). ``` git clone https://github.com/RuslanPeresy/gptchain.git cd gptchain pip install -r requirements-train.txt python gptchain.py chat -m ruslandev/llama-3-70b-tagengo \ --chatml true \ -q '[{"from": "human", "value": "Из чего состоит нейронная сеть?"}]' ``` # Training [gptchain](https://github.com/RuslanPeresy/gptchain) framework has been used for training. ``` python gptchain.py train -m unsloth/llama-3-70b-bnb-4bit \ -dn tagengo_gpt4 \ -sp checkpoints/llama-3-70b-tagengo \ -hf llama-3-70b-tagengo \ --max-steps 2400 ``` # Training hyperparameters - learning_rate: 2e-4 - seed: 3407 - gradient_accumulation_steps: 4 - per_device_train_batch_size: 2 - optimizer: adamw_8bit - lr_scheduler_type: linear - warmup_steps: 5 - max_steps: 2400 - weight_decay: 0.01 # Training results [wandb report](https://api.wandb.ai/links/ruslandev/rilj60ra) 2400 steps took 7 hours on a single H100 [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
fzzhang/mistralv1_lora_r4_25e5_e05	fzzhang	2024-05-18T12:18:51Z	2	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	null	2024-05-18T12:18:49Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 model-index: - name: mistralv1_lora_r4_25e5_e05 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistralv1_lora_r4_25e5_e05 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2.5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.38.2 - Pytorch 2.2.1 - Datasets 2.18.0 - Tokenizers 0.15.2
tinyllava/TinyLLaVA-Gemma-SigLIP-2.4B	tinyllava	2024-05-18T12:15:22Z	140	1	transformers	[ "transformers", "safetensors", "tinyllava", "text-generation", "image-text-to-text", "conversational", "custom_code", "arxiv:2402.14289", "license:apache-2.0", "autotrain_compatible", "region:us" ]	image-text-to-text	2024-05-16T08:35:45Z	--- license: apache-2.0 pipeline_tag: image-text-to-text --- <center><span style="font-size:2em;">TinyLLaVA</span></center> [![arXiv](https://img.shields.io/badge/Arxiv-2402.14289-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2402.14289)[![Github](https://img.shields.io/badge/Github-Github-blue.svg)](https://github.com/TinyLLaVA/TinyLLaVA_Factory)[![Demo](https://img.shields.io/badge/Demo-Demo-red.svg)](http://8843843nmph5.vicp.fun/#/) TinyLLaVA has released a family of small-scale Large Multimodel Models(LMMs), ranging from 1.4B to 3.1B. Our best model, TinyLLaVA-Phi-2-SigLIP-3.1B, achieves better overall performance against existing 7B models such as LLaVA-1.5 and Qwen-VL. Here, we introduce TinyLLaVA-Gemma-SigLIP-2.4B, which is trained by the [TinyLLaVA Factory](https://github.com/TinyLLaVA/TinyLLaVA_Factory) codebase. For LLM and vision tower, we choose [Gemma-2B](https://huggingface.co/google/gemma-2b-it) and [siglip-so400m-patch14-384](https://huggingface.co/google/siglip-so400m-patch14-384), respectively. The dataset used for training this model is the [LLaVA](https://github.com/haotian-liu/LLaVA?tab=readme-ov-file#pretrain-feature-alignment) dataset. ### Usage Before executing the following test code, you need to have the access to [google/gemma-2b-it](https://huggingface.co/google/gemma-2b-it). ```python from transformers import AutoTokenizer, AutoModelForCausalLM hf_path = 'tinyllava/TinyLLaVA-Gemma-SigLIP-2.4B' model = AutoModelForCausalLM.from_pretrained(hf_path, trust_remote_code=True) model.cuda() config = model.config tokenizer = AutoTokenizer.from_pretrained(hf_path, use_fast=False, model_max_length = config.tokenizer_model_max_length,padding_side = config.tokenizer_padding_side) prompt="What are these?" image_url="http://images.cocodataset.org/test-stuff2017/000000000001.jpg" output_text, genertaion_time = model.chat(prompt=prompt, image=image_url, tokenizer=tokenizer) print('model output:', output_text) print('runing time:', genertaion_time) ``` ### Result \| model_name \| vqav2 \| gqa \| sqa \| textvqa \| MM-VET \| POPE \| MME \| MMMU \| \| :----------------------------------------------------------: \| ----- \| ------- \| ----- \| ----- \| ------- \| ----- \| ------ \| ------ \| \| [LLaVA-1.5-7B](https://huggingface.co/llava-hf/llava-1.5-7b-hf) \| 78.5 \| 62.0 \| 66.8 \| 58.2 \| 30.5 \| 85.9 \| 1510.7 \| - \| \| [bczhou/TinyLLaVA-3.1B](https://huggingface.co/bczhou/TinyLLaVA-3.1B) (our legacy model)\| 79.9 \| 62.0 \| 69.1 \| 59.1 \| 32.0 \| 86.4 \| 1464.9 \| - \| \| [tinyllava/TinyLLaVA-Gemma-SigLIP-2.4B](https://huggingface.co/tinyllava/TinyLLaVA-Gemma-SigLIP-2.4B) \| 78.4 \| 61.6 \| 64.4 \| 53.6 \| 26.9 \| 86.4 \| 1339.0 \| 31.7 \| \| [tinyllava/TinyLLaVA-Phi-2-SigLIP-3.1B](https://huggingface.co/tinyllava/TinyLLaVA-Phi-2-SigLIP-3.1B) \| 80.1 \| 62.1 \| 73.0 \| 60.3 \| 37.5 \| 87.2 \| 1466.4 \| 38.4 \| P.S. [TinyLLaVA Factory](https://github.com/TinyLLaVA/TinyLLaVA_Factory) is an open-source modular codebase for small-scale LMMs with a focus on simplicity of code implementations, extensibility of new features, and reproducibility of training results. This code repository provides standard training&evaluating pipelines, flexible data preprocessing&model configurations, and easily extensible architectures. Users can customize their own LMMs with minimal coding effort and less coding mistake. TinyLLaVA Factory integrates a suite of cutting-edge models and methods. - LLM currently supports OpenELM, TinyLlama, StableLM, Qwen, Gemma, and Phi. - Vision tower currently supports CLIP, SigLIP, Dino, and combination of CLIP and Dino. - Connector currently supports MLP, Qformer, and Resampler.
tinyllava/TinyLLaVA-Phi-2-SigLIP-3.1B	tinyllava	2024-05-18T12:13:47Z	3,236	14	transformers	[ "transformers", "safetensors", "tinyllava", "text-generation", "image-text-to-text", "custom_code", "arxiv:2402.14289", "license:apache-2.0", "autotrain_compatible", "region:us" ]	image-text-to-text	2024-05-15T12:19:17Z	--- license: apache-2.0 pipeline_tag: image-text-to-text --- <center><span style="font-size:2em;">TinyLLaVA</span></center> [![arXiv](https://img.shields.io/badge/Arxiv-2402.14289-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2402.14289)[![Github](https://img.shields.io/badge/Github-Github-blue.svg)](https://github.com/TinyLLaVA/TinyLLaVA_Factory)[![Demo](https://img.shields.io/badge/Demo-Demo-red.svg)](http://8843843nmph5.vicp.fun/#/) TinyLLaVA has released a family of small-scale Large Multimodel Models(LMMs), ranging from 1.4B to 3.1B. Our best model, TinyLLaVA-Phi-2-SigLIP-3.1B, achieves better overall performance against existing 7B models such as LLaVA-1.5 and Qwen-VL. Here, we introduce TinyLLaVA-Phi-2-SigLIP-3.1B, which is trained by the [TinyLLaVA Factory](https://github.com/TinyLLaVA/TinyLLaVA_Factory) codebase. For LLM and vision tower, we choose [Phi-2](microsoft/phi-2) and [siglip-so400m-patch14-384](https://huggingface.co/google/siglip-so400m-patch14-384), respectively. The dataset used for training this model is the [ShareGPT4V](https://github.com/InternLM/InternLM-XComposer/blob/main/projects/ShareGPT4V/docs/Data.md) dataset. ### Usage Execute the following test code: ```python from transformers import AutoTokenizer, AutoModelForCausalLM hf_path = 'tinyllava/TinyLLaVA-Phi-2-SigLIP-3.1B' model = AutoModelForCausalLM.from_pretrained(hf_path, trust_remote_code=True) model.cuda() config = model.config tokenizer = AutoTokenizer.from_pretrained(hf_path, use_fast=False, model_max_length = config.tokenizer_model_max_length,padding_side = config.tokenizer_padding_side) prompt="What are these?" image_url="http://images.cocodataset.org/test-stuff2017/000000000001.jpg" output_text, genertaion_time = model.chat(prompt=prompt, image=image_url, tokenizer=tokenizer) print('model output:', output_text) print('runing time:', genertaion_time) ``` ### Result \| model_name \| vqav2 \| gqa \| sqa \| textvqa \| MM-VET \| POPE \| MME \| MMMU \| \| :----------------------------------------------------------: \| ----- \| ------- \| ----- \| ----- \| ------- \| ----- \| ------ \| ------ \| \| [LLaVA-1.5-7B](https://huggingface.co/llava-hf/llava-1.5-7b-hf) \| 78.5 \| 62.0 \| 66.8 \| 58.2 \| 30.5 \| 85.9 \| 1510.7 \| - \| \| [bczhou/TinyLLaVA-3.1B](https://huggingface.co/bczhou/TinyLLaVA-3.1B) (our legacy model) \| 79.9 \| 62.0 \| 69.1 \| 59.1 \| 32.0 \| 86.4 \| 1464.9 \| - \| \| [tinyllava/TinyLLaVA-Gemma-SigLIP-2.4B](https://huggingface.co/tinyllava/TinyLLaVA-Gemma-SigLIP-2.4B) \| 78.4 \| 61.6 \| 64.4 \| 53.6 \| 26.9 \| 86.4 \| 1339.0 \| 31.7 \| \| [tinyllava/TinyLLaVA-Phi-2-SigLIP-3.1B](https://huggingface.co/tinyllava/TinyLLaVA-Phi-2-SigLIP-3.1B) \| 80.1 \| 62.1 \| 73.0 \| 60.3 \| 37.5 \| 87.2 \| 1466.4 \| 38.4 \| P.S. [TinyLLaVA Factory](https://github.com/TinyLLaVA/TinyLLaVA_Factory) is an open-source modular codebase for small-scale LMMs with a focus on simplicity of code implementations, extensibility of new features, and reproducibility of training results. This code repository provides standard training&evaluating pipelines, flexible data preprocessing&model configurations, and easily extensible architectures. Users can customize their own LMMs with minimal coding effort and less coding mistake. TinyLLaVA Factory integrates a suite of cutting-edge models and methods. - LLM currently supports OpenELM, TinyLlama, StableLM, Qwen, Gemma, and Phi. - Vision tower currently supports CLIP, SigLIP, Dino, and combination of CLIP and Dino. - Connector currently supports MLP, Qformer, and Resampler.
fzzhang/mistralv1_lora_r8_25e5_e05	fzzhang	2024-05-18T12:12:30Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	null	2024-05-18T12:12:28Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 model-index: - name: mistralv1_lora_r8_25e5_e05 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistralv1_lora_r8_25e5_e05 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2.5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.38.2 - Pytorch 2.2.1 - Datasets 2.18.0 - Tokenizers 0.15.2
MJerome/V65_LoRA_V63_GPT2-350k-Plus_10k_low_elo_4E_r64	MJerome	2024-05-18T12:10:36Z	0	0	peft	[ "peft", "arxiv:1910.09700", "base_model:Leon-LLM/V63_GPT2_350k_4E_xLANplus_RIGHT_PAD", "base_model:adapter:Leon-LLM/V63_GPT2_350k_4E_xLANplus_RIGHT_PAD", "region:us" ]	null	2024-05-18T12:10:33Z	--- library_name: peft base_model: Leon-LLM/V63_GPT2_350k_4E_xLANplus_RIGHT_PAD --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.10.0
Prince21332/Business	Prince21332	2024-05-18T12:08:13Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-18T12:08:13Z	--- license: apache-2.0 ---
akbargherbal/gemma_7b_en_to_ar_ft_01_LORA	akbargherbal	2024-05-18T12:04:50Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma", "trl", "en", "base_model:unsloth/gemma-7b-it-bnb-4bit", "base_model:finetune:unsloth/gemma-7b-it-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-18T12:04:37Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma - trl base_model: unsloth/gemma-7b-it-bnb-4bit --- # Uploaded model - Developed by: akbargherbal - License: apache-2.0 - Finetuned from model : unsloth/gemma-7b-it-bnb-4bit This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
euiyulsong/ORPO-task-domain-20k-synth3k-semi	euiyulsong	2024-05-18T12:02:17Z	81	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "trl", "sft", "orpo", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-18T11:57:56Z	--- library_name: transformers tags: - trl - sft - orpo --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
ddnahm/ddn_qa_model	ddnahm	2024-05-18T11:59:29Z	69	0	transformers	[ "transformers", "tf", "distilbert", "question-answering", "generated_from_keras_callback", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2024-05-18T09:06:45Z	--- license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_keras_callback model-index: - name: ddnahm/ddn_qa_model results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # ddnahm/ddn_qa_model This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.5135 - Validation Loss: 2.3658 - Epoch: 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: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 3.5135 \| 2.3658 \| 0 \| ### Framework versions - Transformers 4.40.2 - TensorFlow 2.15.0 - Datasets 2.19.1 - Tokenizers 0.19.1
OsherElhadad/ppo-PandaReachJointsDense-v3-750000	OsherElhadad	2024-05-18T11:56:01Z	0	0	stable-baselines3	[ "stable-baselines3", "PandaReachJointsDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-18T11:51:48Z	--- library_name: stable-baselines3 tags: - PandaReachJointsDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachJointsDense-v3 type: PandaReachJointsDense-v3 metrics: - type: mean_reward value: -0.21 +/- 0.13 name: mean_reward verified: false --- # PPO Agent playing PandaReachJointsDense-v3 This is a trained model of a PPO agent playing PandaReachJointsDense-v3 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
RichardErkhov/macadeliccc_-_laser-dolphin-mixtral-2x7b-dpo-8bits	RichardErkhov	2024-05-18T11:54:31Z	6	0	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "arxiv:2312.13558", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-18T11:45:43Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) laser-dolphin-mixtral-2x7b-dpo - bnb 8bits - Model creator: https://huggingface.co/macadeliccc/ - Original model: https://huggingface.co/macadeliccc/laser-dolphin-mixtral-2x7b-dpo/ Original model description: --- license: apache-2.0 library_name: transformers model-index: - name: laser-dolphin-mixtral-2x7b-dpo results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 65.96 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/laser-dolphin-mixtral-2x7b-dpo name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 85.8 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/laser-dolphin-mixtral-2x7b-dpo name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 63.17 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/laser-dolphin-mixtral-2x7b-dpo name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 60.76 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/laser-dolphin-mixtral-2x7b-dpo name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 79.01 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/laser-dolphin-mixtral-2x7b-dpo name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 48.29 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/laser-dolphin-mixtral-2x7b-dpo name: Open LLM Leaderboard --- # Laser-Dolphin-Mixtral-2x7b-dpo ![laser_dolphin_image](./dolphin_moe.png) New Version out now! Credit to Fernando Fernandes and Eric Hartford for their project [laserRMT](https://github.com/cognitivecomputations/laserRMT) ## Overview This model is a medium-sized MoE implementation based on [cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser](https://huggingface.co/cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser) + The new version shows ~1 point increase in evaluation performance on average. ## Process + The process is outlined in this [notebook](https://github.com/cognitivecomputations/laserRMT/blob/main/examples/laser-dolphin-mixtral-2x7b.ipynb) + The mergekit_config is in the files. + The models used in the configuration are not lasered, but the final product is. This is an update from the last version. + This process is experimental. Your mileage may vary. ## Future Goals + [ ] Function Calling + [ ] v2 with new base model to improve performance ## Quantizations ### ExLlamav2 _These are the recommended quantizations for users that are running the model on GPU_ Thanks to user [bartowski](https://huggingface.co/bartowski) we now have exllamav2 quantizations in 3.5 through 8 bpw. They are available here: + [bartowski/laser-dolphin-mixtral-2x7b-dpo-exl2](https://huggingface.co/bartowski/laser-dolphin-mixtral-2x7b-dpo-exl2) \| Branch \| Bits \| lm_head bits \| VRAM (4k) \| VRAM (16k) \| VRAM (32k) \| Description \| \| ----- \| ---- \| ------- \| ------ \| ------ \| ------ \| ------------ \| \| [8_0](https://huggingface.co/bartowski/laser-dolphin-mixtral-2x7b-dpo-exl2/tree/8_0) \| 8.0 \| 8.0 \| 13.7 GB \| 15.1 GB \| 17.2 GB \| Maximum quality that ExLlamaV2 can produce, near unquantized performance. \| \| [6_5](https://huggingface.co/bartowski/laser-dolphin-mixtral-2x7b-dpo-exl2/tree/6_5) \| 6.5 \| 8.0 \| 11.5 GB \| 12.9 GB \| 15.0 GB \| Near unquantized performance at vastly reduced size, recommended. \| \| [5_0](https://huggingface.co/bartowski/laser-dolphin-mixtral-2x7b-dpo-exl2/tree/5_0) \| 5.0 \| 6.0 \| 9.3 GB \| 10.7 GB \| 12.8 GB \| Slightly lower quality vs 6.5, great for 12gb cards with 16k context. \| \| [4_25](https://huggingface.co/bartowski/laser-dolphin-mixtral-2x7b-dpo-exl2/tree/4_25) \| 4.25 \| 6.0 \| 8.2 GB \| 9.6 GB \| 11.7 GB \| GPTQ equivalent bits per weight. \| \| [3_5](https://huggingface.co/bartowski/laser-dolphin-mixtral-2x7b-dpo-exl2/tree/3_5) \| 3.5 \| 6.0 \| 7.0 GB \| 8.4 GB \| 10.5 GB \| Lower quality, not recommended. \| His quantizations represent the first ~13B model with GQA support. Check out his repo for more information! ### GGUF Current GGUF [Quantizations](https://huggingface.co/macadeliccc/laser-dolphin-mixtral-2x7b-dpo-GGUF) ### AWQ Current AWQ [Quantizations](https://huggingface.co/macadeliccc/laser-dolphin-mixtral-2x7b-dpo-AWQ) ### TheBloke These Quants will result in unpredicted behavior. New quants are available as I have updated the model* Quatizations provided by [TheBloke](https://huggingface.co/TheBloke/laser-dolphin-mixtral-2x7b-dpo-GGUF) ## HF Spaces + GGUF chat available [here](https://huggingface.co/spaces/macadeliccc/laser-dolphin-mixtral-chat-GGUF) + 4-bit bnb chat available [here](https://huggingface.co/spaces/macadeliccc/laser-dolphin-mixtral-chat) # Ollama ```bash ollama run macadeliccc/laser-dolphin-mixtral-2x7b-dpo ``` ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6455cc8d679315e4ef16fbec/oVwa7Dwkt00tk8_MtlJdR.png) ## Code Example Switch the commented model definition to use in 4-bit. Should work with 9GB and still exceed the single 7B model by 5-6 points roughly ```python from transformers import AutoModelForCausalLM, AutoTokenizer def generate_response(prompt): """ Generate a response from the model based on the input prompt. Args: prompt (str): Prompt for the model. Returns: str: The generated response from the model. """ # Tokenize the input prompt inputs = tokenizer(prompt, return_tensors="pt") # Generate output tokens outputs = model.generate(**inputs, max_new_tokens=256, eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.pad_token_id) # Decode the generated tokens to a string response = tokenizer.decode(outputs[0], skip_special_tokens=True) return response # Load the model and tokenizer model_id = "macadeliccc/laser-dolphin-mixtral-2x7b-dpo" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id, load_in_4bit=True) prompt = "Write a quicksort algorithm in python" # Generate and print responses for each language print("Response:") print(generate_response(prompt), "\n") ``` [colab](https://colab.research.google.com/drive/1cmRhAkDWItV7utHNqNANVZnqDqQNsTUr?usp=sharing) with usage example ## Eval ## EQ Bench <pre>----Benchmark Complete---- 2024-01-31 16:55:37 Time taken: 31.1 mins Prompt Format: ChatML Model: macadeliccc/laser-dolphin-mixtral-2x7b-dpo-GGUF Score (v2): 72.76 Parseable: 171.0 --------------- Batch completed Time taken: 31.2 mins --------------- </pre> evaluation [colab](https://colab.research.google.com/drive/1FpwgsGzCR4tORTxAwUxpN3PcP22En2xk?usp=sharing) ## Summary of previous evaluation \| Model \|AGIEval\|GPT4All\|TruthfulQA\|Bigbench\|Average\| \|---------------------------------------------------------------------------------------------------\|------:\|------:\|---------:\|-------:\|------:\| \|[laser-dolphin-mixtral-2x7b-dpo](https://huggingface.co/macadeliccc/laser-dolphin-mixtral-2x7b-dpo)\| 41.31\| 73.67\| 61.69\| 42.79\| 54.87\| ## Detailed current evaluation \| Model \|AGIEval\|GPT4All\|TruthfulQA\|Bigbench\|Average\| \|---------------------------------------------------------------------------------------------------\|------:\|------:\|---------:\|-------:\|------:\| \|[laser-dolphin-mixtral-2x7b-dpo](https://huggingface.co/macadeliccc/laser-dolphin-mixtral-2x7b-dpo)\| 42.25\| 73.45\| 63.44\| 43.96\| 55.77\| ### AGIEval \| Task \|Version\| Metric \|Value\| \|Stderr\| \|------------------------------\|------:\|--------\|----:\|---\|-----:\| \|agieval_aqua_rat \| 0\|acc \|21.26\|± \| 2.57\| \| \| \|acc_norm\|21.65\|± \| 2.59\| \|agieval_logiqa_en \| 0\|acc \|34.72\|± \| 1.87\| \| \| \|acc_norm\|35.64\|± \| 1.88\| \|agieval_lsat_ar \| 0\|acc \|26.96\|± \| 2.93\| \| \| \|acc_norm\|26.96\|± \| 2.93\| \|agieval_lsat_lr \| 0\|acc \|45.88\|± \| 2.21\| \| \| \|acc_norm\|46.08\|± \| 2.21\| \|agieval_lsat_rc \| 0\|acc \|59.48\|± \| 3.00\| \| \| \|acc_norm\|59.48\|± \| 3.00\| \|agieval_sat_en \| 0\|acc \|73.79\|± \| 3.07\| \| \| \|acc_norm\|73.79\|± \| 3.07\| \|agieval_sat_en_without_passage\| 0\|acc \|42.23\|± \| 3.45\| \| \| \|acc_norm\|41.26\|± \| 3.44\| \|agieval_sat_math \| 0\|acc \|37.27\|± \| 3.27\| \| \| \|acc_norm\|33.18\|± \| 3.18\| Average: 42.25% ### GPT4All \| Task \|Version\| Metric \|Value\| \|Stderr\| \|-------------\|------:\|--------\|----:\|---\|-----:\| \|arc_challenge\| 0\|acc \|58.36\|± \| 1.44\| \| \| \|acc_norm\|58.02\|± \| 1.44\| \|arc_easy \| 0\|acc \|82.20\|± \| 0.78\| \| \| \|acc_norm\|77.40\|± \| 0.86\| \|boolq \| 1\|acc \|87.52\|± \| 0.58\| \|hellaswag \| 0\|acc \|67.50\|± \| 0.47\| \| \| \|acc_norm\|84.43\|± \| 0.36\| \|openbookqa \| 0\|acc \|34.40\|± \| 2.13\| \| \| \|acc_norm\|47.00\|± \| 2.23\| \|piqa \| 0\|acc \|81.61\|± \| 0.90\| \| \| \|acc_norm\|82.59\|± \| 0.88\| \|winogrande \| 0\|acc \|77.19\|± \| 1.18\| Average: 73.45% ### GSM8K \|Task \|Version\| Metric \|Value\| \|Stderr\| \|-----\|------:\|-----------------------------\|-----\|---\|------\| \|gsm8k\| 2\|exact_match,get-answer \| 0.75\| \| \| \| \| \|exact_match_stderr,get-answer\| 0.01\| \| \| \| \| \|alias \|gsm8k\| \| \| ### TruthfulQA \| Task \|Version\|Metric\|Value\| \|Stderr\| \|-------------\|------:\|------\|----:\|---\|-----:\| \|truthfulqa_mc\| 1\|mc1 \|45.90\|± \| 1.74\| \| \| \|mc2 \|63.44\|± \| 1.56\| Average: 63.44% ### Bigbench \| Task \|Version\| Metric \|Value\| \|Stderr\| \|------------------------------------------------\|------:\|---------------------\|----:\|---\|-----:\| \|bigbench_causal_judgement \| 0\|multiple_choice_grade\|58.42\|± \| 3.59\| \|bigbench_date_understanding \| 0\|multiple_choice_grade\|60.70\|± \| 2.55\| \|bigbench_disambiguation_qa \| 0\|multiple_choice_grade\|38.37\|± \| 3.03\| \|bigbench_geometric_shapes \| 0\|multiple_choice_grade\|21.73\|± \| 2.18\| \| \| \|exact_str_match \| 0.00\|± \| 0.00\| \|bigbench_logical_deduction_five_objects \| 0\|multiple_choice_grade\|35.00\|± \| 2.14\| \|bigbench_logical_deduction_seven_objects \| 0\|multiple_choice_grade\|23.57\|± \| 1.61\| \|bigbench_logical_deduction_three_objects \| 0\|multiple_choice_grade\|50.33\|± \| 2.89\| \|bigbench_movie_recommendation \| 0\|multiple_choice_grade\|45.00\|± \| 2.23\| \|bigbench_navigate \| 0\|multiple_choice_grade\|50.00\|± \| 1.58\| \|bigbench_reasoning_about_colored_objects \| 0\|multiple_choice_grade\|60.35\|± \| 1.09\| \|bigbench_ruin_names \| 0\|multiple_choice_grade\|51.12\|± \| 2.36\| \|bigbench_salient_translation_error_detection \| 0\|multiple_choice_grade\|32.26\|± \| 1.48\| \|bigbench_snarks \| 0\|multiple_choice_grade\|67.96\|± \| 3.48\| \|bigbench_sports_understanding \| 0\|multiple_choice_grade\|70.59\|± \| 1.45\| \|bigbench_temporal_sequences \| 0\|multiple_choice_grade\|35.80\|± \| 1.52\| \|bigbench_tracking_shuffled_objects_five_objects \| 0\|multiple_choice_grade\|22.56\|± \| 1.18\| \|bigbench_tracking_shuffled_objects_seven_objects\| 0\|multiple_choice_grade\|17.20\|± \| 0.90\| \|bigbench_tracking_shuffled_objects_three_objects\| 0\|multiple_choice_grade\|50.33\|± \| 2.89\| Average: 43.96% Average score: 55.77% Elapsed time: 02:43:45 ## Citations Fernando Fernandes Neto and Eric Hartford. "Optimizing Large Language Models Using Layer-Selective Rank Reduction and Random Matrix Theory." 2024. ```bibtex @article{sharma2023truth, title={The Truth is in There: Improving Reasoning in Language Models with Layer-Selective Rank Reduction}, author={Sharma, Pratyusha and Ash, Jordan T and Misra, Dipendra}, journal={arXiv preprint arXiv:2312.13558}, year={2023} } ``` ```bibtex @article{gao2021framework, title={A framework for few-shot language model evaluation}, author={Gao, Leo and Tow, Jonathan and Biderman, Stella and Black, Sid and DiPofi, Anthony and Foster, Charles and Golding, Laurence and Hsu, Jeffrey and McDonell, Kyle and Muennighoff, Niklas and others}, journal={Version v0. 0.1. Sept}, year={2021} } ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_macadeliccc__laser-dolphin-mixtral-2x7b-dpo) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|67.16\| \|AI2 Reasoning Challenge (25-Shot)\|65.96\| \|HellaSwag (10-Shot) \|85.80\| \|MMLU (5-Shot) \|63.17\| \|TruthfulQA (0-shot) \|60.76\| \|Winogrande (5-shot) \|79.01\| \|GSM8k (5-shot) \|48.29\|
uw-vta/bloominzer-0.1	uw-vta	2024-05-18T11:50:49Z	113	3	transformers	[ "transformers", "pytorch", "bert", "text-classification", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-18T11:24:39Z	--- license: apache-2.0 language: - en widget: - text: "What is a goat?" --- # What is the Bloominizer The bloominer is a fine-tuned version of BERT that classifies questions by the Bloom's Taxonomy level: Knowledge, Comprehension, Application, Analysis, Synthesis, Evaluation. Tests during training indicate that the Bloominizer is approximately 93% accurate in its classifications, with most misclassifications being for either one level below or above (for instance, it may misclassify a Comprehension question as a Knowledge question, but rately as an Evaluation question). The Bloominizer has been used for large-scale classification of questions from a corpus. For example, a useful usecase is to run all questions in a long multiple choice exam through the Bloominizer and compute the relative percentages of questions from the six Bloom's levels. This can give you an idea of the approximate cognitive level of the overall exam. # Using in transformers The Bloominizer is easiest to use through a pipeline. Sample code is below: ``` import transformers import torch from transformers import pipeline pipe = pipeline("text-classification", model="uw-vta/bloominzer-0.1") print(pipe("What is a goat?")) ``` If you run this code, the output should be something like: ``` [{'label': 'Knowledge', 'score': 0.9993932247161865}] ```
Audino/my-awesomev3-modelv2-base	Audino	2024-05-18T11:47:21Z	108	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-18T11:46:18Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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]
IntellectusAI/mistral_finetune8x7bcompanylaw	IntellectusAI	2024-05-18T11:39:59Z	3	0	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T21:16:21Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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sidddddddddddd/lora_model_10_examples	sidddddddddddd	2024-05-18T11:38:22Z	3	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:quantized:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-18T11:09:51Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: sidddddddddddd - 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)
geunukj/ppo-LunarLander-v2	geunukj	2024-05-18T11:33:51Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-18T11:33:32Z	--- 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: 255.02 +/- 18.64 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 ... ```
PaulR79/llama_finetuned_synthetic	PaulR79	2024-05-18T11:32:20Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-18T11:32:17Z	--- 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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NikolayKozloff/RoMistral-7b-Instruct-Q8_0-GGUF	NikolayKozloff	2024-05-18T11:30:58Z	0	1	null	[ "gguf", "llama-cpp", "gguf-my-repo", "ro", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-18T11:30:38Z	--- language: - ro license: cc-by-nc-4.0 tags: - llama-cpp - gguf-my-repo --- # NikolayKozloff/RoMistral-7b-Instruct-Q8_0-GGUF This model was converted to GGUF format from [`OpenLLM-Ro/RoMistral-7b-Instruct`](https://huggingface.co/OpenLLM-Ro/RoMistral-7b-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/OpenLLM-Ro/RoMistral-7b-Instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo NikolayKozloff/RoMistral-7b-Instruct-Q8_0-GGUF --model romistral-7b-instruct.Q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo NikolayKozloff/RoMistral-7b-Instruct-Q8_0-GGUF --model romistral-7b-instruct.Q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m romistral-7b-instruct.Q8_0.gguf -n 128 ```
RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-gguf	RichardErkhov	2024-05-18T11:29:56Z	52	0	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2024-05-18T09:29:25Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) KoSOLAR-10.7B-v0.2 - GGUF - Model creator: https://huggingface.co/yanolja/ - Original model: https://huggingface.co/yanolja/KoSOLAR-10.7B-v0.2/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [KoSOLAR-10.7B-v0.2.Q2_K.gguf](https://huggingface.co/RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-gguf/blob/main/KoSOLAR-10.7B-v0.2.Q2_K.gguf) \| Q2_K \| 3.77GB \| \| [KoSOLAR-10.7B-v0.2.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-gguf/blob/main/KoSOLAR-10.7B-v0.2.IQ3_XS.gguf) \| IQ3_XS \| 4.18GB \| \| [KoSOLAR-10.7B-v0.2.IQ3_S.gguf](https://huggingface.co/RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-gguf/blob/main/KoSOLAR-10.7B-v0.2.IQ3_S.gguf) \| IQ3_S \| 4.41GB \| \| [KoSOLAR-10.7B-v0.2.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-gguf/blob/main/KoSOLAR-10.7B-v0.2.Q3_K_S.gguf) \| Q3_K_S \| 4.39GB \| \| [KoSOLAR-10.7B-v0.2.IQ3_M.gguf](https://huggingface.co/RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-gguf/blob/main/KoSOLAR-10.7B-v0.2.IQ3_M.gguf) \| IQ3_M \| 4.56GB \| \| [KoSOLAR-10.7B-v0.2.Q3_K.gguf](https://huggingface.co/RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-gguf/blob/main/KoSOLAR-10.7B-v0.2.Q3_K.gguf) \| Q3_K \| 4.88GB \| \| [KoSOLAR-10.7B-v0.2.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-gguf/blob/main/KoSOLAR-10.7B-v0.2.Q3_K_M.gguf) \| Q3_K_M \| 4.88GB \| \| [KoSOLAR-10.7B-v0.2.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-gguf/blob/main/KoSOLAR-10.7B-v0.2.Q3_K_L.gguf) \| Q3_K_L \| 5.31GB \| \| [KoSOLAR-10.7B-v0.2.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-gguf/blob/main/KoSOLAR-10.7B-v0.2.IQ4_XS.gguf) \| IQ4_XS \| 5.47GB \| \| [KoSOLAR-10.7B-v0.2.Q4_0.gguf](https://huggingface.co/RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-gguf/blob/main/KoSOLAR-10.7B-v0.2.Q4_0.gguf) \| Q4_0 \| 5.7GB \| \| [KoSOLAR-10.7B-v0.2.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-gguf/blob/main/KoSOLAR-10.7B-v0.2.IQ4_NL.gguf) \| IQ4_NL \| 5.77GB \| \| [KoSOLAR-10.7B-v0.2.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-gguf/blob/main/KoSOLAR-10.7B-v0.2.Q4_K_S.gguf) \| Q4_K_S \| 5.75GB \| \| [KoSOLAR-10.7B-v0.2.Q4_K.gguf](https://huggingface.co/RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-gguf/blob/main/KoSOLAR-10.7B-v0.2.Q4_K.gguf) \| Q4_K \| 6.07GB \| \| [KoSOLAR-10.7B-v0.2.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-gguf/blob/main/KoSOLAR-10.7B-v0.2.Q4_K_M.gguf) \| Q4_K_M \| 6.07GB \| \| [KoSOLAR-10.7B-v0.2.Q4_1.gguf](https://huggingface.co/RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-gguf/blob/main/KoSOLAR-10.7B-v0.2.Q4_1.gguf) \| Q4_1 \| 6.32GB \| \| [KoSOLAR-10.7B-v0.2.Q5_0.gguf](https://huggingface.co/RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-gguf/blob/main/KoSOLAR-10.7B-v0.2.Q5_0.gguf) \| Q5_0 \| 6.94GB \| \| [KoSOLAR-10.7B-v0.2.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-gguf/blob/main/KoSOLAR-10.7B-v0.2.Q5_K_S.gguf) \| Q5_K_S \| 6.94GB \| \| [KoSOLAR-10.7B-v0.2.Q5_K.gguf](https://huggingface.co/RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-gguf/blob/main/KoSOLAR-10.7B-v0.2.Q5_K.gguf) \| Q5_K \| 7.13GB \| \| [KoSOLAR-10.7B-v0.2.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-gguf/blob/main/KoSOLAR-10.7B-v0.2.Q5_K_M.gguf) \| Q5_K_M \| 7.13GB \| \| [KoSOLAR-10.7B-v0.2.Q5_1.gguf](https://huggingface.co/RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-gguf/blob/main/KoSOLAR-10.7B-v0.2.Q5_1.gguf) \| Q5_1 \| 7.56GB \| \| [KoSOLAR-10.7B-v0.2.Q6_K.gguf](https://huggingface.co/RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-gguf/blob/main/KoSOLAR-10.7B-v0.2.Q6_K.gguf) \| Q6_K \| 8.26GB \| \| [KoSOLAR-10.7B-v0.2.Q8_0.gguf](https://huggingface.co/RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-gguf/blob/main/KoSOLAR-10.7B-v0.2.Q8_0.gguf) \| Q8_0 \| 10.69GB \| Original model description: --- license: apache-2.0 base_model: upstage/SOLAR-10.7B-v1.0 tags: - generated_from_trainer model-index: - name: yanolja/KoSOLAR-10.7B-v0.2 results: [] --- [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) # KoSOLAR-10.7B-v0.2 ## Join Our Community on Discord! If you're passionate about the field of Large Language Models and wish to exchange knowledge and insights, we warmly invite you to join our Discord server. It's worth noting that Korean is the primary language used in this server. The landscape of LLM is evolving rapidly, and without active sharing, our collective knowledge risks becoming outdated swiftly. Let's collaborate and drive greater impact together! Join us here: [Discord Link](https://discord.gg/b27bAHg95m). ## Our Dedicated Team (Alphabetical Order) \| Research \| Engineering \| Product Management \| UX Design \| \|-----------------\|-----------------\|--------------------\|-------------- \| Myeongho Jeong \| Geon Kim \| Bokyung Huh \| Eunsue Choi \| \| Seungduk Kim \| Rifqi Alfi \| \| \| \| Seungtaek Choi \| Sanghoon Han \| \| \| \| \| Suhyun Kang \| \| \| ## About the Model This model is a Korean vocabulary-extended version of [upstage/SOLAR-10.7B-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-v1.0), specifically fine-tuned on various Korean web-crawled datasets available on HuggingFace. Our approach was to expand the model's understanding of Korean by pre-training the embeddings for new tokens and partially fine-tuning the `lm_head` embeddings for the already existing tokens while preserving the original parameters of the base model. ### Technical Deep Dive Here’s a glimpse into our technical approach: ```python def freeze_partial_embedding_hook(grad): grad[:32000] = 0 return grad for name, param in model.named_parameters(): if ("lm_head" in name or "embed_tokens" in name) and "original" not in name: param.requires_grad = True if "embed_tokens" in name: param.register_hook(freeze_partial_embedding_hook) else: param.requires_grad = False ``` Our strategy involved a selective freeze of model parameters. Specifically, we kept most parameters of the base model unchanged while focusing on enhancing the Korean language capabilities. Through our experiments, we discovered: 1. Freezing the `embed_tokens` layer for existing tokens is crucial to maintain overall performance. 2. Unfreezing the `lm_head` layer for existing tokens actually boosts performance. As a result, we froze the internal layers and the first 32,000 `embed_tokens`, directing our training efforts on a rich mix of Korean and multi-lingual corpora. This balanced approach has notably improved the model’s proficiency in Korean, without compromising its original language capabilities. ### Usage and Limitations Keep in mind that this model hasn't been fine-tuned with instruction-based training. While it excels in Korean language tasks, we advise careful consideration and further training for specific applications. ### Training Details Our model’s training was comprehensive and diverse: - Data Sources: - English to Korean paragraph pairs: 5.86% - Multi-lingual corpus (primarily English): 10.69% - Korean web content: 83.46% - Vocabulary Expansion: We meticulously selected 8,960 Korean tokens based on their frequency in our Korean web corpus. This process involved multiple rounds of tokenizer training, manual curation, and token frequency analysis, ensuring a rich and relevant vocabulary for our model. 1. Initial Tokenizer Training: We trained an intermediate tokenizer on a Korean web corpus, with a vocabulary of 40,000 tokens. 2. Extraction of New Korean Tokens: From the intermediate tokenizer, we identified all Korean tokens not present in the original SOLAR's tokenizer. 3. Manual Tokenizer Construction: We then built the target tokenizer, focusing on these new Korean tokens. 4. Frequency Analysis: Using the target tokenizer, we processed a 100GB Korean corpus to count each token's frequency. 5. Refinement of Token List: We removed tokens appearing less than 6,000 times, ensuring to secure enough tokens to train models later. 6. Inclusion of Single-Letter Characters: Counted missing Korean single-letter characters and added them to the target tokenizer that appeared more than 6,000 times. 7. Iterative Refinement: We repeated steps 2 to 6 until there were no tokens to drop or add. 8. Training Bias Towards New Tokens: Our training data was biased to include more texts with new tokens, for effective learning. This rigorous approach ensured a comprehensive and contextually rich Korean vocabulary for the model.
PQlet/lora-narutoblip-v1-ablation-r16-a16-module_to_q	PQlet	2024-05-18T11:23:37Z	1	0	diffusers	[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers-training", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-05-18T11:23:32Z	--- license: creativeml-openrail-m library_name: diffusers tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - diffusers-training - lora base_model: runwayml/stable-diffusion-v1-5 inference: true --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # LoRA text2image fine-tuning - PQlet/lora-narutoblip-v1-ablation-r16-a16-module_to_q These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the Naruto-BLIP dataset. You can find some example images in the following. ![img_0](./image_a_man_with_glasses_and_a_shirt_on.png) ![img_1](./image_a_group_of_people_sitting_on_the_ground.png) ![img_2](./image_a_man_in_a_green_hoodie_standing_in_front_of_a_mountain.png) ![img_3](./image_a_man_with_a_gun_in_his_hand.png) ![img_4](./image_a_woman_with_red_hair_and_a_cat_on_her_head.png) ![img_5](./image_two_pokemons_sitting_on_top_of_a_cloud.png) ![img_6](./image_a_man_standing_in_front_of_a_bridge.png) ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
LoneStriker/dolphin-2.9.1-yi-1.5-34b-8.0bpw-h8-exl2	LoneStriker	2024-05-18T11:23:14Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "axolotl", "conversational", "dataset:cognitivecomputations/Dolphin-2.9", "dataset:teknium/OpenHermes-2.5", "dataset:m-a-p/CodeFeedback-Filtered-Instruction", "dataset:cognitivecomputations/dolphin-coder", "dataset:cognitivecomputations/samantha-data", "dataset:microsoft/orca-math-word-problems-200k", "dataset:Locutusque/function-calling-chatml", "dataset:internlm/Agent-FLAN", "base_model:01-ai/Yi-1.5-34B", "base_model:quantized:01-ai/Yi-1.5-34B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "exl2", "region:us" ]	text-generation	2024-05-18T11:10:11Z	--- license: apache-2.0 base_model: 01-ai/Yi-1.5-34B tags: - generated_from_trainer - axolotl datasets: - cognitivecomputations/Dolphin-2.9 - teknium/OpenHermes-2.5 - m-a-p/CodeFeedback-Filtered-Instruction - cognitivecomputations/dolphin-coder - cognitivecomputations/samantha-data - microsoft/orca-math-word-problems-200k - Locutusque/function-calling-chatml - internlm/Agent-FLAN --- # Dolphin 2.9.1 Yi 1.5 34b 🐬 Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations This is our most spectacular outcome ever. FFT, all parameters, 16bit. 77.4 MMLU on 34b. And it talks like a dream. Although the max positional embeddings is 4k, we used rope theta of 1000000.0 and we trained with sequence length 8k. We plan to train on the upcoming 32k version as well. Discord: https://discord.gg/8fbBeC7ZGx <img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/ldkN1J0WIDQwU4vutGYiD.png" width="600" /> Our appreciation for the sponsors of Dolphin 2.9.1: - [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 8xH100 node - [OnDemand](https://on-demand.io/) - provided inference sponsorship This model is based on Yi-1.5-34b, and is governed by apache 2.0 license. The base model has 4k context, but we used rope theta of 1000000.0 and the full-weight fine-tuning was with 8k sequence length. Dolphin 2.9.1 uses ChatML prompt template format. example: ``` <\|im_start\|>system You are Dolphin, a helpful AI assistant.<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` Dolphin-2.9.1 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling. Dolphin is uncensored. We have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models You are responsible for any content you create using this model. Enjoy responsibly. Dolphin is licensed according to apache 2.0 license. We grant permission for any use, including commercial. Dolphin was trained on data generated from GPT4, among other models. ## Evals ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/coI4WEJEJD4lhSWgMOjIr.png) ## Training [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.0` ```yaml base_model: 01-ai/Yi-1.5-34B model_type: LlamaForCausalLM tokenizer_type: LlamaTokenizer trust_remote_code: true # load_in_8bit: false # load_in_4bit: true # strict: false # adapter: qlora # lora_modules_to_save: [embed_tokens, lm_head] # lora_r: 32 # lora_alpha: 16 # lora_dropout: 0.05 # lora_target_linear: True # lora_fan_in_fan_out: datasets: - path: /workspace/datasets/dolphin-2.9/dolphin201-sharegpt2.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/dolphin-coder-translate-sharegpt2.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/dolphin-coder-codegen-sharegpt2.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/m-a-p_Code-Feedback-sharegpt-unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/m-a-p_CodeFeedback-Filtered-Instruction-sharegpt-unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/not_samantha_norefusals.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/Orca-Math-resort-unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/agent_instruct_react_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_instruct_j1s1_3k_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_negative_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_react_10p_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_tflan_cot_30p_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/openhermes200k_unfiltered.jsonl type: sharegpt conversation: chatml chat_template: chatml dataset_prepared_path: yi34b val_set_size: 0.01 output_dir: ./out-yi sequence_len: 8192 sample_packing: true pad_to_sequence_len: true wandb_project: dolphin-2.9-yi-34b wandb_watch: wandb_run_id: wandb_log_model: gradient_accumulation_steps: 8 micro_batch_size: 1 num_epochs: 3 optimizer: adamw_8bit lr_scheduler: cosine learning_rate: 1e-5 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: true gradient_checkpointing: true gradient_checkpointing_kwargs: use_reentrant: false early_stopping_patience: # resume_from_checkpoint: /workspace/axolotl/dbrx-checkpoint logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 10 evals_per_epoch: 4 eval_table_size: saves_per_epoch: 4 save_total_limit: 2 save_steps: debug: deepspeed: /workspace/axolotl/deepspeed_configs/zero3_bf16.json weight_decay: 0.05 fsdp: fsdp_config: special_tokens: bos_token: "<\|startoftext\|>" eos_token: "<\|im_end\|>" pad_token: "<unk>" unk_token: "<unk>" tokens: - "<\|im_start\|>" ``` </details><br> # out-yi This model is a fine-tuned version of [01-ai/Yi-1.5-34B](https://huggingface.co/01-ai/Yi-1.5-34B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4425 ## 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: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.6265 \| 0.0 \| 1 \| 0.6035 \| \| 0.4674 \| 0.25 \| 327 \| 0.4344 \| \| 0.4337 \| 0.5 \| 654 \| 0.4250 \| \| 0.4346 \| 0.75 \| 981 \| 0.4179 \| \| 0.3985 \| 1.0 \| 1308 \| 0.4118 \| \| 0.3128 \| 1.23 \| 1635 \| 0.4201 \| \| 0.3261 \| 1.48 \| 1962 \| 0.4157 \| \| 0.3259 \| 1.73 \| 2289 \| 0.4122 \| \| 0.3126 \| 1.98 \| 2616 \| 0.4079 \| \| 0.2265 \| 2.21 \| 2943 \| 0.4441 \| \| 0.2297 \| 2.46 \| 3270 \| 0.4427 \| \| 0.2424 \| 2.71 \| 3597 \| 0.4425 \| ### Framework versions - Transformers 4.40.0.dev0 - Pytorch 2.2.2+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0

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