Datasets:

librarian-bots

/

model_cards_with_metadata

like 10

ineo-ocr/ocr

ineo-ocr

Entry not found

abhishektandon/ddpm

abhishektandon

Entry not found

MarcAmil/esm2_t12_35M_UR50D-finetuned-localization

MarcAmil

Entry not found

TriasAI/UgurYucel

TriasAI

--- license: openrail ---

Adishah31/mistral_4bit_lora_model

Adishah31

peft

--- library_name: peft base_model: unsloth/mistral-7b-bnb-4bit datasets: - yahma/alpaca-cleaned --- # Model Card for Model ID A 4bit Mistral 7B model finetuned using unsloth on T4 GPU ## Model Details ### Model Description - **Finetuned from model:** unsloth/mistral-7b-bnb-4bit - **Repository:** https://github.com/unslothai/unsloth ## Training Details ### Training Data https://huggingface.co/datasets/yahma/alpaca-cleaned ### Training Procedure #### Preprocessing Alpaca prompt template is used: ``` alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. ### Instruction: {} ### Input: {} ### Response: {}""" ``` #### Training Hyperparameters ``` per_device_train_batch_size = 2, gradient_accumulation_steps = 4, warmup_steps = 5, max_steps = 60, learning_rate = 2e-4, fp16 = not torch.cuda.is_bf16_supported(), bf16 = torch.cuda.is_bf16_supported(), logging_steps = 1, optim = "adamw_8bit", weight_decay = 0.01, lr_scheduler_type = "linear", seed = 3407 ``` - **Hardware Type:** T4 GPU - **Cloud Provider:** Google Colab ### Framework versions - PEFT 0.7.1

mahdi1717/jamshid

mahdi1717

Entry not found

smrynrz0220/bart_cg_model

smrynrz0220

Entry not found

daniel-gordon/GradientPolicy-CartPole-v1-500reward

daniel-gordon

reinforcement-learning

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: GradientPolicy-CartPole-v1-500reward 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

abhishektandon/ddpm-butterflies-128

abhishektandon

Entry not found

ib1368/Reinforce-CartPole-v1

ib1368

reinforcement-learning

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

Arzen221/phi-orca-1-percent

Arzen221

peft

--- library_name: peft base_model: microsoft/phi-2 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.1

yy0514/bert-lek-full-train-4epochs

yy0514

transformers

multiple-choice

Entry not found

amuchinaa/lunaai

amuchinaa

Entry not found

inkstar/wuh

inkstar

Entry not found

Olena25/distilgpt2

Olena25

--- license: openrail ---

Loren85/Angel-Dust-Hazbin-Hotel-Ita

Loren85

--- license: openrail ---

jysssacc/bloomz-560m_IA3_lr5e-05_bs4_epoch20_wd0.01

jysssacc

peft

--- license: bigscience-bloom-rail-1.0 library_name: peft tags: - generated_from_trainer base_model: bigscience/bloomz-560m model-index: - name: bloomz-560m_IA3_lr5e-05_bs4_epoch20_wd0.01 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. --> # bloomz-560m_IA3_lr5e-05_bs4_epoch20_wd0.01 This model is a fine-tuned version of [bigscience/bloomz-560m](https://huggingface.co/bigscience/bloomz-560m) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.4710 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 4.3089 | 1.0 | 157 | 4.0251 | | 4.2527 | 2.0 | 314 | 3.9726 | | 4.1732 | 3.0 | 471 | 3.8935 | | 4.0635 | 4.0 | 628 | 3.8054 | | 4.0039 | 5.0 | 785 | 3.7344 | | 3.9169 | 6.0 | 942 | 3.6770 | | 3.8693 | 7.0 | 1099 | 3.6325 | | 3.7869 | 8.0 | 1256 | 3.5966 | | 3.8279 | 9.0 | 1413 | 3.5689 | | 3.7502 | 10.0 | 1570 | 3.5471 | | 3.7021 | 11.0 | 1727 | 3.5299 | | 3.6739 | 12.0 | 1884 | 3.5160 | | 3.6696 | 13.0 | 2041 | 3.5043 | | 3.6395 | 14.0 | 2198 | 3.4947 | | 3.6539 | 15.0 | 2355 | 3.4873 | | 3.601 | 16.0 | 2512 | 3.4812 | | 3.6461 | 17.0 | 2669 | 3.4765 | | 3.657 | 18.0 | 2826 | 3.4734 | | 3.6959 | 19.0 | 2983 | 3.4716 | | 3.6035 | 20.0 | 3140 | 3.4710 | ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.0.1 - Datasets 2.16.1 - Tokenizers 0.15.0

Bruh110/YNWMELLY

Bruh110

--- license: openrail ---

etienne1222/donut-invoice-receipt-test-V1

etienne1222

transformers

Entry not found

homersimpson/4-cat-belebele-fr

homersimpson

transformers

multiple-choice

Entry not found

daniel-gordon/PolicyGradient-Pixelcopter-PLE-v0

daniel-gordon

5000 steps --- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: PolicyGradient-Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 4.00 +/- 5.25 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . 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

CluckRookie/CambioBert-base

CluckRookie

transformers

fill-mask

--- license: apache-2.0 ---

Beckhusen/beckhusen

Beckhusen

--- license: unknown ---

UMI-DUINO/TheDuino-007-13B

UMI-DUINO

Entry not found

kisLottUS/Mafanya

kisLottUS

Entry not found

FilledtotheBrim/falcon_finetuned

FilledtotheBrim

peft

--- library_name: peft base_model: tiiuae/falcon-7b-instruct --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.2.dev0

waveydaveygravy/swap-mukham

waveydaveygravy

Entry not found

lourvalli/code-search-net-tokenizer

lourvalli

Entry not found

NLPProject2023Z/xlnet_regression

NLPProject2023Z

Entry not found

AlbelTec/dpo_mistral_7B_v_0_1

AlbelTec

Entry not found

satendra4u2022/mistral_7b_guanaco_202401

satendra4u2022

--- license: mit ---

ALOQAS/gpt2-aloqas-scientific-papers

ALOQAS

# Algorithmic Learning and Optimized Quantum Artificial Solutions (ALOQAS) <p> <a href="https://huggingface.co/spaces/ALOQAS/aloqas-gradio">Démo. Gradio sur Hugging Face Spaces</a> </p> <p> <a href="https://github.com/LucasAguetai/ALOQAS">Lien vers le repository GitHub</a> </p> <p> <a href="https://drive.google.com/drive/folders/1MrW-UftHd0HVgLjJ_C5HmwBG3ymEY_qY?usp=drive_link">Lien vers les notebooks Google Colaboratory (sur demande)</a> </p> ## Projet : Création d'un Système de Chatbot Conversationnel basé sur GPT-2 Ce projet a pour objectif de développer un chatbot conversationnel intelligent en utilisant le modèle GPT-2 comme base. <br /> Le chatbot sera capable d'engager des conversations naturelles avec les utilisateurs, de répondre à leurs questions et de fournir des informations utiles. ## Membres du projet <ul> <li><b>A</b>urélien ZUFIC</li> <li><b>L</b>ucas AGUETAÏ</li> <li><b>O</b>ny ANDRIATSAHAVOJAONA</li> <li><b>Q</b>uentin VERMEERSCH</li> <li><b>A</b>lexandre HUYNH</li> <li><b>S</b>amuel DORISMOND</li> </ul> ## Jeux de données traité Dataset TensorFlow sur des articles scientifiques : <a href="https://www.tensorflow.org/datasets/catalog/scientific_papers">scientific_papers</a> ## Tâches du projet ### Compréhension de GPT-2 : Étudiez le fonctionnement de GPT-2 en utilisant l'API TensorFlow.<br /> Explorez comment GPT-2 génère du texte en réponse à des stimuli. ### Collecte de Données : Identifiez un domaine spécifique ou une application pour votre chatbot (par exemple, un chatbot de service client, un chatbot éducatif, etc.).<br /> Collectez ou préparez un ensemble de données de dialogue adapté à votre domaine d'application. ### Fine-tuning de GPT-2 : Fine-tunez le modèle GPT-2 en utilisant l'ensemble de données de dialogue.<br /> Optimisez le modèle pour la génération de réponses de chatbot cohérentes et pertinentes.<br /> Évaluez les performances du modèle fine-tuné en utilisant des mesures de qualité de dialogue. ### Intégration de Gradio : Utilisez la bibliothèque Gradio pour intégrer une interface utilisateur conviviale à votre chatbot.<br /> Personnalisez l'interface pour qu'elle corresponde à l'esthétique de votre application. ### Tests et Optimisation : Testez le chatbot avec des utilisateurs pour recueillir des commentaires et des données de performance.<br /> Effectuez des ajustements en fonction des commentaires des utilisateurs pour améliorer la qualité des réponses du chatbot. ### Documentation et Présentation : Rédigez une documentation complète expliquant comment utiliser le chatbot.<br /> Préparez une présentation pour montrer et expliquer votre chatbot à vos pairs et enseignants. ### Ressources : Vous pouvez utiliser l'API GPT-2 de TensorFlow pour le fine-tuning et la génération de réponses de chatbot.<br /> Flask est une bibliothèque Python populaire pour le développement de serveurs web.<br /> Gradio propose des ressources et des exemples pour développer des interfaces utilisateur interactives.

Faithshield/marvel_model

Faithshield

tf-keras

--- license: apache-2.0 ---

JacobLinCool/whisper-small-tw

JacobLinCool

transformers

automatic-speech-recognition

Entry not found

vwxyzjn/models_EleutherAI_pythia-6.9b-deduped_sft_model_77713__reward__tldr

vwxyzjn

Entry not found

vwxyzjn/models_EleutherAI_pythia-6.9b-deduped_sft_model_55513__reward__tldr

vwxyzjn

Entry not found

vwxyzjn/models_EleutherAI_pythia-6.9b-deduped_sft_model_66613__reward__tldr

vwxyzjn

Entry not found

vwxyzjn/models_EleutherAI_pythia-6.9b-deduped_sft_model_44413__reward__tldr

vwxyzjn

Entry not found

johnatanebonilla/fr_pipeline

johnatanebonilla

spacy

token-classification

--- tags: - spacy - token-classification language: - fr model-index: - name: fr_pipeline results: - task: name: TAG type: token-classification metrics: - name: TAG (XPOS) Accuracy type: accuracy value: 0.973761619 - task: name: POS type: token-classification metrics: - name: POS (UPOS) Accuracy type: accuracy value: 0.9726634506 - task: name: MORPH type: token-classification metrics: - name: Morph (UFeats) Accuracy type: accuracy value: 0.9612141653 - task: name: UNLABELED_DEPENDENCIES type: token-classification metrics: - name: Unlabeled Attachment Score (UAS) type: f_score value: 0.8499876513 - task: name: LABELED_DEPENDENCIES type: token-classification metrics: - name: Labeled Attachment Score (LAS) type: f_score value: 0.7929110925 - task: name: SENTS type: token-classification metrics: - name: Sentences F-Score type: f_score value: 0.9819527996 --- | Feature | Description | | --- | --- | | **Name** | `fr_pipeline` | | **Version** | `0.0.0` | | **spaCy** | `>=3.6.1,<3.7.0` | | **Default Pipeline** | `transformer`, `morphologizer`, `parser`, `tagger` | | **Components** | `transformer`, `morphologizer`, `parser`, `tagger` | | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) | | **Sources** | n/a | | **License** | n/a | | **Author** | [n/a]() | ### Label Scheme <details> <summary>View label scheme (240 labels for 3 components)</summary> | Component | Labels | | --- | --- | | **`morphologizer`** | `POS=INTJ`, `POS=PUNCT`, `POS=ADP`, `POS=VERB\|VerbForm=Inf`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `POS=PROPN`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Fem\|Number=Sing\|POS=NOUN`, `Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `POS=ADV`, `Definite=Def\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=PROPN`, `POS=CCONJ`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `POS=PRON\|PronType=Rel`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=ADJ`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `POS=ADV\|Polarity=Neg`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=NOUN`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `POS=X`, `Definite=Def\|Number=Plur\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Plur\|POS=NOUN`, `Number=Plur\|POS=DET\|PronType=Dem`, `ExtPos=INTJ\|POS=INTJ`, `Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `POS=SCONJ`, `POS=VERB`, `Gender=Masc\|Number=Plur\|POS=ADJ`, `Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=ADJ`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `ExtPos=ADV\|POS=ADP`, `Number=Sing\|POS=ADJ`, `Number=Plur\|Number[psor]=Plur\|POS=DET\|Person[psor]=1\|Poss=Yes\|PronType=Prs`, `Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=NOUN`, `Gender=Fem\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Number=Plur\|Number[psor]=Plur\|POS=DET\|Person[psor]=2\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Number=Sing\|Number[psor]=Plur\|POS=DET\|Person[psor]=1\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Number=Plur\|POS=NUM`, `Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Number=Sing\|Number[psor]=Plur\|POS=DET\|Person[psor]=2\|Poss=Yes\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=VERB\|Tense=Pres\|VerbForm=Part`, `Foreign=Yes\|POS=ADJ`, `Foreign=Yes\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Number=Plur\|Number[psor]=Sing\|POS=DET\|Person[psor]=1\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `POS=NUM`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `POS=NOUN`, `Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `ExtPos=SCONJ\|POS=ADV`, `Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Neg`, `Number=Sing\|POS=NOUN`, `Number=Sing\|Number[psor]=Sing\|POS=DET\|Person[psor]=1\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Gender=Masc\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Part`, `ExtPos=CCONJ\|POS=CCONJ`, `Definite=Ind\|Number=Plur\|POS=DET\|PronType=Art`, `Gender=Masc\|POS=VERB\|Tense=Past\|VerbForm=Part`, `POS=PRON`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Imp\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Number=Plur\|Number[psor]=Sing\|POS=DET\|Person[psor]=3\|Poss=Yes\|PronType=Prs`, `Number=Sing\|POS=PROPN`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `POS=ADV\|PronType=Int`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Fut\|VerbForm=Fin`, `ExtPos=PRON\|POS=ADV\|Polarity=Neg`, `POS=PRON\|PronType=Int`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `POS=AUX\|Tense=Pres\|VerbForm=Part`, `POS=AUX\|VerbForm=Inf`, `ExtPos=PROPN\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Number=Sing\|Number[psor]=Sing\|POS=DET\|Person[psor]=3\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|POS=NOUN`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person[psor]=3\|Poss=Yes\|PronType=Prs`, `Number=Plur\|POS=DET\|PronType=Ind`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=PROPN`, `Gender=Masc\|POS=ADJ`, `Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Definite=Ind\|ExtPos=ADV\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `ExtPos=ADP\|POS=ADP`, `ExtPos=INTJ\|POS=VERB`, `ExtPos=ADV\|POS=ADV`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel`, `Gender=Masc\|Number=Plur\|POS=PROPN`, `ExtPos=PRON\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Number=Sing\|POS=NUM`, `ExtPos=ADV\|POS=ADV\|Polarity=Neg`, `ExtPos=ADV\|POS=SCONJ`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person[psor]=1\|Poss=Yes\|PronType=Prs`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Number=Sing\|Number[psor]=Plur\|POS=DET\|Person[psor]=3\|Poss=Yes\|PronType=Prs`, `Number=Plur\|POS=ADJ`, `Number=Plur\|POS=VERB\|Person=1`, `POS=ADV\|PronType=Exc`, `POS=DET`, `Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `ExtPos=PRON\|POS=ADV`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Number=Sing\|POS=DET\|PronType=Ind`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `POS=ADV\|PronType=Neg`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `ExtPos=NOUN\|POS=ADV`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Neg`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `POS=PRON\|Person=3\|PronType=Ind`, `ExtPos=ADV\|POS=CCONJ`, `ExtPos=ADP\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `ExtPos=DET\|POS=ADP`, `ExtPos=ADP\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `POS=ADJ`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Foreign=Yes\|POS=DET`, `Foreign=Yes\|POS=NOUN`, `Number=Plur\|POS=PRON\|Person=2\|PronType=Prs\|Reflex=Yes`, `ExtPos=ADV\|Number=Plur\|POS=NUM`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin`, `ExtPos=PROPN\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Foreign=Yes\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `ExtPos=NOUN\|Gender=Masc\|Number=Sing\|POS=NOUN`, `ExtPos=PROPN\|POS=PROPN`, `ExtPos=VERB\|POS=X`, `Definite=Ind\|ExtPos=ADV\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=1\|PronType=Prs`, `ExtPos=PROPN\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `ExtPos=NOUN\|POS=ADP`, `Number=Plur\|Number[psor]=Plur\|POS=DET\|Person[psor]=3\|Poss=Yes\|PronType=Prs`, `ExtPos=SCONJ\|POS=CCONJ`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin` | | **`parser`** | `ROOT`, `acl`, `acl:relcl`, `advcl`, `advcl:cleft`, `advmod`, `amod`, `appos`, `aux:pass`, `aux:tense`, `case`, `cc`, `ccomp`, `conj`, `cop`, `dep`, `det`, `discourse`, `dislocated`, `expl:comp`, `expl:subj`, `fixed`, `flat:name`, `iobj`, `mark`, `nmod`, `nmod:appos`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obj:lvc`, `obl:arg`, `obl:mod`, `punct`, `reparandum`, `xcomp` | | **`tagger`** | `ADJ`, `ADP`, `ADV`, `AUX`, `CCONJ`, `DET`, `INTJ`, `NOUN`, `NUM`, `PRON`, `PROPN`, `PUNCT`, `SCONJ`, `VERB`, `X` | </details> ### Accuracy | Type | Score | | --- | --- | | `POS_ACC` | 97.27 | | `MORPH_ACC` | 96.12 | | `DEP_UAS` | 85.00 | | `DEP_LAS` | 79.29 | | `SENTS_P` | 98.24 | | `SENTS_R` | 98.15 | | `SENTS_F` | 98.20 | | `TAG_ACC` | 97.38 | | `TRANSFORMER_LOSS` | 670926.24 | | `MORPHOLOGIZER_LOSS` | 1747.33 | | `PARSER_LOSS` | 1719102.10 | | `TAGGER_LOSS` | 198.51 |

AiAF/furrydiffusion.safetensora

AiAF

Entry not found

Dybala10/Test

Dybala10

Entry not found

daniel-gordon/Reinforce-Pixelcopter-PLE-v0

daniel-gordon

reinforcement-learning

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 19.90 +/- 18.44 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . 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

Bananamonkey011/models

Bananamonkey011

Entry not found

Gabriel1945/Velha

Gabriel1945

--- license: openrail ---

Dremmar/juggernaut_v8

Dremmar

Entry not found

satendra4u2022/mistral_7b_guanaco

satendra4u2022

Entry not found

yy0514/llama2-7b-chat-qlora-lek-train-4-epochs-run2

yy0514

--- base_model: meta-llama/Llama-2-7b-chat-hf tags: - generated_from_trainer model-index: - name: llama2-7b-chat-qlora-lek-train-4-epochs-recheck 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. --> # llama2-7b-chat-qlora-lek-train-4-epochs-recheck This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2 - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0

Nick088/Jayne_Secker_Sky_News_Reporter

Nick088

Entry not found

roktimsardar123/riley_reid

roktimsardar123

Entry not found

Keiser41/ModelMaker

Keiser41

--- license: creativeml-openrail-m language: - en - es - ja tags: - music ---

Cool629/OtherModels

Cool629

--- license: openrail ---

AbdulSamad101/llama-2-7b-AbdulSamad-FT

AbdulSamad101

peft

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0 - PEFT 0.4.0

ptailor3/SAINTS_Large

ptailor3

--- license: mit ---

zhan1993/kate_test

zhan1993

Entry not found

ostapeno/library-phi_2-v3-10-flan-clusters-parallel_evol

ostapeno

Number of experts present in the library: 10 | Expert Name | Base Model | Trained on | Adapter Type | | --- | --- | --- | --- | | phi2_joint_lora_embed_clustersc8_2e_3epoch | phi-2 | sordonia/flan-10k-flat/race_middle_Read_the_article_and_answer_the_question_no_option_,race_high_Select_the_best_answer,quail_description_context_question_answer_id,quail_context_question_description_text,race_high_Read_the_article_and_answer_the_question_no_option_,race_high_Select_the_best_answer_no_instructions_,quail_context_description_question_answer_id,race_high_Taking_a_test,super_glue_multirc_1_0_2,race_middle_Select_the_best_answer,quail_context_question_description_answer_id,quail_description_context_question_answer_text,quail_context_question_answer_description_text,race_high_Select_the_best_answer_generate_span_,race_middle_Select_the_best_answer_generate_span_,quail_context_question_answer_description_id,quail_context_description_question_answer_text,quail_context_description_question_text,quail_context_question_description_answer_text,quail_description_context_question_text,race_middle_Taking_a_test,quail_no_prompt_id,quail_no_prompt_text,race_middle_Select_the_best_answer_no_instructions_ | lora | | phi2_joint_lora_embed_clustersc4_2e_3epoch | phi-2 | sordonia/flan-10k-flat/wiki_qa_found_on_google,app_reviews_categorize_rating_using_review,race_middle_Is_this_the_right_answer,super_glue_cb_1_0_2,wiki_qa_Topic_Prediction_Answer_Only,wiki_qa_Direct_Answer_to_Question,super_glue_wsc_fixed_1_0_2,cot_gsm8k_ii,unified_qa_science_inst,race_high_Is_this_the_right_answer,cot_strategyqa,cot_ecqa_ii,quarel_do_not_use,wiki_qa_exercise,wiki_qa_automatic_system,cot_creak_ii,quarel_heres_a_story,quarel_choose_between,stream_qed_ii,wiki_qa_Topic_Prediction_Question_Only,glue_qnli_2_0_0,cot_sensemaking_ii,super_glue_copa_1_0_2,social_i_qa_Generate_the_question_from_the_answer,social_i_qa_Show_choices_and_generate_index,quarel_testing_students,wiki_qa_Topic_Prediction_Question_and_Answer_Pair,wiki_qa_Decide_good_answer,wiki_qa_Jeopardy_style,wiki_qa_Generate_Question_from_Topic,definite_pronoun_resolution_1_1_0,wiqa_effect_with_label_answer,glue_wnli_2_0_0,cot_qasc,cot_strategyqa_ii,quarel_logic_test,stream_aqua_ii | lora | | phi2_joint_lora_embed_clustersc9_2e_3epoch | phi-2 | sordonia/flan-10k-flat/natural_questions_open_1_0_0,web_questions_whats_the_answer,web_questions_question_answer,dbpedia_14_pick_one_category_for_the_following_text,kilt_tasks_hotpotqa_combining_facts,web_questions_short_general_knowledge_q,kilt_tasks_hotpotqa_straighforward_qa,adversarial_qa_dbidaf_generate_question,adversarial_qa_droberta_based_on,web_questions_get_the_answer,kilt_tasks_hotpotqa_complex_question,web_questions_potential_correct_answer,trivia_qa_rc_1_1_0,kilt_tasks_hotpotqa_formulate,adversarial_qa_dbert_based_on,adversarial_qa_dbidaf_based_on,squad_v1_1_3_0_0 | lora | | phi2_joint_lora_embed_clustersc6_2e_3epoch | phi-2 | sordonia/flan-10k-flat/super_glue_rte_1_0_2,cot_sensemaking,super_glue_wic_1_0_2,cos_e_v1_11_rationale,anli_r3_0_1_0,dream_generate_last_utterance,paws_wiki_1_1_0,cos_e_v1_11_generate_explanation_given_text,cot_creak,stream_aqua,snli_1_1_0,cos_e_v1_11_i_think,glue_qqp_2_0_0,cos_e_v1_11_explain_why_human,anli_r2_0_1_0,anli_r1_0_1_0,glue_stsb_2_0_0,cos_e_v1_11_aligned_with_common_sense,glue_mnli_2_0_0,social_i_qa_I_was_wondering,cosmos_qa_1_0_0,glue_mrpc_2_0_0,social_i_qa_Generate_answer | lora | | phi2_joint_lora_embed_clustersc7_2e_3epoch | phi-2 | sordonia/flan-10k-flat/dream_read_the_following_conversation_and_answer_the_question,app_reviews_convert_to_star_rating,cos_e_v1_11_question_option_description_text,social_i_qa_Show_choices_and_generate_answer,quartz_answer_question_based_on,sciq_Direct_Question_Closed_Book_,qasc_qa_with_separated_facts_3,quartz_given_the_fact_answer_the_q,quartz_answer_question_below,kilt_tasks_hotpotqa_final_exam,sciq_Multiple_Choice,wiqa_does_the_supposed_perturbation_have_an_effect,cos_e_v1_11_question_description_option_text,wiki_qa_Is_This_True_,quartz_use_info_from_question_paragraph,sciq_Direct_Question,qasc_qa_with_separated_facts_2,wiqa_which_of_the_following_is_the_supposed_perturbation,app_reviews_convert_to_rating,cos_e_v1_11_question_option_description_id,wiqa_effect_with_string_answer,qasc_qa_with_separated_facts_5,dream_baseline,quartz_having_read_above_passage,cos_e_v1_11_question_description_option_id,qasc_qa_with_separated_facts_1,cos_e_v1_11_description_question_option_text,qasc_qa_with_combined_facts_1,qasc_is_correct_1,cos_e_v1_11_description_question_option_id,social_i_qa_Check_if_a_random_answer_is_valid_or_not,sciq_Multiple_Choice_Closed_Book_,quartz_use_info_from_paragraph_question,qasc_is_correct_2,qasc_qa_with_separated_facts_4,quartz_read_passage_below_choose,quartz_paragraph_question_plain_concat,sciq_Multiple_Choice_Question_First | lora | | phi2_joint_lora_embed_clustersc3_2e_3epoch | phi-2 | sordonia/flan-10k-flat/wiqa_what_might_be_the_first_step_of_the_process,wiqa_what_is_the_final_step_of_the_following_process,wmt16_translate_ro_en_1_0_0,wiqa_what_might_be_the_last_step_of_the_process,wiki_bio_key_content,gem_common_gen_1_1_0,duorc_SelfRC_build_story_around_qa,app_reviews_generate_review,wiki_bio_what_content,wiki_bio_who,gem_e2e_nlg_1_1_0,cot_esnli_ii,wmt16_translate_tr_en_1_0_0,wiqa_what_is_the_missing_first_step,wiki_bio_comprehension,coqa_1_0_0,duorc_ParaphraseRC_build_story_around_qa,multi_news_1_0_0 | lora | | phi2_joint_lora_embed_clustersc2_2e_3epoch | phi-2 | sordonia/flan-10k-flat/adversarial_qa_dbidaf_question_context_answer,super_glue_record_1_0_2,wiki_hop_original_generate_object,adversarial_qa_droberta_tell_what_it_is,dbpedia_14_given_a_choice_of_categories_,wiki_hop_original_choose_best_object_affirmative_3,quac_1_0_0,wiki_hop_original_choose_best_object_interrogative_1,wiki_hop_original_choose_best_object_affirmative_1,adversarial_qa_dbert_answer_the_following_q,wiki_hop_original_choose_best_object_interrogative_2,adversarial_qa_droberta_question_context_answer,squad_v2_0_3_0_0,wiki_hop_original_generate_subject,wiki_bio_guess_person,adversarial_qa_dbidaf_answer_the_following_q,adversarial_qa_droberta_answer_the_following_q,adversarial_qa_dbert_tell_what_it_is,race_high_Write_a_multi_choice_question_options_given_,wiki_hop_original_choose_best_object_affirmative_2,wiki_hop_original_generate_subject_and_object,drop_2_0_0,adversarial_qa_dbert_question_context_answer,adversarial_qa_dbidaf_tell_what_it_is | lora | | phi2_joint_lora_embed_clustersc0_2e_3epoch | phi-2 | sordonia/flan-10k-flat/ropes_background_new_situation_answer,ropes_prompt_bottom_no_hint,ropes_plain_background_situation,ropes_new_situation_background_answer,ropes_given_background_situation,ropes_prompt_bottom_hint_beginning,ropes_prompt_beginning,ropes_read_background_situation,ropes_plain_bottom_hint,ropes_plain_no_background,ropes_prompt_mix,ropes_background_situation_middle | lora | | phi2_joint_lora_embed_clustersc1_2e_3epoch | phi-2 | sordonia/flan-10k-flat/glue_sst2_2_0_0,adversarial_qa_droberta_generate_question,true_case,stream_qed,huggingface_xsum,cot_esnli,cot_gsm8k,trec_1_0_0,yelp_polarity_reviews_0_2_0,lambada_1_0_0,glue_cola_2_0_0,ag_news_subset_1_0_0,gem_dart_1_1_0,math_dataset_algebra__linear_1d_1_0_0,cnn_dailymail_3_4_0,wiki_hop_original_explain_relation,dbpedia_14_given_list_what_category_does_the_paragraph_belong_to,gem_wiki_lingua_english_en_1_1_0,fix_punct,imdb_reviews_plain_text_1_0_0,race_middle_Write_a_multi_choice_question_for_the_following_article,gigaword_1_2_0,dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to,gem_web_nlg_en_1_1_0,word_segment,race_high_Write_a_multi_choice_question_for_the_following_article,wmt16_translate_de_en_1_0_0,cot_ecqa,aeslc_1_0_0,dream_generate_first_utterance,wmt16_translate_fi_en_1_0_0,dream_answer_to_dialogue,para_crawl_enes,adversarial_qa_dbert_generate_question,race_middle_Write_a_multi_choice_question_options_given_,wmt14_translate_fr_en_1_0_0 | lora | | phi2_joint_lora_embed_clustersc5_2e_3epoch | phi-2 | sordonia/flan-10k-flat/quoref_Context_Contains_Answer,duorc_SelfRC_generate_question_by_answer,quoref_Find_Answer,duorc_ParaphraseRC_movie_director,duorc_ParaphraseRC_answer_question,quoref_Found_Context_Online,quoref_Read_And_Extract_,duorc_ParaphraseRC_title_generation,duorc_ParaphraseRC_decide_worth_it,quoref_What_Is_The_Answer,duorc_ParaphraseRC_generate_question,quoref_Guess_Title_For_Context,quoref_Answer_Test,duorc_SelfRC_question_answering,duorc_SelfRC_title_generation,duorc_ParaphraseRC_generate_question_by_answer,duorc_ParaphraseRC_extract_answer,duorc_SelfRC_answer_question,duorc_SelfRC_decide_worth_it,duorc_ParaphraseRC_question_answering,quoref_Answer_Question_Given_Context,duorc_SelfRC_extract_answer,quoref_Guess_Answer,quoref_Answer_Friend_Question,duorc_SelfRC_movie_director,duorc_SelfRC_generate_question,quoref_Given_Context_Answer_Question | lora | Last updated on: 2024-01-07 20:06:29+00:00

ryusangwon/7243_Llama-2-13b-hf

ryusangwon

peft

--- base_model: meta-llama/Llama-2-13b-hf tags: - generated_from_trainer datasets: - cnn_dailymail model-index: - name: 7243_Llama-2-13b-hf results: [] library_name: peft --- <!-- 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. --> # 7243_Llama-2-13b-hf This model is a fine-tuned version of [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.4.0 - Transformers 4.36.2 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0

VoidZeroe/llama6.3-model

VoidZeroe

peft

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0

Kiwihead15/whatsapp_Llama-2-13b-hf-Eternos

Kiwihead15

Entry not found

llananailo/Baseline-1

llananailo

transformers

Entry not found

drakrig/ppo-LunarLander-v2

drakrig

stable-baselines3

reinforcement-learning

--- 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: 250.09 +/- 20.61 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 ... ```

frydziu/speecht5_tts_voxpopuli_nl

frydziu

Entry not found

frydziu/speecht5_tts_voxpopuli_pl

frydziu

Entry not found

AswanthCManoj/azma-tinyllama-instruct-v2-adapter

AswanthCManoj

peft

--- library_name: peft base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.2.dev0

s3nh/BEE-spoke-data-TinyLlama-3T-1.1bee-GGUF

s3nh

transformers

text-generation

--- license: openrail pipeline_tag: text-generation library_name: transformers language: - zh - en --- ## Original model card Buy me a coffee if you like this project ;) <a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a> #### Description GGUF Format model files for [This project](https://huggingface.co/BEE-spoke-data/TinyLlama-3T-1.1bee). ### GGUF Specs GGUF is a format based on the existing GGJT, but makes a few changes to the format to make it more extensible and easier to use. The following features are desired: Single-file deployment: they can be easily distributed and loaded, and do not require any external files for additional information. Extensible: new features can be added to GGML-based executors/new information can be added to GGUF models without breaking compatibility with existing models. mmap compatibility: models can be loaded using mmap for fast loading and saving. Easy to use: models can be easily loaded and saved using a small amount of code, with no need for external libraries, regardless of the language used. Full information: all information needed to load a model is contained in the model file, and no additional information needs to be provided by the user. The key difference between GGJT and GGUF is the use of a key-value structure for the hyperparameters (now referred to as metadata), rather than a list of untyped values. This allows for new metadata to be added without breaking compatibility with existing models, and to annotate the model with additional information that may be useful for inference or for identifying the model. ### Perplexity params Model Measure Q2_K Q3_K_S Q3_K_M Q3_K_L Q4_0 Q4_1 Q4_K_S Q4_K_M Q5_0 Q5_1 Q5_K_S Q5_K_M Q6_K Q8_0 F16 7B perplexity 6.7764 6.4571 6.1503 6.0869 6.1565 6.0912 6.0215 5.9601 5.9862 5.9481 5.9419 5.9208 5.9110 5.9070 5.9066 13B perplexity 5.8545 5.6033 5.4498 5.4063 5.3860 5.3608 5.3404 5.3002 5.2856 5.2706 5.2785 5.2638 5.2568 5.2548 5.2543 ### inference TODO # Original model card

asasamad/llama-2-7b-asasamad_test

asasamad

peft

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0 - PEFT 0.4.0

Gypsy086/Gypsy

Gypsy086

--- license: apache-2.0 ---

pierian-data/cthx-boy-lora

pierian-data

Entry not found

s-gladkykh/sky_diffusion_ddim_512_lr1e-5_bs4_e150

s-gladkykh

Entry not found

pierian-data/tok-boy-lora

pierian-data

Entry not found

Vijish/vits_mongolian_monospeaker

Vijish

transformers

Entry not found

NLPProject2023Z/roberta_regression_corrected

NLPProject2023Z

transformers

--- tags: - generated_from_trainer model-index: - name: roberta_regression_corrected results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta_regression_corrected This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5899 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 25 - eval_batch_size: 25 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 160 | 0.5899 | | No log | 2.0 | 320 | 0.5899 | | No log | 3.0 | 480 | 0.5899 | | 0.5781 | 4.0 | 640 | 0.5899 | | 0.5781 | 5.0 | 800 | 0.5899 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0

AstroZeta7/test_bowl

AstroZeta7

Entry not found

Dontin/Ai_Project

Dontin

--- license: apache-2.0 ---

tjbleo616/my_awesome_swag_model

tjbleo616

transformers

multiple-choice

Entry not found

asasamad/testing_model

asasamad

Entry not found

Minata/method2test-mistral-7B_v0

Minata

peft

--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 model-index: - name: method2test-mistral-7B_v0 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. --> # method2test-mistral-7B_v0 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6527 ## 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: 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: 5 - training_steps: 1000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.9248 | 0.09 | 100 | 1.6111 | | 0.8949 | 0.18 | 200 | 1.5985 | | 0.9269 | 0.27 | 300 | 1.5983 | | 0.8941 | 0.36 | 400 | 1.6074 | | 0.8719 | 0.44 | 500 | 1.6126 | | 0.8133 | 0.53 | 600 | 1.6316 | | 0.7936 | 0.62 | 700 | 1.6385 | | 0.7532 | 0.71 | 800 | 1.6554 | | 0.7424 | 0.8 | 900 | 1.6479 | | 0.7573 | 0.89 | 1000 | 1.6527 | ### Framework versions - PEFT 0.7.2.dev0 - Transformers 4.37.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0

jysssacc/bloomz-560m_lora_lr5e-05_bs4_epoch20_wd0.01

jysssacc

peft

--- license: bigscience-bloom-rail-1.0 library_name: peft tags: - generated_from_trainer base_model: bigscience/bloomz-560m model-index: - name: bloomz-560m_lora_lr5e-05_bs4_epoch20_wd0.01 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. --> # bloomz-560m_lora_lr5e-05_bs4_epoch20_wd0.01 This model is a fine-tuned version of [bigscience/bloomz-560m](https://huggingface.co/bigscience/bloomz-560m) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.8047 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 4.2484 | 1.0 | 157 | 3.6028 | | 3.5565 | 2.0 | 314 | 3.3295 | | 3.3894 | 3.0 | 471 | 3.2916 | | 3.2189 | 4.0 | 628 | 3.2876 | | 3.1204 | 5.0 | 785 | 3.3139 | | 2.9755 | 6.0 | 942 | 3.3707 | | 2.919 | 7.0 | 1099 | 3.4452 | | 2.7196 | 8.0 | 1256 | 3.5097 | | 2.6831 | 9.0 | 1413 | 3.6234 | | 2.5105 | 10.0 | 1570 | 3.7513 | | 2.3998 | 11.0 | 1727 | 3.8193 | | 2.314 | 12.0 | 1884 | 3.9362 | | 2.1941 | 13.0 | 2041 | 4.1743 | | 2.1129 | 14.0 | 2198 | 4.2149 | | 2.0442 | 15.0 | 2355 | 4.3023 | | 1.8967 | 16.0 | 2512 | 4.4912 | | 1.9345 | 17.0 | 2669 | 4.5690 | | 1.8908 | 18.0 | 2826 | 4.6751 | | 1.8891 | 19.0 | 2983 | 4.7869 | | 1.7753 | 20.0 | 3140 | 4.8047 | ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.0.1 - Datasets 2.16.1 - Tokenizers 0.15.0

Sojde/Sojde1

Sojde

Entry not found

tjbleo616/bert-base-uncased-finetuned-swag

tjbleo616

Entry not found

Viivvzz0742/mistral-MistralMiniMed2

Viivvzz0742

peft

--- license: mit library_name: peft tags: - generated_from_trainer base_model: HuggingFaceH4/mistral-7b-sft-beta model-index: - name: mistral-MistralMiniMed2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistral-MistralMiniMed2 This model is a fine-tuned version of [HuggingFaceH4/mistral-7b-sft-beta](https://huggingface.co/HuggingFaceH4/mistral-7b-sft-beta) 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: 2.5e-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: 5 - training_steps: 100 ### Training results ### Framework versions - PEFT 0.7.2.dev0 - Transformers 4.37.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0

Floyd93/eli5_mlm_model

Floyd93

Entry not found

shravanthis/layoutlmv3-finetuned-cord_100

shravanthis

Entry not found

jeiku/Toxic_DPO_StableLM

jeiku

--- license: other datasets: - diffnamehard/toxic-dpo-v0.1-NoWarning-alpaca language: - en ---

Donislebew00/lucintaluna

Donislebew00

--- license: openrail ---

jysssacc/opt-350m_lora_lr5e-05_bs4_epoch20_wd0.01

jysssacc

peft

--- license: other library_name: peft tags: - generated_from_trainer base_model: facebook/opt-350m model-index: - name: opt-350m_lora_lr5e-05_bs4_epoch20_wd0.01 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # opt-350m_lora_lr5e-05_bs4_epoch20_wd0.01 This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.4811 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.9852 | 1.0 | 157 | 3.5147 | | 3.5297 | 2.0 | 314 | 3.4018 | | 3.4497 | 3.0 | 471 | 3.3918 | | 3.3979 | 4.0 | 628 | 3.4111 | | 3.3625 | 5.0 | 785 | 3.3598 | | 3.3397 | 6.0 | 942 | 3.3665 | | 3.2897 | 7.0 | 1099 | 3.3855 | | 3.2289 | 8.0 | 1256 | 3.3887 | | 3.287 | 9.0 | 1413 | 3.3999 | | 3.1938 | 10.0 | 1570 | 3.4151 | | 3.1485 | 11.0 | 1727 | 3.4140 | | 3.0905 | 12.0 | 1884 | 3.4125 | | 3.0712 | 13.0 | 2041 | 3.4385 | | 3.0604 | 14.0 | 2198 | 3.4475 | | 3.0445 | 15.0 | 2355 | 3.4718 | | 2.9898 | 16.0 | 2512 | 3.4659 | | 2.9948 | 17.0 | 2669 | 3.4765 | | 2.984 | 18.0 | 2826 | 3.4740 | | 3.0367 | 19.0 | 2983 | 3.4817 | | 2.9656 | 20.0 | 3140 | 3.4811 | ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.0.1 - Datasets 2.16.1 - Tokenizers 0.15.0

ai-tools-search/genderslider

ai-tools-search

Entry not found

andlock/ppo-LunarLander-v2

andlock

stable-baselines3

reinforcement-learning

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 262.43 +/- 19.68 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 ... ```

leosol/bert-base-uncased-issues-128

leosol

Entry not found

yy0514/bert-lek-full-train-4epochs-run2

yy0514

transformers

multiple-choice

Entry not found

tjbleo616/my_awesome_model

tjbleo616

Entry not found

Loren85/icarly-Opening-Singer

Loren85

--- license: openrail ---

gputrain/rl_course_vizdoom_health_gathering_supreme

gputrain

sample-factory

reinforcement-learning

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 9.44 +/- 3.57 name: mean_reward verified: false --- A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r gputrain/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

pankaj4u4m/code-search-net-tokenizer

pankaj4u4m

Entry not found

bpugnaire/a2c-PandaReachDense-v3

bpugnaire

stable-baselines3

reinforcement-learning

--- 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: -0.17 +/- 0.10 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 ... ```

pihalf/erbd

pihalf

Entry not found

jysssacc/roberta-base_IA3_lr0.0005_bs4_epoch20_wd0.01

jysssacc

peft

--- license: mit library_name: peft tags: - generated_from_trainer base_model: roberta-base model-index: - name: roberta-base_IA3_lr0.0005_bs4_epoch20_wd0.01 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base_IA3_lr0.0005_bs4_epoch20_wd0.01 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: 1.3406 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 19.5038 | 1.0 | 157 | 18.4857 | | 8.9314 | 2.0 | 314 | 5.8495 | | 5.4493 | 3.0 | 471 | 4.2791 | | 3.8453 | 4.0 | 628 | 3.4174 | | 3.4001 | 5.0 | 785 | 2.8767 | | 2.8518 | 6.0 | 942 | 2.5189 | | 2.7181 | 7.0 | 1099 | 2.2672 | | 2.3938 | 8.0 | 1256 | 2.0897 | | 2.2025 | 9.0 | 1413 | 1.9660 | | 2.1035 | 10.0 | 1570 | 1.8055 | | 1.9748 | 11.0 | 1727 | 1.6968 | | 1.8698 | 12.0 | 1884 | 1.6367 | | 1.7843 | 13.0 | 2041 | 1.5600 | | 1.7277 | 14.0 | 2198 | 1.5018 | | 1.6915 | 15.0 | 2355 | 1.4518 | | 1.5865 | 16.0 | 2512 | 1.4089 | | 1.5934 | 17.0 | 2669 | 1.3896 | | 1.5713 | 18.0 | 2826 | 1.3617 | | 1.5521 | 19.0 | 2983 | 1.3453 | | 1.5471 | 20.0 | 3140 | 1.3406 | ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.0.1 - Datasets 2.16.1 - Tokenizers 0.15.0

ppardee/ControlNetSDXL

ppardee

Entry not found

ostapeno/newt_adaNeo1B_quarel_heres_a_story_lora_sim_sgd_full_ft_CG

ostapeno

Number of experts present in the library: 10 | Expert Name | Base Model | Trained on | Adapter Type | | --- | --- | --- | --- | | quarel_heres_a_story_v4 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quarel_heres_a_story | lora | | quarel_heres_a_story_v5 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quarel_heres_a_story | lora | | quarel_heres_a_story_v8 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quarel_heres_a_story | lora | | quarel_heres_a_story_v2 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quarel_heres_a_story | lora | | quarel_heres_a_story_v3 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quarel_heres_a_story | lora | | quarel_heres_a_story_v1 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quarel_heres_a_story | lora | | quarel_heres_a_story_v7 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quarel_heres_a_story | lora | | quarel_heres_a_story | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quarel_heres_a_story | lora | | quarel_heres_a_story_v6 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quarel_heres_a_story | lora | | quarel_heres_a_story_v9 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/quarel_heres_a_story | lora | Last updated on: 2024-01-08 02:48:40+00:00

TheRealheavy/SteveHarwell

TheRealheavy

--- license: openrail ---

cigdemm/xlm-roberta-base-finetuned-ner

cigdemm

Entry not found