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jddllwqa/Qwen-Qwen1.5-0.5B-1719052430	jddllwqa	"2024-06-22T10:33:56Z"	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen1.5-0.5B", "region:us" ]	null	"2024-06-22T10:33:50Z"	--- base_model: Qwen/Qwen1.5-0.5B library_name: peft --- # 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 0.11.1
jddllwqa/Qwen-Qwen1.5-1.8B-1719052486	jddllwqa	"2024-06-22T10:34:50Z"	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen1.5-1.8B", "region:us" ]	null	"2024-06-22T10:34:46Z"	--- base_model: Qwen/Qwen1.5-1.8B library_name: peft --- # 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 0.11.1
friendlyguy774/ToDo_list	friendlyguy774	"2024-06-22T11:42:47Z"	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "unsloth", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-06-22T10:35:25Z"	--- library_name: transformers tags: - unsloth - 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. 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]
c10/eeve-10.8B_B	c10	"2024-06-22T10:51:56Z"	0	null	[ "trl", "sft", "generated_from_trainer", "base_model:yanolja/EEVE-Korean-Instruct-10.8B-v1.0", "license:apache-2.0", "region:us" ]	null	"2024-06-22T10:36:20Z"	--- license: apache-2.0 base_model: yanolja/EEVE-Korean-Instruct-10.8B-v1.0 tags: - trl - sft - generated_from_trainer model-index: - name: eeve-10.8B_B 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. --> # eeve-10.8B_B This model is a fine-tuned version of [yanolja/EEVE-Korean-Instruct-10.8B-v1.0](https://huggingface.co/yanolja/EEVE-Korean-Instruct-10.8B-v1.0) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3710 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 3 - total_train_batch_size: 48 - total_eval_batch_size: 24 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.1773 \| 1.0 \| 291 \| 0.3710 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.1.2 - Datasets 2.20.0 - Tokenizers 0.19.1
jddllwqa/Qwen-Qwen1.5-7B-1719052603	jddllwqa	"2024-06-22T10:36:48Z"	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen1.5-7B", "region:us" ]	null	"2024-06-22T10:36:43Z"	--- base_model: Qwen/Qwen1.5-7B library_name: peft --- # 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 0.11.1
jddllwqa/google-gemma-2b-1719052653	jddllwqa	"2024-06-22T10:37:46Z"	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:google/gemma-2b", "region:us" ]	null	"2024-06-22T10:37:33Z"	--- base_model: google/gemma-2b library_name: peft --- # 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 0.11.1
ShapeKapseln33/EffectXmed55	ShapeKapseln33	"2024-06-22T10:59:33Z"	0	null	[ "region:us" ]	null	"2024-06-22T10:39:04Z"	[Neu] Effectxmed Erfahrungen Bewertungen: EffectXMed ist eine Marke, die sich auf Hautpflegeprodukte spezialisiert hat und Anti-Aging-Seren und -Cremes anbietet. Die Produkte werden mit dem Anspruch hergestellt, den Altersprozess der Haut zu verlangsamen, die Reparaturmechanismen zu unterstützen und die Hautbarriere zu verbessern. Hochwirksame Antioxidantien, Vitamine und feine pflanzliche Öle sowie Aminosäuren sind zentrale Bestandteile der Formulierungen. [Klicken Sie hier, um jetzt auf der offiziellen Website von EffectXmed zu kaufen](https://callednews.com/effectxmed-de) EffectXMed ist eine Marke, die sich auf Hautpflegeprodukte spezialisiert hat und Anti-Aging-Seren und -Cremes anbietet. Die Produkte werden mit dem Anspruch hergestellt, den Altersprozess der Haut zu verlangsamen, die Reparaturmechanismen zu unterstützen und die Hautbarriere zu verbessern. Hochwirksame Antioxidantien, Vitamine und feine pflanzliche Öle sowie Aminosäuren sind zentrale Bestandteile der Formulierungen. Zu den Verbrauchern hat sich EffectXMed durch seine Verpflichtung zu Inhaltsstoffen, die frei von aggressiven Chemikalien, Parabenen und Sulfaten sind, als eine Marke für saubere Schönheit positioniert. Diese Philosophie findet Anklang bei Kunden, die Wert auf Nachhaltigkeit und sanfte Pflege legen. Das Unternehmen Effectxmed AG hat seinen Sitz in Irland, stellt seine Produkte jedoch in Deutschland her und versendet sie auch von dort. Die positiven Kundenbewertungen und Erfahrungsberichte über verschiedene Online-Plattformen hinweg spiegeln die Zufriedenheit der Nutzer mit der Qualität und Wirksamkeit der EffectXMed-Produkte wider. Das Unternehmen stärkt seine Präsenz im Markt kontinuierlich und scheint eine vertrauenswürdige Option für Verbraucher zu sein, die auf der Suche nach effektiven Hautpflegelösungen sind. ##EffectXMed Erfahrungen, Test und Bewertung EffectXMed ist eine Hautpflegelinie, die darauf ausgerichtet ist, den Alterungsprozess der Haut zu verlangsamen. Die Marke bietet eine Palette von Produkten an, die von Dr. med. Margrit Lettko entwickelt wurden, und sie hebt sich durch ihre medizinische Expertise hervor. Produkte wie Cremes und Seren von EffectXMed enthalten eine Kombination aus pflanzlichen Inhaltsstoffen und fortschrittlichen Wirkstoffen. ##Produktentwicklung: Die Rezepturen von EffectXMed werden in Deutschland hergestellt und sollen ohne Nebenwirkungen die Zufriedenheit und das Aussehen der Kunden verbessern. Die Marke nutzt innovative Wissenschaft, um maximale Wirksamkeit zu erzielen. ##Wirkstoffe und Effekte: ##Die Hautcremes und Seren bieten verschiedene Anti-Aging-Vorteile. Sie zielen darauf ab: [Klicken Sie hier, um jetzt auf der offiziellen Website von EffectXmed zu kaufen](https://callednews.com/effectxmed-de) Die Haut zu hydrieren Feine Linien und Falten zu reduzieren Alterserscheinungen wie dunkle Ringe und Altersflecken zu verringern ##EffectXMed Nutzerberichte: Kundenbewertungen deuten auf eine allgemeine Zufriedenheit mit der Wirksamkeit der Produkte hin, wobei berichtet wird, dass das Erscheinungsbild von Falten und Linien deutlich verbessert wurde. ##Textur und Duft: Die Cremes werden für ihre sahnige Textur und ihren zarten Duft geschätzt, welcher häufig mit blühenden Rosenfeldern verglichen wird. EffectXMed integriert somit pflanzliche Inhaltsstoffe mit modernen, effektiven Substanzen für eine Luxus-Hautpflege, die darauf abzielt, die natürlichen Reparaturmechanismen der Haut zu unterstützen und ihre Barrierefunktion zu verbessern. ##EffectXmed Luxury Glow Skin Boost Creme (Dr. med Margit Lettko) EffectXMed Creme ist entwickelt für die tägliche Hautpflege und zielt darauf ab, das Hautbild zu verbessern. Die Anwendung erfolgt idealerweise zweimal täglich — morgens und abends. Dabei bietet die Creme folgende Anwendungsmöglichkeiten: Hydratisierung: Sie trägt dazu bei, die Haut intensiv mit Feuchtigkeit zu versorgen. Nährstoffversorgung: Wichtige Inhaltsstoffe können helfen, die Haut zu nähren und so ihr Erscheinungsbild zu verbessern. [Klicken Sie hier, um jetzt auf der offiziellen Website von EffectXmed zu kaufen](https://callednews.com/effectxmed-de)
mdshamsad/Llama-2-7b-chat-new-finetune	mdshamsad	"2024-06-22T10:47:00Z"	0	null	[ "region:us" ]	null	"2024-06-22T10:45:56Z"	Entry not found
amirhosein-vedadi/whisper-small-dv	amirhosein-vedadi	"2024-06-22T10:46:26Z"	0	null	[ "region:us" ]	null	"2024-06-22T10:46:26Z"	Entry not found
Protector131090/dreambooth-sd3-lora	Protector131090	"2024-06-22T10:46:26Z"	0	null	[ "region:us" ]	null	"2024-06-22T10:46:26Z"	Entry not found
hemantjuyal/q-FrozenLake-v1-4x4-noSlippery	hemantjuyal	"2024-06-22T10:51:01Z"	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	"2024-06-22T10:50:59Z"	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="hemantjuyal/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
Hamed7immortal/filter	Hamed7immortal	"2024-06-22T10:51:53Z"	0	null	[ "license:openrail", "region:us" ]	null	"2024-06-22T10:51:53Z"	--- license: openrail ---
c10/eeve-10.8B_C	c10	"2024-06-22T11:05:17Z"	0	null	[ "trl", "sft", "generated_from_trainer", "base_model:yanolja/EEVE-Korean-Instruct-10.8B-v1.0", "license:apache-2.0", "region:us" ]	null	"2024-06-22T10:52:34Z"	--- license: apache-2.0 base_model: yanolja/EEVE-Korean-Instruct-10.8B-v1.0 tags: - trl - sft - generated_from_trainer model-index: - name: eeve-10.8B_C 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. --> # eeve-10.8B_C This model is a fine-tuned version of [yanolja/EEVE-Korean-Instruct-10.8B-v1.0](https://huggingface.co/yanolja/EEVE-Korean-Instruct-10.8B-v1.0) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3697 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 3 - total_train_batch_size: 48 - total_eval_batch_size: 24 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.2153 \| 1.0 \| 233 \| 0.3697 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.1.2 - Datasets 2.20.0 - Tokenizers 0.19.1
nishantsinha00/finetuned-lora-blur_dataset	nishantsinha00	"2024-06-22T13:12:42Z"	0	null	[ "tensorboard", "safetensors", "region:us" ]	null	"2024-06-22T10:52:50Z"	Entry not found
NotoriousH2/Qwen1.5b-ref	NotoriousH2	"2024-06-22T10:57:34Z"	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-06-22T10:54:03Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
bruvmomentos/zmean	bruvmomentos	"2024-06-22T10:54:16Z"	0	null	[ "license:cc-by-2.0", "region:us" ]	null	"2024-06-22T10:54:16Z"	--- license: cc-by-2.0 ---
adamo1139/Yi-34B-200K-HESOYAM-2206	adamo1139	"2024-06-23T21:44:27Z"	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "galore", "dataset:adamo1139/uninstruct-v1-experimental-chatml", "dataset:adamo1139/HESOYAM_v0.3", "arxiv:2403.03507", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-06-22T10:54:59Z"	--- license: apache-2.0 datasets: - adamo1139/uninstruct-v1-experimental-chatml - adamo1139/HESOYAM_v0.3 tags: - galore --- ## Basic Model Info 1 epoch on adamo1139/uninstruct-v1-experimental-chatml and then 1 epoch on adamo1139/HESOYAM_v0.3. I used [GaLore](https://arxiv.org/abs/2403.03507) for both stages. This is a model trained on only human data, finetuned to behave like a person on 4chan board /x/ or redditor. Data used has comments from 1 4chan board "paranormal" and about 10 reddit subreddits. There's also a pippa in case you want to roleplay. Have a look at dataset to know what to expect. Use ChatML prompt format with a system prompt like those in adamo1139/HESOYAM_v0.3, so `A chat on 4chan` or `A chat on subreddit /r/wallstreetbets`. It behaves like OpenAI slopped model with system prompt `A chat` so I advise you to avoid using that.
hemantjuyal/Taxi-v3	hemantjuyal	"2024-06-22T10:56:07Z"	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	"2024-06-22T10:56:05Z"	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="hemantjuyal/Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
Hamed7immortal/test	Hamed7immortal	"2024-06-28T14:30:54Z"	0	null	[ "region:us" ]	null	"2024-06-22T10:59:46Z"	Entry not found
KirsanovArtem/autotrain-nb940-hbmw5	KirsanovArtem	"2024-06-22T12:04:47Z"	0	transformers	[ "transformers", "tensorboard", "safetensors", "autotrain", "text-generation-inference", "text-generation", "peft", "conversational", "base_model:openai-community/gpt2", "endpoints_compatible", "region:us" ]	text-generation	"2024-06-22T10:59:48Z"	--- tags: - autotrain - text-generation-inference - text-generation - peft library_name: transformers base_model: openai-community/gpt2 widget: - messages: - role: user content: What is your favorite condiment? pipeline_tag: text-generation --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
nnyyaann/poiuytds	nnyyaann	"2024-06-22T11:12:58Z"	0	null	[ "region:us" ]	null	"2024-06-22T11:00:34Z"	Entry not found
welsachy/roberta-base-finetuned-depression	welsachy	"2024-06-22T11:58:28Z"	0	transformers	[ "transformers", "tensorboard", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2024-06-22T11:01:43Z"	--- license: mit base_model: roberta-base tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: roberta-base-finetuned-depression 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-finetuned-depression This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7662 - Precision: 0.8912 - Recall: 0.9136 - F1: 0.9018 - Accuracy: 0.9104 ## 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: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 469 \| 0.5219 \| 0.8220 \| 0.7921 \| 0.8000 \| 0.8603 \| \| 0.602 \| 2.0 \| 938 \| 0.6344 \| 0.9039 \| 0.8257 \| 0.8538 \| 0.8753 \| \| 0.3573 \| 3.0 \| 1407 \| 0.4821 \| 0.8818 \| 0.8902 \| 0.8859 \| 0.8870 \| \| 0.2511 \| 4.0 \| 1876 \| 0.6265 \| 0.8511 \| 0.8965 \| 0.8676 \| 0.8934 \| \| 0.1614 \| 5.0 \| 2345 \| 0.5439 \| 0.8908 \| 0.8992 \| 0.8919 \| 0.9041 \| \| 0.1107 \| 6.0 \| 2814 \| 0.6237 \| 0.8838 \| 0.8990 \| 0.8886 \| 0.9009 \| \| 0.0756 \| 7.0 \| 3283 \| 0.6915 \| 0.8930 \| 0.9062 \| 0.8988 \| 0.9083 \| \| 0.057 \| 8.0 \| 3752 \| 0.6572 \| 0.8736 \| 0.9107 \| 0.8905 \| 0.9062 \| \| 0.0664 \| 9.0 \| 4221 \| 0.8022 \| 0.8692 \| 0.8987 \| 0.8804 \| 0.8977 \| \| 0.0392 \| 10.0 \| 4690 \| 0.7953 \| 0.8931 \| 0.8847 \| 0.8844 \| 0.8977 \| \| 0.0472 \| 11.0 \| 5159 \| 0.7757 \| 0.8951 \| 0.8886 \| 0.8885 \| 0.8998 \| \| 0.0375 \| 12.0 \| 5628 \| 0.7821 \| 0.8881 \| 0.9029 \| 0.8939 \| 0.9072 \| \| 0.0292 \| 13.0 \| 6097 \| 0.8124 \| 0.8793 \| 0.8982 \| 0.8870 \| 0.9009 \| \| 0.0373 \| 14.0 \| 6566 \| 0.9106 \| 0.8774 \| 0.8818 \| 0.8735 \| 0.8934 \| \| 0.0227 \| 15.0 \| 7035 \| 0.8325 \| 0.8876 \| 0.8855 \| 0.8825 \| 0.8966 \| \| 0.0249 \| 16.0 \| 7504 \| 0.7662 \| 0.8912 \| 0.9136 \| 0.9018 \| 0.9104 \| \| 0.0249 \| 17.0 \| 7973 \| 0.8383 \| 0.8804 \| 0.8905 \| 0.8833 \| 0.8955 \| \| 0.0245 \| 18.0 \| 8442 \| 0.8073 \| 0.8844 \| 0.9000 \| 0.8907 \| 0.9030 \| \| 0.0188 \| 19.0 \| 8911 \| 0.8137 \| 0.8850 \| 0.9012 \| 0.8917 \| 0.9041 \| \| 0.0203 \| 20.0 \| 9380 \| 0.8234 \| 0.8850 \| 0.8993 \| 0.8905 \| 0.9030 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
limaatulya/my_awesome_billsum_model_72	limaatulya	"2024-06-22T11:08:29Z"	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text2text-generation	"2024-06-22T11:06:00Z"	--- license: apache-2.0 base_model: google-t5/t5-small tags: - generated_from_trainer model-index: - name: my_awesome_billsum_model_72 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_billsum_model_72 This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 1.4308 - eval_rouge1: 0.4384 - eval_rouge2: 0.3029 - eval_rougeL: 0.4176 - eval_rougeLsum: 0.4167 - eval_gen_len: 15.8125 - eval_runtime: 8.9074 - eval_samples_per_second: 5.389 - eval_steps_per_second: 0.337 - epoch: 2.0 - step: 24 ## 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: 100 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
itisarainyday/llemma-2-7b-ft-test-v7	itisarainyday	"2024-06-22T13:13:22Z"	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-22T11:06:51Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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ArturMad2003/UniqueSoilder	ArturMad2003	"2024-06-22T11:15:48Z"	0	adapter-transformers	[ "adapter-transformers", "dataset:nvidia/HelpSteer2", "license:apache-2.0", "region:us" ]	null	"2024-06-22T11:14:01Z"	--- license: apache-2.0 datasets: - nvidia/HelpSteer2 metrics: - character library_name: adapter-transformers ---
c10/eeve-2.8B_A	c10	"2024-06-22T11:22:54Z"	0	null	[ "trl", "sft", "generated_from_trainer", "base_model:yanolja/EEVE-Korean-Instruct-2.8B-v1.0", "license:apache-2.0", "region:us" ]	null	"2024-06-22T11:14:27Z"	--- license: apache-2.0 base_model: yanolja/EEVE-Korean-Instruct-2.8B-v1.0 tags: - trl - sft - generated_from_trainer model-index: - name: eeve-2.8B_A 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. --> # eeve-2.8B_A This model is a fine-tuned version of [yanolja/EEVE-Korean-Instruct-2.8B-v1.0](https://huggingface.co/yanolja/EEVE-Korean-Instruct-2.8B-v1.0) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3930 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 3 - total_train_batch_size: 48 - total_eval_batch_size: 24 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.2752 \| 1.0 \| 387 \| 0.3930 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.1.2 - Datasets 2.20.0 - Tokenizers 0.19.1
oodapow/welfake_bert_1	oodapow	"2024-06-22T11:16:40Z"	0	null	[ "region:us" ]	null	"2024-06-22T11:16:40Z"	Entry not found
c10/eeve-2.8B_B	c10	"2024-06-22T11:29:59Z"	0	null	[ "trl", "sft", "generated_from_trainer", "base_model:yanolja/EEVE-Korean-Instruct-2.8B-v1.0", "license:apache-2.0", "region:us" ]	null	"2024-06-22T11:23:36Z"	--- license: apache-2.0 base_model: yanolja/EEVE-Korean-Instruct-2.8B-v1.0 tags: - trl - sft - generated_from_trainer model-index: - name: eeve-2.8B_B 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. --> # eeve-2.8B_B This model is a fine-tuned version of [yanolja/EEVE-Korean-Instruct-2.8B-v1.0](https://huggingface.co/yanolja/EEVE-Korean-Instruct-2.8B-v1.0) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3918 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 3 - total_train_batch_size: 48 - total_eval_batch_size: 24 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.2894 \| 1.0 \| 291 \| 0.3918 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.1.2 - Datasets 2.20.0 - Tokenizers 0.19.1
hooollly/kcc_vive_con	hooollly	"2024-06-22T11:26:41Z"	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-22T11:25:46Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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]
am96149/llama3-8b-tuned1	am96149	"2024-06-22T11:29:49Z"	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-06-22T11:29:35Z"	--- base_model: unsloth/llama-3-8b-Instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - Developed by: am96149 - 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)
c10/eeve-2.8B_C	c10	"2024-06-22T13:16:54Z"	0	transformers	[ "transformers", "phi", "text-generation", "trl", "sft", "alignment-handbook", "generated_from_trainer", "custom_code", "base_model:yanolja/EEVE-Korean-Instruct-2.8B-v1.0", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-06-22T11:30:35Z"	--- license: apache-2.0 base_model: yanolja/EEVE-Korean-Instruct-2.8B-v1.0 tags: - trl - sft - alignment-handbook - generated_from_trainer model-index: - name: eeve-2.8B_C 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. --> # eeve-2.8B_C This model is a fine-tuned version of [yanolja/EEVE-Korean-Instruct-2.8B-v1.0](https://huggingface.co/yanolja/EEVE-Korean-Instruct-2.8B-v1.0) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3962 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 3 - total_train_batch_size: 48 - total_eval_batch_size: 24 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.3034 \| 1.0 \| 233 \| 0.3962 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.1.2 - Datasets 2.20.0 - Tokenizers 0.19.1
aengusl/21jun_run1_r2d2_lat_eps1pt5_lr2e-5_ckpt200	aengusl	"2024-06-22T11:32:36Z"	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-22T11:32:22Z"	--- 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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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]
aengusl/21jun_run1_r2d2_lat_eps1pt5_lr2e-5_ckpt240	aengusl	"2024-06-22T11:32:56Z"	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-22T11:32:44Z"	--- 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]
pfchai/asr	pfchai	"2024-06-22T11:33:19Z"	0	null	[ "endpoints_compatible", "region:us" ]	null	"2024-06-22T11:33:19Z"	ASR+Diarization handler that works natively with Inference Endpoints. Example payload: ```python import base64 import requests API_URL = "<your endpoint URL>" filepath = "/path/to/audio" with open(filepath, 'rb') as f: audio_encoded = base64.b64encode(f.read()).decode("utf-8") data = { "inputs": audio_encoded, "parameters": { "batch_size": 24 } } resp = requests.post(API_URL, json=data, headers={"Authorization": "Bearer <your token>"}) print(resp.json()) ```
kspl/depression-llama3-7b-instruct	kspl	"2024-06-22T11:35:04Z"	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-22T11:34:59Z"	--- 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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SoumilB7/School_categoriser	SoumilB7	"2024-06-22T12:10:48Z"	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	"2024-06-22T11:37:29Z"	--- base_model: unsloth/llama-3-8b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl ---
valerielucro/mistral_gsm8k_dpo_cot_beta_0.01	valerielucro	"2024-06-22T11:39:08Z"	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-22T11:38:59Z"	--- 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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Lerian51/JustinBieber_MyWorldEra	Lerian51	"2024-06-22T11:44:36Z"	0	null	[ "license:mit", "region:us" ]	null	"2024-06-22T11:42:49Z"	--- license: mit ---
adamo1139/Yi-34B-200K-Un-Instruct-1906	adamo1139	"2024-06-23T21:47:47Z"	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "dataset:adamo1139/uninstruct-v1-experimental-chatml", "arxiv:2403.03507", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-06-22T11:43:41Z"	--- license: apache-2.0 datasets: - adamo1139/uninstruct-v1-experimental-chatml --- ## Basic Model Info 1 epoch on adamo1139/uninstruct-v1-experimental-chatml. I used [GaLore](https://arxiv.org/abs/2403.03507).\ Purpose of this model is to make the model un-learn to use chatml-specific code words such as: <\|im_start\|>, <\|im_end\|>, user, assistant. This is a base model meant for further finetuning. I think much of OpenAI slop is still left in there, so it's probably best combined with preference optimization method like DPO, ORPO or SPO for best results.
Lars8899/pov_lora_1	Lars8899	"2024-06-22T12:03:20Z"	0	null	[ "region:us" ]	null	"2024-06-22T11:45:24Z"	Entry not found
anilbhatt1/peft_phi2_v4_l4500	anilbhatt1	"2024-06-22T11:47:19Z"	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-22T11:46:44Z"	--- 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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kiranmalape/food_order_function_calling	kiranmalape	"2024-06-22T12:03:38Z"	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:microsoft/Phi-3-mini-4k-instruct", "region:us" ]	null	"2024-06-22T11:48:39Z"	--- base_model: microsoft/Phi-3-mini-4k-instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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jackswie/bi	jackswie	"2024-06-22T13:04:53Z"	0	null	[ "license:openrail", "region:us" ]	null	"2024-06-22T11:51:36Z"	--- license: openrail ---
marsggbo/t2-small-token-pattern-predictor-switch32-xsum	marsggbo	"2024-06-22T11:59:58Z"	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text2text-generation	"2024-06-22T11:59:24Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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SeoulStreamingStation/KLM4.1	SeoulStreamingStation	"2024-06-25T04:48:10Z"	3	null	[ "license:other", "region:us" ]	null	"2024-06-22T11:59:56Z"	--- license: other license_name: sss license_link: LICENSE ---
kiljae/playground	kiljae	"2024-06-23T05:25:52Z"	0	null	[ "license:mit", "region:us" ]	null	"2024-06-22T12:04:18Z"	--- license: mit ---
Tyas79/Coba1	Tyas79	"2024-06-22T12:04:27Z"	0	null	[ "region:us" ]	null	"2024-06-22T12:04:27Z"	Entry not found
valerielucro/mistral_gsm8k_dpo_cot_r64_epoch1	valerielucro	"2024-06-22T12:06:06Z"	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-22T12:05:44Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Scatxyu/gar	Scatxyu	"2024-06-22T12:07:50Z"	0	null	[ "region:us" ]	null	"2024-06-22T12:07:50Z"	Entry not found
passionful7/trained-sd3	passionful7	"2024-06-22T12:08:19Z"	0	null	[ "region:us" ]	null	"2024-06-22T12:08:19Z"	Entry not found
cochaviz/q-FrozenLake-v1-4x4-noSlippery	cochaviz	"2024-06-22T12:09:06Z"	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	"2024-06-22T12:09:04Z"	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="cochaviz/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
CHE-72-ZLab/Baichuan2-7B-Chat-GGUF	CHE-72-ZLab	"2024-06-22T12:09:09Z"	0	null	[ "region:us" ]	null	"2024-06-22T12:09:09Z"	Entry not found
CHE-72-ZLab/01AI-Yi1.5-6B-Chat-GGUF	CHE-72-ZLab	"2024-06-22T12:09:48Z"	0	null	[ "license:apache-2.0", "region:us" ]	null	"2024-06-22T12:09:48Z"	--- license: apache-2.0 ---
rjuggins/instruction_mistral_7b_v1_225_test	rjuggins	"2024-06-22T12:11:24Z"	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-22T12:11:02Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
Major3220/Dragon	Major3220	"2024-06-22T12:12:51Z"	0	null	[ "region:us" ]	null	"2024-06-22T12:12:51Z"	Entry not found
cochaviz/q-Taxi-v3	cochaviz	"2024-06-22T12:14:01Z"	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	"2024-06-22T12:14:00Z"	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.44 +/- 2.65 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="cochaviz/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
alex2020xx/creampie	alex2020xx	"2024-06-22T12:16:51Z"	0	null	[ "region:us" ]	null	"2024-06-22T12:15:52Z"	Entry not found
alex2020xx/cohf	alex2020xx	"2024-06-26T16:59:02Z"	0	null	[ "region:us" ]	null	"2024-06-22T12:16:59Z"	Entry not found
ShiftAddLLM/opt66b-3bit-acc	ShiftAddLLM	"2024-06-22T12:40:52Z"	0	transformers	[ "transformers", "safetensors", "opt", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-06-22T12:17:46Z"	Entry not found
Ilonavissual/Plasma	Ilonavissual	"2024-06-22T12:17:50Z"	0	null	[ "region:us" ]	null	"2024-06-22T12:17:50Z"	Entry not found
tharunkrishna1611/deepseek_finetune_v1	tharunkrishna1611	"2024-06-22T12:18:57Z"	0	null	[ "region:us" ]	null	"2024-06-22T12:18:57Z"	Entry not found
IlyaGusev/saiga_aya_23_35b_sft_m1_d5	IlyaGusev	"2024-06-22T12:33:11Z"	0	transformers	[ "transformers", "safetensors", "cohere", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-06-22T12:19:03Z"	Entry not found
valerielucro/mistral_gsm8k_dpo_cot_epoch2	valerielucro	"2024-06-22T12:20:26Z"	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-22T12:20:18Z"	--- 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. 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]
Kibalama/q-FrozenLake-v1-4x4-noSlippery	Kibalama	"2024-06-22T12:29:52Z"	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	"2024-06-22T12:29:49Z"	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="Kibalama/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
VKapseln475/ShapeKapseln45	VKapseln475	"2024-06-22T12:31:01Z"	0	null	[ "region:us" ]	null	"2024-06-22T12:30:26Z"	# <kaufen> Shape Kapseln Erfahrungen - Shape Kapseln Schweiz Bewertungen Zutaten Preis Shape Kapseln Schweiz ist ein kürzlich auf den Markt gebrachtes, den Stoffwechsel ankurbelndes Nahrungsergänzungsmittel, das in den letzten Wochen auf dem Markt begeisterte Kritiken erhalten hat. In dieser Rezension zu Shape Kapseln werde ich jeden Aspekt dieser Ergänzung im Detail untersuchen, um herauszufinden, ob sie ihr Geld wert ist. ## [Klicken Sie hier, um jetzt auf der offiziellen Website von Shape Kapseln zu kaufen](https://capsules24x7.com/shape-kapseln-ch) ## Was sind Shape diätkapseln Schweiz? Shape diätkapseln Germany sind eine Art Gummibonbons, die speziell für Personen entwickelt wurden, die eine ketogene Diät einhalten. Die ketogene Diät, allgemein bekannt als Keto-Diät, ist ein fettreicher, kohlenhydratarmer Ernährungsplan, der darauf abzielt, einen Stoffwechselzustand namens Ketose herbeizuführen. Bei der Ketose nutzt der Körper zur Energiegewinnung hauptsächlich Fett statt Kohlenhydrate. Shape diätkapseln sind so formuliert, dass sie nur minimale Kohlenhydrate und Zucker enthalten und gleichzeitig einen hohen Anteil an gesunden Fetten aufweisen. Sie enthalten oft Zutaten wie: Gelatine oder alternative Geliermittel: Diese sorgen für die gummiartige Textur. Gesunde Fette: Oft aus Quellen wie Kokosnussöl, MCT-Öl oder anderen ketofreundlichen Fetten gewonnen. Low-Carb-Süßstoffe: Wie Erythrit, Stevia oder Mönchsfruchtextrakt für mehr Süße, ohne den Blutzuckerspiegel in die Höhe zu treiben. Aromen und Farbstoffe: Natürliche Aromen und Farbstoffe werden häufig verwendet, um Geschmack und Aussehen zu verbessern. Diese Gummibonbons haben normalerweise einen niedrigen Nettokohlenhydratgehalt und eignen sich daher für Menschen mit einer ketogenen Diät, die eine süße Leckerei wünschen, ohne regelmäßig Süßigkeiten mit hohem Zuckergehalt zu sich zu nehmen, die die Ketose stören würden. Es ist jedoch wichtig, die spezifischen Inhaltsstoffe und Nährwertangaben der Shape Kapseln Germany zu prüfen, da einige davon möglicherweise noch versteckte Kohlenhydrate oder Inhaltsstoffe enthalten, die die Ketose beeinflussen könnten. ## Anweisungen: Gießen Sie eine halbe Tasse kaltes Wasser in einen Topf und streuen Sie die Gelatine darüber. Lassen Sie es einige Minuten ruhen, damit es blüht. Stellen Sie den Topf auf schwache Hitze und rühren Sie ständig um, bis sich die Gelatine vollständig aufgelöst hat. Achten Sie darauf, dass es nicht kocht. Sobald sich die Gelatine aufgelöst hat, den Topf vom Herd nehmen. Geben Sie die restliche halbe Tasse Wasser und Süßstoff zur Gelatinemischung. Rühren, bis sich der Süßstoff vollständig aufgelöst hat. Wenn Sie Aromen oder Extrakte verwenden, fügen Sie diese der Mischung hinzu und rühren Sie gut um. Bei Bedarf können Sie zu diesem Zeitpunkt auch Lebensmittelfarbe hinzufügen. ## [Klicken Sie hier, um jetzt auf der offiziellen Website von Shape Kapseln zu kaufen](https://capsules24x7.com/shape-kapseln-ch)
Hasher08/summarizer	Hasher08	"2024-06-22T12:30:40Z"	0	null	[ "region:us" ]	null	"2024-06-22T12:30:40Z"	Entry not found
MyNiuuu/MOFA-Video-Hybrid	MyNiuuu	"2024-06-25T23:33:05Z"	20	diffusers	[ "diffusers", "safetensors", "arxiv:2405.20222", "license:apache-2.0", "region:us" ]	null	"2024-06-22T12:33:47Z"	--- license: apache-2.0 --- ## Paper arxiv.org/abs/2405.20222 ## Introduction This repo provides the inference Gradio demo for Hybrid (Trajectory + Landmark) Control of [MOFA-Video](https://myniuuu.github.io/MOFA_Video/). ## Environment Setup ``` cd MOFA-Hybrid conda create -n mofa python==3.10 conda activate mofa pip install -r requirements.txt pip install opencv-python-headless pip install "git+https://github.com/facebookresearch/pytorch3d.git" ``` IMPORTANT: Gradio Version of 4.5.0 should be used since other versions may cause errors. ## Checkpoints Download 1. Download the checkpoint of CMP from [here](https://huggingface.co/MyNiuuu/MOFA-Video-Hybrid/blob/main/models/cmp/experiments/semiauto_annot/resnet50_vip%2Bmpii_liteflow/checkpoints/ckpt_iter_42000.pth.tar) and put it into `./models/cmp/experiments/semiauto_annot/resnet50_vip+mpii_liteflow/checkpoints`. 2. Downloading the necessary pretrained checkpoints from [huggingface](https://huggingface.co/MyNiuuu/MOFA-Video-Hybrid). It is recommended to directly using git lfs to clone the [huggingface repo](https://huggingface.co/MyNiuuu/MOFA-Video-Hybrid). The checkpoints should be orgnized as `./ckpt_tree.md` (they will be automatically organized if you use git lfs to clone the [huggingface repo](https://huggingface.co/MyNiuuu/MOFA-Video-Hybrid)). ## Run Gradio Demo ### Using audio to animate the facial part `python run_gradio_audio_driven.py` ### Using refernce video to animate the facial part `python run_gradio_video_driven.py` IMPORTANT: Please refer to the instructions on the gradio interface during the inference process.
karthikmit/openai-whisper-medium-LORA-EN-v1	karthikmit	"2024-06-22T12:33:58Z"	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-22T12:33:49Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Justin3355/Arianaeternalshine	Justin3355	"2024-06-22T12:33:59Z"	0	null	[ "license:openrail", "region:us" ]	null	"2024-06-22T12:33:59Z"	--- license: openrail ---
Ramikan-BR/TiamaPY-LORA-v33	Ramikan-BR	"2024-06-22T12:39:27Z"	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/tinyllama-chat-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-06-22T12:38:33Z"	--- base_model: unsloth/tinyllama-chat-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - Developed by: Ramikan-BR - License: apache-2.0 - Finetuned from model : unsloth/tinyllama-chat-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
linwf/dit4v	linwf	"2024-06-24T05:33:55Z"	0	null	[ "license:openrail++", "region:us" ]	null	"2024-06-22T12:40:54Z"	--- license: openrail++ ---
Kibalama/q-Taxi-v3	Kibalama	"2024-06-22T12:41:45Z"	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	"2024-06-22T12:41:43Z"	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="Kibalama/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
oodapow/welfake_bert	oodapow	"2024-06-22T12:42:46Z"	0	null	[ "region:us" ]	null	"2024-06-22T12:42:46Z"	Entry not found
Kibalama/q-Taxi-v3-2	Kibalama	"2024-06-22T12:53:29Z"	0	null	[ "region:us" ]	null	"2024-06-22T12:53:29Z"	Entry not found
Wsassi/whisper_minds_14_merged	Wsassi	"2024-06-22T13:54:44Z"	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]	automatic-speech-recognition	"2024-06-22T12:56:45Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
hamidei/arianaeternalsunshein	hamidei	"2024-06-22T13:02:18Z"	0	null	[ "music", "en", "license:openrail", "region:us" ]	null	"2024-06-22T12:59:28Z"	--- license: openrail language: - en tags: - music ---
andreeadumitru/welfake_bert	andreeadumitru	"2024-06-22T14:35:08Z"	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2024-06-22T13:02:29Z"	--- license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_trainer model-index: - name: welfake_bert 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. --> # welfake_bert This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) 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: 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 ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
IrsadDh/Shiroko	IrsadDh	"2024-06-22T13:17:07Z"	0	null	[ "license:openrail", "region:us" ]	null	"2024-06-22T13:03:24Z"	--- license: openrail ---
Aminrhmni/Persian_text_classification	Aminrhmni	"2024-06-22T13:45:04Z"	0	adapter-transformers	[ "adapter-transformers", "safetensors", "bert", "text-classification", "fa", "dataset:Msobhi/virgool_62k", "license:mit", "region:us" ]	text-classification	"2024-06-22T13:04:13Z"	--- license: mit language: - fa metrics: - accuracy library_name: adapter-transformers datasets: - Msobhi/virgool_62k pipeline_tag: text-classification --- Hi I have fine-tuned ParsBert for classifying Persian text using the "Msobhi/virgool_62k" dataset, with the labels listed below: {'استارتاپ': 0, 'اقتصاد': 1, 'امنیت سایبری': 2, 'اینترنت اشیا': 3, 'بازاریابی': 4, 'بازی رایانه ای': 5, 'برنامه نویسی': 6, 'بلاک چین': 7, 'بهره وری': 8, 'تاریخ': 9, 'تجربه کاربری': 10, 'تحصیلی و آموزشی': 11, 'حقوقی': 12, 'خانواده': 13, 'خودشناسی': 14, 'داستان': 15, 'رابطه': 16, 'روانشناسی': 17, 'زنان': 18, 'سفر': 19, 'سلامت': 20, 'سلامت روانی': 21, 'سیاست': 22, 'شبکه اجتماعی': 23, 'شغل و کار': 24, 'طراحی دیجیتال': 25, 'علوم': 26, 'عکاسی': 27, 'غذا': 28, 'فرهنگ': 29, 'فریلنسری': 30, 'فلسفه': 31, 'فیلم و سینما': 32, 'فین تک': 33, 'محیط زیست': 34, 'مذهبی': 35, 'مهاجرت': 36, 'مهندسی نرم افزار': 37, 'موسیقی': 38, 'موفقیت': 39, 'نویسندگی': 40, 'هنر': 41, 'هوا فضا': 42, 'هوش مصنوعی': 43, 'ورزشی': 44, 'پادکست': 45, 'پول رمزی': 46, 'کارآفرینی': 47, 'کتاب': 48, 'یادگیری ماشین': 49} Utilize this code snippet to test the model. It may not be very accurate, but further training epochs could enhance its performance. model_path = "Aminrhmni/Persian_text_classification" from transformers import pipeline, BertForSequenceClassification, BertTokenizerFast model = BertForSequenceClassification.from_pretrained(model_path) tokenizer= BertTokenizerFast.from_pretrained(model_path) nlp= pipeline("text-classification", model=model, tokenizer=tokenizer) nlp("متن ورودی")
ZidanAf/Zidan_model_output_v2	ZidanAf	"2024-06-22T15:31:26Z"	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:indolem/indobert-base-uncased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2024-06-22T13:04:31Z"	--- license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: Zidan_model_output_v2 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. --> # Zidan_model_output_v2 This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7630 - Accuracy: 0.6818 ## 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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 220 \| 1.0479 \| 0.5091 \| \| No log \| 2.0 \| 440 \| 0.9568 \| 0.5455 \| \| 1.0321 \| 3.0 \| 660 \| 0.9094 \| 0.5818 \| \| 1.0321 \| 4.0 \| 880 \| 0.8748 \| 0.6182 \| \| 0.8487 \| 5.0 \| 1100 \| 0.8410 \| 0.6182 \| \| 0.8487 \| 6.0 \| 1320 \| 0.8203 \| 0.6727 \| \| 0.7405 \| 7.0 \| 1540 \| 0.8106 \| 0.6273 \| \| 0.7405 \| 8.0 \| 1760 \| 0.7971 \| 0.6636 \| \| 0.7405 \| 9.0 \| 1980 \| 0.7852 \| 0.6636 \| \| 0.6747 \| 10.0 \| 2200 \| 0.7788 \| 0.6636 \| \| 0.6747 \| 11.0 \| 2420 \| 0.7754 \| 0.6455 \| \| 0.6304 \| 12.0 \| 2640 \| 0.7688 \| 0.6545 \| \| 0.6304 \| 13.0 \| 2860 \| 0.7656 \| 0.6727 \| \| 0.6064 \| 14.0 \| 3080 \| 0.7626 \| 0.6727 \| \| 0.6064 \| 15.0 \| 3300 \| 0.7630 \| 0.6818 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
hamidei/ag202444	hamidei	"2024-06-22T13:07:13Z"	0	null	[ "license:openrail", "region:us" ]	null	"2024-06-22T13:05:39Z"	--- license: openrail ---
marsggbo/t2-small-token-pattern-predictor-switch64-xsum	marsggbo	"2024-06-22T13:06:58Z"	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text2text-generation	"2024-06-22T13:06:22Z"	--- 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]
Bredmak/Yana	Bredmak	"2024-06-22T13:10:28Z"	0	null	[ "license:openrail", "region:us" ]	null	"2024-06-22T13:06:59Z"	--- license: openrail ---
slelab/AES5	slelab	"2024-06-22T13:51:34Z"	0	null	[ "region:us" ]	null	"2024-06-22T13:07:11Z"	Entry not found
hyperspaceai/hyperEngine_8B_v3	hyperspaceai	"2024-06-22T13:11:06Z"	1	null	[ "region:us" ]	null	"2024-06-22T13:11:06Z"	Entry not found
Romanos575/hassaku-xl-hentai-v13-better-eyes-sdxl	Romanos575	"2024-06-22T17:46:48Z"	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	"2024-06-22T13:12:09Z"	--- license: creativeml-openrail-m ---
ShiftAddLLM/Llama-3-70b-wbits3-acc	ShiftAddLLM	"2024-06-22T13:37:43Z"	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-06-22T13:16:02Z"	Entry not found
jddllwqa/Qwen-Qwen1.5-0.5B-1719062203	jddllwqa	"2024-06-22T13:16:50Z"	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen1.5-0.5B", "region:us" ]	null	"2024-06-22T13:16:43Z"	--- base_model: Qwen/Qwen1.5-0.5B library_name: peft --- # 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 0.11.1
jddllwqa/Qwen-Qwen1.5-1.8B-1719062257	jddllwqa	"2024-06-22T13:17:42Z"	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen1.5-1.8B", "region:us" ]	null	"2024-06-22T13:17:38Z"	--- base_model: Qwen/Qwen1.5-1.8B library_name: peft --- # 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. 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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 0.11.1
Yash-Shindey/q-FrozenLake-v1-4x4-noSlippery	Yash-Shindey	"2024-06-22T13:18:32Z"	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	"2024-06-22T13:18:29Z"	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="Yash-Shindey/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
jddllwqa/Qwen-Qwen1.5-7B-1719062349	jddllwqa	"2024-06-22T13:19:14Z"	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen1.5-7B", "region:us" ]	null	"2024-06-22T13:19:09Z"	--- base_model: Qwen/Qwen1.5-7B library_name: peft --- # 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. 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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 0.11.1
inetnuc/nuclear_model	inetnuc	"2024-06-22T13:43:59Z"	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "dataset:inetnuc/nuclear_report", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-06-22T13:19:36Z"	--- base_model: unsloth/llama-3-8b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl datasets: - inetnuc/nuclear_report --- # Uploaded model #Model Summary This Hub repository contains a Hugging Face's transformers implementation of a fine-tuned LLaMA-3 model by inetnuc. This model, based on the LLaMA-3 architecture, has been fine-tuned using a specialized nuclear dataset developed by inetnuc. The model excels in understanding and generating text related to nuclear safety, security, and operational details, making it highly suitable for tasks requiring domain-specific knowledge in the nuclear field. Fine-tuned Model Details Developed by: inetnuc License: apache-2.0 Finetuned from model: unsloth/llama-3-8b-bnb-4bit Training: This LLaMA-3 model was trained 2x faster with Unsloth and Hugging Face's TRL library. Pipeline: Unable to determine this model’s pipeline type. Check the docs for more information. Datasets Used for Training inetnuc/nuclear_model inetnuc/nuclear_report Model Capabilities This model is adept at: Answering questions related to nuclear safety and operational protocols. Assisting in the interpretation of complex nuclear-related documents. - Developed by: inetnuc - 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)
jddllwqa/google-gemma-2b-1719062397	jddllwqa	"2024-06-22T13:20:04Z"	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:google/gemma-2b", "region:us" ]	null	"2024-06-22T13:19:58Z"	--- base_model: google/gemma-2b library_name: peft --- # 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 0.11.1
AriaRahmati1/222ghesmat4part1	AriaRahmati1	"2024-06-22T13:53:53Z"	0	null	[ "license:openrail", "region:us" ]	null	"2024-06-22T13:20:02Z"	--- license: openrail ---
Yash-Shindey/taxi-v3	Yash-Shindey	"2024-06-22T13:22:25Z"	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	"2024-06-22T13:22:23Z"	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.52 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="Yash-Shindey/taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
hchcsuim/batch-size16_Celeb-DF-v2_opencv-1FPS_faces-expand30-aligned_unaugmentation	hchcsuim	"2024-06-22T14:18:53Z"	0	transformers	[ "transformers", "tensorboard", "safetensors", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/swin-tiny-patch4-window7-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	"2024-06-22T13:22:39Z"	--- license: apache-2.0 base_model: microsoft/swin-tiny-patch4-window7-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy - precision - recall - f1 model-index: - name: batch-size16_Celeb-DF-v2_opencv-1FPS_faces-expand30-aligned_unaugmentation results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: test args: default metrics: - name: Accuracy type: accuracy value: 0.9931273636466434 - name: Precision type: precision value: 0.994805662520431 - name: Recall type: recall value: 0.9976108124626689 - name: F1 type: f1 value: 0.9962062627873912 --- <!-- 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. --> # batch-size16_Celeb-DF-v2_opencv-1FPS_faces-expand30-aligned_unaugmentation This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0198 - Accuracy: 0.9931 - Precision: 0.9948 - Recall: 0.9976 - F1: 0.9962 - Roc Auc: 0.9991 ## 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: 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 - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| Roc Auc \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\|:-------:\| \| 0.0338 \| 0.9994 \| 1243 \| 0.0198 \| 0.9931 \| 0.9948 \| 0.9976 \| 0.9962 \| 0.9991 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.1 - Datasets 2.20.0 - Tokenizers 0.19.1
rzzat/example-model	rzzat	"2024-06-22T13:35:43Z"	0	null	[ "region:us" ]	null	"2024-06-22T13:22:49Z"	# Test Model readme Readme dummy commit. --- license: mit ---
Plachta/FAcodec-redecoder	Plachta	"2024-06-22T13:49:07Z"	6	null	[ "pytorch", "audio-to-audio", "arxiv:2403.03100", "license:mit", "region:us" ]	audio-to-audio	"2024-06-22T13:28:16Z"	--- license: mit pipeline_tag: audio-to-audio --- [FAcodec](https://arxiv.org/pdf/2403.03100) trained on 50k hours speech data, with more timbre diversity and better at reconstructing speakers from podcasts, videos, games or animations. This is a separate decoder designed and trained based on the pretrained [encoder](https://huggingface.co/Plachta/FAcodec) specifically for voice conversion task. It is capable of zero-shot voice conversion, stream voice conversion and has outstanding timbre generalization ability. See [main repository](https://github.com/Plachtaa/FAcodec) for example usages.
hchcsuim/batch-size16_Celeb-DF-v2_opencv-1FPS_faces-expand40-aligned_unaugmentation	hchcsuim	"2024-06-22T14:28:11Z"	0	transformers	[ "transformers", "tensorboard", "safetensors", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/swin-tiny-patch4-window7-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	"2024-06-22T13:31:13Z"	--- license: apache-2.0 base_model: microsoft/swin-tiny-patch4-window7-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy - precision - recall - f1 model-index: - name: batch-size16_Celeb-DF-v2_opencv-1FPS_faces-expand40-aligned_unaugmentation results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: test args: default metrics: - name: Accuracy type: accuracy value: 0.992276459211251 - name: Precision type: precision value: 0.9963001289993337 - name: Recall type: recall value: 0.9951574535568645 - name: F1 type: f1 value: 0.9957284634510899 --- <!-- 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. --> # batch-size16_Celeb-DF-v2_opencv-1FPS_faces-expand40-aligned_unaugmentation This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0217 - Accuracy: 0.9923 - Precision: 0.9963 - Recall: 0.9952 - F1: 0.9957 - Roc Auc: 0.9990 ## 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: 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 - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| Roc Auc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\|:-------:\| \| 0.0388 \| 1.0 \| 1220 \| 0.0217 \| 0.9923 \| 0.9963 \| 0.9952 \| 0.9957 \| 0.9990 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.1 - Datasets 2.20.0 - Tokenizers 0.19.1
Nugik/Rainbow_Ruby	Nugik	"2024-06-22T13:31:56Z"	0	null	[ "region:us" ]	null	"2024-06-22T13:31:55Z"	Entry not found