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librarian-bots
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model_cards_with_metadata_test

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modelId stringlengths 5 122	author stringlengths 2 42	last_modified unknown	likes int64 0 11k	library_name stringclasses 245 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 48 values	createdAt unknown	card stringlengths 1 901k
swoos/llama-3-8b-unsloth-KoCoT-1000-final	swoos	"2024-06-14T12:08:16Z"	0	transformers	[ "transformers", "pytorch", "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", "region:us" ]	text-generation	"2024-06-14T11:58:24Z"	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: swoos - 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)
ar08/Tinyllama-bengali	ar08	"2024-06-14T11:58:41Z"	0	transformers	[ "transformers", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/tinyllama-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-06-14T11:58:37Z"	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/tinyllama-bnb-4bit --- # Uploaded model - Developed by: ar08 - License: apache-2.0 - Finetuned from model : unsloth/tinyllama-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)
harsh4733/support_lora	harsh4733	"2024-06-14T12:03:01Z"	0	null	[ "region:us" ]	null	"2024-06-14T12:00:10Z"	Entry not found
DavidLacour/hyumnoZephyr32bitsmerged2	DavidLacour	"2024-06-14T12:00:36Z"	0	transformers	[ "transformers", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/zephyr-sft", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-06-14T12:00:35Z"	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/zephyr-sft --- # Uploaded model - Developed by: DavidLacour - License: apache-2.0 - Finetuned from model : unsloth/zephyr-sft This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
HCHSmost/Terry-ft	HCHSmost	"2024-06-14T12:04:31Z"	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-14T12:04:29Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
HCHSmost/shawgpt-ft	HCHSmost	"2024-06-14T12:04:34Z"	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:TheBloke/Mistral-7B-Instruct-v0.2-GPTQ", "license:apache-2.0", "region:us" ]	null	"2024-06-14T12:04:31Z"	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: TheBloke/Mistral-7B-Instruct-v0.2-GPTQ model-index: - name: shawgpt-ft results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # shawgpt-ft This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.2-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GPTQ) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.8893 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 4.5931 \| 0.9231 \| 3 \| 3.9639 \| \| 4.0415 \| 1.8462 \| 6 \| 3.4366 \| \| 3.4638 \| 2.7692 \| 9 \| 2.9860 \| \| 2.2596 \| 4.0 \| 13 \| 2.5615 \| \| 2.6798 \| 4.9231 \| 16 \| 2.3235 \| \| 2.3696 \| 5.8462 \| 19 \| 2.1400 \| \| 2.1782 \| 6.7692 \| 22 \| 2.0297 \| \| 1.5459 \| 8.0 \| 26 \| 1.9520 \| \| 2.0084 \| 8.9231 \| 29 \| 1.9031 \| \| 1.3897 \| 9.2308 \| 30 \| 1.8893 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.41.2 - Pytorch 2.1.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
rajputbittusitaram/stampdetection	rajputbittusitaram	"2024-06-14T12:13:20Z"	0	null	[ "license:mit", "region:us" ]	null	"2024-06-14T12:05:43Z"	--- license: mit --- this is model for logistics pod stamp detection which has every stamp a different sign it is traine of 1000 images of different pods although it has two differnet models first on has round pod and the second one is square stamp.
cgteen/mistral7bHRBot	cgteen	"2024-06-14T12:06:05Z"	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-v0.3-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-06-14T12:05:53Z"	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/mistral-7b-v0.3-bnb-4bit --- # Uploaded model - Developed by: cgteen - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-v0.3-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
ssnsn/470pretrain444	ssnsn	"2024-06-14T12:07:26Z"	0	null	[ "license:openrail", "region:us" ]	null	"2024-06-14T12:06:50Z"	--- license: openrail ---
savan14/swinv2-large-patch4-window12to16-192to256-22kto1k-ft	savan14	"2024-06-14T12:31:03Z"	0	transformers	[ "transformers", "safetensors", "swinv2", "image-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	"2024-06-14T12:07:29Z"	Entry not found
rgres/modelout	rgres	"2024-06-14T12:07:55Z"	0	null	[ "region:us" ]	null	"2024-06-14T12:07:55Z"	Entry not found
dbg-adapter/chien	dbg-adapter	"2024-06-14T12:08:26Z"	0	peft	[ "peft", "region:us" ]	null	"2024-06-14T12:08:20Z"	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0
Rxplore/Llava1.5-quant	Rxplore	"2024-06-14T12:09:17Z"	0	null	[ "region:us" ]	null	"2024-06-14T12:09:17Z"	Entry not found
licyk/sd-3-model	licyk	"2024-06-17T14:43:28Z"	0	null	[ "license:other", "region:us" ]	null	"2024-06-14T12:09:38Z"	--- license: other --- 个人存储模型的仓库 ## 仓库列表 [sd-model](https://huggingface.co/licyk/sd-model) Stable Diffusion 模型仓库 [sd-3-model](https://huggingface.co/licyk/sd-3-model) Stable Diffusion 3 模型仓库 [sd-vae](https://huggingface.co/licyk/sd-vae) VAE 模型仓库 [sd-upscaler-models](https://huggingface.co/licyk/sd-upscaler-models) 放大模型仓库 [sd-embeddings](https://huggingface.co/licyk/sd-embeddings) Embedding 模型仓库 [sd-lora](https://huggingface.co/licyk/sd-lora) LoRA 模型仓库 [sd3_lora](https://huggingface.co/licyk/sd3_lora) 适用于 Stable Diffusion 3 的 LoRA 模型 [controlnet_v1.1](https://huggingface.co/licyk/controlnet_v1.1) 适用于 Stable Diffusion 1.5 的 ControlNet 模型仓库 [sd_control_collection](https://huggingface.co/licyk/sd_control_collection) 适用于 Stable Diffusion 1.5 / Stable Diffusion XL 的 ControlNet 模型仓库 [control-lora](https://huggingface.co/licyk/control-lora) 适用于 Stable Diffusion 1.5 / Stable Diffusion XL 的 ControlNet 模型仓库 [sd3_controlnet](https://huggingface.co/licyk/sd3_controlnet) 适用于 Stable Diffusion 3 的 ControlNet 模型仓库 [controlnet_v1.1_annotator](https://huggingface.co/licyk/controlnet_v1.1_annotator) 搭配 ControlNet 的预处理器模型仓库 [layerdiffusion](https://huggingface.co/licyk/layerdiffusion) LayerDiffusion 模型仓库
cgteen/mistral7bHRBotFull	cgteen	"2024-06-14T12:12:32Z"	0	transformers	[ "transformers", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-v0.3-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-06-14T12:10:06Z"	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/mistral-7b-v0.3-bnb-4bit --- # Uploaded model - Developed by: cgteen - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-v0.3-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
dbg-adapter/avocat	dbg-adapter	"2024-06-14T12:12:39Z"	0	peft	[ "peft", "region:us" ]	null	"2024-06-14T12:11:00Z"	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0
DavidLacour/gguftosafe	DavidLacour	"2024-06-14T12:13:44Z"	0	null	[ "region:us" ]	null	"2024-06-14T12:13:44Z"	Entry not found
VKapseln475/Vigorys4155	VKapseln475	"2024-06-14T12:21:26Z"	0	null	[ "region:us" ]	null	"2024-06-14T12:19:52Z"	# Vigorys Avis Expériences Dose et action – Vigorys France Commentaires Prix officiel, acheter Vigorys Avis Expériences Dose et action Vigorys n’est rien d’autre qu’un complément alimentaire mis au point par Biovancia pour aider les seniors à retrouver la vigueur sexuelle de leur jeunesse. Ce laboratoire de santé reconnu en France pour la qualité et l’efficacité de ses produits de santé est resté fidèle à lui-même en ce qui concerne la conception du complément alimentaire de virilité sexuelle. ## [cliquez ici pour acheter maintenant sur le site officiel de Vigorys](https://callednews.com/vigorys-fr) ## Quels sont les avantages de l’utilisation de Vigorys ? L’usage de Vigorys présente des avantages indéniables pour soutenir la virilité et la libido de l’homme. Ce produit contribue efficacement à l’amélioration de la santé masculine sur le plan de la sexualité. À la base, il assure une triple fonction sur les différents facteurs influençant la vie sexuelle. La combinaison de ses différents ingrédients contribue à redonner l’endurance et le désir à tous ceux qui connaissent une certaine baisse de forme au lit. Vous devenez un super amant avec l’utilisation de cette cure naturelle. La qualité de l’érection, sa durée ainsi que sa fréquence sont fortement impactées de façon positive par le recours à Vigorys. Il agit profondément sur le corps de son utilisateur pour lui donner l’énergie physique et sexuelle qu’il faut. Il régénère le corps en lui donnant le tonus indispensable pour une virilité retrouvée. ## Où est-ce possible de trouver en vente la formule Vigorys de Biovancia ? Avant de pouvoir bénéficier des avantages de l’utilisation de Vigorys, il faut l’acheter au bon endroit. À l’instar du supplément nutritionnel Nutrilim 24 et de tous les produits mis sur le marché par l’institut Biovancia, il n’est pas possible de trouver ce traitement dans une pharmacie. En effet, Vigorys n’est pas disponible en pharmacie ou dans les centres de santé. Si vous allez dans les rayons des grandes surfaces de distribution, vous ne pourrez pas trouver ce traitement. De même, il est absent des plateformes de vente en ligne habituellement utilisées pour des achats de certains compléments alimentaires. Pour acheter Vigorys, il faut se rendre sur le site officiel de l’institut Biovancia qui vend exclusivement ce traitement. Il n’y a donc pas d’intermédiaire entre le fabricant et les clients qui peuvent bénéficier du produit au juste prix. Il suffit de remplir le formulaire de commande pour lancer l’achat de ce traitement. Le prix d’un flacon de gélules Vigorys est de 69 euros. L’achat de 3 ou 6 flacons permet de bénéficier davantage de réductions pour faire des économies. Pour tout achat, vous bénéficiez d’une livraison gratuite et de la garantie « 100 % satisfait ou remboursé » valable pour 12 mois. ## Le complément d’amélioration de la virilité est-il disponible sur Amazon ? Toutes les personnes qui désirent suivre un traitement à base de Vigorys ne pourront pas trouver ce produit sur les plateformes de vente en ligne telles que Amazon. Tout simplement parce que le concepteur du supplément n’autorise pas ladite plateforme à s’insérer dans la chaine de distribution. Pour avoir Vigorys à un prix intéressant, la seule adresse demeure à ce jour le site officiel de la marque Biovancia. ## L’achat du Vigorys Biovancia en pharmacie est-elle une bonne idée ? La véritable question à se poser est de savoir si ce complément de restauration de la virilité masculine se vend dans les surfaces telles que les pharmacies. Et la réponse à ladite interrogation est négative. Il en ressort donc qu’aucun consommateur ne devrait même penser à aller rechercher Vigorys dans une pharmacie ou en parapharmacie, encore moins dans un autre type de boutique physique. Si l’entreprise prend toutes ces précautions, c’est principalement pour sécuriser les compléments venant de ses laboratoires et qui sont déversés par milliers sur le marché. De cette manière, la santé des utilisateurs est également sécurisée et préservée des effets néfastes de produits contrefaits. ## À quel prix le supplément Vigorys de Biovancia est-il vendu ? La philosophie de la marque française Biovancia est de permettre à toutes les personnes dans le besoin d’accéder facilement à l’intégralité des produits développés et mis sur le marché par la marque. Dans le cas du Vigorys par exemple, l’entreprise propose aux acheteurs sur son site de se procurer le produit au prix unitaire de 69 euros. De plus, Biovancia a mis en place une politique permettant d’acheter le produit moins cher lorsque l’on prend un certain nombre. Ainsi pour 3 flacons à acheter vous verserez 138 euros et pour 6 flacons achetés il faudra débourser la somme de 198 euros. Notez que plus la cure est renouvelée et plus les chances de jouir entièrement des bienfaits du supplément repas sont élevées. ## Vigorys Biovancia : posologie et potentiels effets secondaires du supplément repas L’utilisation de tout médicament requiert le respect des recommandations de sa fabrication. Le respect de la bonne posologie de Vigorys est donc indispensable pour bénéficier de tous ses effets. À cet effet, chaque flacon de ce traitement contient 90 gélules qui sont destinées à un mois d’utilisation. Il est donc recommandé de prendre 3 gélules par jour pour profiter pleinement de ce traitement. La prise de Vigorys doit se faire tout simplement avec un grand verre d’eau. ## [cliquez ici pour acheter maintenant sur le site officiel de Vigorys](https://callednews.com/vigorys-fr)
saykl76/test	saykl76	"2024-06-14T12:24:42Z"	0	null	[ "region:us" ]	null	"2024-06-14T12:24:42Z"	Entry not found
ArtChicken/mohwx_datassRev3	ArtChicken	"2024-06-14T15:11:03Z"	0	null	[ "region:us" ]	null	"2024-06-14T12:25:29Z"	Entry not found
inventwithdean/q-FrozenLake-v1-4x4-noSlippery	inventwithdean	"2024-06-14T13:58:13Z"	1	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	"2024-06-14T12:25:30Z"	--- 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="inventwithdean/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"]) ```
DBangshu/Base_GPT2_e7_2_0	DBangshu	"2024-06-14T12:26:24Z"	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-06-14T12:26:04Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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whizzzzkid/ft_G_050000_222	whizzzzkid	"2024-06-14T12:27:15Z"	0	null	[ "region:us" ]	null	"2024-06-14T12:26:43Z"	Entry not found
whizzzzkid/ft_G_030000_222	whizzzzkid	"2024-06-14T12:28:33Z"	0	null	[ "region:us" ]	null	"2024-06-14T12:28:00Z"	Entry not found
kdcyberdude/gemma-2B-pa_66000base_sft_final	kdcyberdude	"2024-06-14T12:29:48Z"	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-14T12:28:55Z"	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
whizzzzkid/ft_G_040000_222	whizzzzkid	"2024-06-14T12:29:56Z"	0	null	[ "region:us" ]	null	"2024-06-14T12:29:25Z"	Entry not found
tharunkrishna1611/llama2_finetune_chatbot	tharunkrishna1611	"2024-06-14T13:35:35Z"	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:NousResearch/Hermes-2-Pro-Llama-3-8B", "region:us" ]	null	"2024-06-14T12:30:37Z"	--- library_name: peft tags: - trl - sft - generated_from_trainer base_model: NousResearch/Hermes-2-Pro-Llama-3-8B model-index: - name: llama2_finetune_chatbot results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/nitktharun/huggingface/runs/jovcov98) # llama2_finetune_chatbot This model is a fine-tuned version of [NousResearch/Hermes-2-Pro-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.11.2.dev0 - Transformers 4.42.0.dev0 - Pytorch 2.2.2 - Datasets 2.19.1 - Tokenizers 0.19.1
GeorgiaCh96/mini_XCEPTION	GeorgiaCh96	"2024-06-14T12:31:18Z"	0	null	[ "region:us" ]	null	"2024-06-14T12:30:58Z"	Entry not found
swoos/llama-2-7b-unsloth-KoCoT-2000-final-adapter	swoos	"2024-06-14T12:31:42Z"	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-2-7b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-06-14T12:31:30Z"	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-2-7b-bnb-4bit --- # Uploaded model - Developed by: swoos - License: apache-2.0 - Finetuned from model : unsloth/llama-2-7b-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)
Kijai/DepthAnythingV2-safetensors	Kijai	"2024-06-23T00:37:50Z"	13	null	[ "license:cc-by-4.0", "region:us" ]	null	"2024-06-14T12:36:19Z"	--- license: cc-by-4.0 --- Safetensors versions of DepthAnythingV2 models: https://huggingface.co/depth-anything Can be used in ComfyUI with: https://github.com/kijai/ComfyUI-DepthAnythingV2
shiyill/lora_test	shiyill	"2024-06-14T12:38:38Z"	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/llama-3-8b", "region:us" ]	null	"2024-06-14T12:36:32Z"	--- library_name: peft base_model: unsloth/llama-3-8b --- # 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
lukru/digitarium_migra	lukru	"2024-06-14T12:40:03Z"	0	bertopic	[ "bertopic", "de", "license:cc0-1.0", "region:us" ]	null	"2024-06-14T12:36:51Z"	--- license: cc0-1.0 language: - de library_name: bertopic ---
Americo/uma_model_gemma_finetuned	Americo	"2024-06-14T12:40:16Z"	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "bitsandbytes", "region:us" ]	text-generation	"2024-06-14T12:38:38Z"	--- 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]
damgomz/ft_8_6e6_x1	damgomz	"2024-06-15T06:25:58Z"	0	transformers	[ "transformers", "safetensors", "albert", "text-classification", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2024-06-14T12:39:59Z"	--- language: en tags: - text-classification pipeline_tag: text-classification widget: - text: GEPS Techno is the pioneer of hybridization of renewable energies at sea. We imagine, design and commercialize innovative off-grid systems that aim to generate power at sea, stabilize and collect data. The success of our low power platforms WAVEPEAL enabled us to scale-up the device up to WAVEGEM, the 150-kW capacity platform. --- ## Environmental Impact (CODE CARBON DEFAULT) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| Duration (in seconds) \| 66323.7196905613 \| \| Emissions (Co2eq in kg) \| 0.0401335068619164 \| \| CPU power (W) \| 42.5 \| \| GPU power (W) \| [No GPU] \| \| RAM power (W) \| 3.75 \| \| CPU energy (kWh) \| 0.7829867703366625 \| \| GPU energy (kWh) \| [No GPU] \| \| RAM energy (kWh) \| 0.069086579739054 \| \| Consumed energy (kWh) \| 0.852073350075719 \| \| Country name \| Switzerland \| \| Cloud provider \| nan \| \| Cloud region \| nan \| \| CPU count \| 2 \| \| CPU model \| Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz \| \| GPU count \| nan \| \| GPU model \| nan \| ## Environmental Impact (for one core) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| CPU energy (kWh) \| 0.1276731604043305 \| \| Emissions (Co2eq in kg) \| 0.025976790212136506 \| ## Note 14 juin 2024 ## My Config \| Config \| Value \| \|--------------------------\|-----------------\| \| checkpoint \| damgomz/ThunBERT_bs16_lr5_MLM \| \| model_name \| ft_8_6e6_x1 \| \| sequence_length \| 400 \| \| num_epoch \| 6 \| \| learning_rate \| 6e-06 \| \| batch_size \| 8 \| \| weight_decay \| 0.0 \| \| warm_up_prop \| 0.0 \| \| drop_out_prob \| 0.1 \| \| packing_length \| 100 \| \| train_test_split \| 0.2 \| \| num_steps \| 29328 \| ## Training and Testing steps Epoch \| Train Loss \| Test Loss \| F-beta Score ---\|---\|---\|--- \| 0 \| 0.000000 \| 0.715953 \| 0.602088 \| \| 1 \| 0.277613 \| 0.189666 \| 0.929988 \| \| 2 \| 0.158410 \| 0.190735 \| 0.935253 \| \| 3 \| 0.106650 \| 0.200901 \| 0.922741 \| \| 4 \| 0.059783 \| 0.237250 \| 0.920546 \| \| 5 \| 0.028820 \| 0.302216 \| 0.917541 \| \| 6 \| 0.015656 \| 0.306303 \| 0.928960 \|
DavidLacour/model	DavidLacour	"2024-06-14T12:47:01Z"	0	transformers	[ "transformers", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/zephyr-sft", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-06-14T12:40:09Z"	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/zephyr-sft --- # Uploaded model - Developed by: DavidLacour - License: apache-2.0 - Finetuned from model : unsloth/zephyr-sft This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
hardjhonatan/MyTestPublic	hardjhonatan	"2024-06-14T12:41:45Z"	0	null	[ "license:gpl", "region:us" ]	null	"2024-06-14T12:41:45Z"	--- license: gpl ---
damgomz/ft_8_1e6_x4	damgomz	"2024-06-15T07:20:11Z"	0	transformers	[ "transformers", "safetensors", "albert", "text-classification", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2024-06-14T12:42:03Z"	--- language: en tags: - text-classification pipeline_tag: text-classification widget: - text: GEPS Techno is the pioneer of hybridization of renewable energies at sea. We imagine, design and commercialize innovative off-grid systems that aim to generate power at sea, stabilize and collect data. The success of our low power platforms WAVEPEAL enabled us to scale-up the device up to WAVEGEM, the 150-kW capacity platform. --- ## Environmental Impact (CODE CARBON DEFAULT) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| Duration (in seconds) \| 69629.05299854279 \| \| Emissions (Co2eq in kg) \| 0.0421336251564296 \| \| CPU power (W) \| 42.5 \| \| GPU power (W) \| [No GPU] \| \| RAM power (W) \| 3.75 \| \| CPU energy (kWh) \| 0.8220081630764721 \| \| GPU energy (kWh) \| [No GPU] \| \| RAM energy (kWh) \| 0.0725296419898667 \| \| Consumed energy (kWh) \| 0.8945378050663388 \| \| Country name \| Switzerland \| \| Cloud provider \| nan \| \| Cloud region \| nan \| \| CPU count \| 2 \| \| CPU model \| Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz \| \| GPU count \| nan \| \| GPU model \| nan \| ## Environmental Impact (for one core) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| CPU energy (kWh) \| 0.13403592702219486 \| \| Emissions (Co2eq in kg) \| 0.027271379091095924 \| ## Note 14 juin 2024 ## My Config \| Config \| Value \| \|--------------------------\|-----------------\| \| checkpoint \| damgomz/fp_bs16_lr5_x4 \| \| model_name \| ft_8_1e6_x4 \| \| sequence_length \| 400 \| \| num_epoch \| 6 \| \| learning_rate \| 1e-06 \| \| batch_size \| 8 \| \| weight_decay \| 0.0 \| \| warm_up_prop \| 0.0 \| \| drop_out_prob \| 0.1 \| \| packing_length \| 100 \| \| train_test_split \| 0.2 \| \| num_steps \| 29328 \| ## Training and Testing steps Epoch \| Train Loss \| Test Loss \| F-beta Score ---\|---\|---\|--- \| 0 \| 0.000000 \| 0.703632 \| 0.165280 \| \| 1 \| 0.414205 \| 0.290184 \| 0.907074 \| \| 2 \| 0.246584 \| 0.242877 \| 0.890588 \| \| 3 \| 0.203273 \| 0.219180 \| 0.913207 \| \| 4 \| 0.177256 \| 0.214220 \| 0.911893 \| \| 5 \| 0.154187 \| 0.217406 \| 0.909596 \| \| 6 \| 0.134142 \| 0.223097 \| 0.916680 \|
Batuhan-Sener/ybs-garment-model	Batuhan-Sener	"2024-06-14T12:42:24Z"	0	null	[ "region:us" ]	null	"2024-06-14T12:42:24Z"	Entry not found
abhinit27052001/dummy-output-dir	abhinit27052001	"2024-06-14T12:42:49Z"	0	null	[ "region:us" ]	null	"2024-06-14T12:42:48Z"	Entry not found
Famestar6/Anushsdxl	Famestar6	"2024-06-14T12:44:18Z"	0	null	[ "region:us" ]	null	"2024-06-14T12:43:07Z"	![image/png](https://cdn-uploads.huggingface.co/production/uploads/6332c2870487d0f95b7767c8/Mug0uunGK0QiY-1Q-tAx-.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6332c2870487d0f95b7767c8/MhqBlstHBjZIq9P_JgFEH.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6332c2870487d0f95b7767c8/o-Xspvnkuf8xZwxMl2XWA.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6332c2870487d0f95b7767c8/OCvK8J3a0j0X5VgaGpLBg.png)
Mohammedxo51/squad-bloom-3b	Mohammedxo51	"2024-06-14T13:21:33Z"	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-14T12:43:53Z"	--- 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]
kzipa/vikhr-7b-kspitza	kzipa	"2024-06-14T12:47:51Z"	0	null	[ "tensorboard", "safetensors", "region:us" ]	null	"2024-06-14T12:44:36Z"	Entry not found
ADT109119/llama-3-chinese-8b-instruct.flm	ADT109119	"2024-06-14T14:43:18Z"	0	null	[ "license:apache-2.0", "region:us" ]	null	"2024-06-14T12:45:53Z"	--- license: apache-2.0 --- fastllm model for llama3-chinese-8b-Instruct-int8 Github address: https://github.com/ztxz16/fastllm build by [The Walking Fish步行魚](https://www.youtube.com/@the_walking_fish)
inventwithdean/q-Taxi-v3	inventwithdean	"2024-06-14T13:58:23Z"	1	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	"2024-06-14T12:46:58Z"	--- 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="inventwithdean/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"]) ```
damgomz/ft_8_8e6_x12	damgomz	"2024-06-15T09:39:50Z"	0	transformers	[ "transformers", "safetensors", "albert", "text-classification", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2024-06-14T12:47:28Z"	--- language: en tags: - text-classification pipeline_tag: text-classification widget: - text: GEPS Techno is the pioneer of hybridization of renewable energies at sea. We imagine, design and commercialize innovative off-grid systems that aim to generate power at sea, stabilize and collect data. The success of our low power platforms WAVEPEAL enabled us to scale-up the device up to WAVEGEM, the 150-kW capacity platform. --- ## Environmental Impact (CODE CARBON DEFAULT) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| Duration (in seconds) \| 77959.85770082474 \| \| Emissions (Co2eq in kg) \| 0.0471747096374891 \| \| CPU power (W) \| 42.5 \| \| GPU power (W) \| [No GPU] \| \| RAM power (W) \| 3.75 \| \| CPU energy (kWh) \| 0.92035751372311 \| \| GPU energy (kWh) \| [No GPU] \| \| RAM energy (kWh) \| 0.0812074135075013 \| \| Consumed energy (kWh) \| 1.001564927230613 \| \| Country name \| Switzerland \| \| Cloud provider \| nan \| \| Cloud region \| nan \| \| CPU count \| 2 \| \| CPU model \| Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz \| \| GPU count \| nan \| \| GPU model \| nan \| ## Environmental Impact (for one core) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| CPU energy (kWh) \| 0.15007272607408761 \| \| Emissions (Co2eq in kg) \| 0.03053427759948969 \| ## Note 14 juin 2024 ## My Config \| Config \| Value \| \|--------------------------\|-----------------\| \| checkpoint \| damgomz/fp_bs16_lr5_x12 \| \| model_name \| ft_8_8e6_x12 \| \| sequence_length \| 400 \| \| num_epoch \| 6 \| \| learning_rate \| 8e-06 \| \| batch_size \| 8 \| \| weight_decay \| 0.0 \| \| warm_up_prop \| 0.0 \| \| drop_out_prob \| 0.1 \| \| packing_length \| 100 \| \| train_test_split \| 0.2 \| \| num_steps \| 29328 \| ## Training and Testing steps Epoch \| Train Loss \| Test Loss \| F-beta Score ---\|---\|---\|--- \| 0 \| 0.000000 \| 0.704644 \| 0.367745 \| \| 1 \| 0.280690 \| 0.237452 \| 0.896514 \| \| 2 \| 0.173360 \| 0.215660 \| 0.925404 \| \| 3 \| 0.117897 \| 0.234173 \| 0.925939 \| \| 4 \| 0.070056 \| 0.297896 \| 0.915701 \| \| 5 \| 0.039130 \| 0.324509 \| 0.926595 \| \| 6 \| 0.025837 \| 0.384530 \| 0.913883 \|
nihal-mp/clarkson_worst_cars_llama3_8b_updated	nihal-mp	"2024-06-14T12:50:21Z"	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-06-14T12:50:03Z"	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: nihal-mp - 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)
Yong-Hoon/trained-sd3	Yong-Hoon	"2024-06-14T12:51:08Z"	0	null	[ "region:us" ]	null	"2024-06-14T12:51:08Z"	Entry not found
HealTether-Healthcare/openbiollm-8b-lora-finetuned-v1	HealTether-Healthcare	"2024-06-14T12:52:14Z"	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:aaditya/OpenBioLLM-Llama3-8B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-06-14T12:51:48Z"	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: aaditya/OpenBioLLM-Llama3-8B --- # Uploaded model - Developed by: HealTether-Healthcare - License: apache-2.0 - Finetuned from model : aaditya/OpenBioLLM-Llama3-8B This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
damgomz/ft_4_8e6_x1	damgomz	"2024-06-15T12:50:43Z"	0	transformers	[ "transformers", "safetensors", "albert", "text-classification", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2024-06-14T12:54:20Z"	--- language: en tags: - text-classification pipeline_tag: text-classification widget: - text: GEPS Techno is the pioneer of hybridization of renewable energies at sea. We imagine, design and commercialize innovative off-grid systems that aim to generate power at sea, stabilize and collect data. The success of our low power platforms WAVEPEAL enabled us to scale-up the device up to WAVEGEM, the 150-kW capacity platform. --- ## Environmental Impact (CODE CARBON DEFAULT) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| Duration (in seconds) \| 89404.58606696129 \| \| Emissions (Co2eq in kg) \| 0.0541000503080068 \| \| CPU power (W) \| 42.5 \| \| GPU power (W) \| [No GPU] \| \| RAM power (W) \| 3.75 \| \| CPU energy (kWh) \| 1.0554679654199204 \| \| GPU energy (kWh) \| [No GPU] \| \| RAM energy (kWh) \| 0.093128673887253 \| \| Consumed energy (kWh) \| 1.1485966393071665 \| \| Country name \| Switzerland \| \| Cloud provider \| nan \| \| Cloud region \| nan \| \| CPU count \| 2 \| \| CPU model \| Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz \| \| GPU count \| nan \| \| GPU model \| nan \| ## Environmental Impact (for one core) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| CPU energy (kWh) \| 0.17210382817890046 \| \| Emissions (Co2eq in kg) \| 0.03501679620955984 \| ## Note 14 juin 2024 ## My Config \| Config \| Value \| \|--------------------------\|-----------------\| \| checkpoint \| damgomz/ThunBERT_bs16_lr5_MLM \| \| model_name \| ft_4_8e6_x1 \| \| sequence_length \| 400 \| \| num_epoch \| 6 \| \| learning_rate \| 8e-06 \| \| batch_size \| 4 \| \| weight_decay \| 0.0 \| \| warm_up_prop \| 0.0 \| \| drop_out_prob \| 0.1 \| \| packing_length \| 100 \| \| train_test_split \| 0.2 \| \| num_steps \| 29328 \| ## Training and Testing steps Epoch \| Train Loss \| Test Loss \| F-beta Score ---\|---\|---\|--- \| 0 \| 0.000000 \| 0.719633 \| 0.447858 \| \| 1 \| 0.246374 \| 0.195433 \| 0.937301 \| \| 2 \| 0.149021 \| 0.190475 \| 0.935192 \| \| 3 \| 0.087900 \| 0.215916 \| 0.931942 \| \| 4 \| 0.041989 \| 0.292436 \| 0.924949 \| \| 5 \| 0.019791 \| 0.313287 \| 0.927317 \| \| 6 \| 0.010160 \| 0.364459 \| 0.923813 \|
HealTether-Healthcare/openbiollm-8b-lora-finetuned-v1-4bit	HealTether-Healthcare	"2024-06-14T12:57:35Z"	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:aaditya/OpenBioLLM-Llama3-8B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	"2024-06-14T12:56:01Z"	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft base_model: aaditya/OpenBioLLM-Llama3-8B --- # Uploaded model - Developed by: HealTether-Healthcare - License: apache-2.0 - Finetuned from model : aaditya/OpenBioLLM-Llama3-8B This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
yangyida/llama_3_ecc_transcript_small_3	yangyida	"2024-06-14T13:06:41Z"	0	transformers	[ "transformers", "pytorch", "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", "region:us" ]	text-generation	"2024-06-14T12:58:59Z"	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: yangyida - 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)
leo20000306/EVAL	leo20000306	"2024-06-18T16:13:01Z"	0	null	[ "region:us" ]	null	"2024-06-14T13:01:40Z"	Entry not found
trustvare/TrustVare-OST-to-MBOX-Converter	trustvare	"2024-06-14T13:03:05Z"	0	null	[ "region:us" ]	null	"2024-06-14T13:02:39Z"	TrusVare OST to MBOX Converter is a superb tool for converting Outlook OST files to MBOX format. It aids in the resolution of OST file errors before their presentation in the preview mode. Export OST files to MBOX format with no file size limits. The application allows you to examine a preview of all OST file data items, such as emails with attachments, etc. This expert program can convert an unlimited number of offline OST files to MBOX format at once with complete correctness. This OST to MBOX converter generates unique output while preserving folder organization, text formatting, email characteristics, and HTML formatting. Furthermore, users do not need Microsoft Outlook or Exchange Server to complete the transfer process. It supports any version of Microsoft Outlook, such as Outlook 2021, 2019, 2016, 2013, 2010, 2007, etc. Visit Here - https://www.trustvare.com/ost/mbox/
Osru/prueba-gguf-mistral	Osru	"2024-06-17T11:18:21Z"	0	transformers	[ "transformers", "text-generation-inference", "unsloth", "mistral", "gguf", "en", "base_model:unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-06-14T13:02:43Z"	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - gguf base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit --- # Uploaded model - Developed by: Osru - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-instruct-v0.3-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
vbanonyme/VIXTTS	vbanonyme	"2024-06-14T13:02:53Z"	0	null	[ "region:us" ]	null	"2024-06-14T13:02:53Z"	Entry not found
Ansh007/lora_model	Ansh007	"2024-06-14T23:28:22Z"	1	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-06-14T13:04:00Z"	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: Ansh007 - 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)
decepticonsIsAllYouNeed/root_gc_multilingual_bert_classifier_v6	decepticonsIsAllYouNeed	"2024-06-14T13:04:12Z"	0	null	[ "region:us" ]	null	"2024-06-14T13:04:12Z"	Entry not found
DavidLacour/robertom2	DavidLacour	"2024-06-14T13:05:49Z"	0	null	[ "region:us" ]	null	"2024-06-14T13:05:49Z"	Entry not found
ar9av/phi-finetuned-llava-fixed	ar9av	"2024-06-14T13:11:00Z"	0	transformers	[ "transformers", "pytorch", "phi3_v", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "region:us" ]	text-generation	"2024-06-14T13:09:07Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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goreactdev/dir	goreactdev	"2024-06-14T13:10:02Z"	0	null	[ "region:us" ]	null	"2024-06-14T13:10:02Z"	Entry not found
Xavi99/test-chatbot	Xavi99	"2024-06-14T13:11:52Z"	0	null	[ "arxiv:1910.09700", "region:us" ]	null	"2024-06-14T13:10:45Z"	--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. 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DBangshu/Base_GPT2_e7_3_0	DBangshu	"2024-06-14T13:11:19Z"	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-06-14T13:10:59Z"	--- 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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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]
SunShineFlower/Taeyeon_700epoch	SunShineFlower	"2024-06-14T13:11:37Z"	0	null	[ "license:openrail", "region:us" ]	null	"2024-06-14T13:11:06Z"	--- license: openrail ---
EooPanda/h	EooPanda	"2024-06-14T13:15:38Z"	0	null	[ "region:us" ]	null	"2024-06-14T13:15:38Z"	Entry not found
kevinchen123/Qwen-Qwen1.5-0.5B-1718371128	kevinchen123	"2024-06-14T13:18:52Z"	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen1.5-0.5B", "region:us" ]	null	"2024-06-14T13:18:48Z"	--- library_name: peft base_model: Qwen/Qwen1.5-0.5B --- # 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
sukga/Breeze7B-QLoRA-weight	sukga	"2024-06-14T13:19:58Z"	0	peft	[ "peft", "safetensors", "llama-factory", "lora", "generated_from_trainer", "base_model:taide/TAIDE-LX-7B", "license:other", "region:us" ]	null	"2024-06-14T13:19:49Z"	--- license: other library_name: peft tags: - llama-factory - lora - generated_from_trainer base_model: taide/TAIDE-LX-7B model-index: - name: train_2024-06-14-12-51-33 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. --> # train_2024-06-14-12-51-33 This model is a fine-tuned version of [taide/TAIDE-LX-7B](https://huggingface.co/taide/TAIDE-LX-7B) on the medical_data dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.11.1 - Transformers 4.42.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
nihal-mp/outputs	nihal-mp	"2024-06-14T13:20:39Z"	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "unsloth", "generated_from_trainer", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:llama2", "region:us" ]	null	"2024-06-14T13:20:15Z"	--- license: llama2 library_name: peft tags: - trl - sft - unsloth - generated_from_trainer base_model: unsloth/llama-3-8b-bnb-4bit model-index: - name: outputs 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. --> # outputs This model is a fine-tuned version of [unsloth/llama-3-8b-bnb-4bit](https://huggingface.co/unsloth/llama-3-8b-bnb-4bit) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 8 - seed: 3407 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - training_steps: 60 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.11.1 - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
almost/ai3	almost	"2024-06-14T13:30:47Z"	0	null	[ "region:us" ]	null	"2024-06-14T13:20:54Z"	Entry not found
clementdevarieux/local_PA4A_training	clementdevarieux	"2024-06-14T13:22:14Z"	0	null	[ "region:us" ]	null	"2024-06-14T13:22:14Z"	Entry not found
kakife3586/testmerg16bit_s	kakife3586	"2024-06-14T13:22:51Z"	0	transformers	[ "transformers", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:Kukedlc/NeuralLLaMa-3-8b-DT-v0.1", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-06-14T13:22:49Z"	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: Kukedlc/NeuralLLaMa-3-8b-DT-v0.1 --- # Uploaded model - Developed by: kakife3586 - License: apache-2.0 - Finetuned from model : Kukedlc/NeuralLLaMa-3-8b-DT-v0.1 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)
OneFly7/lora-t5-base-KW-DQE-Q1-kelm-r256-alpha32	OneFly7	"2024-06-14T13:24:03Z"	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text2text-generation	"2024-06-14T13:23:31Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
DavidLacour/mergedAndUnloaded	DavidLacour	"2024-06-14T14:50:10Z"	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-06-14T13:23:56Z"	Entry not found
OneFly7/lora-t5-base-KW-DQE-Q1-webnlg-r256-alpha32	OneFly7	"2024-06-14T13:24:57Z"	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text2text-generation	"2024-06-14T13:24:29Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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WBoton/llama-3-8b-chat-doctor	WBoton	"2024-06-14T14:02:04Z"	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-14T13:25:19Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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OneFly7/lora-t5-base-only-KW-DQE-Q1-r256-alpha32	OneFly7	"2024-06-14T13:26:15Z"	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text2text-generation	"2024-06-14T13:25:42Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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fydhfzh/wav2vec2-custom-tokenizer-asr	fydhfzh	"2024-06-14T13:26:51Z"	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-14T13:26:49Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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fydhfzh/wav2vec2-custom-processor-asr	fydhfzh	"2024-06-14T13:26:54Z"	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-14T13:26:52Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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spunkrock/fake_news_classifier	spunkrock	"2024-06-14T13:27:02Z"	0	null	[ "region:us" ]	null	"2024-06-14T13:27:02Z"	Entry not found
OneFly7/lora-t5-base-only-KW-facts-Q1-r256-alpha32	OneFly7	"2024-06-14T13:28:03Z"	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text2text-generation	"2024-06-14T13:27:34Z"	--- 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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OneFly7/model-webnlg-DQE-Q1-e10	OneFly7	"2024-06-14T13:28:55Z"	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text2text-generation	"2024-06-14T13:28:21Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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OneFly7/model-webnlg-DQE-Q4-e10-ct	OneFly7	"2024-06-14T13:36:19Z"	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text2text-generation	"2024-06-14T13:29:46Z"	--- 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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OneFly7/model-webnlg-DQE-Q4-e10	OneFly7	"2024-06-14T13:31:15Z"	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text2text-generation	"2024-06-14T13:30:42Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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OneFly7/model-webnlg-facts-Q4-e10	OneFly7	"2024-06-14T13:32:00Z"	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text2text-generation	"2024-06-14T13:31:30Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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OneFly7/model-webnlg-facts-Q1-e10	OneFly7	"2024-06-14T13:33:11Z"	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text2text-generation	"2024-06-14T13:32:41Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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tipep12782/testmerg16bit_s	tipep12782	"2024-06-14T14:01:57Z"	0	transformers	[ "transformers", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:Kukedlc/NeuralLLaMa-3-8b-DT-v0.1", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-06-14T13:33:04Z"	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: Kukedlc/NeuralLLaMa-3-8b-DT-v0.1 --- # Uploaded model - Developed by: tipep12782 - License: apache-2.0 - Finetuned from model : Kukedlc/NeuralLLaMa-3-8b-DT-v0.1 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)
OneFly7/model-webnlg-facts-Q1-e10-ct	OneFly7	"2024-06-14T13:34:15Z"	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text2text-generation	"2024-06-14T13:33:38Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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(2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Nestorthera/Titanic	Nestorthera	"2024-06-14T13:33:58Z"	0	null	[ "region:us" ]	null	"2024-06-14T13:33:58Z"	Entry not found
HyperdustProtocol/ImHyperAGI-llama2-7b-1004	HyperdustProtocol	"2024-06-14T13:34:33Z"	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-2-7b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-06-14T13:34:12Z"	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-2-7b-bnb-4bit --- # Uploaded model - Developed by: HyperdustProtocol - License: apache-2.0 - Finetuned from model : unsloth/llama-2-7b-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)
yangyangyang666/sdmodel	yangyangyang666	"2024-06-30T07:34:10Z"	0	null	[ "region:us" ]	null	"2024-06-14T13:34:16Z"	Entry not found
OneFly7/model-webnlg-DQE-Q1-e10-ct	OneFly7	"2024-06-14T13:35:55Z"	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text2text-generation	"2024-06-14T13:35:26Z"	--- 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]
andrewoh/chembert_cased	andrewoh	"2024-06-14T13:35:52Z"	0	null	[ "region:us" ]	null	"2024-06-14T13:35:52Z"	Entry not found
Coolwowsocoolwow/SML_Anthony	Coolwowsocoolwow	"2024-06-14T13:38:20Z"	0	null	[ "license:openrail", "region:us" ]	null	"2024-06-14T13:36:14Z"	--- license: openrail ---
sagnikrayc/opt-350m-bn-adapter-squad-model2	sagnikrayc	"2024-06-14T13:38:15Z"	0	null	[ "region:us" ]	null	"2024-06-14T13:38:13Z"	Entry not found
sagnikrayc/opt-350m-bn-adapter-squad-model3	sagnikrayc	"2024-06-14T13:38:48Z"	0	null	[ "region:us" ]	null	"2024-06-14T13:38:46Z"	Entry not found
Kontek/Jxbsysnsnsh	Kontek	"2024-06-15T09:06:54Z"	0	null	[ "region:us" ]	null	"2024-06-14T13:44:07Z"	Entry not found
datvtn/RealVisXL_V4.0_TRT	datvtn	"2024-06-26T14:10:14Z"	0	null	[ "onnx", "license:apache-2.0", "region:us" ]	null	"2024-06-14T13:44:50Z"	--- license: apache-2.0 ---
Hiariel/Hiariel	Hiariel	"2024-06-14T13:45:26Z"	0	null	[ "region:us" ]	null	"2024-06-14T13:45:26Z"	Entry not found
cristianmiranda/bloom-7b1-english	cristianmiranda	"2024-06-14T13:46:45Z"	0	null	[ "license:mit", "region:us" ]	null	"2024-06-14T13:46:45Z"	--- license: mit ---
debenoist/mistral-7b-instruct-cube-merged_groussard-1	debenoist	"2024-06-14T13:47:29Z"	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-instruct-v0.3", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-06-14T13:47:19Z"	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/mistral-7b-instruct-v0.3 --- # Uploaded model - Developed by: debenoist - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-instruct-v0.3 This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
damgomz/ft_8_5e6_x12	damgomz	"2024-06-15T13:53:33Z"	0	transformers	[ "transformers", "safetensors", "albert", "text-classification", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2024-06-14T13:48:03Z"	--- language: en tags: - text-classification pipeline_tag: text-classification widget: - text: GEPS Techno is the pioneer of hybridization of renewable energies at sea. We imagine, design and commercialize innovative off-grid systems that aim to generate power at sea, stabilize and collect data. The success of our low power platforms WAVEPEAL enabled us to scale-up the device up to WAVEGEM, the 150-kW capacity platform. --- ## Environmental Impact (CODE CARBON DEFAULT) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| Duration (in seconds) \| 93178.99680066109 \| \| Emissions (Co2eq in kg) \| 0.0563840170531791 \| \| CPU power (W) \| 42.5 \| \| GPU power (W) \| [No GPU] \| \| RAM power (W) \| 3.75 \| \| CPU energy (kWh) \| 1.100027112062775 \| \| GPU energy (kWh) \| [No GPU] \| \| RAM energy (kWh) \| 0.0970603606698415 \| \| Consumed energy (kWh) \| 1.1970874727326195 \| \| Country name \| Switzerland \| \| Cloud provider \| nan \| \| Cloud region \| nan \| \| CPU count \| 2 \| \| CPU model \| Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz \| \| GPU count \| nan \| \| GPU model \| nan \| ## Environmental Impact (for one core) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| CPU energy (kWh) \| 0.1793695688412726 \| \| Emissions (Co2eq in kg) \| 0.03649510708025892 \| ## Note 14 juin 2024 ## My Config \| Config \| Value \| \|--------------------------\|-----------------\| \| checkpoint \| damgomz/fp_bs16_lr5_x12 \| \| model_name \| ft_8_5e6_x12 \| \| sequence_length \| 400 \| \| num_epoch \| 6 \| \| learning_rate \| 5e-06 \| \| batch_size \| 8 \| \| weight_decay \| 0.0 \| \| warm_up_prop \| 0.0 \| \| drop_out_prob \| 0.1 \| \| packing_length \| 100 \| \| train_test_split \| 0.2 \| \| num_steps \| 29328 \| ## Training and Testing steps Epoch \| Train Loss \| Test Loss \| F-beta Score ---\|---\|---\|--- \| 0 \| 0.000000 \| 0.698153 \| 0.330179 \| \| 1 \| 0.299857 \| 0.229570 \| 0.918500 \| \| 2 \| 0.182588 \| 0.207300 \| 0.925103 \| \| 3 \| 0.133614 \| 0.219128 \| 0.933382 \| \| 4 \| 0.087488 \| 0.263445 \| 0.912547 \| \| 5 \| 0.051254 \| 0.322440 \| 0.914601 \| \| 6 \| 0.030591 \| 0.359180 \| 0.898750 \|