librarian-bots/model_cards_with_metadata_test

modelId stringlengths 5 122	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]	likes int64 0 11k	library_name stringclasses 245 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 48 values	createdAt timestamp[us, tz=UTC]	card stringlengths 1 901k
zacharyxxxxcr/Meta-Llama-3-8B-Instruct-verilog	zacharyxxxxcr	2024-06-26T06:47:45Z	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-26T06:47:43Z	--- 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]
HyperdustProtocol/ImHyperAGI-cog-llama2-7b-5909	HyperdustProtocol	2024-06-26T06:50:00Z	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	2024-06-26T06:49:43Z	--- base_model: unsloth/llama-2-7b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # 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)
rushilJariwala/bert-base-uncased-ccdv-patent-classification	rushilJariwala	2024-06-26T06:51:55Z	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-26T06:51:55Z	--- license: apache-2.0 ---
Roshgupta/tiny-llama	Roshgupta	2024-06-26T06:53:36Z	0	null	[ "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "region:us" ]	null	2024-06-26T06:53:33Z	--- license: apache-2.0 base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 tags: - trl - sft - generated_from_trainer model-index: - name: tiny llama 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. --> # tiny llama This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 150 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.35.2 - Pytorch 2.3.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.2
gmanxin/sidrvc	gmanxin	2024-06-26T06:58:25Z	0	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2024-06-26T06:54:10Z	--- license: bigscience-openrail-m ---
yraziel/lior_narkis_v2_yr	yraziel	2024-06-26T06:57:29Z	0	null	[ "region:us" ]	null	2024-06-26T06:55:03Z	Entry not found
howarudo/paligemma-3b-pt-224-vqa-final	howarudo	2024-06-26T20:51:20Z	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-26T06:56:00Z	--- 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]
solojungle/browser-model	solojungle	2024-06-26T09:11:59Z	0	null	[ "region:us" ]	null	2024-06-26T06:56:34Z	Entry not found
Shivanikumar/pix2pix_model_h5format	Shivanikumar	2024-06-26T06:58:30Z	0	null	[ "region:us" ]	null	2024-06-26T06:57:20Z	Entry not found
Lxt115/fine_tunning_0626	Lxt115	2024-06-26T06:57:54Z	0	null	[ "region:us" ]	null	2024-06-26T06:57:54Z	Entry not found
Shivanikumar/pix2pix_model_tensorflow	Shivanikumar	2024-06-26T06:59:36Z	0	keras	[ "keras", "region:us" ]	null	2024-06-26T06:59:31Z	Entry not found
Joinus/test	Joinus	2024-06-26T07:01:32Z	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-26T07:01:32Z	--- license: apache-2.0 ---
slelab/AES13	slelab	2024-06-26T13:40:28Z	0	null	[ "region:us" ]	null	2024-06-26T07:01:56Z	Entry not found
jacklin1978/test	jacklin1978	2024-06-26T07:02:55Z	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-26T07:02:55Z	--- license: apache-2.0 ---
ShapeKapseln33/Veelobooster33	ShapeKapseln33	2024-06-26T07:08:02Z	0	null	[ "region:us" ]	null	2024-06-26T07:04:10Z	VeeloBooster België Recensies voor VeeloBooster Op zoek naar optimale prestaties en tevredenheid zijn mannen voortdurend op zoek naar manieren om hun vitaliteit en bekwaamheid te verbeteren. Onder de talloze beschikbare opties schittert één naam: VeeloBooster. VeeloBooster is ontworpen om uw zelfvertrouwen, uithoudingsvermogen en algeheel welzijn te vergroten en is de ultieme oplossing voor mannen die klaar zijn om hun volledige potentieel te ontsluiten. [Klik hier om nu te kopen via de officiële Veelobooster-website](https://capsules24x7.com/veelobooster-be) Dans un monde où la santé et la forme physique prennent de plus en plus Belangrijk is dat de markt voor fitnessapparatuur en accessoires snel groeit. Onder de vele producten die om aandacht strijden, is VeeloBooster naar voren gekomen als een veelbelovende kandidaat, die belooft een revolutie teweeg te brengen in de manier waarop we onze fitnessroutines benaderen. Maar maakt het de hype waar? Laten we onze VeeloBooster-recensie eens nader bekijken om de waarheid te achterhalen. ##Wat is VeeloBooster? VeeloBooster is een geavanceerd fitnessapparaat dat is ontworpen om uw trainingen te verbeteren door weerstand te bieden via een innovatief ontwerp. In tegenstelling tot traditionele weerstandsbanden of gewichten maakt VeeloBooster gebruik van pneumatische technologie om instelbare weerstand te creëren voor gebruikers van alle fitnessniveaus. Het compacte, draagbare ontwerp zorgt voor een naadloze integratie in een verscheidenheid aan trainingen, van krachttraining tot cardio. ##De belofte van VeeloBooster ##De aantrekkingskracht van de VeeloBooster ligt in zijn belofte: Effectiviteit: VeeloBooster claimt de effectiviteit van de training te maximaliseren door weerstand in elke beweging op te nemen, waardoor gebruikers betere resultaten kunnen behalen in minder tijd. Veelzijdigheid: Of u nu een beginner of een fitnessliefhebber bent, VeeloBooster past zich aan uw behoeften aan door instelbare weerstand te bieden voor een verscheidenheid aan trainingen en fitnessniveaus. Draagbaarheid: Voorbij zijn de dagen van omvangrijke fitnessapparatuur. Het compacte formaat en het lichtgewicht ontwerp van de VeeloBooster maken hem perfect voor thuistrainingen, buitentrainingen en zelfs trainingen onderweg. INNOVATIE: Baanbrekende pneumatische technologie onderscheidt VeeloBooster van traditionele weerstandsinstrumenten en belooft een soepelere, meer gecontroleerde weerstandservaring. ##VeeloBooster Review: wat onze gebruikers zeggen Laten we, om de effectiviteit van VeeloBooster te evalueren, naar echte gebruikerservaringen kijken. [Klik hier om nu te kopen via de officiële Veelobooster-website](https://capsules24x7.com/veelobooster-be) Fitnessliefhebber Anna prijst de VeeloBooster om zijn veelzijdigheid. “Ik vind het leuk dat de VeeloBooster kan worden gebruikt voor een verscheidenheid aan oefeningen, van squats tot biceps curls. “Het is alsof je je eigen sportschool in je zak hebt!» Als drukke professional waardeert James de draagbaarheid van de VeeloBooster. “Met drukke schema’s kan het moeilijk zijn om tijd te vinden om naar de sportschool te gaan. Met VeeloBooster kan ik snel en efficiënt trainen, waar ik ook ben. Sara, een fitness newbie, benadrukt het gebruiksvriendelijke ontwerp van VeeloBooster. “Als nieuwkomer op het gebied van sporten werd ik geïntimideerd door traditionele fitnessapparatuur. Dankzij de instelbare weerstand van de VeeloBooster kon ik langzaam beginnen en geleidelijk de intensiteit verhogen naarmate ik sterker werd. De meeste recensies van de VeeloBooster zijn positief, maar sommige gebruikers hebben problemen gemeld met duurzaamheid en weerstandsconsistentie. Fitnesscoach Michael vertelt: “De VeeloBooster is geschikt voor lichte tot matige inspanning, maar zwaardere gebruikers kunnen merken dat de weerstand na verloop van tijd zwakker wordt. Bovendien kunnen de weerstandsniveaus inconsistent lijken, waardoor het moeilijk wordt om de voortgang nauwkeurig te volgen. ##definitieve uitspraak VeeloBooster biedt een krachtige oplossing voor mensen die hun fitnessroutine willen verbeteren. Het innovatieve ontwerp, dat efficiëntie, veelzijdigheid en draagbaarheid garandeert, heeft door veel gebruikers lovende kritieken gekregen. Vanwege zorgen over duurzaamheid en weerstandsconsistentie is de VeeloBooster echter mogelijk niet geschikt voor trainingen met hoge intensiteit of geavanceerde fitnessdoelen. Uiteindelijk hangt de waarde van investeren in de VeeloBooster af van uw fitnessbehoeften en -verwachtingen. Als u op zoek bent naar een handig en aanpasbaar fitnesshulpmiddel voor lichte tot matige lichaamsbeweging, dan kan de VeeloBooster de perfecte metgezel zijn voor uw fitnessreis. Als je echter apparatuur nodig hebt die bestand is tegen een intensieve training, wil je misschien alternatieve opties verkennen. Concluderend heeft VeeloBooster het potentieel om uw conditie te verbeteren, maar zoals bij elk product is het belangrijk om de voor- en nadelen af te wegen voordat u een beslissing neemt. Met een scherp oog en realistische verwachtingen kan VeeloBooster de sleutel zijn om uw fitnesspotentieel te ontsluiten. [Klik hier om nu te kopen via de officiële Veelobooster-website](https://capsules24x7.com/veelobooster-be)
akshay107/NeBuLa	akshay107	2024-06-27T05:18:24Z	2	null	[ "safetensors", "minecraft", "action prediction", "other", "en", "arxiv:2406.18164", "region:us" ]	other	2024-06-26T07:09:20Z	--- pipeline_tag: other tags: - minecraft - action prediction language: - en --- # NeBuLa: A Minecraft Neural Builder NeBuLa is an LLM (Llama3-8B) finetuned on the minecraft action prediction task (https://aclanthology.org/2020.acl-main.232/). ## Model Details We provide two variants of the model: NeBuLa-org, which was trained on minecraft data set; and NeBuLa-synth, which was trained on level-1 and level-2 synthetic data along with the minecraft data set. Both the models achieve similar net-action F1 score on minecraft test set. ### Model Description - Language(s) (NLP): English - Finetuned from model: Llama3-8B ### Model Sources Paper: https://arxiv.org/abs/2406.18164
dodoritu/ddd	dodoritu	2024-06-26T07:27:56Z	0	null	[ "license:unknown", "region:us" ]	null	2024-06-26T07:11:07Z	--- license: unknown ---
Aswathinidhi/protein	Aswathinidhi	2024-06-26T07:11:38Z	0	null	[ "region:us" ]	null	2024-06-26T07:11:38Z	Entry not found
Coolwowsocoolwow/Timmy	Coolwowsocoolwow	2024-06-26T07:29:24Z	0	null	[ "license:openrail", "region:us" ]	null	2024-06-26T07:13:10Z	--- license: openrail ---
ScottZetta/new_model	ScottZetta	2024-06-26T07:14:48Z	0	null	[ "license:mit", "region:us" ]	null	2024-06-26T07:14:48Z	--- license: mit ---
sunriseXu/t5-sunrise	sunriseXu	2024-06-26T07:15:45Z	0	null	[ "region:us" ]	null	2024-06-26T07:15:45Z	Entry not found
HikariLight/Mistral-7B-v0.2_ACI_MTS_Backtranslation_SFT	HikariLight	2024-06-26T07:16:06Z	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-26T07:15:54Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
kaiimran/malaysian-llama2-7b-32k-instructions-lora-sentiment-analysis-v2	kaiimran	2024-06-26T07:17:24Z	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-26T07:16:07Z	--- 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. 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]
TopperThijs/Llama2-Picture-Des-Finetuned-6epochs15mlm	TopperThijs	2024-06-26T07:16:26Z	0	null	[ "region:us" ]	null	2024-06-26T07:16:26Z	Entry not found
anmolshrvstv/condrft	anmolshrvstv	2024-06-26T07:20:32Z	0	null	[ "license:llama2", "region:us" ]	null	2024-06-26T07:20:32Z	--- license: llama2 ---
geraldabrhm/llama-3-8b-regular-simplecontext-32lora-verylowlr	geraldabrhm	2024-06-26T08:23:05Z	0	null	[ "safetensors", "region:us" ]	null	2024-06-26T07:21:20Z	Entry not found
choah/llama3-ko-IronMan	choah	2024-06-26T07:21:31Z	0	null	[ "region:us" ]	null	2024-06-26T07:21:31Z	Entry not found
azureted/SEYGPT	azureted	2024-06-26T07:25:18Z	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-26T07:25:17Z	--- license: apache-2.0 ---
TioPanda/pandev-blocks-v2	TioPanda	2024-06-26T07:27:39Z	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	2024-06-26T07:27:19Z	--- base_model: unsloth/llama-3-8b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - Developed by: TioPanda - 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)
PradyumSomebody/finetunedLlamaTest5	PradyumSomebody	2024-06-26T07:29:07Z	0	peft	[ "peft", "region:us" ]	null	2024-06-26T07:29:04Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0 - PEFT 0.4.0
abhayesian/LLama3_HarmBench_LAT_4	abhayesian	2024-06-26T07:29:15Z	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-26T07:29:07Z	--- 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. 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]
muhammadmidhat81/database-admin	muhammadmidhat81	2024-06-26T07:31:33Z	0	null	[ "region:us" ]	null	2024-06-26T07:31:33Z	Entry not found
JhuTheBunny999/Cinderace	JhuTheBunny999	2024-06-26T08:00:03Z	0	null	[ "region:us" ]	null	2024-06-26T07:33:38Z	Entry not found
svercoutere/bpmn-compare-0.1.0	svercoutere	2024-06-26T09:38:56Z	0	null	[ "abb", "agentschap binnenlands bestuur", "lblod", "nl", "license:mit", "region:us" ]	null	2024-06-26T07:33:50Z	--- license: mit language: - nl tags: - abb - agentschap binnenlands bestuur - lblod --- # Custum ML model for BPMN-similarity using spektral and keras. ## Overview This project aims to create embeddings for BPMN files to facilitate tasks like search, similarity, and clustering. The embeddings capture both the semantics and structure of BPMN files, allowing for effective retrieval and comparison of similar BPMN diagrams. ## Important Note The current model uses embeddings created by the paraphrase-multilingual-MiniLM-L12-v2 with an embedding dimension of 384. Using a different sentence-transformer will result in unexpected behavior. Ensure to use the correct sentence-transformer with the kerasEmbedder and adjust the 'dims' parameter in mu-search accordingly. ## Motivation The goal is to: Capture the semantics of BPMN files, making similar BPMN files have similar embeddings. Capture the structure of BPMN files, making structurally similar BPMN files have similar embeddings. Enable measuring the similarity between two BPMN diagrams by estimating the number of changes needed to transform one into another (trained on Minimum Edit Distance). ## Design Choices: Preprocessing BPMN Files: Adjust the input size to fit the fixed input size of embedding models or handle large inputs by splitting them into smaller parts. Encoding Structure: Use graph embedding techniques (e.g., GNNs, GCNs) to encode the structure of BPMN diagrams. Graph Representation: Convert BPMN diagrams into graph representations using NetworkX. Node and Edge Information: Extract labels and documentation fields from nodes and edges, converting them into numerical vectors using pre-trained embeddings or custom-trained embeddings. ## Current Approach: Convert BPMN Files to Graphs: Use NetworkX to represent BPMN files as graphs. Node and Edge Embeddings: Use pre-trained embeddings to create vector representations of nodes and edges. Graph Embedding: Use these embeddings as features for a GNN or GCN model (e.g., Spektral) to create a single embedding for each BPMN file. ## Similarity Model Specifics: Input: two BPMN files: (Batchsize, nodes, node_features) and Adjacency matrix Similarity Calculation: Uses precomputed embeddings and cosine similarity to rank BPMN files based on query similarity. Efficiency: Designed to handle large volumes of BPMN files and queries efficiently. ## Shortcomings: Data Availability: Lack of sufficient BPMN files within ABB, we trained on the data of [camunda/bpmn-for-research](https://github.com/camunda/bpmn-for-research) by converting the BPMN files to Networkx graphs and measuring the Minimum Edit Distance between random pairs. Minimum Edit Distance: Does this translate to perceived similarity? It might require a different approach. Training data in the form of (BPMN, BPMN, similarity) is needed. If user interactions can be captured, this might be a better approach for gauging perceived similarity or general 'useful' similarity. ## Future Steps: Gather more BPMN files from the correct domain (OPH). Train custom text embeddings for nodes and edges (e.g., using [robbert-2023-dutch-base-abb](https://huggingface.co/svercoutere/robbert-2023-dutch-base-abb)). Validate and refine the current model with new data. Potentially merge the graph and text models into a unified architecture. ### Suggestions: Data Collection: Store search results and user interactions anonymously to gather diverse query data. User Interaction Analysis: Use interaction data to train models for better search result ranking. ### Requirements for future steps: A large and varied dataset of BPMN files to ensure the model generalizes well. Real user interactions (recommendation interface based on BPMN-BPMN: user uploads file -> what suggestion did he interact with) to validate and improve the model's effectiveness.
TifTifR/orpo-future1000-Llama-3-8B-Instruct	TifTifR	2024-06-26T19:03:25Z	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-06-26T07:34:38Z	--- base_model: unsloth/llama-3-8b-instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - Developed by: TifTifR - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
PradyumSomebody/finetunedLlamaTest6	PradyumSomebody	2024-06-26T07:36:01Z	0	peft	[ "peft", "region:us" ]	null	2024-06-26T07:35:56Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0
julientfai/qwen2-0.5B-pii-masking-q4f16_1-Opilot	julientfai	2024-06-26T08:18:57Z	0	null	[ "region:us" ]	null	2024-06-26T07:36:26Z	Entry not found
cherifkhalifah/my_awesome_model	cherifkhalifah	2024-06-26T07:37:35Z	0	null	[ "region:us" ]	null	2024-06-26T07:37:35Z	Entry not found
rajtest/tiny_llama_v3	rajtest	2024-06-26T07:38:09Z	0	null	[ "license:unknown", "region:us" ]	null	2024-06-26T07:38:09Z	--- license: unknown ---
wenkai26/pony_lora	wenkai26	2024-06-26T07:40:48Z	0	null	[ "region:us" ]	null	2024-06-26T07:39:24Z	Entry not found
arham6/deepseek7b_finetuned	arham6	2024-06-26T07:39:56Z	0	null	[ "region:us" ]	null	2024-06-26T07:39:56Z	Entry not found
lalok/ililgu_medium_model_wer	lalok	2024-06-26T07:42:44Z	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-26T07:42:35Z	--- 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]
gnsepili/shakespeare-rnn	gnsepili	2024-06-26T09:04:36Z	0	null	[ "rnn", "shakespeare", "text-generation", "en", "dataset:Trelis/tiny-shakespeare", "license:mit", "region:us" ]	text-generation	2024-06-26T07:42:58Z	--- license: mit datasets: - Trelis/tiny-shakespeare language: - en metrics: - accuracy pipeline_tag: text-generation tags: - rnn - shakespeare --- # Shakespeare RNN This project implements a character-level Recurrent Neural Network (RNN) trained on Shakespeare's works. The model can generate Shakespeare-like text based on a given prompt. ## Table of Contents - [Shakespeare RNN](#shakespeare-rnn) - [Table of Contents](#table-of-contents) - [Project Overview](#project-overview) - [Installation](#installation) - [Project Structure](#project-structure) - [Usage](#usage) - [Training](#training) - [Inference](#inference) - [Model Architecture](#model-architecture) - [Dataset](#dataset) - [Configuration](#configuration) - [Results](#results) - [Contributing](#contributing) - [License](#license) ## Project Overview This project uses a Long Short-Term Memory (LSTM) network to generate text in the style of Shakespeare. The model is trained on a dataset of Shakespeare's works and can generate new text based on a given prompt. Key features: - Character-level text generation - LSTM-based RNN architecture - Customizable hyperparameters - Training with Weights & Biases logging - Interactive inference script ## Installation 1. Clone the repository: ``` git clone https://github.com/your-username/shakespeare-rnn.git cd shakespeare-rnn ``` 2. Create a virtual environment: ``` python -m venv venv source venv/bin/activate # On Windows, use `venv\Scripts\activate` ``` 3. Install the required packages: ``` pip install -r requirements.txt ``` ## Project Structure ``` shakespeare-rnn/ │ ├── data/ │ ├── __init__.py │ └── dataset.py │ ├── model/ │ ├── __init__.py │ └── rnn.py │ ├── utils/ │ ├── __init__.py │ └── tokenizer.py │ ├── config.py ├── train.py ├── inference.py ├── requirements.txt └── README.md ``` ## Usage ### Training To train the model, run: ``` python train.py ``` This will start the training process and log the results to Weights & Biases. You can monitor the training progress in real-time through the W&B dashboard. ### Inference To generate text using the trained model, run: ``` python inference.py ``` This will load the trained model and allow you to enter prompts for text generation. The script will also generate text for a few predefined prompts. ## Model Architecture The model uses a character-level LSTM network with the following architecture: - Embedding layer - LSTM layer(s) - Fully connected output layer The exact architecture (number of layers, hidden dimensions, etc.) can be configured in the `config.py` file. ## Dataset The model is trained on the Tiny Shakespeare dataset, which is a collection of Shakespeare's works. The dataset is automatically downloaded using the Hugging Face `datasets` library. ## Configuration You can modify the model's hyperparameters and training settings in the `config.py` file. Key configurations include: - Batch size - Sequence length - Embedding dimension - Hidden dimension - Number of LSTM layers - Learning rate - Number of training epochs ## Results After training, you can find the training logs and performance metrics on the Weights & Biases dashboard. The trained model will be saved as `shakespeare_model.pth`, and the tokenizer will be saved as `tokenizer.pkl`. Example generated text: [Include some example outputs from your trained model here] ## Contributing Contributions to this project are welcome! Please follow these steps: 1. Fork the repository 2. Create a new branch (`git checkout -b feature/your-feature-name`) 3. Make your changes 4. Commit your changes (`git commit -am 'Add some feature'`) 5. Push to the branch (`git push origin feature/your-feature-name`) 6. Create a new Pull Request ## License This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. ``` This README provides a comprehensive overview of your project, including installation instructions, usage guidelines, project structure, and other relevant information. You may want to customize some parts, such as the repository URL, example outputs, and any specific instructions or results from your implementation.
jkushwaha/paddle_GPU_test	jkushwaha	2024-06-28T04:53:51Z	0	null	[ "region:us" ]	null	2024-06-26T07:43:40Z	Entry not found
Dazzak/phi3_intruct_aci_3e	Dazzak	2024-06-26T07:53:37Z	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-26T07:48:38Z	--- 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. 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]
dddd322/lora03	dddd322	2024-06-26T07:49:51Z	0	null	[ "region:us" ]	null	2024-06-26T07:49:51Z	Entry not found
hossay/textual_inversion_cat	hossay	2024-06-26T07:52:49Z	0	null	[ "region:us" ]	null	2024-06-26T07:52:49Z	Entry not found
v0dkapapi/shuffled	v0dkapapi	2024-06-26T08:20:47Z	0	null	[ "generated_from_trainer", "base_model:ybelkada/falcon-7b-sharded-bf16", "region:us" ]	null	2024-06-26T07:53:40Z	--- base_model: ybelkada/falcon-7b-sharded-bf16 tags: - generated_from_trainer model-index: - name: shuffled 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. --> # shuffled This model is a fine-tuned version of [ybelkada/falcon-7b-sharded-bf16](https://huggingface.co/ybelkada/falcon-7b-sharded-bf16) 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: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - training_steps: 100 ### Training results ### Framework versions - Transformers 4.32.0 - Pytorch 2.3.0+cu121 - Datasets 2.13.1 - Tokenizers 0.13.3
Perriewang/aaa	Perriewang	2024-06-26T08:01:03Z	0	null	[ "region:us" ]	null	2024-06-26T08:01:03Z	Entry not found
stojchet/b255231ae44a52f878c6b2cf4a0fe1fd	stojchet	2024-06-26T08:01:29Z	0	null	[ "region:us" ]	null	2024-06-26T08:01:29Z	Entry not found
jayoohwang/alphamath_round1	jayoohwang	2024-06-27T11:56:32Z	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-06-26T08:06:53Z	--- 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]
IreNkweke/code-search-net-tokenizer	IreNkweke	2024-06-26T08:09:16Z	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-26T08:09:15Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
RobertML/sn3-hawk-tuah	RobertML	2024-06-26T08:14:24Z	0	null	[ "region:us" ]	null	2024-06-26T08:14:14Z	Entry not found
makhataei/wav2vec2-xls-r-300m-emotion-ru	makhataei	2024-06-26T08:19:08Z	0	null	[ "region:us" ]	null	2024-06-26T08:19:08Z	Entry not found
iitrsamrat/Phi-3-mini-4k-instruct	iitrsamrat	2024-06-26T08:22:24Z	0	null	[ "region:us" ]	null	2024-06-26T08:22:24Z	Entry not found
Woooooong/output	Woooooong	2024-06-26T08:24:24Z	0	null	[ "region:us" ]	null	2024-06-26T08:24:24Z	Entry not found
KaonashiYume/Kolya	KaonashiYume	2024-06-26T08:30:16Z	0	null	[ "license:afl-3.0", "region:us" ]	null	2024-06-26T08:27:48Z	--- license: afl-3.0 ---
nieshen/gpt2_ce	nieshen	2024-06-26T08:44:06Z	0	null	[ "region:us" ]	null	2024-06-26T08:30:05Z	Entry not found
acl-srw-2024/llama-3-typhoon-v1.5-8b-instruct-unsloth-sft	acl-srw-2024	2024-06-26T08:31:31Z	0	null	[ "safetensors", "region:us" ]	null	2024-06-26T08:30:46Z	Entry not found
mouhebMehdoui/mitstralFinetuned	mouhebMehdoui	2024-06-26T08:30:56Z	0	null	[ "region:us" ]	null	2024-06-26T08:30:56Z	Entry not found
lalok/oneonenine_medium_model	lalok	2024-06-26T08:31:52Z	0	null	[ "region:us" ]	null	2024-06-26T08:31:52Z	Entry not found
weiquan/MU	weiquan	2024-06-27T02:34:49Z	0	null	[ "region:us" ]	null	2024-06-26T08:32:13Z	Entry not found
lizihua/q-FrozenLake-v1-4x4-noSlippery	lizihua	2024-06-26T08:32:35Z	0	null	[ "region:us" ]	null	2024-06-26T08:32:35Z	Entry not found
BossBattlar/G97	BossBattlar	2024-06-26T08:35:34Z	0	null	[ "license:mit", "region:us" ]	null	2024-06-26T08:35:34Z	--- license: mit ---
multimodalart/fofr-sdxl-emoji-older-diffusers	multimodalart	2024-06-26T11:07:19Z	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "license:creativeml-openrail-m", "endpoints_compatible", "region:us" ]	text-to-image	2024-06-26T08:36:06Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion - lora - diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 --- ## Inference Endpoints ready version of https://huggingface.co/fofr/sdxl-emoji ### sdxl-emoji LoRA by [fofr](https://replicate.com/fofr) #### An SDXL fine-tune based on Apple Emojis ![lora_image](https://replicate.delivery/pbxt/a3z81v5vwlKfLq1H5uBqpVmkHalOVup0jSLma9E2UaF3tawIA/out-0.png) >
michellehu/ok	michellehu	2024-06-26T08:37:08Z	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-26T08:37:08Z	--- license: apache-2.0 ---
ClinBAY/hkunlp-instructor-base	ClinBAY	2024-06-26T08:37:29Z	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-26T08:37:28Z	--- license: apache-2.0 ---
Pandita-IA/rl_course_vizdoom_health_gathering_supreme	Pandita-IA	2024-06-26T09:54:13Z	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-06-26T08:38:32Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 12.17 +/- 6.74 name: mean_reward verified: false --- A(n) APPO model trained on the doom_health_gathering_supreme environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r Pandita-IA/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
HyperdustProtocol/HyperAuto-cog-llama2-7b-3778	HyperdustProtocol	2024-06-26T08:40:12Z	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	2024-06-26T08:39:58Z	--- base_model: unsloth/llama-2-7b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # 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)
DDoesGames/B.Awesome	DDoesGames	2024-06-26T10:38:56Z	0	null	[ "license:cc", "region:us" ]	null	2024-06-26T08:40:18Z	--- license: cc ---
PradyumSomebody/finetunedLlamaTest7	PradyumSomebody	2024-06-26T08:40:25Z	0	peft	[ "peft", "region:us" ]	null	2024-06-26T08:40:19Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0 - PEFT 0.4.0
mona112/trained-sd3	mona112	2024-06-26T08:46:44Z	0	null	[ "region:us" ]	null	2024-06-26T08:46:44Z	Entry not found
sataayu/molt5-augmented-default-45-large-smiles2caption	sataayu	2024-06-26T08:48:17Z	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text2text-generation	2024-06-26T08:46:54Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
fsw/llm_rosebleu_lora	fsw	2024-06-26T08:54:34Z	0	null	[ "region:us" ]	null	2024-06-26T08:52:41Z	# これはなに？ `stabilityai/japanese-stablelm-base-alpha-7b` を `rosebleu` データセットで学習した Lora ## 使い方 .\text-generation-webui\loras\Rosebleu となるように配置。 `text-generation-webui` の `model` の LoRAから読み込める。 ## データセット https://gitlab.com/open_contents_datasets/Rosebleu Hシーンも含まれているみたいですが、特に区別せずに突っ込んでいます。 ## 前処理とデータロード中にあるすべてのtsvを全部繋て、csvにしただけ。学習プロンプトはこの用にしているので、このフォーマットだと良くなったりすることがあるかもしれないし、ないかもしれない。コンテキスト長は2048にしているので、1行が2048トークン以上は切り捨てられているはず。 ``` result = f'### name:\n{data_point["name"]}\n\n### text:\n{data_point["text"]}' ```
HeroadZ/q-FrozenLake-v1-4x4-noSlippery	HeroadZ	2024-06-26T08:53:21Z	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-06-26T08:53:17Z	--- 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="HeroadZ/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"]) ```
Dazzak/gemma-7b_aci_3e	Dazzak	2024-06-26T08:53:37Z	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-06-26T08:53:26Z	--- base_model: unsloth/llama-3-8b-Instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - Developed by: Dazzak - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
iremmd/thy_model_21	iremmd	2024-06-26T08:54:11Z	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	2024-06-26T08:53:46Z	--- base_model: unsloth/llama-3-8b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - Developed by: iremmd - 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)
HeroadZ/q-Taxi-v3	HeroadZ	2024-06-26T08:55:10Z	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-06-26T08:55:07Z	--- 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="HeroadZ/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"]) ```
apersonnaz/crystalDetect_bin_uv_512_20240626-105516	apersonnaz	2024-06-26T10:37:33Z	0	null	[ "safetensors", "region:us" ]	null	2024-06-26T08:55:18Z	Entry not found
apersonnaz/crystalDetect_bin_vis_512_20240626-105516	apersonnaz	2024-06-26T11:07:57Z	0	null	[ "safetensors", "region:us" ]	null	2024-06-26T08:55:18Z	Entry not found
darynka-xo/SupremeCourt	darynka-xo	2024-06-26T09:02:22Z	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-26T09:02:01Z	--- 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]
iremmd/thy_model_22	iremmd	2024-06-26T09:03:53Z	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	2024-06-26T09:03:20Z	--- base_model: unsloth/llama-3-8b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - Developed by: iremmd - 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)
Andres29/Mymodel	Andres29	2024-06-26T09:05:11Z	0	null	[ "region:us" ]	null	2024-06-26T09:05:11Z	Entry not found
iremmd/thy_model_23	iremmd	2024-06-26T09:06:14Z	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	2024-06-26T09:05:49Z	--- base_model: unsloth/llama-3-8b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - Developed by: iremmd - 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)
Floriankidev/deit-base-distilled-patch16-224-finetuned-eurosat	Floriankidev	2024-06-26T09:06:32Z	0	null	[ "region:us" ]	null	2024-06-26T09:06:32Z	Entry not found
xiaoxuanzi/CT-GAT	xiaoxuanzi	2024-06-26T09:07:05Z	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-26T09:07:04Z	--- license: apache-2.0 ---
3dalgo/tmp	3dalgo	2024-06-26T09:08:15Z	0	null	[ "region:us" ]	null	2024-06-26T09:08:14Z	Entry not found
Bajiyo/5gram-correct-arpa	Bajiyo	2024-06-26T09:08:41Z	0	null	[ "region:us" ]	null	2024-06-26T09:08:41Z	Entry not found
adham674y/r	adham674y	2024-06-26T09:09:52Z	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-26T09:09:52Z	--- license: apache-2.0 ---
rnu/medical_llama3_finetune_1	rnu	2024-06-26T10:15:19Z	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-26T09:11:02Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Floriankidev/deit-base-patch16-224-finetuned-eurosat	Floriankidev	2024-06-26T09:11:14Z	0	null	[ "region:us" ]	null	2024-06-26T09:11:14Z	Entry not found
killiankopp/datascientest-oct23ml	killiankopp	2024-06-26T09:22:36Z	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-26T09:11:42Z	--- license: apache-2.0 ---
Kqte/LLaMIPa	Kqte	2024-06-26T09:21:58Z	0	null	[ "safetensors", "region:us" ]	null	2024-06-26T09:17:11Z	Entry not found
mohityadav/mental-health-advisor-gpt	mohityadav	2024-06-26T09:21:38Z	0	null	[ "safetensors", "license:apache-2.0", "region:us" ]	null	2024-06-26T09:18:07Z	--- license: apache-2.0 ---
askari1122/llama3	askari1122	2024-06-26T09:18:35Z	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-26T09:18:35Z	--- license: apache-2.0 ---
PradyumSomebody/finetunedLlamaTest8	PradyumSomebody	2024-06-26T09:21:15Z	0	peft	[ "peft", "region:us" ]	null	2024-06-26T09:21:09Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0 - PEFT 0.4.0
szcjerry/smat-vit-sup21k-small	szcjerry	2024-07-02T09:22:13Z	0	null	[ "license:cc-by-4.0", "region:us" ]	null	2024-06-26T09:21:37Z	--- license: cc-by-4.0 --- This repo contains the SMAT meta-tuned vit-sup21k-small model checkpoint for PyTorch. ### How to use With our implementation here on [github](https://github.com/szc12153/sparse_meta_tuning), you can load the pre-trained weights by calling ``` model.load_state_dict(torch.load(/path/to/checkpoint.pt)) ``` For inference with ProtoNet on a few-shot learning task: ``` # outputs is a dictionary outputs = model(x_s=x_s, # support inputs y_s=y_s, # support labels x_q=x_q, # query inputs y_q=None, # predict for query labels finetune_model=None # None for direct inference with a ProtoNet classifier ) y_q_pred = outputs['y_q_pred'] ``` For inference with task-specific full fine-tuning then inference: ``` # outputs is a dictionary model.args.meta_learner.inner_lr.lr = lr # set the learning rate for fine-tuning model.args.meta_learner.num_finetune_steps = num_finetune_steps # set the number of fine-tuning steps outputs = model(x_s=x_s, # support inputs y_s=y_s, # support labels x_q=x_q, # query inputs y_q=None, # predict for query labels finetune_model="full" # {'full','lora'} ) y_q_pred = outputs['y_q_pred'] ``` You can visit our [github](https://github.com/szc12153/sparse_meta_tuning) repo for more details on training and inference!
szcjerry/smat-vit-sup21k-base	szcjerry	2024-07-02T09:11:02Z	0	null	[ "license:cc-by-4.0", "region:us" ]	null	2024-06-26T09:22:00Z	--- license: cc-by-4.0 --- This repo contains the SMAT meta-tuned vit-sup21k-base model checkpoint for PyTorch. ### How to use With our implementation here on [github](https://github.com/szc12153/sparse_meta_tuning), you can load the pre-trained weights by calling ``` model.load_state_dict(torch.load(/path/to/checkpoint.pt)) ``` For inference with ProtoNet on a few-shot learning task: ``` # outputs is a dictionary outputs = model(x_s=x_s, # support inputs y_s=y_s, # support labels x_q=x_q, # query inputs y_q=None, # predict for query labels finetune_model=None # None for direct inference with a ProtoNet classifier ) y_q_pred = outputs['y_q_pred'] ``` For inference with task-specific full fine-tuning then inference: ``` # outputs is a dictionary model.args.meta_learner.inner_lr.lr = lr # set the learning rate for fine-tuning model.args.meta_learner.num_finetune_steps = num_finetune_steps # set the number of fine-tuning steps outputs = model(x_s=x_s, # support inputs y_s=y_s, # support labels x_q=x_q, # query inputs y_q=None, # predict for query labels finetune_model="full" # {'full','lora'} ) y_q_pred = outputs['y_q_pred'] ``` You can visit our [github](https://github.com/szc12153/sparse_meta_tuning) repo for more details on training and inference!
nagrajn/gpt2-imdb-PPO_KD	nagrajn	2024-06-26T09:30:02Z	0	null	[ "region:us" ]	null	2024-06-26T09:30:02Z	Entry not found
RimZrelli/CTL_Mistral_Instruct_12Fold_Model	RimZrelli	2024-06-26T09:30:36Z	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-06-26T09:30:28Z	--- base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl --- # Uploaded model - Developed by: RimZrelli - 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)

zacharyxxxxcr/Meta-Llama-3-8B-Instruct-verilog

zacharyxxxxcr

2024-06-26T06:47:45Z

0

transformers

[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-06-26T06:47:43Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

HyperdustProtocol/ImHyperAGI-cog-llama2-7b-5909

HyperdustProtocol

2024-06-26T06:50:00Z

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

2024-06-26T06:49:43Z

--- base_model: unsloth/llama-2-7b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # 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)

rushilJariwala/bert-base-uncased-ccdv-patent-classification

rushilJariwala

2024-06-26T06:51:55Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2024-06-26T06:51:55Z

--- license: apache-2.0 ---

Roshgupta/tiny-llama

Roshgupta

2024-06-26T06:53:36Z

0

null

[ "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "region:us" ]

null

2024-06-26T06:53:33Z

--- license: apache-2.0 base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 tags: - trl - sft - generated_from_trainer model-index: - name: tiny llama results: [] ---  # tiny llama This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 150 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.35.2 - Pytorch 2.3.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.2

gmanxin/sidrvc

gmanxin

2024-06-26T06:58:25Z

0

null

[ "license:bigscience-openrail-m", "region:us" ]

null

2024-06-26T06:54:10Z

--- license: bigscience-openrail-m ---

yraziel/lior_narkis_v2_yr

yraziel

2024-06-26T06:57:29Z

0

null

[ "region:us" ]

null

2024-06-26T06:55:03Z

Entry not found

howarudo/paligemma-3b-pt-224-vqa-final

howarudo

2024-06-26T20:51:20Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-06-26T06:56:00Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

solojungle/browser-model

solojungle

2024-06-26T09:11:59Z

0

null

[ "region:us" ]

null

2024-06-26T06:56:34Z

Entry not found

Shivanikumar/pix2pix_model_h5format

Shivanikumar

2024-06-26T06:58:30Z

0

null

[ "region:us" ]

null

2024-06-26T06:57:20Z

Entry not found

Lxt115/fine_tunning_0626

Lxt115

2024-06-26T06:57:54Z

0

null

[ "region:us" ]

null

2024-06-26T06:57:54Z

Entry not found

Shivanikumar/pix2pix_model_tensorflow

Shivanikumar

2024-06-26T06:59:36Z

0

keras

[ "keras", "region:us" ]

null

2024-06-26T06:59:31Z

Entry not found

Joinus/test

Joinus

2024-06-26T07:01:32Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2024-06-26T07:01:32Z

--- license: apache-2.0 ---

slelab/AES13

slelab

2024-06-26T13:40:28Z

0

null

[ "region:us" ]

null

2024-06-26T07:01:56Z

Entry not found

jacklin1978/test

jacklin1978

2024-06-26T07:02:55Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2024-06-26T07:02:55Z

--- license: apache-2.0 ---

ShapeKapseln33/Veelobooster33

ShapeKapseln33

2024-06-26T07:08:02Z

0

null

[ "region:us" ]

null

2024-06-26T07:04:10Z

VeeloBooster België Recensies voor VeeloBooster Op zoek naar optimale prestaties en tevredenheid zijn mannen voortdurend op zoek naar manieren om hun vitaliteit en bekwaamheid te verbeteren. Onder de talloze beschikbare opties schittert één naam: VeeloBooster. VeeloBooster is ontworpen om uw zelfvertrouwen, uithoudingsvermogen en algeheel welzijn te vergroten en is de ultieme oplossing voor mannen die klaar zijn om hun volledige potentieel te ontsluiten. **[Klik hier om nu te kopen via de officiële Veelobooster-website](https://capsules24x7.com/veelobooster-be)** Dans un monde où la santé et la forme physique prennent de plus en plus Belangrijk is dat de markt voor fitnessapparatuur en accessoires snel groeit. Onder de vele producten die om aandacht strijden, is VeeloBooster naar voren gekomen als een veelbelovende kandidaat, die belooft een revolutie teweeg te brengen in de manier waarop we onze fitnessroutines benaderen. Maar maakt het de hype waar? Laten we onze VeeloBooster-recensie eens nader bekijken om de waarheid te achterhalen. ##Wat is VeeloBooster? VeeloBooster is een geavanceerd fitnessapparaat dat is ontworpen om uw trainingen te verbeteren door weerstand te bieden via een innovatief ontwerp. In tegenstelling tot traditionele weerstandsbanden of gewichten maakt VeeloBooster gebruik van pneumatische technologie om instelbare weerstand te creëren voor gebruikers van alle fitnessniveaus. Het compacte, draagbare ontwerp zorgt voor een naadloze integratie in een verscheidenheid aan trainingen, van krachttraining tot cardio. ##De belofte van VeeloBooster ##De aantrekkingskracht van de VeeloBooster ligt in zijn belofte: Effectiviteit: VeeloBooster claimt de effectiviteit van de training te maximaliseren door weerstand in elke beweging op te nemen, waardoor gebruikers betere resultaten kunnen behalen in minder tijd. Veelzijdigheid: Of u nu een beginner of een fitnessliefhebber bent, VeeloBooster past zich aan uw behoeften aan door instelbare weerstand te bieden voor een verscheidenheid aan trainingen en fitnessniveaus. Draagbaarheid: Voorbij zijn de dagen van omvangrijke fitnessapparatuur. Het compacte formaat en het lichtgewicht ontwerp van de VeeloBooster maken hem perfect voor thuistrainingen, buitentrainingen en zelfs trainingen onderweg. INNOVATIE: Baanbrekende pneumatische technologie onderscheidt VeeloBooster van traditionele weerstandsinstrumenten en belooft een soepelere, meer gecontroleerde weerstandservaring. ##VeeloBooster Review: wat onze gebruikers zeggen Laten we, om de effectiviteit van VeeloBooster te evalueren, naar echte gebruikerservaringen kijken. **[Klik hier om nu te kopen via de officiële Veelobooster-website](https://capsules24x7.com/veelobooster-be)** Fitnessliefhebber Anna prijst de VeeloBooster om zijn veelzijdigheid. “Ik vind het leuk dat de VeeloBooster kan worden gebruikt voor een verscheidenheid aan oefeningen, van squats tot biceps curls. “Het is alsof je je eigen sportschool in je zak hebt!» Als drukke professional waardeert James de draagbaarheid van de VeeloBooster. “Met drukke schema’s kan het moeilijk zijn om tijd te vinden om naar de sportschool te gaan. Met VeeloBooster kan ik snel en efficiënt trainen, waar ik ook ben. Sara, een fitness newbie, benadrukt het gebruiksvriendelijke ontwerp van VeeloBooster. “Als nieuwkomer op het gebied van sporten werd ik geïntimideerd door traditionele fitnessapparatuur. Dankzij de instelbare weerstand van de VeeloBooster kon ik langzaam beginnen en geleidelijk de intensiteit verhogen naarmate ik sterker werd. De meeste recensies van de VeeloBooster zijn positief, maar sommige gebruikers hebben problemen gemeld met duurzaamheid en weerstandsconsistentie. Fitnesscoach Michael vertelt: “De VeeloBooster is geschikt voor lichte tot matige inspanning, maar zwaardere gebruikers kunnen merken dat de weerstand na verloop van tijd zwakker wordt. Bovendien kunnen de weerstandsniveaus inconsistent lijken, waardoor het moeilijk wordt om de voortgang nauwkeurig te volgen. ##definitieve uitspraak VeeloBooster biedt een krachtige oplossing voor mensen die hun fitnessroutine willen verbeteren. Het innovatieve ontwerp, dat efficiëntie, veelzijdigheid en draagbaarheid garandeert, heeft door veel gebruikers lovende kritieken gekregen. Vanwege zorgen over duurzaamheid en weerstandsconsistentie is de VeeloBooster echter mogelijk niet geschikt voor trainingen met hoge intensiteit of geavanceerde fitnessdoelen. Uiteindelijk hangt de waarde van investeren in de VeeloBooster af van uw fitnessbehoeften en -verwachtingen. Als u op zoek bent naar een handig en aanpasbaar fitnesshulpmiddel voor lichte tot matige lichaamsbeweging, dan kan de VeeloBooster de perfecte metgezel zijn voor uw fitnessreis. Als je echter apparatuur nodig hebt die bestand is tegen een intensieve training, wil je misschien alternatieve opties verkennen. Concluderend heeft VeeloBooster het potentieel om uw conditie te verbeteren, maar zoals bij elk product is het belangrijk om de voor- en nadelen af te wegen voordat u een beslissing neemt. Met een scherp oog en realistische verwachtingen kan VeeloBooster de sleutel zijn om uw fitnesspotentieel te ontsluiten. **[Klik hier om nu te kopen via de officiële Veelobooster-website](https://capsules24x7.com/veelobooster-be)**

akshay107/NeBuLa

akshay107

2024-06-27T05:18:24Z

0

2

null

[ "safetensors", "minecraft", "action prediction", "other", "en", "arxiv:2406.18164", "region:us" ]

other

2024-06-26T07:09:20Z

--- pipeline_tag: other tags: - minecraft - action prediction language: - en --- # NeBuLa: A Minecraft Neural Builder NeBuLa is an LLM (Llama3-8B) finetuned on the minecraft action prediction task (https://aclanthology.org/2020.acl-main.232/). ## Model Details We provide two variants of the model: NeBuLa-org, which was trained on minecraft data set; and NeBuLa-synth, which was trained on level-1 and level-2 synthetic data along with the minecraft data set. Both the models achieve similar net-action F1 score on minecraft test set. ### Model Description - **Language(s) (NLP):** English - **Finetuned from model:** Llama3-8B ### Model Sources **Paper:** https://arxiv.org/abs/2406.18164

dodoritu/ddd

dodoritu

2024-06-26T07:27:56Z

0

null

[ "license:unknown", "region:us" ]

null

2024-06-26T07:11:07Z

--- license: unknown ---

Aswathinidhi/protein

Aswathinidhi

2024-06-26T07:11:38Z

0

null

[ "region:us" ]

null

2024-06-26T07:11:38Z

Entry not found

Coolwowsocoolwow/Timmy

Coolwowsocoolwow

2024-06-26T07:29:24Z

0

null

[ "license:openrail", "region:us" ]

null

2024-06-26T07:13:10Z

--- license: openrail ---

ScottZetta/new_model

ScottZetta

2024-06-26T07:14:48Z

0

null

[ "license:mit", "region:us" ]

null

2024-06-26T07:14:48Z

--- license: mit ---

sunriseXu/t5-sunrise

sunriseXu

2024-06-26T07:15:45Z

0

null

[ "region:us" ]

null

2024-06-26T07:15:45Z

Entry not found

HikariLight/Mistral-7B-v0.2_ACI_MTS_Backtranslation_SFT

HikariLight

2024-06-26T07:16:06Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-06-26T07:15:54Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

kaiimran/malaysian-llama2-7b-32k-instructions-lora-sentiment-analysis-v2

kaiimran

2024-06-26T07:17:24Z

0

transformers

[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-06-26T07:16:07Z

--- library_name: transformers tags: - unsloth --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

TopperThijs/Llama2-Picture-Des-Finetuned-6epochs15mlm

TopperThijs

2024-06-26T07:16:26Z

0

null

[ "region:us" ]

null

2024-06-26T07:16:26Z

Entry not found

anmolshrvstv/condrft

anmolshrvstv

2024-06-26T07:20:32Z

0

null

[ "license:llama2", "region:us" ]

null

2024-06-26T07:20:32Z

--- license: llama2 ---

geraldabrhm/llama-3-8b-regular-simplecontext-32lora-verylowlr

geraldabrhm

2024-06-26T08:23:05Z

0

null

[ "safetensors", "region:us" ]

null

2024-06-26T07:21:20Z

Entry not found

choah/llama3-ko-IronMan

choah

2024-06-26T07:21:31Z

0

null

[ "region:us" ]

null

2024-06-26T07:21:31Z

Entry not found

azureted/SEYGPT

azureted

2024-06-26T07:25:18Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2024-06-26T07:25:17Z

--- license: apache-2.0 ---

TioPanda/pandev-blocks-v2

TioPanda

2024-06-26T07:27:39Z

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

2024-06-26T07:27:19Z

--- base_model: unsloth/llama-3-8b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** TioPanda - **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)

PradyumSomebody/finetunedLlamaTest5

PradyumSomebody

2024-06-26T07:29:07Z

0

peft

[ "peft", "region:us" ]

null

2024-06-26T07:29:04Z

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

abhayesian/LLama3_HarmBench_LAT_4

abhayesian

2024-06-26T07:29:15Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-06-26T07:29:07Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

muhammadmidhat81/database-admin

muhammadmidhat81

2024-06-26T07:31:33Z

0

null

[ "region:us" ]

null

2024-06-26T07:31:33Z

Entry not found

JhuTheBunny999/Cinderace

JhuTheBunny999

2024-06-26T08:00:03Z

0

null

[ "region:us" ]

null

2024-06-26T07:33:38Z

Entry not found

svercoutere/bpmn-compare-0.1.0

svercoutere

2024-06-26T09:38:56Z

0

null

[ "abb", "agentschap binnenlands bestuur", "lblod", "nl", "license:mit", "region:us" ]

null

2024-06-26T07:33:50Z

--- license: mit language: - nl tags: - abb - agentschap binnenlands bestuur - lblod --- # Custum ML model for BPMN-similarity using spektral and keras. ## Overview This project aims to create embeddings for BPMN files to facilitate tasks like search, similarity, and clustering. The embeddings capture both the semantics and structure of BPMN files, allowing for effective retrieval and comparison of similar BPMN diagrams. ## Important Note The current model uses embeddings created by the paraphrase-multilingual-MiniLM-L12-v2 with an embedding dimension of 384. Using a different sentence-transformer will result in unexpected behavior. Ensure to use the correct sentence-transformer with the kerasEmbedder and adjust the 'dims' parameter in mu-search accordingly. ## Motivation The goal is to: Capture the semantics of BPMN files, making similar BPMN files have similar embeddings. Capture the structure of BPMN files, making structurally similar BPMN files have similar embeddings. Enable measuring the similarity between two BPMN diagrams by estimating the number of changes needed to transform one into another (trained on Minimum Edit Distance). ## Design Choices: Preprocessing BPMN Files: Adjust the input size to fit the fixed input size of embedding models or handle large inputs by splitting them into smaller parts. Encoding Structure: Use graph embedding techniques (e.g., GNNs, GCNs) to encode the structure of BPMN diagrams. Graph Representation: Convert BPMN diagrams into graph representations using NetworkX. Node and Edge Information: Extract labels and documentation fields from nodes and edges, converting them into numerical vectors using pre-trained embeddings or custom-trained embeddings. ## Current Approach: Convert BPMN Files to Graphs: Use NetworkX to represent BPMN files as graphs. Node and Edge Embeddings: Use pre-trained embeddings to create vector representations of nodes and edges. Graph Embedding: Use these embeddings as features for a GNN or GCN model (e.g., Spektral) to create a single embedding for each BPMN file. ## Similarity Model Specifics: Input: two BPMN files: (Batchsize, nodes, node_features) and Adjacency matrix Similarity Calculation: Uses precomputed embeddings and cosine similarity to rank BPMN files based on query similarity. Efficiency: Designed to handle large volumes of BPMN files and queries efficiently. ## Shortcomings: Data Availability: Lack of sufficient BPMN files within ABB, we trained on the data of [camunda/bpmn-for-research](https://github.com/camunda/bpmn-for-research) by converting the BPMN files to Networkx graphs and measuring the Minimum Edit Distance between random pairs. Minimum Edit Distance: **Does this translate to perceived similarity?** It might require a different approach. Training data in the form of (BPMN, BPMN, similarity) is needed. If user interactions can be captured, this might be a better approach for gauging perceived similarity or general 'useful' similarity. ## Future Steps: Gather more BPMN files from the correct domain (OPH). Train custom text embeddings for nodes and edges (e.g., using [robbert-2023-dutch-base-abb](https://huggingface.co/svercoutere/robbert-2023-dutch-base-abb)). Validate and refine the current model with new data. Potentially merge the graph and text models into a unified architecture. ### Suggestions: Data Collection: Store search results and user interactions anonymously to gather diverse query data. User Interaction Analysis: Use interaction data to train models for better search result ranking. ### Requirements for future steps: A large and varied dataset of BPMN files to ensure the model generalizes well. Real user interactions (recommendation interface based on BPMN-BPMN: user uploads file -> what suggestion did he interact with) to validate and improve the model's effectiveness.

TifTifR/orpo-future1000-Llama-3-8B-Instruct

TifTifR

2024-06-26T19:03:25Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-06-26T07:34:38Z

--- base_model: unsloth/llama-3-8b-instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** TifTifR - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

PradyumSomebody/finetunedLlamaTest6

PradyumSomebody

2024-06-26T07:36:01Z

0

peft

[ "peft", "region:us" ]

null

2024-06-26T07:35:56Z

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

julientfai/qwen2-0.5B-pii-masking-q4f16_1-Opilot

julientfai

2024-06-26T08:18:57Z

0

null

[ "region:us" ]

null

2024-06-26T07:36:26Z

Entry not found

cherifkhalifah/my_awesome_model

cherifkhalifah

2024-06-26T07:37:35Z

0

null

[ "region:us" ]

null

2024-06-26T07:37:35Z

Entry not found

rajtest/tiny_llama_v3

rajtest

2024-06-26T07:38:09Z

0

null

[ "license:unknown", "region:us" ]

null

2024-06-26T07:38:09Z

--- license: unknown ---

wenkai26/pony_lora

wenkai26

2024-06-26T07:40:48Z

0

null

[ "region:us" ]

null

2024-06-26T07:39:24Z

Entry not found

arham6/deepseek7b_finetuned

arham6

2024-06-26T07:39:56Z

0

null

[ "region:us" ]

null

2024-06-26T07:39:56Z

Entry not found

lalok/ililgu_medium_model_wer

lalok

2024-06-26T07:42:44Z

0

transformers

[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-06-26T07:42:35Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

gnsepili/shakespeare-rnn

gnsepili

2024-06-26T09:04:36Z

0

null

[ "rnn", "shakespeare", "text-generation", "en", "dataset:Trelis/tiny-shakespeare", "license:mit", "region:us" ]

text-generation

2024-06-26T07:42:58Z

--- license: mit datasets: - Trelis/tiny-shakespeare language: - en metrics: - accuracy pipeline_tag: text-generation tags: - rnn - shakespeare --- # Shakespeare RNN This project implements a character-level Recurrent Neural Network (RNN) trained on Shakespeare's works. The model can generate Shakespeare-like text based on a given prompt. ## Table of Contents - [Shakespeare RNN](#shakespeare-rnn) - [Table of Contents](#table-of-contents) - [Project Overview](#project-overview) - [Installation](#installation) - [Project Structure](#project-structure) - [Usage](#usage) - [Training](#training) - [Inference](#inference) - [Model Architecture](#model-architecture) - [Dataset](#dataset) - [Configuration](#configuration) - [Results](#results) - [Contributing](#contributing) - [License](#license) ## Project Overview This project uses a Long Short-Term Memory (LSTM) network to generate text in the style of Shakespeare. The model is trained on a dataset of Shakespeare's works and can generate new text based on a given prompt. Key features: - Character-level text generation - LSTM-based RNN architecture - Customizable hyperparameters - Training with Weights & Biases logging - Interactive inference script ## Installation 1. Clone the repository: ``` git clone https://github.com/your-username/shakespeare-rnn.git cd shakespeare-rnn ``` 2. Create a virtual environment: ``` python -m venv venv source venv/bin/activate # On Windows, use `venv\Scripts\activate` ``` 3. Install the required packages: ``` pip install -r requirements.txt ``` ## Project Structure ``` shakespeare-rnn/ │ ├── data/ │ ├── __init__.py │ └── dataset.py │ ├── model/ │ ├── __init__.py │ └── rnn.py │ ├── utils/ │ ├── __init__.py │ └── tokenizer.py │ ├── config.py ├── train.py ├── inference.py ├── requirements.txt └── README.md ``` ## Usage ### Training To train the model, run: ``` python train.py ``` This will start the training process and log the results to Weights & Biases. You can monitor the training progress in real-time through the W&B dashboard. ### Inference To generate text using the trained model, run: ``` python inference.py ``` This will load the trained model and allow you to enter prompts for text generation. The script will also generate text for a few predefined prompts. ## Model Architecture The model uses a character-level LSTM network with the following architecture: - Embedding layer - LSTM layer(s) - Fully connected output layer The exact architecture (number of layers, hidden dimensions, etc.) can be configured in the `config.py` file. ## Dataset The model is trained on the Tiny Shakespeare dataset, which is a collection of Shakespeare's works. The dataset is automatically downloaded using the Hugging Face `datasets` library. ## Configuration You can modify the model's hyperparameters and training settings in the `config.py` file. Key configurations include: - Batch size - Sequence length - Embedding dimension - Hidden dimension - Number of LSTM layers - Learning rate - Number of training epochs ## Results After training, you can find the training logs and performance metrics on the Weights & Biases dashboard. The trained model will be saved as `shakespeare_model.pth`, and the tokenizer will be saved as `tokenizer.pkl`. Example generated text: [Include some example outputs from your trained model here] ## Contributing Contributions to this project are welcome! Please follow these steps: 1. Fork the repository 2. Create a new branch (`git checkout -b feature/your-feature-name`) 3. Make your changes 4. Commit your changes (`git commit -am 'Add some feature'`) 5. Push to the branch (`git push origin feature/your-feature-name`) 6. Create a new Pull Request ## License This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. ``` This README provides a comprehensive overview of your project, including installation instructions, usage guidelines, project structure, and other relevant information. You may want to customize some parts, such as the repository URL, example outputs, and any specific instructions or results from your implementation.

jkushwaha/paddle_GPU_test

jkushwaha

2024-06-28T04:53:51Z

0

null

[ "region:us" ]

null

2024-06-26T07:43:40Z

Entry not found

Dazzak/phi3_intruct_aci_3e

Dazzak

2024-06-26T07:53:37Z

0

transformers

[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-06-26T07:48:38Z

--- library_name: transformers tags: - unsloth --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

dddd322/lora03

dddd322

2024-06-26T07:49:51Z

0

null

[ "region:us" ]

null

2024-06-26T07:49:51Z

Entry not found

hossay/textual_inversion_cat

hossay

2024-06-26T07:52:49Z

0

null

[ "region:us" ]

null

2024-06-26T07:52:49Z

Entry not found

v0dkapapi/shuffled

v0dkapapi

2024-06-26T08:20:47Z

0

null

[ "generated_from_trainer", "base_model:ybelkada/falcon-7b-sharded-bf16", "region:us" ]

null

2024-06-26T07:53:40Z

--- base_model: ybelkada/falcon-7b-sharded-bf16 tags: - generated_from_trainer model-index: - name: shuffled results: [] ---  # shuffled This model is a fine-tuned version of [ybelkada/falcon-7b-sharded-bf16](https://huggingface.co/ybelkada/falcon-7b-sharded-bf16) 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: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - training_steps: 100 ### Training results ### Framework versions - Transformers 4.32.0 - Pytorch 2.3.0+cu121 - Datasets 2.13.1 - Tokenizers 0.13.3

Perriewang/aaa

Perriewang

2024-06-26T08:01:03Z

0

null

[ "region:us" ]

null

2024-06-26T08:01:03Z

Entry not found

stojchet/b255231ae44a52f878c6b2cf4a0fe1fd

stojchet

2024-06-26T08:01:29Z

0

null

[ "region:us" ]

null

2024-06-26T08:01:29Z

Entry not found

jayoohwang/alphamath_round1

jayoohwang

2024-06-27T11:56:32Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-06-26T08:06:53Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

IreNkweke/code-search-net-tokenizer

IreNkweke

2024-06-26T08:09:16Z

0

transformers

[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-06-26T08:09:15Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

RobertML/sn3-hawk-tuah

RobertML

2024-06-26T08:14:24Z

0

null

[ "region:us" ]

null

2024-06-26T08:14:14Z

Entry not found

makhataei/wav2vec2-xls-r-300m-emotion-ru

makhataei

2024-06-26T08:19:08Z

0

null

[ "region:us" ]

null

2024-06-26T08:19:08Z

Entry not found

iitrsamrat/Phi-3-mini-4k-instruct

iitrsamrat

2024-06-26T08:22:24Z

0

null

[ "region:us" ]

null

2024-06-26T08:22:24Z

Entry not found

Woooooong/output

Woooooong

2024-06-26T08:24:24Z

0

null

[ "region:us" ]

null

2024-06-26T08:24:24Z

Entry not found

KaonashiYume/Kolya

KaonashiYume

2024-06-26T08:30:16Z

0

null

[ "license:afl-3.0", "region:us" ]

null

2024-06-26T08:27:48Z

--- license: afl-3.0 ---

nieshen/gpt2_ce

nieshen

2024-06-26T08:44:06Z

0

null

[ "region:us" ]

null

2024-06-26T08:30:05Z

Entry not found

acl-srw-2024/llama-3-typhoon-v1.5-8b-instruct-unsloth-sft

acl-srw-2024

2024-06-26T08:31:31Z

0

null

[ "safetensors", "region:us" ]

null

2024-06-26T08:30:46Z

Entry not found

mouhebMehdoui/mitstralFinetuned

mouhebMehdoui

2024-06-26T08:30:56Z

0

null

[ "region:us" ]

null

2024-06-26T08:30:56Z

Entry not found

lalok/oneonenine_medium_model

lalok

2024-06-26T08:31:52Z

0

null

[ "region:us" ]

null

2024-06-26T08:31:52Z

Entry not found

weiquan/MU

weiquan

2024-06-27T02:34:49Z

0

null

[ "region:us" ]

null

2024-06-26T08:32:13Z

Entry not found

lizihua/q-FrozenLake-v1-4x4-noSlippery

lizihua

2024-06-26T08:32:35Z

0

null

[ "region:us" ]

null

2024-06-26T08:32:35Z

Entry not found

BossBattlar/G97

BossBattlar

2024-06-26T08:35:34Z

0

null

[ "license:mit", "region:us" ]

null

2024-06-26T08:35:34Z

--- license: mit ---

multimodalart/fofr-sdxl-emoji-older-diffusers

multimodalart

2024-06-26T11:07:19Z

0

diffusers

[ "diffusers", "text-to-image", "stable-diffusion", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "license:creativeml-openrail-m", "endpoints_compatible", "region:us" ]

text-to-image

2024-06-26T08:36:06Z

--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion - lora - diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 --- ## Inference Endpoints ready version of https://huggingface.co/fofr/sdxl-emoji ### sdxl-emoji LoRA by [fofr](https://replicate.com/fofr) #### An SDXL fine-tune based on Apple Emojis ![lora_image](https://replicate.delivery/pbxt/a3z81v5vwlKfLq1H5uBqpVmkHalOVup0jSLma9E2UaF3tawIA/out-0.png) >

michellehu/ok

michellehu

2024-06-26T08:37:08Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2024-06-26T08:37:08Z

--- license: apache-2.0 ---

ClinBAY/hkunlp-instructor-base

ClinBAY

2024-06-26T08:37:29Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2024-06-26T08:37:28Z

--- license: apache-2.0 ---

Pandita-IA/rl_course_vizdoom_health_gathering_supreme

Pandita-IA

2024-06-26T09:54:13Z

0

sample-factory

[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2024-06-26T08:38:32Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 12.17 +/- 6.74 name: mean_reward verified: false --- A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r Pandita-IA/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

HyperdustProtocol/HyperAuto-cog-llama2-7b-3778

HyperdustProtocol

2024-06-26T08:40:12Z

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

2024-06-26T08:39:58Z

--- base_model: unsloth/llama-2-7b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # 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)

DDoesGames/B.Awesome

DDoesGames

2024-06-26T10:38:56Z

0

null

[ "license:cc", "region:us" ]

null

2024-06-26T08:40:18Z

--- license: cc ---

PradyumSomebody/finetunedLlamaTest7

PradyumSomebody

2024-06-26T08:40:25Z

0

peft

[ "peft", "region:us" ]

null

2024-06-26T08:40:19Z

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

mona112/trained-sd3

mona112

2024-06-26T08:46:44Z

0

null

[ "region:us" ]

null

2024-06-26T08:46:44Z

Entry not found

sataayu/molt5-augmented-default-45-large-smiles2caption

sataayu

2024-06-26T08:48:17Z

0

transformers

[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text2text-generation

2024-06-26T08:46:54Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

fsw/llm_rosebleu_lora

fsw

2024-06-26T08:54:34Z

0

null

[ "region:us" ]

null

2024-06-26T08:52:41Z

# これはなに？ `stabilityai/japanese-stablelm-base-alpha-7b` を `rosebleu` データセットで学習した Lora ## 使い方 .\text-generation-webui\loras\Rosebleu となるように配置。 `text-generation-webui` の `model` の LoRAから読み込める。 ## データセット https://gitlab.com/open_contents_datasets/Rosebleu Hシーンも含まれているみたいですが、特に区別せずに突っ込んでいます。 ## 前処理とデータロード中にあるすべてのtsvを全部繋て、csvにしただけ。学習プロンプトはこの用にしているので、このフォーマットだと良くなったりすることがあるかもしれないし、ないかもしれない。コンテキスト長は2048にしているので、1行が2048トークン以上は切り捨てられているはず。 ``` result = f'### name:\n{data_point["name"]}\n\n### text:\n{data_point["text"]}' ```

HeroadZ/q-FrozenLake-v1-4x4-noSlippery

HeroadZ

2024-06-26T08:53:21Z

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2024-06-26T08:53:17Z

--- 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="HeroadZ/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"]) ```

Dazzak/gemma-7b_aci_3e

Dazzak

2024-06-26T08:53:37Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-06-26T08:53:26Z

--- base_model: unsloth/llama-3-8b-Instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** Dazzak - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

iremmd/thy_model_21

iremmd

2024-06-26T08:54:11Z

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

2024-06-26T08:53:46Z

--- base_model: unsloth/llama-3-8b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** iremmd - **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)

HeroadZ/q-Taxi-v3

HeroadZ

2024-06-26T08:55:10Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2024-06-26T08:55:07Z

--- 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="HeroadZ/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"]) ```

apersonnaz/crystalDetect_bin_uv_512_20240626-105516

apersonnaz

2024-06-26T10:37:33Z

0

null

[ "safetensors", "region:us" ]

null

2024-06-26T08:55:18Z

Entry not found

apersonnaz/crystalDetect_bin_vis_512_20240626-105516

apersonnaz

2024-06-26T11:07:57Z

0

null

[ "safetensors", "region:us" ]

null

2024-06-26T08:55:18Z

Entry not found

darynka-xo/SupremeCourt

darynka-xo

2024-06-26T09:02:22Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-06-26T09:02:01Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

iremmd/thy_model_22

iremmd

2024-06-26T09:03:53Z

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

2024-06-26T09:03:20Z

--- base_model: unsloth/llama-3-8b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** iremmd - **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)

Andres29/Mymodel

Andres29

2024-06-26T09:05:11Z

0

null

[ "region:us" ]

null

2024-06-26T09:05:11Z

Entry not found

iremmd/thy_model_23

iremmd

2024-06-26T09:06:14Z

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

2024-06-26T09:05:49Z

--- base_model: unsloth/llama-3-8b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** iremmd - **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)

Floriankidev/deit-base-distilled-patch16-224-finetuned-eurosat

Floriankidev

2024-06-26T09:06:32Z

0

null

[ "region:us" ]

null

2024-06-26T09:06:32Z

Entry not found

xiaoxuanzi/CT-GAT

xiaoxuanzi

2024-06-26T09:07:05Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2024-06-26T09:07:04Z

--- license: apache-2.0 ---

3dalgo/tmp

3dalgo

2024-06-26T09:08:15Z

0

null

[ "region:us" ]

null

2024-06-26T09:08:14Z

Entry not found

Bajiyo/5gram-correct-arpa

Bajiyo

2024-06-26T09:08:41Z

0

null

[ "region:us" ]

null

2024-06-26T09:08:41Z

Entry not found

adham674y/r

adham674y

2024-06-26T09:09:52Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2024-06-26T09:09:52Z

--- license: apache-2.0 ---

rnu/medical_llama3_finetune_1

rnu

2024-06-26T10:15:19Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-06-26T09:11:02Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Floriankidev/deit-base-patch16-224-finetuned-eurosat

Floriankidev

2024-06-26T09:11:14Z

0

null

[ "region:us" ]

null

2024-06-26T09:11:14Z

Entry not found

killiankopp/datascientest-oct23ml

killiankopp

2024-06-26T09:22:36Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2024-06-26T09:11:42Z

--- license: apache-2.0 ---

Kqte/LLaMIPa

Kqte

2024-06-26T09:21:58Z

0

null

[ "safetensors", "region:us" ]

null

2024-06-26T09:17:11Z

Entry not found

mohityadav/mental-health-advisor-gpt

mohityadav

2024-06-26T09:21:38Z

0

null

[ "safetensors", "license:apache-2.0", "region:us" ]

null

2024-06-26T09:18:07Z

--- license: apache-2.0 ---

askari1122/llama3

askari1122

2024-06-26T09:18:35Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2024-06-26T09:18:35Z

--- license: apache-2.0 ---

PradyumSomebody/finetunedLlamaTest8

PradyumSomebody

2024-06-26T09:21:15Z

0

peft

[ "peft", "region:us" ]

null

2024-06-26T09:21:09Z

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

szcjerry/smat-vit-sup21k-small

szcjerry

2024-07-02T09:22:13Z

0

null

[ "license:cc-by-4.0", "region:us" ]

null

2024-06-26T09:21:37Z

--- license: cc-by-4.0 --- This repo contains the SMAT meta-tuned vit-sup21k-small model checkpoint for PyTorch. ### How to use With our implementation here on [github](https://github.com/szc12153/sparse_meta_tuning), you can load the pre-trained weights by calling ``` model.load_state_dict(torch.load(/path/to/checkpoint.pt)) ``` For inference with ProtoNet on a few-shot learning task: ``` # outputs is a dictionary outputs = model(x_s=x_s, # support inputs y_s=y_s, # support labels x_q=x_q, # query inputs y_q=None, # predict for query labels finetune_model=None # None for direct inference with a ProtoNet classifier ) y_q_pred = outputs['y_q_pred'] ``` For inference with task-specific full fine-tuning then inference: ``` # outputs is a dictionary model.args.meta_learner.inner_lr.lr = lr # set the learning rate for fine-tuning model.args.meta_learner.num_finetune_steps = num_finetune_steps # set the number of fine-tuning steps outputs = model(x_s=x_s, # support inputs y_s=y_s, # support labels x_q=x_q, # query inputs y_q=None, # predict for query labels finetune_model="full" # {'full','lora'} ) y_q_pred = outputs['y_q_pred'] ``` You can visit our [github](https://github.com/szc12153/sparse_meta_tuning) repo for more details on training and inference!

szcjerry/smat-vit-sup21k-base

szcjerry

2024-07-02T09:11:02Z

0

null

[ "license:cc-by-4.0", "region:us" ]

null

2024-06-26T09:22:00Z

--- license: cc-by-4.0 --- This repo contains the SMAT meta-tuned vit-sup21k-base model checkpoint for PyTorch. ### How to use With our implementation here on [github](https://github.com/szc12153/sparse_meta_tuning), you can load the pre-trained weights by calling ``` model.load_state_dict(torch.load(/path/to/checkpoint.pt)) ``` For inference with ProtoNet on a few-shot learning task: ``` # outputs is a dictionary outputs = model(x_s=x_s, # support inputs y_s=y_s, # support labels x_q=x_q, # query inputs y_q=None, # predict for query labels finetune_model=None # None for direct inference with a ProtoNet classifier ) y_q_pred = outputs['y_q_pred'] ``` For inference with task-specific full fine-tuning then inference: ``` # outputs is a dictionary model.args.meta_learner.inner_lr.lr = lr # set the learning rate for fine-tuning model.args.meta_learner.num_finetune_steps = num_finetune_steps # set the number of fine-tuning steps outputs = model(x_s=x_s, # support inputs y_s=y_s, # support labels x_q=x_q, # query inputs y_q=None, # predict for query labels finetune_model="full" # {'full','lora'} ) y_q_pred = outputs['y_q_pred'] ``` You can visit our [github](https://github.com/szc12153/sparse_meta_tuning) repo for more details on training and inference!

nagrajn/gpt2-imdb-PPO_KD

nagrajn

2024-06-26T09:30:02Z

0

null

[ "region:us" ]

null

2024-06-26T09:30:02Z

Entry not found

RimZrelli/CTL_Mistral_Instruct_12Fold_Model

RimZrelli

2024-06-26T09:30:36Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-06-26T09:30:28Z

--- base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl --- # Uploaded model - **Developed by:** RimZrelli - **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)