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
wkingyu666/qwen2	wkingyu666	2024-06-26T12:00:26Z	0	null	[ "region:us" ]	null	2024-06-26T12:00:26Z	Entry not found
SilvioLima/absa_3_domains	SilvioLima	2024-07-02T13:36:03Z	0	null	[ "safetensors", "region:us" ]	null	2024-06-26T12:01:28Z	#### Domain Restaurant 68.763668 Laptop 64.611111 pet 37.000000 grocery 36.815789 home 36.000000 electronics 35.427419 book 34.227273 beauty 33.382353 fashion 28.500000 toy 27.413793 ### F1-score% mean = 53.4454 ![3_dominios.png](https://cdn-uploads.huggingface.co/production/uploads/659820c0ada2ade50bc44f71/69ia8Up2WD_zqrchaKVYh.png)
hansa15100/openimage_r16_epoch25_model	hansa15100	2024-06-26T12:36:55Z	0	null	[ "tensorboard", "safetensors", "region:us" ]	null	2024-06-26T12:01:54Z	Entry not found
VKapseln475/SlimXmed122	VKapseln475	2024-06-26T12:12:43Z	0	null	[ "region:us" ]	null	2024-06-26T12:02:11Z	# <Kaufen> Slimxmed Deutschland - SlimXmed Erfahrungen Test, Einnahme Zutaten Preis <Kaufen> Slimxmed Deutschland SlimXmed tritt auf den Plan im umkämpften Markt der Nahrungsergänzungsmittel zur Gewichtsreduktion. Dabei sind sie mehr als nur ein weiteres Produkt – sie sind ein Versprechen für eine gesündere und aktivere Zukunft für alle, die nicht nur abnehmen, sondern ihre Lebensqualität verbessern wollen. Dieser Blog gibt Einblicke in unsere Bewertungen und teilt echte Erfahrungen von Benutzern. ## [Klicken Sie hier, um jetzt auf der offiziellen Website von SlimXmed zu kaufen](https://adtocart.xyz/slimxmed-de) ## Wissenschaftliche Belege zur Wirksamkeit von Polyphenolen zum Abnehmen (Studien) Ein Schlüsselelement im Testbericht von SlimXmed Premium Effect Kapseln ist die Evaluation der wissenschaftlichen Belege, die die behaupteten Wirkungen unterstützen. Mehrere Studien haben die positiven Effekte von Polyphenolen auf die Gewichtsreduktion untersucht. Eine Meta-Analyse, veröffentlicht im „Journal of Nutritional Science and Vitaminology„, untersuchte die Auswirkungen von Grüntee-Extrakten, reich an Catechinen, auf die Gewichtsreduktion und Gewichtserhaltung. Die Analyse von vierzehn randomisierten kontrollierten Studien ergab eine signifikante Reduktion des Körpergewichts bei Teilnehmern, die Grüntee-Extrakte konsumierten, verglichen mit den Kontrollgruppen. Resveratrol wurde in einer Studie im „International Journal of Obesity“ untersucht. Die Studie zeigte, dass eine Supplementierung mit Resveratrol den Stoffwechsel verbessern und die Fettmasse bei übergewichtigen Personen reduzieren kann. Quercetin, bekannt für seine entzündungshemmenden und antioxidativen Eigenschaften, wurde ebenfalls hinsichtlich seiner Wirkung auf das Körpergewicht erforscht. Eine im „Journal of Clinical Endocrinology & Metabolism“ veröffentlichte Studie fand heraus, dass Quercetin die Fettverbrennung erhöhen und die Fettaufnahme im Darm reduzieren kann. ## Einschränkungen und Sicherheitsprofil Während die vorhandenen Studien vielversprechende Ergebnisse bezüglich der Wirksamkeit von Polyphenolen bei der Gewichtsreduktion liefern, ist es wichtig, die Einschränkungen dieser Forschung zu berücksichtigen. Viele Studien wurden im kleinen Rahmen oder mit tierischen Modellen durchgeführt, was die Generalisierbarkeit der Ergebnisse auf den Menschen einschränkt. Zudem variiert die Dosierung der Polyphenole in den Studien erheblich, was einen direkten Vergleich der Ergebnisse erschwert. Das Sicherheitsprofil von SlimXmed Stiftung Warentest ist im Allgemeinen als gut einzustufen, da Polyphenole in den verwendeten Konzentrationen selten ernsthafte Nebenwirkungen verursachen. Dennoch sollten Personen mit bestimmten gesundheitlichen Voraussetzungen oder diejenigen, die Medikamente einnehmen, vor der Anwendung einen Arzt konsultieren. ## [Klicken Sie hier, um jetzt auf der offiziellen Website von SlimXmed zu kaufen](https://adtocart.xyz/slimxmed-de)
zmlapq18/example-model	zmlapq18	2024-06-26T12:02:59Z	0	null	[ "license:mit", "region:us" ]	null	2024-06-26T12:02:59Z	--- license: mit ---
wufan/PDF-EXTRACT-KIT	wufan	2024-06-26T12:04:14Z	0	null	[ "region:us" ]	null	2024-06-26T12:04:14Z	Entry not found
elaistu/Salesperson	elaistu	2024-06-26T12:04:39Z	0	null	[ "region:us" ]	null	2024-06-26T12:04:39Z	Entry not found
alternativerealitystudio/Llama-3-8B-F	alternativerealitystudio	2024-06-26T12:05:34Z	0	null	[ "license:mit", "region:us" ]	null	2024-06-26T12:05:34Z	--- license: mit ---
jasonk19/mistral-7b-gec	jasonk19	2024-06-26T12:07:12Z	0	null	[ "region:us" ]	null	2024-06-26T12:07:12Z	Entry not found
rzarno/llama-3-8b-industry-code-with-adapter	rzarno	2024-06-26T12:08:28Z	0	null	[ "region:us" ]	null	2024-06-26T12:08:28Z	Entry not found
Atonemo/meeting-recorder	Atonemo	2024-06-26T12:09:38Z	0	null	[ "region:us" ]	null	2024-06-26T12:09:38Z	Entry not found
msonali/xlm-roberta-base-finetuned-panx-de	msonali	2024-06-26T12:09:51Z	0	null	[ "region:us" ]	null	2024-06-26T12:09:51Z	Entry not found
anileo1/llama3-8B-instruct-lora-finetuned-v1.2-16bit	anileo1	2024-06-26T12:11:36Z	0	null	[ "region:us" ]	null	2024-06-26T12:11:35Z	Entry not found
Suhash/my_awesome_billsum_model	Suhash	2024-06-26T12:12:05Z	0	null	[ "region:us" ]	null	2024-06-26T12:12:05Z	Entry not found
Divy12/Forest	Divy12	2024-06-26T12:12:36Z	0	null	[ "region:us" ]	null	2024-06-26T12:12:36Z	Entry not found
nsugianto/detr-resnet50_tuned_detrresnet50_lsdocelementdetv1type7_plusb5_5389s_adjparam6_lr5e5_dec1e4_b14	nsugianto	2024-06-26T12:13:15Z	0	null	[ "region:us" ]	null	2024-06-26T12:13:15Z	Entry not found
nsugianto/detr-resnet50_tuned_detrresnet50_lsdocelementdetv1type7_plusb5_5389s_adjparam6_lr5e5_dec1e4_b10	nsugianto	2024-06-26T12:13:52Z	0	null	[ "region:us" ]	null	2024-06-26T12:13:52Z	Entry not found
ozgung/red-bowl-SD3	ozgung	2024-06-26T12:14:49Z	0	null	[ "region:us" ]	null	2024-06-26T12:14:49Z	Entry not found
Grayx/john_paul_van_damme_37	Grayx	2024-06-26T12:15:36Z	0	null	[ "region:us" ]	null	2024-06-26T12:15:25Z	Entry not found
nsugianto/detr-resnet50_tuned_detrresnet50_lsdocelementdetv1type7_plusb5_5389s_adjparam6_lr5e5_dec5e4_b12	nsugianto	2024-06-26T12:16:15Z	0	null	[ "region:us" ]	null	2024-06-26T12:16:15Z	Entry not found
Cynor/cynorllama3	Cynor	2024-06-26T12:17:14Z	0	null	[ "license:llama3", "region:us" ]	null	2024-06-26T12:17:09Z	--- license: llama3 ---
diproger/llama3-8b-loss-fine-tuned-test	diproger	2024-06-26T12:17:29Z	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	2024-06-26T12:17:15Z	--- 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: diproger - 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)
kheopss/kheops_quantized	kheopss	2024-06-26T12:18:02Z	0	null	[ "region:us" ]	null	2024-06-26T12:18:02Z	Entry not found
Ak1104/snapshot	Ak1104	2024-07-02T11:42:46Z	0	null	[ "region:us" ]	null	2024-06-26T12:19:03Z	Entry not found
fwdfsdf/Mysey	fwdfsdf	2024-06-26T12:27:48Z	0	null	[ "license:openrail", "region:us" ]	null	2024-06-26T12:21:50Z	--- license: openrail ---
R0obin/email-spam-classifier	R0obin	2024-06-26T12:27:34Z	0	null	[ "region:us" ]	null	2024-06-26T12:27:34Z	Entry not found
valerielucro/mistral_gsm8k_dpo_cot_beta_0.9	valerielucro	2024-06-26T12:29:58Z	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-26T12:29:51Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
valerielucro/mistral_gsm8k_dpo_cot_beta_0.7	valerielucro	2024-06-26T12:31:24Z	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-26T12:31:15Z	--- 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]
henilp105/InjecAgent-llama-7b-optim-all	henilp105	2024-06-26T12:33:53Z	0	null	[ "safetensors", "region:us" ]	null	2024-06-26T12:31:38Z	Entry not found
valerielucro/mistral_gsm8k_dpo_cot_beta_0.5	valerielucro	2024-06-26T12:35:32Z	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-26T12:35:23Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
prabinpanta0/image_classification_with_cnns	prabinpanta0	2024-06-26T12:44:17Z	1	tensorflow	[ "tensorflow", "keras", "image-classification", "image-classification-cnns", "Fasion_image-classification", "neural-network", "en", "license:mit", "region:us" ]	image-classification	2024-06-26T12:36:07Z	--- license: mit language: en metrics: mean_squared_error library_name: tensorflow tags: - image-classification - image-classification-cnns - Fasion_image-classification - tensorflow - neural-network pipeline_tag: image-classification --- This model was created as a practice exercise for the course "Intro to TensorFlow for Deep Learning" from Udacity, given by TensorFlow. It was trained on a dataset of TenserFlow Fashion MNIST using the cnns method. The model uses a small neural network built with TensorFlow. ## License This model is released under the MIT license.
Ikhsan1/hugging	Ikhsan1	2024-06-26T12:36:12Z	0	null	[ "region:us" ]	null	2024-06-26T12:36:12Z	Entry not found
DarbyTan/Test	DarbyTan	2024-06-26T12:37:04Z	0	null	[ "region:us" ]	null	2024-06-26T12:37:04Z	Entry not found
Truepeak/ORPO-PM01-0.4	Truepeak	2024-06-26T12:39:02Z	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-26T12:37:44Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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valerielucro/mistral_gsm8k_dpo_cot_beta_0.8	valerielucro	2024-06-26T12:38:37Z	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-26T12:38:31Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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(2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
kaya-kedi/Toadette-TITANPretrain	kaya-kedi	2024-06-26T12:44:26Z	0	null	[ "region:us" ]	null	2024-06-26T12:38:34Z	Entry not found
Hasano20/Mask2Former_Clean_Set1_95images_mask2former-swin-large-ade-semantic	Hasano20	2024-06-26T12:40:07Z	0	null	[ "region:us" ]	null	2024-06-26T12:40:07Z	Entry not found
pinkyprakash/Llama-3-8b-chat-finetune	pinkyprakash	2024-06-26T12:43:49Z	0	null	[ "region:us" ]	null	2024-06-26T12:43:49Z	Entry not found
TopperThijs/Llama2-Open-ended-Finetuned-6epochs15mlm	TopperThijs	2024-06-26T12:44:38Z	0	null	[ "region:us" ]	null	2024-06-26T12:44:38Z	Entry not found
Janibicigo/Misscake	Janibicigo	2024-06-26T12:46:36Z	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-26T12:46:36Z	--- license: apache-2.0 ---
shayantreylon2/lora_model4	shayantreylon2	2024-06-26T12:47:41Z	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	2024-06-26T12:47:16Z	--- 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: shayantreylon2 - 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)
jurieyel/77cdm-llama3-sqlcoder-8b-500s-1000d	jurieyel	2024-06-26T12:48:01Z	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:defog/llama-3-sqlcoder-8b", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-06-26T12:47:50Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: defog/llama-3-sqlcoder-8b --- # Uploaded model - Developed by: jurieyel - License: apache-2.0 - Finetuned from model : defog/llama-3-sqlcoder-8b This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
charlieoneill/jsalt-astroph-data	charlieoneill	2024-06-26T12:53:49Z	0	null	[ "region:us" ]	null	2024-06-26T12:48:43Z	Entry not found
geraldabrhm/llama-3-8b-regular-nocontext-32lora-lr8_5	geraldabrhm	2024-06-26T13:42:07Z	0	null	[ "safetensors", "region:us" ]	null	2024-06-26T12:49:01Z	Entry not found
newih/western	newih	2024-06-26T13:04:09Z	0	null	[ "region:us" ]	null	2024-06-26T12:49:26Z	Entry not found
nam194/llama3-8b-qlora-ultrachat-unsloth	nam194	2024-06-26T15:19:29Z	0	null	[ "tensorboard", "safetensors", "region:us" ]	null	2024-06-26T12:51:01Z	Entry not found
bug7/longchat_1080	bug7	2024-06-26T12:53:00Z	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-26T12:52:49Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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]
X1Rexords/Sidhu-Moosewala-AI-Model	X1Rexords	2024-06-26T13:01:44Z	0	null	[ "license:openrail", "region:us" ]	null	2024-06-26T12:56:43Z	--- license: openrail ---
kevin009/deepseek	kevin009	2024-06-26T18:40:17Z	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-26T13:03:37Z	--- 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. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
ekaterina-blatova-jb/model_lr1e-5_v0	ekaterina-blatova-jb	2024-06-26T13:05:44Z	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-06-26T13:03:46Z	--- {} --- ## Evaluation results Validation loss on the whole input: 0.7532717230496928 Validation loss on completion: 0.785313343279995
bug7/longchat_960	bug7	2024-06-26T13:04:09Z	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-26T13:03:59Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
wadiea/voice_model	wadiea	2024-06-26T13:04:09Z	0	null	[ "region:us" ]	null	2024-06-26T13:04:09Z	Entry not found
taric49/LLAMA3_Summarization_16k_2ep_b4g16_2024	taric49	2024-06-26T13:07:01Z	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-26T13:05:52Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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schaturv/llama2-7b-key-value-pairings-adapter	schaturv	2024-06-26T13:26:06Z	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-26T13:07:59Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
scrfur/parukorvcmmodel	scrfur	2024-06-26T13:10:45Z	0	null	[ "license:unknown", "region:us" ]	null	2024-06-26T13:08:06Z	--- license: unknown ---
Darius07/UNER_subword_tk_en_lora_alpha_1024_drop_0.3_rank_512_seed_42	Darius07	2024-06-26T13:27:23Z	0	null	[ "safetensors", "generated_from_trainer", "dataset:universalner/universal_ner", "base_model:xlm-roberta-base", "license:mit", "model-index", "region:us" ]	null	2024-06-26T13:08:30Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - universalner/universal_ner metrics: - precision - recall - f1 - accuracy model-index: - name: UNER_subword_tk_en_lora_alpha_1024_drop_0.3_rank_512_seed_42 results: - task: name: Token Classification type: token-classification dataset: name: universalner/universal_ner en_ewt type: universalner/universal_ner config: en_ewt split: validation args: en_ewt metrics: - name: Precision type: precision value: 0.7731660231660231 - name: Recall type: recall value: 0.8291925465838509 - name: F1 type: f1 value: 0.8001998001998001 - name: Accuracy type: accuracy value: 0.9844128991212374 --- <!-- 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. --> # UNER_subword_tk_en_lora_alpha_1024_drop_0.3_rank_512_seed_42 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the universalner/universal_ner en_ewt dataset. It achieves the following results on the evaluation set: - Loss: 0.0633 - Precision: 0.7732 - Recall: 0.8292 - F1: 0.8002 - Accuracy: 0.9844 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 35.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 392 \| 0.1362 \| 0.2922 \| 0.3903 \| 0.3342 \| 0.9569 \| \| 0.2046 \| 2.0 \| 784 \| 0.0889 \| 0.5868 \| 0.6822 \| 0.6309 \| 0.9745 \| \| 0.085 \| 3.0 \| 1176 \| 0.0772 \| 0.6687 \| 0.7940 \| 0.7260 \| 0.9778 \| \| 0.0591 \| 4.0 \| 1568 \| 0.0692 \| 0.7085 \| 0.7950 \| 0.7493 \| 0.9802 \| \| 0.0591 \| 5.0 \| 1960 \| 0.0692 \| 0.6894 \| 0.8251 \| 0.7512 \| 0.9791 \| \| 0.0496 \| 6.0 \| 2352 \| 0.0664 \| 0.6937 \| 0.8157 \| 0.7498 \| 0.9791 \| \| 0.0448 \| 7.0 \| 2744 \| 0.0671 \| 0.7007 \| 0.8313 \| 0.7604 \| 0.9797 \| \| 0.0409 \| 8.0 \| 3136 \| 0.0674 \| 0.7200 \| 0.8147 \| 0.7644 \| 0.9814 \| \| 0.0388 \| 9.0 \| 3528 \| 0.0635 \| 0.7306 \| 0.8478 \| 0.7849 \| 0.9816 \| \| 0.0388 \| 10.0 \| 3920 \| 0.0620 \| 0.7481 \| 0.8209 \| 0.7828 \| 0.9832 \| \| 0.0357 \| 11.0 \| 4312 \| 0.0586 \| 0.7758 \| 0.8240 \| 0.7992 \| 0.9844 \| \| 0.0333 \| 12.0 \| 4704 \| 0.0611 \| 0.7606 \| 0.8354 \| 0.7963 \| 0.9840 \| \| 0.0323 \| 13.0 \| 5096 \| 0.0601 \| 0.7819 \| 0.8240 \| 0.8024 \| 0.9844 \| \| 0.0323 \| 14.0 \| 5488 \| 0.0638 \| 0.7203 \| 0.8292 \| 0.7709 \| 0.9812 \| \| 0.0303 \| 15.0 \| 5880 \| 0.0600 \| 0.7737 \| 0.8354 \| 0.8034 \| 0.9841 \| \| 0.0293 \| 16.0 \| 6272 \| 0.0602 \| 0.7703 \| 0.8333 \| 0.8006 \| 0.9841 \| \| 0.0271 \| 17.0 \| 6664 \| 0.0609 \| 0.7634 \| 0.8416 \| 0.8006 \| 0.9841 \| \| 0.0269 \| 18.0 \| 7056 \| 0.0641 \| 0.7569 \| 0.8478 \| 0.7998 \| 0.9835 \| \| 0.0269 \| 19.0 \| 7448 \| 0.0594 \| 0.7793 \| 0.8261 \| 0.8020 \| 0.9849 \| \| 0.0263 \| 20.0 \| 7840 \| 0.0608 \| 0.7873 \| 0.8199 \| 0.8032 \| 0.9850 \| \| 0.025 \| 21.0 \| 8232 \| 0.0606 \| 0.7812 \| 0.8240 \| 0.8020 \| 0.9850 \| \| 0.0236 \| 22.0 \| 8624 \| 0.0639 \| 0.7558 \| 0.8364 \| 0.7941 \| 0.9839 \| \| 0.0228 \| 23.0 \| 9016 \| 0.0620 \| 0.7668 \| 0.8375 \| 0.8006 \| 0.9845 \| \| 0.0228 \| 24.0 \| 9408 \| 0.0612 \| 0.7647 \| 0.8344 \| 0.7980 \| 0.9842 \| \| 0.0229 \| 25.0 \| 9800 \| 0.0618 \| 0.7584 \| 0.8385 \| 0.7965 \| 0.9839 \| \| 0.0227 \| 26.0 \| 10192 \| 0.0631 \| 0.7678 \| 0.8385 \| 0.8016 \| 0.9842 \| \| 0.0216 \| 27.0 \| 10584 \| 0.0628 \| 0.7883 \| 0.8364 \| 0.8117 \| 0.9850 \| \| 0.0216 \| 28.0 \| 10976 \| 0.0611 \| 0.7765 \| 0.8344 \| 0.8044 \| 0.9849 \| \| 0.0203 \| 29.0 \| 11368 \| 0.0615 \| 0.7755 \| 0.8406 \| 0.8068 \| 0.9847 \| \| 0.02 \| 30.0 \| 11760 \| 0.0629 \| 0.7743 \| 0.8344 \| 0.8032 \| 0.9847 \| \| 0.0197 \| 31.0 \| 12152 \| 0.0620 \| 0.7763 \| 0.8333 \| 0.8038 \| 0.9843 \| \| 0.0197 \| 32.0 \| 12544 \| 0.0633 \| 0.7750 \| 0.8271 \| 0.8002 \| 0.9845 \| \| 0.0197 \| 33.0 \| 12936 \| 0.0631 \| 0.7813 \| 0.8323 \| 0.8060 \| 0.9845 \| \| 0.0192 \| 34.0 \| 13328 \| 0.0629 \| 0.7768 \| 0.8323 \| 0.8036 \| 0.9845 \| \| 0.0188 \| 35.0 \| 13720 \| 0.0633 \| 0.7732 \| 0.8292 \| 0.8002 \| 0.9844 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.18.0 - Tokenizers 0.19.1
jazzxxx/my_awesome_mind_model	jazzxxx	2024-06-26T13:10:14Z	0	null	[ "region:us" ]	null	2024-06-26T13:10:14Z	Entry not found
jvv7/ppo-Huggy	jvv7	2024-06-26T13:10:33Z	0	null	[ "region:us" ]	null	2024-06-26T13:10:33Z	Entry not found
jjgerbo/stable-diffusion-embeddings-lora	jjgerbo	2024-06-27T14:05:35Z	0	null	[ "license:mit", "region:us" ]	null	2024-06-26T13:12:07Z	--- license: mit ---
raidelcarballo/Arboles	raidelcarballo	2024-06-26T13:19:35Z	0	null	[ "region:us" ]	null	2024-06-26T13:19:35Z	Entry not found
anushaporwal/wav2vec2-common_voice-tr-demo	anushaporwal	2024-07-01T11:31:22Z	0	transformers	[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-06-26T13:21:15Z	Entry not found
m-faraz-ali/kaggle2	m-faraz-ali	2024-06-26T13:22:41Z	0	null	[ "region:us" ]	null	2024-06-26T13:22:41Z	Entry not found
anhnguyen1010/QWEN-7B-Instruct-Elementary-Math	anhnguyen1010	2024-06-26T13:25:21Z	0	null	[ "region:us" ]	null	2024-06-26T13:25:21Z	Entry not found
stewhsource/GovernmentGPT	stewhsource	2024-06-26T14:22:30Z	1	null	[ "tensorboard", "politics", "debate", "text-generation", "en", "base_model:mistralai/Mistral-7B-v0.3", "license:mit", "region:us" ]	text-generation	2024-06-26T13:27:10Z	--- license: mit base_model: mistralai/Mistral-7B-v0.3 language: - en tags: - politics - debate pipeline_tag: text-generation --- # GovernmentGPT _An LLM fine-tuned on the British Commons Parliamentary Hansard to simulate the debate of political topics like members of parliament._ I wanted to see whether we can teach an LLM to do the job of elected British Members of Parliament (MPs) and debate any issue like they do in the House of Commons. GovernmentGPT is an LLM fine-tuned with a LoRA adapter. This git repo contains all the code and data necessary to build the datasets, perform fine-tuning and do inference: https://github.com/stewhsource/GovernmentGPT/ If you're looking to see an interesting end-to-end example of an LLM fine-tuning pipeline on real-world data, then look no further! The key parts of the data processing pipeline are described in the following sections: ## Raw Data Extraction The raw Hansard transcript and speaker data needed to create the training datasets sits in a few places and needs to be processed and linked together, ready to prepare the final training dataset. We only used Hansard data from 1997 onwards because it was easiest to link to the speaker data. The code to do that is here: https://github.com/stewhsource/GovernmentGPT/tree/main/DatasetPreparation. ## Training Dataset Preparation The code samples 'sequences' of real British Commons Parlimentary Hansard debate transcripts. It attaches the speaker data (eg affiliation, location, additional roles such as committee memberships), and then structures it in a format ready for LLM fine-tuning. It strips dates, MP names and some numeric linking identifiers present in the text to try and avoid the LLM reproducing with bias. There is much more work that can be done to aid generalisability in this regard. You can download the final prepared JSONL datasets ready for fine-tuning here: - [100k instances (700mb compressed)](https://stewh-publicdata.s3.eu-west-2.amazonaws.com/governmentgpt/2024-06-07/datasets/HansardSequences_100k.big.txt.zip) - [250k instances (1.7gb compressed)](https://stewh-publicdata.s3.eu-west-2.amazonaws.com/governmentgpt/2024-06-07/datasets/HansardSequences_250k.big.txt.zip) ## Fine-tuning All code for fine-tuning is in this [[link](https://github.com/stewhsource/GovernmentGPT/blob/main/FineTuning/GovernmentGPT_FineTune_Mistral_7b.ipynb)](notebook). You can easily run this on your local machine if it has a GPU, or on Google Colab. ## LLM Adapter The Mistral 7b v0.3 adapter is available for download here on HuggingFace, ready for you to plug into your own inference pipeline. ## Inference You can run the fine-tuned model easily to generate your own debates using this [[link](https://github.com/stewhsource/GovernmentGPT/blob/main/Inference/GovernmentGPT_Inference.ipynb)](notebook). As with fine-tuning, you can easily run this on your local machine if it has a GPU, or on Google Colab. ## Acknowledgements This work has been made possible through the hard work of others - thank you. Parlimentary Hansard data We make heavy use of [British Commons Parliamentary Hansard](https://hansard.parliament.uk) data. While this data is openly available to use, a number of individual and organisations have kindly worked hard to make this data more accessible for machine processing: - [mySociety](https://www.mysociety.org) (eg their data in: https://github.com/mysociety/parlparse/blob/master/members/ministers-2010.json) - [mySociety TheyWorkForYou](https://www.theyworkforyou.com) - Data APIs and dumps at https://data.theyworkforyou.com - [Parlparse](https://github.com/mysociety/parlparse) - Extracting structured data from the published Hansard - [Government datasets](https://www.parliament.uk/business/publications/research/parliament-facts-and-figures/members-of-parliament/)
Flamenco43/FSDP-2	Flamenco43	2024-06-26T13:41:19Z	0	null	[ "tensorboard", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "license:apache-2.0", "region:us" ]	null	2024-06-26T13:30:54Z	--- license: apache-2.0 base_model: google-bert/bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: FSDP-2 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. --> # FSDP-2 This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6596 - Accuracy: 0.633 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.005 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 256 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.7834 \| 1.0 \| 625 \| 0.6586 \| 0.633 \| \| 0.7112 \| 2.0 \| 1250 \| 0.6596 \| 0.633 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0 - Datasets 2.20.0 - Tokenizers 0.19.1
Darius07/UNER_subword_tk_en_lora_alpha_512_drop_0.3_rank_256_seed_42_lr_3e-5	Darius07	2024-06-26T13:42:36Z	0	null	[ "safetensors", "generated_from_trainer", "dataset:universalner/universal_ner", "base_model:xlm-roberta-base", "license:mit", "model-index", "region:us" ]	null	2024-06-26T13:31:20Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - universalner/universal_ner metrics: - precision - recall - f1 - accuracy model-index: - name: UNER_subword_tk_en_lora_alpha_512_drop_0.3_rank_256_seed_42_lr_3e-5 results: - task: name: Token Classification type: token-classification dataset: name: universalner/universal_ner en_ewt type: universalner/universal_ner config: en_ewt split: validation args: en_ewt metrics: - name: Precision type: precision value: 0.7810361681329423 - name: Recall type: recall value: 0.8271221532091098 - name: F1 type: f1 value: 0.8034188034188033 - name: Accuracy type: accuracy value: 0.9842538470714541 --- <!-- 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. --> # UNER_subword_tk_en_lora_alpha_512_drop_0.3_rank_256_seed_42_lr_3e-5 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the universalner/universal_ner en_ewt dataset. It achieves the following results on the evaluation set: - Loss: 0.0619 - Precision: 0.7810 - Recall: 0.8271 - F1: 0.8034 - Accuracy: 0.9843 ## 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: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 392 \| 0.1040 \| 0.4625 \| 0.5870 \| 0.5173 \| 0.9677 \| \| 0.168 \| 2.0 \| 784 \| 0.0696 \| 0.7047 \| 0.7733 \| 0.7374 \| 0.9789 \| \| 0.0614 \| 3.0 \| 1176 \| 0.0695 \| 0.7149 \| 0.8023 \| 0.7561 \| 0.9807 \| \| 0.0471 \| 4.0 \| 1568 \| 0.0629 \| 0.7233 \| 0.8064 \| 0.7626 \| 0.9812 \| \| 0.0471 \| 5.0 \| 1960 \| 0.0637 \| 0.7037 \| 0.8261 \| 0.76 \| 0.9801 \| \| 0.0408 \| 6.0 \| 2352 \| 0.0594 \| 0.7354 \| 0.8199 \| 0.7753 \| 0.9823 \| \| 0.036 \| 7.0 \| 2744 \| 0.0623 \| 0.7397 \| 0.8209 \| 0.7782 \| 0.9820 \| \| 0.0327 \| 8.0 \| 3136 \| 0.0601 \| 0.7686 \| 0.8219 \| 0.7944 \| 0.9846 \| \| 0.03 \| 9.0 \| 3528 \| 0.0570 \| 0.7678 \| 0.8251 \| 0.7954 \| 0.9839 \| \| 0.03 \| 10.0 \| 3920 \| 0.0588 \| 0.7765 \| 0.8199 \| 0.7976 \| 0.9847 \| \| 0.0271 \| 11.0 \| 4312 \| 0.0573 \| 0.7671 \| 0.8251 \| 0.7950 \| 0.9835 \| \| 0.0252 \| 12.0 \| 4704 \| 0.0595 \| 0.7776 \| 0.8323 \| 0.804 \| 0.9849 \| \| 0.0245 \| 13.0 \| 5096 \| 0.0578 \| 0.7858 \| 0.8240 \| 0.8044 \| 0.9844 \| \| 0.0245 \| 14.0 \| 5488 \| 0.0596 \| 0.7646 \| 0.8271 \| 0.7946 \| 0.9836 \| \| 0.0224 \| 15.0 \| 5880 \| 0.0600 \| 0.7869 \| 0.8219 \| 0.8041 \| 0.9844 \| \| 0.0216 \| 16.0 \| 6272 \| 0.0616 \| 0.7786 \| 0.8230 \| 0.8002 \| 0.9841 \| \| 0.02 \| 17.0 \| 6664 \| 0.0615 \| 0.7804 \| 0.8313 \| 0.8050 \| 0.9847 \| \| 0.0199 \| 18.0 \| 7056 \| 0.0626 \| 0.7727 \| 0.8271 \| 0.7990 \| 0.9840 \| \| 0.0199 \| 19.0 \| 7448 \| 0.0621 \| 0.7747 \| 0.8292 \| 0.801 \| 0.9841 \| \| 0.0193 \| 20.0 \| 7840 \| 0.0619 \| 0.7810 \| 0.8271 \| 0.8034 \| 0.9843 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.18.0 - Tokenizers 0.19.1
lit9003code/melotts218	lit9003code	2024-06-26T13:31:48Z	0	null	[ "region:us" ]	null	2024-06-26T13:31:35Z	Entry not found
lit9003code/melotts220	lit9003code	2024-06-26T13:34:51Z	0	null	[ "region:us" ]	null	2024-06-26T13:33:27Z	Entry not found
Ikblox/Ikblox	Ikblox	2024-06-26T13:35:23Z	0	null	[ "region:us" ]	null	2024-06-26T13:35:23Z	Entry not found
lit9003code/melotts221	lit9003code	2024-06-26T13:37:26Z	0	null	[ "region:us" ]	null	2024-06-26T13:36:06Z	Entry not found
lit9003code/melotts222	lit9003code	2024-06-26T13:38:55Z	0	null	[ "region:us" ]	null	2024-06-26T13:38:39Z	Entry not found
lit9003code/melotts223	lit9003code	2024-06-26T13:41:38Z	0	null	[ "region:us" ]	null	2024-06-26T13:40:14Z	Entry not found
lit9003code/melotts224	lit9003code	2024-06-26T13:43:05Z	0	null	[ "region:us" ]	null	2024-06-26T13:42:50Z	Entry not found
vilkahyilka/go	vilkahyilka	2024-06-26T13:42:54Z	0	null	[ "region:us" ]	null	2024-06-26T13:42:53Z	Entry not found
Darius07/UNER_subword_tk_en_lora_alpha_512_drop_0.2_rank_256_seed_42_lr_3e-5	Darius07	2024-06-27T20:10:14Z	0	null	[ "safetensors", "generated_from_trainer", "dataset:universalner/universal_ner", "base_model:xlm-roberta-base", "license:mit", "region:us" ]	null	2024-06-26T13:42:58Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - universalner/universal_ner model-index: - name: UNER_subword_tk_en_lora_alpha_512_drop_0.2_rank_256_seed_42_lr_3e-5 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. --> # UNER_subword_tk_en_lora_alpha_512_drop_0.2_rank_256_seed_42_lr_3e-5 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the universalner/universal_ner en_ewt dataset. It achieves the following results on the evaluation set: - eval_loss: 1.6475 - eval_precision: 0.0045 - eval_recall: 0.0269 - eval_f1: 0.0077 - eval_accuracy: 0.3062 - eval_runtime: 1.3804 - eval_samples_per_second: 1449.554 - eval_steps_per_second: 45.638 - step: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20.0 ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.18.0 - Tokenizers 0.19.1
lit9003code/melotts225	lit9003code	2024-06-26T13:44:25Z	0	null	[ "region:us" ]	null	2024-06-26T13:44:12Z	Entry not found
lit9003code/melotts226	lit9003code	2024-06-26T13:46:52Z	0	null	[ "region:us" ]	null	2024-06-26T13:45:37Z	Entry not found
Fakeacc007/GPT	Fakeacc007	2024-06-26T13:46:38Z	0	null	[ "region:us" ]	null	2024-06-26T13:46:38Z	Entry not found
gustavomacedo/Llama_3_Canarim	gustavomacedo	2024-06-26T13:47:14Z	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	2024-06-26T13:47:03Z	--- 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: gustavomacedo - 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)
jjsprockel/Patologia_lora_model1	jjsprockel	2024-06-27T14:01:10Z	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	2024-06-26T13:47:27Z	--- base_model: unsloth/llama-3-8b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # LLM basado en LLaMA Ajustado al Dominio de Patología Primera Versión de un LLM ajustado para responder preguntas de Patología # Uploaded model - Developed by: jjsprockel - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit Código para descarga: El siguiente es el código sugerido para descargar el modelo usando Unslot: ``` import torch from unsloth import FastLanguageModel model, tokenizer = FastLanguageModel.from_pretrained( model_name = "jjsprockel/Patologia_lora_model1", max_seq_length = 2048, # Choose any! Llama 3 is up to 8k dtype = None, load_in_4bit = True, ) FastLanguageModel.for_inference(model) alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. ### Instruction: {} ### Input: {} ### Response: {}""" ``` Código para la inferencia: El siguiente codigo demuestra como se puede llevar a cabo la inferencia. ``` instruction = input("Ingresa la pregunta que tengas de Patología: ") inputs = tokenizer( [ alpaca_prompt.format( instruction, # instruction "", # input "", # output - leave this blank for generation! ) ], return_tensors = "pt").to("cuda") from transformers import TextStreamer text_streamer = TextStreamer(tokenizer) _ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 2048) ``` 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)
lit9003code/melotts227	lit9003code	2024-06-26T13:48:26Z	0	null	[ "region:us" ]	null	2024-06-26T13:48:03Z	Entry not found
Adam3/Tim-The-Baldhead-V2	Adam3	2024-06-26T13:50:12Z	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]	text-to-image	2024-06-26T13:49:02Z	--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: '-' output: url: images/1001111177.jpg - text: '-' output: url: images/1001130185.jpg base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: null --- # Tim The Baldhead V2 <Gallery /> ## Download model [Download](/Adam3/Tim-The-Baldhead-V2/tree/main) them in the Files & versions tab.
konstantindobler/mistral7b-de-tokenizer-swap-pure-bf16-v2	konstantindobler	2024-06-26T13:51:22Z	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "de", "dataset:uonlp/CulturaX", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-06-26T13:49:37Z	--- language: de license: apache-2.0 datasets: uonlp/CulturaX --- # mistral7b-de-tokenizer-swap-pure-bf16-v2 Mistral-7B-v0.1 adapted to German as part of our study on efficient language adaptation: "Language Adaptation on a Tight Academic Compute Budget: Tokenizer Swapping Works and Pure bfloat16 Is Enough". Code: https://github.com/konstantinjdobler/tight-budget-llm-adaptation Paper: https://openreview.net/forum?id=VYfJaHeVod ## Usage ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("konstantindobler/mistral7b-de-tokenizer-swap-pure-bf16-v2") model = AutoModelForCausalLM.from_pretrained("konstantindobler/mistral7b-de-tokenizer-swap-pure-bf16-v2") # Use model and tokenizer as usual ``` ## Details The model is based on [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) and was adapted to German. The original tokenizer was replaced by a language-specific German tokenizer with a vocabulary of 32768 tokens. The new embeddings were initialized with [FOCUS](https://github.com/konstantinjdobler/focus). The model was then trained on 8 billion German tokens from [uonlp/CulturaX](https://huggingface.co/uonlp/CulturaX) with pure bfloat16 precision (no mixed precision). More details and hyperparameters can be found [in the paper](https://openreview.net/forum?id=VYfJaHeVod). ## Disclaimer The web-scale dataset used for pretraining and tokenizer training ([uonlp/CulturaX](https://huggingface.co/uonlp/CulturaX)) might contain personal and sensitive information. Such behavior needs to be assessed carefully before any real-world deployment of the models. ## Citation Please cite as follows: ```bibtex @inproceedings{dobler2024language, title={Language Adaptation on a Tight Academic Compute Budget: Tokenizer Swapping Works and Pure bfloat16 Is Enough}, author={Konstantin Dobler and Gerard de Melo}, booktitle={2nd Workshop on Advancing Neural Network Training: Computational Efficiency, Scalability, and Resource Optimization (WANT@ICML 2024)}, year={2024}, url={https://openreview.net/forum?id=VYfJaHeVod} } ```
lit9003code/melotts228	lit9003code	2024-06-26T13:49:54Z	0	null	[ "region:us" ]	null	2024-06-26T13:49:38Z	Entry not found
spjabech/Twitch_Highlighter_audio_phi	spjabech	2024-06-26T13:49:57Z	0	null	[ "region:us" ]	null	2024-06-26T13:49:57Z	Entry not found
lit9003code/melotts229	lit9003code	2024-06-26T13:51:11Z	0	null	[ "region:us" ]	null	2024-06-26T13:51:01Z	Entry not found
lit9003code/melotts230	lit9003code	2024-06-26T13:52:28Z	0	null	[ "region:us" ]	null	2024-06-26T13:52:14Z	Entry not found
lit9003code/melotts231	lit9003code	2024-06-26T13:53:56Z	0	null	[ "region:us" ]	null	2024-06-26T13:53:36Z	Entry not found
FevenTad/v1_0.65_Base	FevenTad	2024-06-26T17:16:27Z	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-26T13:54:02Z	--- 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]
safdarzeeshan/example-model	safdarzeeshan	2024-06-26T14:07:03Z	0	null	[ "region:us" ]	null	2024-06-26T13:54:49Z	# This is my first HF --- license: mit ---
lit9003code/melotts232	lit9003code	2024-06-26T13:55:30Z	0	null	[ "region:us" ]	null	2024-06-26T13:55:14Z	Entry not found
vic1215/sft_openassistant-guanaco	vic1215	2024-06-26T13:55:15Z	0	null	[ "region:us" ]	null	2024-06-26T13:55:15Z	Entry not found
Temo27Anas/videomae-base-finetuned-ucf101-subset-1	Temo27Anas	2024-06-26T13:55:52Z	0	null	[ "region:us" ]	null	2024-06-26T13:55:52Z	Entry not found
geraldabrhm/llama-3-8b-regular-complexcontext-32lora-lr8_5	geraldabrhm	2024-06-26T14:48:22Z	0	null	[ "safetensors", "region:us" ]	null	2024-06-26T13:56:24Z	Entry not found
Loren85/Domenico-Bini	Loren85	2024-06-26T14:00:06Z	0	null	[ "license:openrail", "region:us" ]	null	2024-06-26T13:57:57Z	--- license: openrail ---
spacejot/inspect_server_conditions	spacejot	2024-06-27T08:13:43Z	0	null	[ "endpoints_compatible", "region:us" ]	null	2024-06-26T13:58:14Z	Entry not found
Valeille/David	Valeille	2024-06-26T14:00:05Z	0	null	[ "region:us" ]	null	2024-06-26T14:00:05Z	Entry not found
lit9003code/melotts219	lit9003code	2024-06-26T14:01:58Z	0	null	[ "region:us" ]	null	2024-06-26T14:00:42Z	Entry not found
Vare/mist4	Vare	2024-06-26T14:04:56Z	0	null	[ "region:us" ]	null	2024-06-26T14:04:56Z	Entry not found
agrajpaudel/corgy_dog_LoRA	agrajpaudel	2024-06-26T14:11:41Z	0	null	[ "region:us" ]	null	2024-06-26T14:11:41Z	Entry not found

wkingyu666/qwen2

wkingyu666

2024-06-26T12:00:26Z

0

null

[ "region:us" ]

null

2024-06-26T12:00:26Z

Entry not found

SilvioLima/absa_3_domains

SilvioLima

2024-07-02T13:36:03Z

0

null

[ "safetensors", "region:us" ]

null

2024-06-26T12:01:28Z

#### Domain Restaurant 68.763668 Laptop 64.611111 pet 37.000000 grocery 36.815789 home 36.000000 electronics 35.427419 book 34.227273 beauty 33.382353 fashion 28.500000 toy 27.413793 ### F1-score% mean = 53.4454 ![3_dominios.png](https://cdn-uploads.huggingface.co/production/uploads/659820c0ada2ade50bc44f71/69ia8Up2WD_zqrchaKVYh.png)

hansa15100/openimage_r16_epoch25_model

hansa15100

2024-06-26T12:36:55Z

0

null

[ "tensorboard", "safetensors", "region:us" ]

null

2024-06-26T12:01:54Z

Entry not found

VKapseln475/SlimXmed122

VKapseln475

2024-06-26T12:12:43Z

0

null

[ "region:us" ]

null

2024-06-26T12:02:11Z

# <Kaufen> Slimxmed Deutschland - SlimXmed Erfahrungen Test, Einnahme Zutaten Preis <Kaufen> Slimxmed Deutschland SlimXmed tritt auf den Plan im umkämpften Markt der Nahrungsergänzungsmittel zur Gewichtsreduktion. Dabei sind sie mehr als nur ein weiteres Produkt – sie sind ein Versprechen für eine gesündere und aktivere Zukunft für alle, die nicht nur abnehmen, sondern ihre Lebensqualität verbessern wollen. Dieser Blog gibt Einblicke in unsere Bewertungen und teilt echte Erfahrungen von Benutzern. ## **[Klicken Sie hier, um jetzt auf der offiziellen Website von SlimXmed zu kaufen](https://adtocart.xyz/slimxmed-de)** ## Wissenschaftliche Belege zur Wirksamkeit von Polyphenolen zum Abnehmen (Studien) Ein Schlüsselelement im Testbericht von SlimXmed Premium Effect Kapseln ist die Evaluation der wissenschaftlichen Belege, die die behaupteten Wirkungen unterstützen. Mehrere Studien haben die positiven Effekte von Polyphenolen auf die Gewichtsreduktion untersucht. Eine Meta-Analyse, veröffentlicht im „Journal of Nutritional Science and Vitaminology„, untersuchte die Auswirkungen von Grüntee-Extrakten, reich an Catechinen, auf die Gewichtsreduktion und Gewichtserhaltung. Die Analyse von vierzehn randomisierten kontrollierten Studien ergab eine signifikante Reduktion des Körpergewichts bei Teilnehmern, die Grüntee-Extrakte konsumierten, verglichen mit den Kontrollgruppen. Resveratrol wurde in einer Studie im „International Journal of Obesity“ untersucht. Die Studie zeigte, dass eine Supplementierung mit Resveratrol den Stoffwechsel verbessern und die Fettmasse bei übergewichtigen Personen reduzieren kann. Quercetin, bekannt für seine entzündungshemmenden und antioxidativen Eigenschaften, wurde ebenfalls hinsichtlich seiner Wirkung auf das Körpergewicht erforscht. Eine im „Journal of Clinical Endocrinology & Metabolism“ veröffentlichte Studie fand heraus, dass Quercetin die Fettverbrennung erhöhen und die Fettaufnahme im Darm reduzieren kann. ## Einschränkungen und Sicherheitsprofil Während die vorhandenen Studien vielversprechende Ergebnisse bezüglich der Wirksamkeit von Polyphenolen bei der Gewichtsreduktion liefern, ist es wichtig, die Einschränkungen dieser Forschung zu berücksichtigen. Viele Studien wurden im kleinen Rahmen oder mit tierischen Modellen durchgeführt, was die Generalisierbarkeit der Ergebnisse auf den Menschen einschränkt. Zudem variiert die Dosierung der Polyphenole in den Studien erheblich, was einen direkten Vergleich der Ergebnisse erschwert. Das Sicherheitsprofil von SlimXmed Stiftung Warentest ist im Allgemeinen als gut einzustufen, da Polyphenole in den verwendeten Konzentrationen selten ernsthafte Nebenwirkungen verursachen. Dennoch sollten Personen mit bestimmten gesundheitlichen Voraussetzungen oder diejenigen, die Medikamente einnehmen, vor der Anwendung einen Arzt konsultieren. ## **[Klicken Sie hier, um jetzt auf der offiziellen Website von SlimXmed zu kaufen](https://adtocart.xyz/slimxmed-de)**

zmlapq18/example-model

zmlapq18

2024-06-26T12:02:59Z

0

null

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

null

2024-06-26T12:02:59Z

--- license: mit ---

wufan/PDF-EXTRACT-KIT

wufan

2024-06-26T12:04:14Z

0

null

[ "region:us" ]

null

2024-06-26T12:04:14Z

Entry not found

elaistu/Salesperson

elaistu

2024-06-26T12:04:39Z

0

null

[ "region:us" ]

null

2024-06-26T12:04:39Z

Entry not found

alternativerealitystudio/Llama-3-8B-F

alternativerealitystudio

2024-06-26T12:05:34Z

0

null

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

null

2024-06-26T12:05:34Z

--- license: mit ---

jasonk19/mistral-7b-gec

jasonk19

2024-06-26T12:07:12Z

0

null

[ "region:us" ]

null

2024-06-26T12:07:12Z

Entry not found

rzarno/llama-3-8b-industry-code-with-adapter

rzarno

2024-06-26T12:08:28Z

0

null

[ "region:us" ]

null

2024-06-26T12:08:28Z

Entry not found

Atonemo/meeting-recorder

Atonemo

2024-06-26T12:09:38Z

0

null

[ "region:us" ]

null

2024-06-26T12:09:38Z

Entry not found

msonali/xlm-roberta-base-finetuned-panx-de

msonali

2024-06-26T12:09:51Z

0

null

[ "region:us" ]

null

2024-06-26T12:09:51Z

Entry not found

anileo1/llama3-8B-instruct-lora-finetuned-v1.2-16bit

anileo1

2024-06-26T12:11:36Z

0

null

[ "region:us" ]

null

2024-06-26T12:11:35Z

Entry not found

Suhash/my_awesome_billsum_model

Suhash

2024-06-26T12:12:05Z

0

null

[ "region:us" ]

null

2024-06-26T12:12:05Z

Entry not found

Divy12/Forest

Divy12

2024-06-26T12:12:36Z

0

null

[ "region:us" ]

null

2024-06-26T12:12:36Z

Entry not found

nsugianto/detr-resnet50_tuned_detrresnet50_lsdocelementdetv1type7_plusb5_5389s_adjparam6_lr5e5_dec1e4_b14

nsugianto

2024-06-26T12:13:15Z

0

null

[ "region:us" ]

null

2024-06-26T12:13:15Z

Entry not found

nsugianto/detr-resnet50_tuned_detrresnet50_lsdocelementdetv1type7_plusb5_5389s_adjparam6_lr5e5_dec1e4_b10

nsugianto

2024-06-26T12:13:52Z

0

null

[ "region:us" ]

null

2024-06-26T12:13:52Z

Entry not found

ozgung/red-bowl-SD3

ozgung

2024-06-26T12:14:49Z

0

null

[ "region:us" ]

null

2024-06-26T12:14:49Z

Entry not found

Grayx/john_paul_van_damme_37

Grayx

2024-06-26T12:15:36Z

0

null

[ "region:us" ]

null

2024-06-26T12:15:25Z

Entry not found

nsugianto/detr-resnet50_tuned_detrresnet50_lsdocelementdetv1type7_plusb5_5389s_adjparam6_lr5e5_dec5e4_b12

nsugianto

2024-06-26T12:16:15Z

0

null

[ "region:us" ]

null

2024-06-26T12:16:15Z

Entry not found

Cynor/cynorllama3

Cynor

2024-06-26T12:17:14Z

0

null

[ "license:llama3", "region:us" ]

null

2024-06-26T12:17:09Z

--- license: llama3 ---

diproger/llama3-8b-loss-fine-tuned-test

diproger

2024-06-26T12:17:29Z

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

2024-06-26T12:17:15Z

--- 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:** diproger - **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)

kheopss/kheops_quantized

kheopss

2024-06-26T12:18:02Z

0

null

[ "region:us" ]

null

2024-06-26T12:18:02Z

Entry not found

Ak1104/snapshot

Ak1104

2024-07-02T11:42:46Z

0

null

[ "region:us" ]

null

2024-06-26T12:19:03Z

Entry not found

fwdfsdf/Mysey

fwdfsdf

2024-06-26T12:27:48Z

0

null

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

null

2024-06-26T12:21:50Z

--- license: openrail ---

R0obin/email-spam-classifier

R0obin

2024-06-26T12:27:34Z

0

null

[ "region:us" ]

null

2024-06-26T12:27:34Z

Entry not found

valerielucro/mistral_gsm8k_dpo_cot_beta_0.9

valerielucro

2024-06-26T12:29:58Z

0

transformers

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

null

2024-06-26T12:29:51Z

--- 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]

valerielucro/mistral_gsm8k_dpo_cot_beta_0.7

valerielucro

2024-06-26T12:31:24Z

0

transformers

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

null

2024-06-26T12:31:15Z

--- 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]

henilp105/InjecAgent-llama-7b-optim-all

henilp105

2024-06-26T12:33:53Z

0

null

[ "safetensors", "region:us" ]

null

2024-06-26T12:31:38Z

Entry not found

valerielucro/mistral_gsm8k_dpo_cot_beta_0.5

valerielucro

2024-06-26T12:35:32Z

0

transformers

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

null

2024-06-26T12:35:23Z

--- 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]

prabinpanta0/image_classification_with_cnns

prabinpanta0

2024-06-26T12:44:17Z

0

1

tensorflow

[ "tensorflow", "keras", "image-classification", "image-classification-cnns", "Fasion_image-classification", "neural-network", "en", "license:mit", "region:us" ]

image-classification

2024-06-26T12:36:07Z

--- license: mit language: en metrics: mean_squared_error library_name: tensorflow tags: - image-classification - image-classification-cnns - Fasion_image-classification - tensorflow - neural-network pipeline_tag: image-classification --- This model was created as a practice exercise for the course "Intro to TensorFlow for Deep Learning" from Udacity, given by TensorFlow. It was trained on a dataset of TenserFlow Fashion MNIST using the cnns method. The model uses a small neural network built with TensorFlow. ## License This model is released under the MIT license.

Ikhsan1/hugging

Ikhsan1

2024-06-26T12:36:12Z

0

null

[ "region:us" ]

null

2024-06-26T12:36:12Z

Entry not found

DarbyTan/Test

DarbyTan

2024-06-26T12:37:04Z

0

null

[ "region:us" ]

null

2024-06-26T12:37:04Z

Entry not found

Truepeak/ORPO-PM01-0.4

Truepeak

2024-06-26T12:39:02Z

0

transformers

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

null

2024-06-26T12:37:44Z

--- 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]

valerielucro/mistral_gsm8k_dpo_cot_beta_0.8

valerielucro

2024-06-26T12:38:37Z

0

transformers

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

null

2024-06-26T12:38:31Z

--- 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]

kaya-kedi/Toadette-TITANPretrain

kaya-kedi

2024-06-26T12:44:26Z

0

null

[ "region:us" ]

null

2024-06-26T12:38:34Z

Entry not found

Hasano20/Mask2Former_Clean_Set1_95images_mask2former-swin-large-ade-semantic

Hasano20

2024-06-26T12:40:07Z

0

null

[ "region:us" ]

null

2024-06-26T12:40:07Z

Entry not found

pinkyprakash/Llama-3-8b-chat-finetune

pinkyprakash

2024-06-26T12:43:49Z

0

null

[ "region:us" ]

null

2024-06-26T12:43:49Z

Entry not found

TopperThijs/Llama2-Open-ended-Finetuned-6epochs15mlm

TopperThijs

2024-06-26T12:44:38Z

0

null

[ "region:us" ]

null

2024-06-26T12:44:38Z

Entry not found

Janibicigo/Misscake

Janibicigo

2024-06-26T12:46:36Z

0

null

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

null

2024-06-26T12:46:36Z

--- license: apache-2.0 ---

shayantreylon2/lora_model4

shayantreylon2

2024-06-26T12:47:41Z

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

2024-06-26T12:47:16Z

--- 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:** shayantreylon2 - **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)

jurieyel/77cdm-llama3-sqlcoder-8b-500s-1000d

jurieyel

2024-06-26T12:48:01Z

0

transformers

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

null

2024-06-26T12:47:50Z

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

charlieoneill/jsalt-astroph-data

charlieoneill

2024-06-26T12:53:49Z

0

null

[ "region:us" ]

null

2024-06-26T12:48:43Z

Entry not found

geraldabrhm/llama-3-8b-regular-nocontext-32lora-lr8_5

geraldabrhm

2024-06-26T13:42:07Z

0

null

[ "safetensors", "region:us" ]

null

2024-06-26T12:49:01Z

Entry not found

newih/western

newih

2024-06-26T13:04:09Z

0

null

[ "region:us" ]

null

2024-06-26T12:49:26Z

Entry not found

nam194/llama3-8b-qlora-ultrachat-unsloth

nam194

2024-06-26T15:19:29Z

0

null

[ "tensorboard", "safetensors", "region:us" ]

null

2024-06-26T12:51:01Z

Entry not found

bug7/longchat_1080

bug7

2024-06-26T12:53:00Z

0

transformers

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

null

2024-06-26T12:52:49Z

--- 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]

X1Rexords/Sidhu-Moosewala-AI-Model

X1Rexords

2024-06-26T13:01:44Z

0

null

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

null

2024-06-26T12:56:43Z

--- license: openrail ---

kevin009/deepseek

kevin009

2024-06-26T18:40:17Z

0

transformers

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

null

2024-06-26T13:03:37Z

--- 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]

ekaterina-blatova-jb/model_lr1e-5_v0

ekaterina-blatova-jb

2024-06-26T13:05:44Z

0

transformers

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

text-generation

2024-06-26T13:03:46Z

--- {} --- ## Evaluation results Validation loss on the whole input: 0.7532717230496928 Validation loss on completion: 0.785313343279995

bug7/longchat_960

bug7

2024-06-26T13:04:09Z

0

transformers

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

null

2024-06-26T13:03:59Z

--- 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]

wadiea/voice_model

wadiea

2024-06-26T13:04:09Z

0

null

[ "region:us" ]

null

2024-06-26T13:04:09Z

Entry not found

taric49/LLAMA3_Summarization_16k_2ep_b4g16_2024

taric49

2024-06-26T13:07:01Z

0

transformers

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

null

2024-06-26T13:05:52Z

--- 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]

schaturv/llama2-7b-key-value-pairings-adapter

schaturv

2024-06-26T13:26:06Z

0

transformers

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

null

2024-06-26T13:07:59Z

--- 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]

scrfur/parukorvcmmodel

scrfur

2024-06-26T13:10:45Z

0

null

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

null

2024-06-26T13:08:06Z

--- license: unknown ---

Darius07/UNER_subword_tk_en_lora_alpha_1024_drop_0.3_rank_512_seed_42

Darius07

2024-06-26T13:27:23Z

0

null

[ "safetensors", "generated_from_trainer", "dataset:universalner/universal_ner", "base_model:xlm-roberta-base", "license:mit", "model-index", "region:us" ]

null

2024-06-26T13:08:30Z

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - universalner/universal_ner metrics: - precision - recall - f1 - accuracy model-index: - name: UNER_subword_tk_en_lora_alpha_1024_drop_0.3_rank_512_seed_42 results: - task: name: Token Classification type: token-classification dataset: name: universalner/universal_ner en_ewt type: universalner/universal_ner config: en_ewt split: validation args: en_ewt metrics: - name: Precision type: precision value: 0.7731660231660231 - name: Recall type: recall value: 0.8291925465838509 - name: F1 type: f1 value: 0.8001998001998001 - name: Accuracy type: accuracy value: 0.9844128991212374 ---  # UNER_subword_tk_en_lora_alpha_1024_drop_0.3_rank_512_seed_42 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the universalner/universal_ner en_ewt dataset. It achieves the following results on the evaluation set: - Loss: 0.0633 - Precision: 0.7732 - Recall: 0.8292 - F1: 0.8002 - Accuracy: 0.9844 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 35.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 392 | 0.1362 | 0.2922 | 0.3903 | 0.3342 | 0.9569 | | 0.2046 | 2.0 | 784 | 0.0889 | 0.5868 | 0.6822 | 0.6309 | 0.9745 | | 0.085 | 3.0 | 1176 | 0.0772 | 0.6687 | 0.7940 | 0.7260 | 0.9778 | | 0.0591 | 4.0 | 1568 | 0.0692 | 0.7085 | 0.7950 | 0.7493 | 0.9802 | | 0.0591 | 5.0 | 1960 | 0.0692 | 0.6894 | 0.8251 | 0.7512 | 0.9791 | | 0.0496 | 6.0 | 2352 | 0.0664 | 0.6937 | 0.8157 | 0.7498 | 0.9791 | | 0.0448 | 7.0 | 2744 | 0.0671 | 0.7007 | 0.8313 | 0.7604 | 0.9797 | | 0.0409 | 8.0 | 3136 | 0.0674 | 0.7200 | 0.8147 | 0.7644 | 0.9814 | | 0.0388 | 9.0 | 3528 | 0.0635 | 0.7306 | 0.8478 | 0.7849 | 0.9816 | | 0.0388 | 10.0 | 3920 | 0.0620 | 0.7481 | 0.8209 | 0.7828 | 0.9832 | | 0.0357 | 11.0 | 4312 | 0.0586 | 0.7758 | 0.8240 | 0.7992 | 0.9844 | | 0.0333 | 12.0 | 4704 | 0.0611 | 0.7606 | 0.8354 | 0.7963 | 0.9840 | | 0.0323 | 13.0 | 5096 | 0.0601 | 0.7819 | 0.8240 | 0.8024 | 0.9844 | | 0.0323 | 14.0 | 5488 | 0.0638 | 0.7203 | 0.8292 | 0.7709 | 0.9812 | | 0.0303 | 15.0 | 5880 | 0.0600 | 0.7737 | 0.8354 | 0.8034 | 0.9841 | | 0.0293 | 16.0 | 6272 | 0.0602 | 0.7703 | 0.8333 | 0.8006 | 0.9841 | | 0.0271 | 17.0 | 6664 | 0.0609 | 0.7634 | 0.8416 | 0.8006 | 0.9841 | | 0.0269 | 18.0 | 7056 | 0.0641 | 0.7569 | 0.8478 | 0.7998 | 0.9835 | | 0.0269 | 19.0 | 7448 | 0.0594 | 0.7793 | 0.8261 | 0.8020 | 0.9849 | | 0.0263 | 20.0 | 7840 | 0.0608 | 0.7873 | 0.8199 | 0.8032 | 0.9850 | | 0.025 | 21.0 | 8232 | 0.0606 | 0.7812 | 0.8240 | 0.8020 | 0.9850 | | 0.0236 | 22.0 | 8624 | 0.0639 | 0.7558 | 0.8364 | 0.7941 | 0.9839 | | 0.0228 | 23.0 | 9016 | 0.0620 | 0.7668 | 0.8375 | 0.8006 | 0.9845 | | 0.0228 | 24.0 | 9408 | 0.0612 | 0.7647 | 0.8344 | 0.7980 | 0.9842 | | 0.0229 | 25.0 | 9800 | 0.0618 | 0.7584 | 0.8385 | 0.7965 | 0.9839 | | 0.0227 | 26.0 | 10192 | 0.0631 | 0.7678 | 0.8385 | 0.8016 | 0.9842 | | 0.0216 | 27.0 | 10584 | 0.0628 | 0.7883 | 0.8364 | 0.8117 | 0.9850 | | 0.0216 | 28.0 | 10976 | 0.0611 | 0.7765 | 0.8344 | 0.8044 | 0.9849 | | 0.0203 | 29.0 | 11368 | 0.0615 | 0.7755 | 0.8406 | 0.8068 | 0.9847 | | 0.02 | 30.0 | 11760 | 0.0629 | 0.7743 | 0.8344 | 0.8032 | 0.9847 | | 0.0197 | 31.0 | 12152 | 0.0620 | 0.7763 | 0.8333 | 0.8038 | 0.9843 | | 0.0197 | 32.0 | 12544 | 0.0633 | 0.7750 | 0.8271 | 0.8002 | 0.9845 | | 0.0197 | 33.0 | 12936 | 0.0631 | 0.7813 | 0.8323 | 0.8060 | 0.9845 | | 0.0192 | 34.0 | 13328 | 0.0629 | 0.7768 | 0.8323 | 0.8036 | 0.9845 | | 0.0188 | 35.0 | 13720 | 0.0633 | 0.7732 | 0.8292 | 0.8002 | 0.9844 | ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.18.0 - Tokenizers 0.19.1

jazzxxx/my_awesome_mind_model

jazzxxx

2024-06-26T13:10:14Z

0

null

[ "region:us" ]

null

2024-06-26T13:10:14Z

Entry not found

jvv7/ppo-Huggy

jvv7

2024-06-26T13:10:33Z

0

null

[ "region:us" ]

null

2024-06-26T13:10:33Z

Entry not found

jjgerbo/stable-diffusion-embeddings-lora

jjgerbo

2024-06-27T14:05:35Z

0

null

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

null

2024-06-26T13:12:07Z

--- license: mit ---

raidelcarballo/Arboles

raidelcarballo

2024-06-26T13:19:35Z

0

null

[ "region:us" ]

null

2024-06-26T13:19:35Z

Entry not found

anushaporwal/wav2vec2-common_voice-tr-demo

anushaporwal

2024-07-01T11:31:22Z

0

transformers

[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-06-26T13:21:15Z

Entry not found

m-faraz-ali/kaggle2

m-faraz-ali

2024-06-26T13:22:41Z

0

null

[ "region:us" ]

null

2024-06-26T13:22:41Z

Entry not found

anhnguyen1010/QWEN-7B-Instruct-Elementary-Math

anhnguyen1010

2024-06-26T13:25:21Z

0

null

[ "region:us" ]

null

2024-06-26T13:25:21Z

Entry not found

stewhsource/GovernmentGPT

stewhsource

2024-06-26T14:22:30Z

0

1

null

[ "tensorboard", "politics", "debate", "text-generation", "en", "base_model:mistralai/Mistral-7B-v0.3", "license:mit", "region:us" ]

text-generation

2024-06-26T13:27:10Z

--- license: mit base_model: mistralai/Mistral-7B-v0.3 language: - en tags: - politics - debate pipeline_tag: text-generation --- # GovernmentGPT _An LLM fine-tuned on the British Commons Parliamentary Hansard to simulate the debate of political topics like members of parliament._ I wanted to see whether we can teach an LLM to do the job of elected British Members of Parliament (MPs) and debate any issue like they do in the House of Commons. GovernmentGPT is an LLM fine-tuned with a LoRA adapter. This git repo contains all the code and data necessary to build the datasets, perform fine-tuning and do inference: https://github.com/stewhsource/GovernmentGPT/ If you're looking to see an interesting end-to-end example of an LLM fine-tuning pipeline on real-world data, then look no further! The key parts of the data processing pipeline are described in the following sections: ## Raw Data Extraction The raw Hansard transcript and speaker data needed to create the training datasets sits in a few places and needs to be processed and linked together, ready to prepare the final training dataset. We only used Hansard data from 1997 onwards because it was easiest to link to the speaker data. The code to do that is here: https://github.com/stewhsource/GovernmentGPT/tree/main/DatasetPreparation. ## Training Dataset Preparation The code samples 'sequences' of real British Commons Parlimentary Hansard debate transcripts. It attaches the speaker data (eg affiliation, location, additional roles such as committee memberships), and then structures it in a format ready for LLM fine-tuning. It strips dates, MP names and some numeric linking identifiers present in the text to try and avoid the LLM reproducing with bias. There is much more work that can be done to aid generalisability in this regard. You can download the final prepared JSONL datasets ready for fine-tuning here: - [100k instances (700mb compressed)](https://stewh-publicdata.s3.eu-west-2.amazonaws.com/governmentgpt/2024-06-07/datasets/HansardSequences_100k.big.txt.zip) - [250k instances (1.7gb compressed)](https://stewh-publicdata.s3.eu-west-2.amazonaws.com/governmentgpt/2024-06-07/datasets/HansardSequences_250k.big.txt.zip) ## Fine-tuning All code for fine-tuning is in this [[link](https://github.com/stewhsource/GovernmentGPT/blob/main/FineTuning/GovernmentGPT_FineTune_Mistral_7b.ipynb)](notebook). You can easily run this on your local machine if it has a GPU, or on Google Colab. ## LLM Adapter The Mistral 7b v0.3 adapter is available for download here on HuggingFace, ready for you to plug into your own inference pipeline. ## Inference You can run the fine-tuned model easily to generate your own debates using this [[link](https://github.com/stewhsource/GovernmentGPT/blob/main/Inference/GovernmentGPT_Inference.ipynb)](notebook). As with fine-tuning, you can easily run this on your local machine if it has a GPU, or on Google Colab. ## Acknowledgements This work has been made possible through the hard work of others - thank you. *Parlimentary Hansard data* We make heavy use of [British Commons Parliamentary Hansard](https://hansard.parliament.uk) data. While this data is openly available to use, a number of individual and organisations have kindly worked hard to make this data more accessible for machine processing: - [mySociety](https://www.mysociety.org) (eg their data in: https://github.com/mysociety/parlparse/blob/master/members/ministers-2010.json) - [mySociety TheyWorkForYou](https://www.theyworkforyou.com) - Data APIs and dumps at https://data.theyworkforyou.com - [Parlparse](https://github.com/mysociety/parlparse) - Extracting structured data from the published Hansard - [Government datasets](https://www.parliament.uk/business/publications/research/parliament-facts-and-figures/members-of-parliament/)

Flamenco43/FSDP-2

Flamenco43

2024-06-26T13:41:19Z

0

null

[ "tensorboard", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "license:apache-2.0", "region:us" ]

null

2024-06-26T13:30:54Z

--- license: apache-2.0 base_model: google-bert/bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: FSDP-2 results: [] ---  # FSDP-2 This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6596 - Accuracy: 0.633 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.005 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 256 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7834 | 1.0 | 625 | 0.6586 | 0.633 | | 0.7112 | 2.0 | 1250 | 0.6596 | 0.633 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0 - Datasets 2.20.0 - Tokenizers 0.19.1

Darius07/UNER_subword_tk_en_lora_alpha_512_drop_0.3_rank_256_seed_42_lr_3e-5

Darius07

2024-06-26T13:42:36Z

0

null

[ "safetensors", "generated_from_trainer", "dataset:universalner/universal_ner", "base_model:xlm-roberta-base", "license:mit", "model-index", "region:us" ]

null

2024-06-26T13:31:20Z

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - universalner/universal_ner metrics: - precision - recall - f1 - accuracy model-index: - name: UNER_subword_tk_en_lora_alpha_512_drop_0.3_rank_256_seed_42_lr_3e-5 results: - task: name: Token Classification type: token-classification dataset: name: universalner/universal_ner en_ewt type: universalner/universal_ner config: en_ewt split: validation args: en_ewt metrics: - name: Precision type: precision value: 0.7810361681329423 - name: Recall type: recall value: 0.8271221532091098 - name: F1 type: f1 value: 0.8034188034188033 - name: Accuracy type: accuracy value: 0.9842538470714541 ---  # UNER_subword_tk_en_lora_alpha_512_drop_0.3_rank_256_seed_42_lr_3e-5 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the universalner/universal_ner en_ewt dataset. It achieves the following results on the evaluation set: - Loss: 0.0619 - Precision: 0.7810 - Recall: 0.8271 - F1: 0.8034 - Accuracy: 0.9843 ## 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: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 392 | 0.1040 | 0.4625 | 0.5870 | 0.5173 | 0.9677 | | 0.168 | 2.0 | 784 | 0.0696 | 0.7047 | 0.7733 | 0.7374 | 0.9789 | | 0.0614 | 3.0 | 1176 | 0.0695 | 0.7149 | 0.8023 | 0.7561 | 0.9807 | | 0.0471 | 4.0 | 1568 | 0.0629 | 0.7233 | 0.8064 | 0.7626 | 0.9812 | | 0.0471 | 5.0 | 1960 | 0.0637 | 0.7037 | 0.8261 | 0.76 | 0.9801 | | 0.0408 | 6.0 | 2352 | 0.0594 | 0.7354 | 0.8199 | 0.7753 | 0.9823 | | 0.036 | 7.0 | 2744 | 0.0623 | 0.7397 | 0.8209 | 0.7782 | 0.9820 | | 0.0327 | 8.0 | 3136 | 0.0601 | 0.7686 | 0.8219 | 0.7944 | 0.9846 | | 0.03 | 9.0 | 3528 | 0.0570 | 0.7678 | 0.8251 | 0.7954 | 0.9839 | | 0.03 | 10.0 | 3920 | 0.0588 | 0.7765 | 0.8199 | 0.7976 | 0.9847 | | 0.0271 | 11.0 | 4312 | 0.0573 | 0.7671 | 0.8251 | 0.7950 | 0.9835 | | 0.0252 | 12.0 | 4704 | 0.0595 | 0.7776 | 0.8323 | 0.804 | 0.9849 | | 0.0245 | 13.0 | 5096 | 0.0578 | 0.7858 | 0.8240 | 0.8044 | 0.9844 | | 0.0245 | 14.0 | 5488 | 0.0596 | 0.7646 | 0.8271 | 0.7946 | 0.9836 | | 0.0224 | 15.0 | 5880 | 0.0600 | 0.7869 | 0.8219 | 0.8041 | 0.9844 | | 0.0216 | 16.0 | 6272 | 0.0616 | 0.7786 | 0.8230 | 0.8002 | 0.9841 | | 0.02 | 17.0 | 6664 | 0.0615 | 0.7804 | 0.8313 | 0.8050 | 0.9847 | | 0.0199 | 18.0 | 7056 | 0.0626 | 0.7727 | 0.8271 | 0.7990 | 0.9840 | | 0.0199 | 19.0 | 7448 | 0.0621 | 0.7747 | 0.8292 | 0.801 | 0.9841 | | 0.0193 | 20.0 | 7840 | 0.0619 | 0.7810 | 0.8271 | 0.8034 | 0.9843 | ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.18.0 - Tokenizers 0.19.1

lit9003code/melotts218

lit9003code

2024-06-26T13:31:48Z

0

null

[ "region:us" ]

null

2024-06-26T13:31:35Z

Entry not found

lit9003code/melotts220

lit9003code

2024-06-26T13:34:51Z

0

null

[ "region:us" ]

null

2024-06-26T13:33:27Z

Entry not found

Ikblox/Ikblox

Ikblox

2024-06-26T13:35:23Z

0

null

[ "region:us" ]

null

2024-06-26T13:35:23Z

Entry not found

lit9003code/melotts221

lit9003code

2024-06-26T13:37:26Z

0

null

[ "region:us" ]

null

2024-06-26T13:36:06Z

Entry not found

lit9003code/melotts222

lit9003code

2024-06-26T13:38:55Z

0

null

[ "region:us" ]

null

2024-06-26T13:38:39Z

Entry not found

lit9003code/melotts223

lit9003code

2024-06-26T13:41:38Z

0

null

[ "region:us" ]

null

2024-06-26T13:40:14Z

Entry not found

lit9003code/melotts224

lit9003code

2024-06-26T13:43:05Z

0

null

[ "region:us" ]

null

2024-06-26T13:42:50Z

Entry not found

vilkahyilka/go

vilkahyilka

2024-06-26T13:42:54Z

0

null

[ "region:us" ]

null

2024-06-26T13:42:53Z

Entry not found

Darius07/UNER_subword_tk_en_lora_alpha_512_drop_0.2_rank_256_seed_42_lr_3e-5

Darius07

2024-06-27T20:10:14Z

0

null

[ "safetensors", "generated_from_trainer", "dataset:universalner/universal_ner", "base_model:xlm-roberta-base", "license:mit", "region:us" ]

null

2024-06-26T13:42:58Z

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - universalner/universal_ner model-index: - name: UNER_subword_tk_en_lora_alpha_512_drop_0.2_rank_256_seed_42_lr_3e-5 results: [] ---  # UNER_subword_tk_en_lora_alpha_512_drop_0.2_rank_256_seed_42_lr_3e-5 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the universalner/universal_ner en_ewt dataset. It achieves the following results on the evaluation set: - eval_loss: 1.6475 - eval_precision: 0.0045 - eval_recall: 0.0269 - eval_f1: 0.0077 - eval_accuracy: 0.3062 - eval_runtime: 1.3804 - eval_samples_per_second: 1449.554 - eval_steps_per_second: 45.638 - step: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20.0 ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.18.0 - Tokenizers 0.19.1

lit9003code/melotts225

lit9003code

2024-06-26T13:44:25Z

0

null

[ "region:us" ]

null

2024-06-26T13:44:12Z

Entry not found

lit9003code/melotts226

lit9003code

2024-06-26T13:46:52Z

0

null

[ "region:us" ]

null

2024-06-26T13:45:37Z

Entry not found

Fakeacc007/GPT

Fakeacc007

2024-06-26T13:46:38Z

0

null

[ "region:us" ]

null

2024-06-26T13:46:38Z

Entry not found

gustavomacedo/Llama_3_Canarim

gustavomacedo

2024-06-26T13:47:14Z

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

2024-06-26T13:47:03Z

--- 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:** gustavomacedo - **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)

jjsprockel/Patologia_lora_model1

jjsprockel

2024-06-27T14:01:10Z

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

2024-06-26T13:47:27Z

--- base_model: unsloth/llama-3-8b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # LLM basado en LLaMA Ajustado al Dominio de Patología Primera Versión de un LLM ajustado para responder preguntas de Patología # Uploaded model - **Developed by:** jjsprockel - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit **Código para descarga:** El siguiente es el código sugerido para descargar el modelo usando Unslot: ``` import torch from unsloth import FastLanguageModel model, tokenizer = FastLanguageModel.from_pretrained( model_name = "jjsprockel/Patologia_lora_model1", max_seq_length = 2048, # Choose any! Llama 3 is up to 8k dtype = None, load_in_4bit = True, ) FastLanguageModel.for_inference(model) alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. ### Instruction: {} ### Input: {} ### Response: {}""" ``` **Código para la inferencia:** El siguiente codigo demuestra como se puede llevar a cabo la inferencia. ``` instruction = input("Ingresa la pregunta que tengas de Patología: ") inputs = tokenizer( [ alpaca_prompt.format( instruction, # instruction "", # input "", # output - leave this blank for generation! ) ], return_tensors = "pt").to("cuda") from transformers import TextStreamer text_streamer = TextStreamer(tokenizer) _ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 2048) ``` 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)

lit9003code/melotts227

lit9003code

2024-06-26T13:48:26Z

0

null

[ "region:us" ]

null

2024-06-26T13:48:03Z

Entry not found

Adam3/Tim-The-Baldhead-V2

Adam3

2024-06-26T13:50:12Z

0

diffusers

[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]

text-to-image

2024-06-26T13:49:02Z

--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: '-' output: url: images/1001111177.jpg - text: '-' output: url: images/1001130185.jpg base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: null --- # Tim The Baldhead V2 <Gallery /> ## Download model [Download](/Adam3/Tim-The-Baldhead-V2/tree/main) them in the Files & versions tab.

konstantindobler/mistral7b-de-tokenizer-swap-pure-bf16-v2

konstantindobler

2024-06-26T13:51:22Z

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "de", "dataset:uonlp/CulturaX", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-06-26T13:49:37Z

--- language: de license: apache-2.0 datasets: uonlp/CulturaX --- # mistral7b-de-tokenizer-swap-pure-bf16-v2 Mistral-7B-v0.1 adapted to German as part of our study on efficient language adaptation: "Language Adaptation on a Tight Academic Compute Budget: Tokenizer Swapping Works and Pure bfloat16 Is Enough". Code: https://github.com/konstantinjdobler/tight-budget-llm-adaptation Paper: https://openreview.net/forum?id=VYfJaHeVod ## Usage ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("konstantindobler/mistral7b-de-tokenizer-swap-pure-bf16-v2") model = AutoModelForCausalLM.from_pretrained("konstantindobler/mistral7b-de-tokenizer-swap-pure-bf16-v2") # Use model and tokenizer as usual ``` ## Details The model is based on [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) and was adapted to German. The original tokenizer was replaced by a language-specific German tokenizer with a vocabulary of 32768 tokens. The new embeddings were initialized with [FOCUS](https://github.com/konstantinjdobler/focus). The model was then trained on 8 billion German tokens from [uonlp/CulturaX](https://huggingface.co/uonlp/CulturaX) with pure bfloat16 precision (no mixed precision). More details and hyperparameters can be found [in the paper](https://openreview.net/forum?id=VYfJaHeVod). ## Disclaimer The web-scale dataset used for pretraining and tokenizer training ([uonlp/CulturaX](https://huggingface.co/uonlp/CulturaX)) might contain personal and sensitive information. Such behavior needs to be assessed carefully before any real-world deployment of the models. ## Citation Please cite as follows: ```bibtex @inproceedings{dobler2024language, title={Language Adaptation on a Tight Academic Compute Budget: Tokenizer Swapping Works and Pure bfloat16 Is Enough}, author={Konstantin Dobler and Gerard de Melo}, booktitle={2nd Workshop on Advancing Neural Network Training: Computational Efficiency, Scalability, and Resource Optimization (WANT@ICML 2024)}, year={2024}, url={https://openreview.net/forum?id=VYfJaHeVod} } ```

lit9003code/melotts228

lit9003code

2024-06-26T13:49:54Z

0

null

[ "region:us" ]

null

2024-06-26T13:49:38Z

Entry not found

spjabech/Twitch_Highlighter_audio_phi

spjabech

2024-06-26T13:49:57Z

0

null

[ "region:us" ]

null

2024-06-26T13:49:57Z

Entry not found

lit9003code/melotts229

lit9003code

2024-06-26T13:51:11Z

0

null

[ "region:us" ]

null

2024-06-26T13:51:01Z

Entry not found

lit9003code/melotts230

lit9003code

2024-06-26T13:52:28Z

0

null

[ "region:us" ]

null

2024-06-26T13:52:14Z

Entry not found

lit9003code/melotts231

lit9003code

2024-06-26T13:53:56Z

0

null

[ "region:us" ]

null

2024-06-26T13:53:36Z

Entry not found

FevenTad/v1_0.65_Base

FevenTad

2024-06-26T17:16:27Z

0

transformers

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

null

2024-06-26T13:54:02Z

--- 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]

safdarzeeshan/example-model

safdarzeeshan

2024-06-26T14:07:03Z

0

null

[ "region:us" ]

null

2024-06-26T13:54:49Z

# This is my first HF --- license: mit ---

lit9003code/melotts232

lit9003code

2024-06-26T13:55:30Z

0

null

[ "region:us" ]

null

2024-06-26T13:55:14Z

Entry not found

vic1215/sft_openassistant-guanaco

vic1215

2024-06-26T13:55:15Z

0

null

[ "region:us" ]

null

2024-06-26T13:55:15Z

Entry not found

Temo27Anas/videomae-base-finetuned-ucf101-subset-1

Temo27Anas

2024-06-26T13:55:52Z

0

null

[ "region:us" ]

null

2024-06-26T13:55:52Z

Entry not found

geraldabrhm/llama-3-8b-regular-complexcontext-32lora-lr8_5

geraldabrhm

2024-06-26T14:48:22Z

0

null

[ "safetensors", "region:us" ]

null

2024-06-26T13:56:24Z

Entry not found

Loren85/Domenico-Bini

Loren85

2024-06-26T14:00:06Z

0

null

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

null

2024-06-26T13:57:57Z

--- license: openrail ---

spacejot/inspect_server_conditions

spacejot

2024-06-27T08:13:43Z

0

null

[ "endpoints_compatible", "region:us" ]

null

2024-06-26T13:58:14Z

Entry not found

Valeille/David

Valeille

2024-06-26T14:00:05Z

0

null

[ "region:us" ]

null

2024-06-26T14:00:05Z

Entry not found

lit9003code/melotts219

lit9003code

2024-06-26T14:01:58Z

0

null

[ "region:us" ]

null

2024-06-26T14:00:42Z

Entry not found

Vare/mist4

Vare

2024-06-26T14:04:56Z

0

null

[ "region:us" ]

null

2024-06-26T14:04:56Z

Entry not found

agrajpaudel/corgy_dog_LoRA

agrajpaudel

2024-06-26T14:11:41Z

0

null

[ "region:us" ]

null

2024-06-26T14:11:41Z

Entry not found