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train · 842k rows

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JoseGR1702/output_Nutsedge_0	JoseGR1702	2024-09-16T21:34:46Z	27	0	diffusers	[ "diffusers", "safetensors", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-09-16T21:33:21Z	--- library_name: diffusers --- # 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 🧨 diffusers 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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JoseGR1702/output_Ecliptpros_2	JoseGR1702	2024-09-16T21:33:16Z	27	0	diffusers	[ "diffusers", "safetensors", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-09-16T21:31:49Z	--- library_name: diffusers --- # 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 🧨 diffusers model that has been pushed on the Hub. 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JoseGR1702/output_Ecliptpros_1	JoseGR1702	2024-09-16T21:31:44Z	27	0	diffusers	[ "diffusers", "safetensors", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-09-16T21:30:00Z	--- library_name: diffusers --- # 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 🧨 diffusers 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. 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JoseGR1702/output_Dighitariya_2	JoseGR1702	2024-09-16T21:28:25Z	27	0	diffusers	[ "diffusers", "safetensors", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-09-16T21:27:15Z	--- library_name: diffusers --- # 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 🧨 diffusers 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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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. 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JoseGR1702/output_Dighitariya_1	JoseGR1702	2024-09-16T21:27:09Z	29	0	diffusers	[ "diffusers", "safetensors", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-09-16T21:25:50Z	--- library_name: diffusers --- # 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 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
A-POR-LOS-8000/distilhubert-finetuned-mixed-data	A-POR-LOS-8000	2024-09-16T21:26:45Z	6	0	transformers	[ "transformers", "tensorboard", "safetensors", "hubert", "audio-classification", "generated_from_trainer", "base_model:ntu-spml/distilhubert", "base_model:finetune:ntu-spml/distilhubert", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2024-09-01T16:36:42Z	--- library_name: transformers license: apache-2.0 base_model: ntu-spml/distilhubert tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: distilhubert-finetuned-mixed-data 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. --> # distilhubert-finetuned-mixed-data This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8806 - Accuracy: 0.7912 - F1: 0.7772 - Precision: 0.8022 - Recall: 0.7912 - Confusion Matrix: [[59, 1, 1, 2], [20, 35, 22, 0], [2, 7, 68, 0], [2, 0, 0, 54]] ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 123 - gradient_accumulation_steps: 2 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 40 - mixed_precision_training: Native AMP - label_smoothing_factor: 0.1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| Confusion Matrix \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\|:--------------------------------------------------------------:\| \| 0.4221 \| 22.2222 \| 100 \| 0.8806 \| 0.7912 \| 0.7772 \| 0.8022 \| 0.7912 \| [[59, 1, 1, 2], [20, 35, 22, 0], [2, 7, 68, 0], [2, 0, 0, 54]] \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.1+cu121 - Tokenizers 0.19.1
JoseGR1702/output_Dighitariya_0	JoseGR1702	2024-09-16T21:25:45Z	27	0	diffusers	[ "diffusers", "safetensors", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-09-16T21:24:13Z	--- library_name: diffusers --- # 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 🧨 diffusers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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SongTonyLi/Phi-3.5-mini-instruct-SFT-D_chosen-dpo-mix_skywork_infinity	SongTonyLi	2024-09-16T21:23:35Z	6	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "trl", "sft", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-09-16T21:19:32Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MattiaTintori/ABSA_Aspect_IT	MattiaTintori	2024-09-16T21:17:49Z	6	0	setfit	[ "setfit", "safetensors", "xlm-roberta", "absa", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "base_model:sentence-transformers/paraphrase-multilingual-mpnet-base-v2", "base_model:finetune:sentence-transformers/paraphrase-multilingual-mpnet-base-v2", "model-index", "region:us" ]	text-classification	2024-09-16T21:16:48Z	--- base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2 library_name: setfit metrics: - f1 pipeline_tag: text-classification tags: - setfit - absa - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: Locale:Locale molto bene arredato, con stile e atmosfera tipica valtellinese. Cucina ottima, dal bastone di carne al pesce, dai pizzoccheri agli gnocchetti, dal vino ai dolci, tutto perfetto e soprattutto di grande qualità... Filippo poi è un’autentica forza della natura, molto simpatico, cordiale e amichevole,...Altro - text: cucina:Locale accogliente e familiare...bravissima la ragazza in cucina, come le ragazze al banco e in sala! CONSIGLIATO - text: servizio:Il servizio era impeccabile e il tortello di zucca era sublime. - text: cucina:Il ristorante propone piatti vegetariani che NON sono vegetariani. Dopo aver specificato al servizio la nostra etica alimentare, ci è stata consigliata una portata che durante la consumazione abbiamo constatato con amarezza che avesse parti di maiale come ingredienti (confermato dalla cucina). Poco valgono le...scuse del servizio, trovo assurdo e inconcepibile che situazioni del genere possano accadere nel 2024. Evidentemente questo è indice della poca professionalità di questo ristorante.Altro - text: servizio:La polenta con formaggio era saporita, ma il servizio è stato lento. inference: false model-index: - name: SetFit Aspect Model with sentence-transformers/paraphrase-multilingual-mpnet-base-v2 results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: f1 value: 0.8096514745308312 name: F1 --- # SetFit Aspect Model with sentence-transformers/paraphrase-multilingual-mpnet-base-v2 This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) as the Sentence Transformer embedding model. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance is used for classification. In particular, this model is in charge of filtering aspect span candidates. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. This model was trained within the context of a larger system for ABSA, which looks like so: 1. Use a spaCy model to select possible aspect span candidates. 2. Use this SetFit model to filter these possible aspect span candidates. 3. Use a SetFit model to classify the filtered aspect span candidates. ## Model Details ### Model Description - Model Type: SetFit - Sentence Transformer body: [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) - Classification head: a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance - spaCy Model: it_core_news_lg - SetFitABSA Aspect Model: [MattiaTintori/Final_aspect_Colab_It](https://huggingface.co/MattiaTintori/Final_aspect_Colab_It) - SetFitABSA Polarity Model: [setfit-absa-polarity](https://huggingface.co/setfit-absa-polarity) - Maximum Sequence Length: 128 tokens - Number of Classes: 2 classes <!-- - Training Dataset: [Unknown](https://huggingface.co/datasets/unknown) --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Repository: [SetFit on GitHub](https://github.com/huggingface/setfit) - Paper: [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - Blogpost: [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels \| Label \| Examples \| \|:----------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| aspect \| <ul><li>"tavolo:Purtroppo tutte le volte, ed è anni, che tento di prenotare non sono mai stato fortunato........devo dirvi che ora ho un po' perso la poesia!!!!!! O aggiungono tavoli o cambiano location......mai fatta cosi tanta fatica per trovare un tavolo!!!!! Non so francamente se comporro' ancora...Altro"</li><li>'spesa:Devo premettere che sono sempre stato ospite e non so la spesa.Da quanto posso intuire la carne la fa da padrona ed essendo io ve non posso giudicare.Per me trovo sempre cose piacevoli come antipasti a buffet,primi veg riso alle verdure, trofie al pesto patate...Altro'</li><li>'carne:Devo premettere che sono sempre stato ospite e non so la spesa.Da quanto posso intuire la carne la fa da padrona ed essendo io ve non posso giudicare.Per me trovo sempre cose piacevoli come antipasti a buffet,primi veg riso alle verdure, trofie al pesto patate...Altro'</li></ul> \| \| no aspect \| <ul><li>"volte:Purtroppo tutte le volte, ed è anni, che tento di prenotare non sono mai stato fortunato........devo dirvi che ora ho un po' perso la poesia!!!!!! O aggiungono tavoli o cambiano location......mai fatta cosi tanta fatica per trovare un tavolo!!!!! Non so francamente se comporro' ancora...Altro"</li><li>"anni:Purtroppo tutte le volte, ed è anni, che tento di prenotare non sono mai stato fortunato........devo dirvi che ora ho un po' perso la poesia!!!!!! O aggiungono tavoli o cambiano location......mai fatta cosi tanta fatica per trovare un tavolo!!!!! Non so francamente se comporro' ancora...Altro"</li><li>"poesia:Purtroppo tutte le volte, ed è anni, che tento di prenotare non sono mai stato fortunato........devo dirvi che ora ho un po' perso la poesia!!!!!! O aggiungono tavoli o cambiano location......mai fatta cosi tanta fatica per trovare un tavolo!!!!! Non so francamente se comporro' ancora...Altro"</li></ul> \| ## Evaluation ### Metrics \| Label \| F1 \| \|:--------\|:-------\| \| all \| 0.8097 \| ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import AbsaModel # Download from the 🤗 Hub model = AbsaModel.from_pretrained( "MattiaTintori/Final_aspect_Colab_It", "setfit-absa-polarity", ) # Run inference preds = model("The food was great, but the venue is just way too busy.") ``` <!-- ### Downstream Use List how someone could finetune this model on their own dataset. --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Set Metrics \| Training set \| Min \| Median \| Max \| \|:-------------\|:----\|:--------\|:----\| \| Word count \| 9 \| 40.3192 \| 137 \| \| Label \| Training Sample Count \| \|:----------\|:----------------------\| \| no aspect \| 1379 \| \| aspect \| 1378 \| ### Training Hyperparameters - batch_size: (128, 32) - num_epochs: (5, 32) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 10 - body_learning_rate: (5e-05, 5e-05) - head_learning_rate: 0.01 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: True - warmup_proportion: 0.1 - l2_weight: 0.02 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: True ### Training Results \| Epoch \| Step \| Training Loss \| Validation Loss \| \|:----------:\|:-------:\|:-------------:\|:---------------:\| \| 0.0023 \| 1 \| 0.2484 \| - \| \| 0.0464 \| 20 \| 0.2718 \| 0.259 \| \| 0.0928 \| 40 \| 0.2581 \| 0.2544 \| \| 0.1392 \| 60 \| 0.2266 \| 0.2475 \| \| 0.1856 \| 80 \| 0.233 \| 0.2298 \| \| 0.2320 \| 100 \| 0.2104 \| 0.2145 \| \| 0.2784 \| 120 \| 0.1487 \| 0.2106 \| \| 0.3248 \| 140 \| 0.1615 \| 0.2314 \| \| 0.3712 \| 160 \| 0.1328 \| 0.2164 \| \| 0.4176 \| 180 \| 0.0905 \| 0.2164 \| \| 0.4640 \| 200 \| 0.0934 \| 0.2517 \| \| 0.5104 \| 220 \| 0.0942 \| 0.2185 \| \| 0.5568 \| 240 \| 0.0774 \| 0.2469 \| \| 0.6032 \| 260 \| 0.1013 \| 0.2248 \| \| 0.6497 \| 280 \| 0.0781 \| 0.2221 \| \| 0.6961 \| 300 \| 0.0386 \| 0.2362 \| \| 0.7425 \| 320 \| 0.084 \| 0.2386 \| * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.12 - SetFit: 1.0.3 - Sentence Transformers: 3.1.0 - spaCy: 3.7.6 - Transformers: 4.39.0 - PyTorch: 2.4.0+cu121 - Datasets: 3.0.0 - Tokenizers: 0.15.2 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
yasmineee/araT5-Base	yasmineee	2024-09-16T21:17:40Z	7	0	null	[ "safetensors", "t5", "generated_from_trainer", "base_model:UBC-NLP/AraT5v2-base-1024", "base_model:finetune:UBC-NLP/AraT5v2-base-1024", "region:us" ]	null	2024-09-07T21:46:20Z	--- base_model: UBC-NLP/AraT5v2-base-1024 tags: - generated_from_trainer metrics: - bleu - rouge model-index: - name: araT5-Base 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. --> # araT5-Base This model is a fine-tuned version of [UBC-NLP/AraT5v2-base-1024](https://huggingface.co/UBC-NLP/AraT5v2-base-1024) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3080 - Bleu: 19.9507 - Rouge: 0.6204 - Gen Len: 14.3392 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Rouge \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:------:\|:-------:\| \| 2.7135 \| 1.0 \| 7500 \| 1.6843 \| 15.9171 \| 0.5533 \| 14.33 \| \| 1.6024 \| 2.0 \| 15000 \| 1.4055 \| 18.3573 \| 0.5965 \| 14.27 \| \| 1.1542 \| 3.0 \| 22500 \| 1.3082 \| 19.3343 \| 0.6112 \| 14.3792 \| \| 0.8608 \| 4.0 \| 30000 \| 1.3080 \| 19.9507 \| 0.6204 \| 14.3392 \| \| 0.6687 \| 5.0 \| 37500 \| 1.3430 \| 20.2683 \| 0.6234 \| 14.3436 \| ### Framework versions - Transformers 4.44.0 - Pytorch 2.4.0 - Datasets 2.21.0 - Tokenizers 0.19.1
Triangle104/MN-12B-Mag-Mell-R1-Q5_K_M-GGUF	Triangle104	2024-09-16T21:09:44Z	350	1	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:inflatebot/MN-12B-Mag-Mell-R1", "base_model:quantized:inflatebot/MN-12B-Mag-Mell-R1", "endpoints_compatible", "region:us", "conversational" ]	null	2024-09-16T21:09:06Z	--- base_model: inflatebot/MN-12B-Mag-Mell-R1 library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # Triangle104/MN-12B-Mag-Mell-R1-Q5_K_M-GGUF This model was converted to GGUF format from [`inflatebot/MN-12B-Mag-Mell-R1`](https://huggingface.co/inflatebot/MN-12B-Mag-Mell-R1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/inflatebot/MN-12B-Mag-Mell-R1) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/MN-12B-Mag-Mell-R1-Q5_K_M-GGUF --hf-file mn-12b-mag-mell-r1-q5_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/MN-12B-Mag-Mell-R1-Q5_K_M-GGUF --hf-file mn-12b-mag-mell-r1-q5_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/MN-12B-Mag-Mell-R1-Q5_K_M-GGUF --hf-file mn-12b-mag-mell-r1-q5_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/MN-12B-Mag-Mell-R1-Q5_K_M-GGUF --hf-file mn-12b-mag-mell-r1-q5_k_m.gguf -c 2048 ```
cyixiao/qwen-1.5B-cqa	cyixiao	2024-09-16T20:36:59Z	83	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-09-16T20:34:41Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
lukebhan/PDEControlGymModels	lukebhan	2024-09-16T20:36:37Z	0	0	null	[ "en", "license:apache-2.0", "region:us" ]	null	2023-12-10T05:00:07Z	--- license: apache-2.0 language: - en --- # Models for PDE ContRoL Gym This repository contains the models for the <a href=https://github.com/lukebhan/PDEControlGym/tree/main>PDE ContRoL Gym</a>. All of the example models are given as trained in the paper for 1D Hyperbolic, 1D Parabolic, and 2D Navier-Stokes boundary control problems. If there are any questions, feel free to make a github issue or reach out to [email protected].
Triangle104/MN-12B-Mag-Mell-R1-Q4_K_S-GGUF	Triangle104	2024-09-16T20:35:06Z	6	1	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:inflatebot/MN-12B-Mag-Mell-R1", "base_model:quantized:inflatebot/MN-12B-Mag-Mell-R1", "endpoints_compatible", "region:us", "conversational" ]	null	2024-09-16T20:34:33Z	--- base_model: inflatebot/MN-12B-Mag-Mell-R1 library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # Triangle104/MN-12B-Mag-Mell-R1-Q4_K_S-GGUF This model was converted to GGUF format from [`inflatebot/MN-12B-Mag-Mell-R1`](https://huggingface.co/inflatebot/MN-12B-Mag-Mell-R1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/inflatebot/MN-12B-Mag-Mell-R1) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/MN-12B-Mag-Mell-R1-Q4_K_S-GGUF --hf-file mn-12b-mag-mell-r1-q4_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/MN-12B-Mag-Mell-R1-Q4_K_S-GGUF --hf-file mn-12b-mag-mell-r1-q4_k_s.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/MN-12B-Mag-Mell-R1-Q4_K_S-GGUF --hf-file mn-12b-mag-mell-r1-q4_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/MN-12B-Mag-Mell-R1-Q4_K_S-GGUF --hf-file mn-12b-mag-mell-r1-q4_k_s.gguf -c 2048 ```
mradermacher/Gemma-Ataraxy-Dare-9b-GGUF	mradermacher	2024-09-16T20:28:37Z	15	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:BarBarickoza/Gemma-Ataraxy-Dare-9b", "base_model:quantized:BarBarickoza/Gemma-Ataraxy-Dare-9b", "endpoints_compatible", "region:us" ]	null	2024-09-16T18:32:27Z	--- base_model: BarBarickoza/Gemma-Ataraxy-Dare-9b language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/BarBarickoza/Gemma-Ataraxy-Dare-9b <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Gemma-Ataraxy-Dare-9b-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Gemma-Ataraxy-Dare-9b-GGUF/resolve/main/Gemma-Ataraxy-Dare-9b.Q2_K.gguf) \| Q2_K \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-Ataraxy-Dare-9b-GGUF/resolve/main/Gemma-Ataraxy-Dare-9b.IQ3_XS.gguf) \| IQ3_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-Ataraxy-Dare-9b-GGUF/resolve/main/Gemma-Ataraxy-Dare-9b.IQ3_S.gguf) \| IQ3_S \| 4.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-Ataraxy-Dare-9b-GGUF/resolve/main/Gemma-Ataraxy-Dare-9b.Q3_K_S.gguf) \| Q3_K_S \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-Ataraxy-Dare-9b-GGUF/resolve/main/Gemma-Ataraxy-Dare-9b.IQ3_M.gguf) \| IQ3_M \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-Ataraxy-Dare-9b-GGUF/resolve/main/Gemma-Ataraxy-Dare-9b.Q3_K_M.gguf) \| Q3_K_M \| 4.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-Ataraxy-Dare-9b-GGUF/resolve/main/Gemma-Ataraxy-Dare-9b.Q3_K_L.gguf) \| Q3_K_L \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-Ataraxy-Dare-9b-GGUF/resolve/main/Gemma-Ataraxy-Dare-9b.IQ4_XS.gguf) \| IQ4_XS \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-Ataraxy-Dare-9b-GGUF/resolve/main/Gemma-Ataraxy-Dare-9b.Q4_K_S.gguf) \| Q4_K_S \| 5.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-Ataraxy-Dare-9b-GGUF/resolve/main/Gemma-Ataraxy-Dare-9b.Q4_K_M.gguf) \| Q4_K_M \| 5.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-Ataraxy-Dare-9b-GGUF/resolve/main/Gemma-Ataraxy-Dare-9b.Q5_K_S.gguf) \| Q5_K_S \| 6.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-Ataraxy-Dare-9b-GGUF/resolve/main/Gemma-Ataraxy-Dare-9b.Q5_K_M.gguf) \| Q5_K_M \| 6.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-Ataraxy-Dare-9b-GGUF/resolve/main/Gemma-Ataraxy-Dare-9b.Q6_K.gguf) \| Q6_K \| 7.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-Ataraxy-Dare-9b-GGUF/resolve/main/Gemma-Ataraxy-Dare-9b.Q8_0.gguf) \| Q8_0 \| 9.9 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-Ataraxy-Dare-9b-GGUF/resolve/main/Gemma-Ataraxy-Dare-9b.f16.gguf) \| f16 \| 18.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
automatichamster/gpt2-cia-world-factbook-augmented	automatichamster	2024-09-16T20:17:39Z	19	0	transformers	[ "transformers", "safetensors", "gpt2", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2024-09-16T20:07:23Z	--- 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. 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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]
JunyaoPu/segformer-b0-scene-parse-150	JunyaoPu	2024-09-16T20:15:48Z	19	0	transformers	[ "transformers", "tensorboard", "safetensors", "segformer", "generated_from_trainer", "dataset:scene_parse_150", "base_model:nvidia/mit-b0", "base_model:finetune:nvidia/mit-b0", "license:other", "endpoints_compatible", "region:us" ]	null	2024-09-16T19:27:05Z	--- library_name: transformers license: other base_model: nvidia/mit-b0 tags: - generated_from_trainer datasets: - scene_parse_150 model-index: - name: segformer-b0-scene-parse-150 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. --> # segformer-b0-scene-parse-150 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the scene_parse_150 dataset. It achieves the following results on the evaluation set: - Loss: 2.4363 ## 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: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 25 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 4.9247 \| 1.0 \| 20 \| 4.9059 \| \| 4.5746 \| 2.0 \| 40 \| 4.6125 \| \| 4.5749 \| 3.0 \| 60 \| 4.0543 \| \| 4.2089 \| 4.0 \| 80 \| 3.7518 \| \| 3.9694 \| 5.0 \| 100 \| 3.5456 \| \| 3.4601 \| 6.0 \| 120 \| 3.2747 \| \| 3.5999 \| 7.0 \| 140 \| 3.2017 \| \| 3.2724 \| 8.0 \| 160 \| 2.9125 \| \| 3.3775 \| 9.0 \| 180 \| 2.9575 \| \| 2.9466 \| 10.0 \| 200 \| 2.7341 \| \| 2.8341 \| 11.0 \| 220 \| 2.7660 \| \| 3.0457 \| 12.0 \| 240 \| 2.8243 \| \| 3.8739 \| 13.0 \| 260 \| 2.6063 \| \| 2.6287 \| 14.0 \| 280 \| 2.6507 \| \| 2.6291 \| 15.0 \| 300 \| 2.5783 \| \| 3.1508 \| 16.0 \| 320 \| 2.4557 \| \| 2.7062 \| 17.0 \| 340 \| 2.3839 \| \| 3.0388 \| 18.0 \| 360 \| 2.5159 \| \| 2.1882 \| 19.0 \| 380 \| 2.4833 \| \| 2.4467 \| 20.0 \| 400 \| 2.4029 \| \| 2.2945 \| 21.0 \| 420 \| 2.4240 \| \| 2.5678 \| 22.0 \| 440 \| 2.4354 \| \| 2.8432 \| 23.0 \| 460 \| 2.4213 \| \| 2.6837 \| 24.0 \| 480 \| 2.3866 \| \| 2.398 \| 25.0 \| 500 \| 2.4363 \| ### Framework versions - Transformers 4.44.2 - Pytorch 1.11.0 - Datasets 2.21.0 - Tokenizers 0.19.1
Vishwas1/bert-base-imdb2	Vishwas1	2024-09-16T19:44:05Z	90	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-09-16T19:31:12Z	--- base_model: bert-base-uncased library_name: transformers license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert-base-imdb2 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. --> # bert-base-imdb2 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.1+cu121 - Datasets 3.0.0 - Tokenizers 0.19.1
NVEagle/Eagle-X5-13B	NVEagle	2024-09-16T19:43:11Z	27	15	transformers	[ "transformers", "safetensors", "eagle_llama", "text-generation", "Eagle", "VLM", "image-text-to-text", "arxiv:2408.15998", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-08-23T04:46:17Z	--- license: cc-by-nc-sa-4.0 library_name: transformers pipeline_tag: image-text-to-text tags: - Eagle - VLM --- # Eagle Model Card ## Model details Model type: Eagle is a family of Vision-Centric High-Resolution Multimodal LLMs. It presents a thorough exploration to strengthen multimodal LLM perception with a mixture of vision encoders and different input resolutions. The model contains a channel-concatenation-based "CLIP+X" fusion for vision experts with different architectures (ViT/ConvNets) and knowledge (detection/segmentation/OCR/SSL). The resulting family of Eagle models support up to over 1K input resolution and obtain strong results on multimodal LLM benchmarks, especially resolution-sensitive tasks such as optical character recognition and document understanding. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64618b9496259bec21d44704/BdAIMvo--yG7SpG5xDeYN.png) Paper or resources for more information: https://github.com/NVlabs/Eagle [arXiv](https://arxiv.org/pdf/2408.15998) / [Demo](https://huggingface.co/spaces/NVEagle/Eagle-X5-13B-Chat) / [Huggingface](https://huggingface.co/papers/2408.15998) ``` @article{shi2024eagle, title = {Eagle: Exploring The Design Space for Multimodal LLMs with Mixture of Encoders}, author={Min Shi and Fuxiao Liu and Shihao Wang and Shijia Liao and Subhashree Radhakrishnan and De-An Huang and Hongxu Yin and Karan Sapra and Yaser Yacoob and Humphrey Shi and Bryan Catanzaro and Andrew Tao and Jan Kautz and Zhiding Yu and Guilin Liu}, journal={arXiv:2408.15998}, year={2024} } ``` ## License - The code is released under the Apache 2.0 license as found in the [LICENSE](./LICENSE) file. - The pretrained weights are released under the [CC-BY-NC-SA-4.0 license](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en). - The service is a research preview intended for non-commercial use only, and is subject to the following licenses and terms: - [Model License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA - [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI - [Dataset Licenses](https://github.com/Efficient-Large-Model/VILA/blob/main/data_prepare/LICENSE) for each one used during training. Where to send questions or comments about the model: https://github.com/NVlabs/Eagle/issues ## Model Architecture: Architecture Type: Transformer ## Input: Input Type: Image, Text Input Format: Red, Green, Blue; String ## Output: Output Type: Text Output Format: String ## Inference: ``` import os import torch import numpy as np from eagle import conversation as conversation_lib from eagle.constants import DEFAULT_IMAGE_TOKEN from eagle.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN from eagle.conversation import conv_templates, SeparatorStyle from eagle.model.builder import load_pretrained_model from eagle.utils import disable_torch_init from eagle.mm_utils import tokenizer_image_token, get_model_name_from_path, process_images, KeywordsStoppingCriteria from PIL import Image import argparse from transformers import TextIteratorStreamer from threading import Thread model_path = "NVEagle/Eagle-X5-13B-Chat" conv_mode = "vicuna_v1" image_path = "assets/georgia-tech.jpeg" input_prompt = "Describe this image." model_name = get_model_name_from_path(model_path) tokenizer, model, image_processor, context_len = load_pretrained_model(model_path,None,model_name,False,False) if model.config.mm_use_im_start_end: input_prompt = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + input_prompt else: input_prompt = DEFAULT_IMAGE_TOKEN + '\n' + input_prompt conv = conv_templates[conv_mode].copy() conv.append_message(conv.roles[0], input_prompt) conv.append_message(conv.roles[1], None) prompt = conv.get_prompt() image = Image.open(image_path).convert('RGB') image_tensor = process_images([image], image_processor, model.config)[0] input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt') input_ids = input_ids.to(device='cuda', non_blocking=True) image_tensor = image_tensor.to(dtype=torch.float16, device='cuda', non_blocking=True) with torch.inference_mode(): output_ids = model.generate( input_ids.unsqueeze(0), images=image_tensor.unsqueeze(0), image_sizes=[image.size], do_sample=True, temperature=0.2, top_p=0.5, num_beams=1, max_new_tokens=256, use_cache=True) outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip() print(f"Image:{image_path} \nPrompt:{input_prompt} \nOutput:{outputs}") ``` [Preferred/Supported] Operating System(s): <br> Linux ## Intended use Primary intended uses: The primary use of Eagle is research on large multimodal models and chatbots. Primary intended users: The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence. ## Ethical Considerations NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
NVEagle/Eagle-X4-8B-Plus	NVEagle	2024-09-16T19:42:48Z	4,592	3	transformers	[ "transformers", "safetensors", "eagle_llama", "text-generation", "Eagle", "VLM", "image-text-to-text", "conversational", "arxiv:2408.15998", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-09-07T18:54:39Z	--- license: cc-by-nc-sa-4.0 library_name: transformers pipeline_tag: image-text-to-text tags: - Eagle - VLM --- # Eagle Model Card ## Model details Model type: Eagle is a family of Vision-Centric High-Resolution Multimodal LLMs. It presents a thorough exploration to strengthen multimodal LLM perception with a mixture of vision encoders and different input resolutions. The model contains a channel-concatenation-based "CLIP+X" fusion for vision experts with different architectures (ViT/ConvNets) and knowledge (detection/segmentation/OCR/SSL). The resulting family of Eagle models support up to over 1K input resolution and obtain strong results on multimodal LLM benchmarks, especially resolution-sensitive tasks such as optical character recognition and document understanding. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64618b9496259bec21d44704/BdAIMvo--yG7SpG5xDeYN.png) Paper or resources for more information: https://github.com/NVlabs/Eagle [arXiv](https://arxiv.org/pdf/2408.15998) / [Demo](https://huggingface.co/spaces/NVEagle/Eagle-X5-13B-Chat) / [Huggingface](https://huggingface.co/papers/2408.15998) ``` @article{shi2024eagle, title = {Eagle: Exploring The Design Space for Multimodal LLMs with Mixture of Encoders}, author={Min Shi and Fuxiao Liu and Shihao Wang and Shijia Liao and Subhashree Radhakrishnan and De-An Huang and Hongxu Yin and Karan Sapra and Yaser Yacoob and Humphrey Shi and Bryan Catanzaro and Andrew Tao and Jan Kautz and Zhiding Yu and Guilin Liu}, journal={arXiv:2408.15998}, year={2024} } ``` ## License - The code is released under the Apache 2.0 license as found in the [LICENSE](./LICENSE) file. - The pretrained weights are released under the [CC-BY-NC-SA-4.0 license](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en). - The service is a research preview intended for non-commercial use only, and is subject to the following licenses and terms: - [Model License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA - [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI - [Dataset Licenses](https://github.com/Efficient-Large-Model/VILA/blob/main/data_prepare/LICENSE) for each one used during training. Where to send questions or comments about the model: https://github.com/NVlabs/Eagle/issues ## Model Architecture: Architecture Type: Transformer ## Input: Input Type: Image, Text Input Format: Red, Green, Blue; String ## Output: Output Type: Text Output Format: String ## Inference: ``` import os import torch import numpy as np from eagle import conversation as conversation_lib from eagle.constants import DEFAULT_IMAGE_TOKEN from eagle.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN from eagle.conversation import conv_templates, SeparatorStyle from eagle.model.builder import load_pretrained_model from eagle.utils import disable_torch_init from eagle.mm_utils import tokenizer_image_token, get_model_name_from_path, process_images, KeywordsStoppingCriteria from PIL import Image import argparse from transformers import TextIteratorStreamer from threading import Thread model_path = "NVEagle/Eagle-X5-13B-Chat" conv_mode = "vicuna_v1" image_path = "assets/georgia-tech.jpeg" input_prompt = "Describe this image." model_name = get_model_name_from_path(model_path) tokenizer, model, image_processor, context_len = load_pretrained_model(model_path,None,model_name,False,False) if model.config.mm_use_im_start_end: input_prompt = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + input_prompt else: input_prompt = DEFAULT_IMAGE_TOKEN + '\n' + input_prompt conv = conv_templates[conv_mode].copy() conv.append_message(conv.roles[0], input_prompt) conv.append_message(conv.roles[1], None) prompt = conv.get_prompt() image = Image.open(image_path).convert('RGB') image_tensor = process_images([image], image_processor, model.config)[0] input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt') input_ids = input_ids.to(device='cuda', non_blocking=True) image_tensor = image_tensor.to(dtype=torch.float16, device='cuda', non_blocking=True) with torch.inference_mode(): output_ids = model.generate( input_ids.unsqueeze(0), images=image_tensor.unsqueeze(0), image_sizes=[image.size], do_sample=True, temperature=0.2, top_p=0.5, num_beams=1, max_new_tokens=256, use_cache=True) outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip() print(f"Image:{image_path} \nPrompt:{input_prompt} \nOutput:{outputs}") ``` [Preferred/Supported] Operating System(s): <br> Linux ## Intended use Primary intended uses: The primary use of Eagle is research on large multimodal models and chatbots. Primary intended users: The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence. ## Ethical Considerations NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
NVEagle/Eagle-X5-13B-Chat	NVEagle	2024-09-16T19:41:06Z	9,947	28	transformers	[ "transformers", "safetensors", "eagle_llama", "text-generation", "Eagle", "VLM", "image-text-to-text", "arxiv:2408.15998", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-08-23T04:41:24Z	--- license: cc-by-nc-sa-4.0 library_name: transformers pipeline_tag: image-text-to-text tags: - Eagle - VLM --- # Eagle Model Card ## Model details Model type: Eagle is a family of Vision-Centric High-Resolution Multimodal LLMs. It presents a thorough exploration to strengthen multimodal LLM perception with a mixture of vision encoders and different input resolutions. The model contains a channel-concatenation-based "CLIP+X" fusion for vision experts with different architectures (ViT/ConvNets) and knowledge (detection/segmentation/OCR/SSL). The resulting family of Eagle models support up to over 1K input resolution and obtain strong results on multimodal LLM benchmarks, especially resolution-sensitive tasks such as optical character recognition and document understanding. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64618b9496259bec21d44704/BdAIMvo--yG7SpG5xDeYN.png) Paper or resources for more information: https://github.com/NVlabs/Eagle [arXiv](https://arxiv.org/pdf/2408.15998) / [Demo](https://huggingface.co/spaces/NVEagle/Eagle-X5-13B-Chat) / [Huggingface](https://huggingface.co/papers/2408.15998) ``` @article{shi2024eagle, title = {Eagle: Exploring The Design Space for Multimodal LLMs with Mixture of Encoders}, author={Min Shi and Fuxiao Liu and Shihao Wang and Shijia Liao and Subhashree Radhakrishnan and De-An Huang and Hongxu Yin and Karan Sapra and Yaser Yacoob and Humphrey Shi and Bryan Catanzaro and Andrew Tao and Jan Kautz and Zhiding Yu and Guilin Liu}, journal={arXiv:2408.15998}, year={2024} } ``` ## License - The code is released under the Apache 2.0 license as found in the [LICENSE](./LICENSE) file. - The pretrained weights are released under the [CC-BY-NC-SA-4.0 license](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en). - The service is a research preview intended for non-commercial use only, and is subject to the following licenses and terms: - [Model License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA - [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI - [Dataset Licenses](https://github.com/Efficient-Large-Model/VILA/blob/main/data_prepare/LICENSE) for each one used during training. Where to send questions or comments about the model: https://github.com/NVlabs/Eagle/issues ## Model Architecture: Architecture Type: Transformer ## Input: Input Type: Image, Text Input Format: Red, Green, Blue; String ## Output: Output Type: Text Output Format: String ## Inference: ``` import os import torch import numpy as np from eagle import conversation as conversation_lib from eagle.constants import DEFAULT_IMAGE_TOKEN from eagle.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN from eagle.conversation import conv_templates, SeparatorStyle from eagle.model.builder import load_pretrained_model from eagle.utils import disable_torch_init from eagle.mm_utils import tokenizer_image_token, get_model_name_from_path, process_images, KeywordsStoppingCriteria from PIL import Image import argparse from transformers import TextIteratorStreamer from threading import Thread model_path = "NVEagle/Eagle-X5-13B-Chat" conv_mode = "vicuna_v1" image_path = "assets/georgia-tech.jpeg" input_prompt = "Describe this image." model_name = get_model_name_from_path(model_path) tokenizer, model, image_processor, context_len = load_pretrained_model(model_path,None,model_name,False,False) if model.config.mm_use_im_start_end: input_prompt = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + input_prompt else: input_prompt = DEFAULT_IMAGE_TOKEN + '\n' + input_prompt conv = conv_templates[conv_mode].copy() conv.append_message(conv.roles[0], input_prompt) conv.append_message(conv.roles[1], None) prompt = conv.get_prompt() image = Image.open(image_path).convert('RGB') image_tensor = process_images([image], image_processor, model.config)[0] input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt') input_ids = input_ids.to(device='cuda', non_blocking=True) image_tensor = image_tensor.to(dtype=torch.float16, device='cuda', non_blocking=True) with torch.inference_mode(): output_ids = model.generate( input_ids.unsqueeze(0), images=image_tensor.unsqueeze(0), image_sizes=[image.size], do_sample=True, temperature=0.2, top_p=0.5, num_beams=1, max_new_tokens=256, use_cache=True) outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip() print(f"Image:{image_path} \nPrompt:{input_prompt} \nOutput:{outputs}") ``` [Preferred/Supported] Operating System(s): <br> Linux ## Intended use Primary intended uses: The primary use of Eagle is research on large multimodal models and chatbots. Primary intended users: The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence. ## Ethical Considerations NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
NVEagle/Eagle-X5-34B-Chat	NVEagle	2024-09-16T19:40:43Z	267	0	transformers	[ "transformers", "safetensors", "eagle_llama", "text-generation", "Eagle", "VLM", "image-text-to-text", "conversational", "arxiv:2408.15998", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-09-14T19:18:15Z	--- license: cc-by-nc-sa-4.0 library_name: transformers pipeline_tag: image-text-to-text tags: - Eagle - VLM --- # Eagle Model Card ## Model details Model type: Eagle is a family of Vision-Centric High-Resolution Multimodal LLMs. It presents a thorough exploration to strengthen multimodal LLM perception with a mixture of vision encoders and different input resolutions. The model contains a channel-concatenation-based "CLIP+X" fusion for vision experts with different architectures (ViT/ConvNets) and knowledge (detection/segmentation/OCR/SSL). The resulting family of Eagle models support up to over 1K input resolution and obtain strong results on multimodal LLM benchmarks, especially resolution-sensitive tasks such as optical character recognition and document understanding. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64618b9496259bec21d44704/BdAIMvo--yG7SpG5xDeYN.png) Paper or resources for more information: https://github.com/NVlabs/Eagle [arXiv](https://arxiv.org/pdf/2408.15998) / [Demo](https://huggingface.co/spaces/NVEagle/Eagle-X5-13B-Chat) / [Huggingface](https://huggingface.co/papers/2408.15998) ``` @article{shi2024eagle, title = {Eagle: Exploring The Design Space for Multimodal LLMs with Mixture of Encoders}, author={Min Shi and Fuxiao Liu and Shihao Wang and Shijia Liao and Subhashree Radhakrishnan and De-An Huang and Hongxu Yin and Karan Sapra and Yaser Yacoob and Humphrey Shi and Bryan Catanzaro and Andrew Tao and Jan Kautz and Zhiding Yu and Guilin Liu}, journal={arXiv:2408.15998}, year={2024} } ``` ## License - The code is released under the Apache 2.0 license as found in the [LICENSE](./LICENSE) file. - The pretrained weights are released under the [CC-BY-NC-SA-4.0 license](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en). - The service is a research preview intended for non-commercial use only, and is subject to the following licenses and terms: - [Model License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA - [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI - [Dataset Licenses](https://github.com/Efficient-Large-Model/VILA/blob/main/data_prepare/LICENSE) for each one used during training. Where to send questions or comments about the model: https://github.com/NVlabs/Eagle/issues ## Model Architecture: Architecture Type: Transformer ## Input: Input Type: Image, Text Input Format: Red, Green, Blue; String ## Output: Output Type: Text Output Format: String ## Inference: ``` import os import torch import numpy as np from eagle import conversation as conversation_lib from eagle.constants import DEFAULT_IMAGE_TOKEN from eagle.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN from eagle.conversation import conv_templates, SeparatorStyle from eagle.model.builder import load_pretrained_model from eagle.utils import disable_torch_init from eagle.mm_utils import tokenizer_image_token, get_model_name_from_path, process_images, KeywordsStoppingCriteria from PIL import Image import argparse from transformers import TextIteratorStreamer from threading import Thread model_path = "NVEagle/Eagle-X5-13B-Chat" conv_mode = "vicuna_v1" image_path = "assets/georgia-tech.jpeg" input_prompt = "Describe this image." model_name = get_model_name_from_path(model_path) tokenizer, model, image_processor, context_len = load_pretrained_model(model_path,None,model_name,False,False) if model.config.mm_use_im_start_end: input_prompt = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + input_prompt else: input_prompt = DEFAULT_IMAGE_TOKEN + '\n' + input_prompt conv = conv_templates[conv_mode].copy() conv.append_message(conv.roles[0], input_prompt) conv.append_message(conv.roles[1], None) prompt = conv.get_prompt() image = Image.open(image_path).convert('RGB') image_tensor = process_images([image], image_processor, model.config)[0] input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt') input_ids = input_ids.to(device='cuda', non_blocking=True) image_tensor = image_tensor.to(dtype=torch.float16, device='cuda', non_blocking=True) with torch.inference_mode(): output_ids = model.generate( input_ids.unsqueeze(0), images=image_tensor.unsqueeze(0), image_sizes=[image.size], do_sample=True, temperature=0.2, top_p=0.5, num_beams=1, max_new_tokens=256, use_cache=True) outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip() print(f"Image:{image_path} \nPrompt:{input_prompt} \nOutput:{outputs}") ``` [Preferred/Supported] Operating System(s): <br> Linux ## Intended use Primary intended uses: The primary use of Eagle is research on large multimodal models and chatbots. Primary intended users: The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence. ## Ethical Considerations NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
samuelwaterberry/illrpn	samuelwaterberry	2024-09-16T19:38:22Z	6	0	diffusers	[ "diffusers", "flux", "text-to-image", "lora", "fal", "license:other", "region:us" ]	text-to-image	2024-09-16T19:38:16Z	--- tags: - flux - text-to-image - lora - diffusers - fal base_model: undefined instance_prompt: illrpn license: other --- # illrpn <Gallery /> ## Model description ## Trigger words You should use `illrpn` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/samuelwaterberry/illrpn/tree/main) them in the Files & versions tab. ## Training at fal.ai Training was done using [fal.ai/models/fal-ai/style-lora](https://fal.ai/models/fal-ai/style-lora).
osanseviero/Reflection-Llama-3.1-70B-GGUF	osanseviero	2024-09-16T19:36:14Z	192	0	transformers	[ "transformers", "gguf", "text-generation", "base_model:mattshumer/Reflection-Llama-3.1-70B", "base_model:quantized:mattshumer/Reflection-Llama-3.1-70B", "license:llama3", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	text-generation	2024-09-16T19:35:54Z	--- base_model: mattshumer/Reflection-Llama-3.1-70B library_name: transformers license: llama3 pipeline_tag: text-generation quantized_by: bartowski --- # DO NOT DOWNLOAD It has been rediscovered that these are again the wrong weights, this warning will go away when the proper files are up https://x.com/mattshumer_/status/1832424499054309804?s=46 ## Llamacpp imatrix Quantizations of Reflection-Llama-3.1-70B <b>Yes, this is with the fix to the tokenizer!</b> If you want to make sure it's using the thought and output tokens, be sure to enable rendering of special tokens (in llama.cpp this is the `--special` tag) It is able to use them without rendering them, much like chat tokens, this will just let you see them as they're getting used by the model. Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3658">b3658</a> for quantization. Original model: https://huggingface.co/mattshumer/Reflection-Llama-3.1-70B All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8) Run them in [LM Studio](https://lmstudio.ai/) ## Prompt format For improved reasoning, its suggested you use this system prompt: ``` You are a world-class AI system, capable of complex reasoning and reflection. Reason through the query inside <thinking> tags, and then provide your final response inside <output> tags. If you detect that you made a mistake in your reasoning at any point, correct yourself inside <reflection> tags.<\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|> ``` ``` <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> {system_prompt}<\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|> {prompt}<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> ``` ## Download a file (not the whole branch) from below: \| Filename \| Quant type \| File Size \| Split \| Description \| \| -------- \| ---------- \| --------- \| ----- \| ----------- \| \| [Reflection-Llama-3.1-70B-Q8_0.gguf](https://huggingface.co/bartowski/Reflection-Llama-3.1-70B-GGUF/tree/main/Reflection-Llama-3.1-70B-Q8_0) \| Q8_0 \| 74.98GB \| true \| Extremely high quality, generally unneeded but max available quant. \| \| [Reflection-Llama-3.1-70B-Q6_K_L.gguf](https://huggingface.co/bartowski/Reflection-Llama-3.1-70B-GGUF/tree/main/Reflection-Llama-3.1-70B-Q6_K_L) \| Q6_K_L \| 58.40GB \| true \| Uses Q8_0 for embed and output weights. Very high quality, near perfect, recommended. \| \| [Reflection-Llama-3.1-70B-Q6_K.gguf](https://huggingface.co/bartowski/Reflection-Llama-3.1-70B-GGUF/tree/main/Reflection-Llama-3.1-70B-Q6_K) \| Q6_K \| 57.89GB \| true \| Very high quality, near perfect, recommended. \| \| [Reflection-Llama-3.1-70B-Q5_K_L.gguf](https://huggingface.co/bartowski/Reflection-Llama-3.1-70B-GGUF/tree/main/Reflection-Llama-3.1-70B-Q5_K_L) \| Q5_K_L \| 50.60GB \| true \| Uses Q8_0 for embed and output weights. High quality, recommended. \| \| [Reflection-Llama-3.1-70B-Q5_K_M.gguf](https://huggingface.co/bartowski/Reflection-Llama-3.1-70B-GGUF/tree/main/Reflection-Llama-3.1-70B-Q5_K_M) \| Q5_K_M \| 49.95GB \| true \| High quality, recommended. \| \| [Reflection-Llama-3.1-70B-Q5_K_S.gguf](https://huggingface.co/bartowski/Reflection-Llama-3.1-70B-GGUF/blob/main/Reflection-Llama-3.1-70B-Q5_K_S.gguf) \| Q5_K_S \| 48.66GB \| false \| High quality, recommended. \| \| [Reflection-Llama-3.1-70B-Q4_K_L.gguf](https://huggingface.co/bartowski/Reflection-Llama-3.1-70B-GGUF/blob/main/Reflection-Llama-3.1-70B-Q4_K_L.gguf) \| Q4_K_L \| 43.30GB \| false \| Uses Q8_0 for embed and output weights. Good quality, recommended. \| \| [Reflection-Llama-3.1-70B-Q4_K_M.gguf](https://huggingface.co/bartowski/Reflection-Llama-3.1-70B-GGUF/blob/main/Reflection-Llama-3.1-70B-Q4_K_M.gguf) \| Q4_K_M \| 42.52GB \| false \| Good quality, default size for must use cases, recommended. \| \| [Reflection-Llama-3.1-70B-Q4_K_S.gguf](https://huggingface.co/bartowski/Reflection-Llama-3.1-70B-GGUF/blob/main/Reflection-Llama-3.1-70B-Q4_K_S.gguf) \| Q4_K_S \| 40.35GB \| false \| Slightly lower quality with more space savings, recommended. \| \| [Reflection-Llama-3.1-70B-Q4_0.gguf](https://huggingface.co/bartowski/Reflection-Llama-3.1-70B-GGUF/blob/main/Reflection-Llama-3.1-70B-Q4_0.gguf) \| Q4_0 \| 40.12GB \| false \| Legacy format, generally not worth using over similarly sized formats \| \| [Reflection-Llama-3.1-70B-Q3_K_XL.gguf](https://huggingface.co/bartowski/Reflection-Llama-3.1-70B-GGUF/blob/main/Reflection-Llama-3.1-70B-Q3_K_XL.gguf) \| Q3_K_XL \| 38.06GB \| false \| Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. \| \| [Reflection-Llama-3.1-70B-IQ4_XS.gguf](https://huggingface.co/bartowski/Reflection-Llama-3.1-70B-GGUF/blob/main/Reflection-Llama-3.1-70B-IQ4_XS.gguf) \| IQ4_XS \| 37.90GB \| false \| Decent quality, smaller than Q4_K_S with similar performance, recommended. \| \| [Reflection-Llama-3.1-70B-Q3_K_L.gguf](https://huggingface.co/bartowski/Reflection-Llama-3.1-70B-GGUF/blob/main/Reflection-Llama-3.1-70B-Q3_K_L.gguf) \| Q3_K_L \| 37.14GB \| false \| Lower quality but usable, good for low RAM availability. \| \| [Reflection-Llama-3.1-70B-Q3_K_M.gguf](https://huggingface.co/bartowski/Reflection-Llama-3.1-70B-GGUF/blob/main/Reflection-Llama-3.1-70B-Q3_K_M.gguf) \| Q3_K_M \| 34.27GB \| false \| Low quality. \| \| [Reflection-Llama-3.1-70B-IQ3_M.gguf](https://huggingface.co/bartowski/Reflection-Llama-3.1-70B-GGUF/blob/main/Reflection-Llama-3.1-70B-IQ3_M.gguf) \| IQ3_M \| 31.94GB \| false \| Medium-low quality, new method with decent performance comparable to Q3_K_M. \| \| [Reflection-Llama-3.1-70B-Q3_K_S.gguf](https://huggingface.co/bartowski/Reflection-Llama-3.1-70B-GGUF/blob/main/Reflection-Llama-3.1-70B-Q3_K_S.gguf) \| Q3_K_S \| 30.91GB \| false \| Low quality, not recommended. \| \| [Reflection-Llama-3.1-70B-IQ3_XS.gguf](https://huggingface.co/bartowski/Reflection-Llama-3.1-70B-GGUF/blob/main/Reflection-Llama-3.1-70B-IQ3_XS.gguf) \| IQ3_XS \| 29.31GB \| false \| Lower quality, new method with decent performance, slightly better than Q3_K_S. \| \| [Reflection-Llama-3.1-70B-Q2_K_L.gguf](https://huggingface.co/bartowski/Reflection-Llama-3.1-70B-GGUF/blob/main/Reflection-Llama-3.1-70B-Q2_K_L.gguf) \| Q2_K_L \| 27.40GB \| false \| Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. \| \| [Reflection-Llama-3.1-70B-Q2_K.gguf](https://huggingface.co/bartowski/Reflection-Llama-3.1-70B-GGUF/blob/main/Reflection-Llama-3.1-70B-Q2_K.gguf) \| Q2_K \| 26.38GB \| false \| Very low quality but surprisingly usable. \| \| [Reflection-Llama-3.1-70B-IQ2_M.gguf](https://huggingface.co/bartowski/Reflection-Llama-3.1-70B-GGUF/blob/main/Reflection-Llama-3.1-70B-IQ2_M.gguf) \| IQ2_M \| 24.12GB \| false \| Relatively low quality, uses SOTA techniques to be surprisingly usable. \| \| [Reflection-Llama-3.1-70B-IQ2_S.gguf](https://huggingface.co/bartowski/Reflection-Llama-3.1-70B-GGUF/blob/main/Reflection-Llama-3.1-70B-IQ2_S.gguf) \| IQ2_S \| 22.24GB \| false \| Low quality, uses SOTA techniques to be usable. \| ## Embed/output weights Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to. Some say that this improves the quality, others don't notice any difference. If you use these models PLEASE COMMENT with your findings. I would like feedback that these are actually used and useful so I don't keep uploading quants no one is using. Thanks! ## Downloading using huggingface-cli First, make sure you have hugginface-cli installed: ``` pip install -U "huggingface_hub[cli]" ``` Then, you can target the specific file you want: ``` huggingface-cli download bartowski/Reflection-Llama-3.1-70B-GGUF --include "Reflection-Llama-3.1-70B-Q4_K_M.gguf" --local-dir ./ ``` If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: ``` huggingface-cli download bartowski/Reflection-Llama-3.1-70B-GGUF --include "Reflection-Llama-3.1-70B-Q8_0/" --local-dir ./ ``` You can either specify a new local-dir (Reflection-Llama-3.1-70B-Q8_0) or download them all in place (./) ## Which file should I choose? A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have. If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM. If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total. Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'. If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M. If you want to get more into the weeds, you can check out this extremely useful feature chart: [llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix) But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size. These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide. The I-quants are not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm. ## Credits Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset Thank you ZeroWw for the inspiration to experiment with embed/output Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
Triangle104/llama3.1-cc-8B-Q4_K_S-GGUF	Triangle104	2024-09-16T19:32:45Z	7	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "dataset:flammenai/casual-conversation-DPO", "base_model:nbeerbower/llama3.1-cc-8B", "base_model:quantized:nbeerbower/llama3.1-cc-8B", "license:llama3", "model-index", "endpoints_compatible", "region:us", "conversational" ]	null	2024-09-16T19:32:22Z	--- base_model: nbeerbower/llama3.1-cc-8B datasets: - flammenai/casual-conversation-DPO library_name: transformers license: llama3 tags: - llama-cpp - gguf-my-repo model-index: - name: llama3.1-cc-8B results: - task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: HuggingFaceH4/ifeval args: num_few_shot: 0 metrics: - type: inst_level_strict_acc and prompt_level_strict_acc value: 50.68 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/llama3.1-cc-8B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: BBH args: num_few_shot: 3 metrics: - type: acc_norm value: 26.48 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/llama3.1-cc-8B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: hendrycks/competition_math args: num_few_shot: 4 metrics: - type: exact_match value: 6.34 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/llama3.1-cc-8B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa args: num_few_shot: 0 metrics: - type: acc_norm value: 4.7 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/llama3.1-cc-8B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics: - type: acc_norm value: 6.5 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/llama3.1-cc-8B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 26.08 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/llama3.1-cc-8B name: Open LLM Leaderboard --- # Triangle104/llama3.1-cc-8B-Q4_K_S-GGUF This model was converted to GGUF format from [`nbeerbower/llama3.1-cc-8B`](https://huggingface.co/nbeerbower/llama3.1-cc-8B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/nbeerbower/llama3.1-cc-8B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/llama3.1-cc-8B-Q4_K_S-GGUF --hf-file llama3.1-cc-8b-q4_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/llama3.1-cc-8B-Q4_K_S-GGUF --hf-file llama3.1-cc-8b-q4_k_s.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/llama3.1-cc-8B-Q4_K_S-GGUF --hf-file llama3.1-cc-8b-q4_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/llama3.1-cc-8B-Q4_K_S-GGUF --hf-file llama3.1-cc-8b-q4_k_s.gguf -c 2048 ```
Pearush/phi_moe_25_attn	Pearush	2024-09-16T19:18:45Z	5	0	transformers	[ "transformers", "safetensors", "phimoe", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "region:us" ]	text-generation	2024-09-16T18:20:14Z	--- 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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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]
rdli/rdl-k8s-4bit_incremental_dpo	rdli	2024-09-16T19:16:45Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "dpo", "conversational", "en", "base_model:rdli/rdl-k8s-v2", "base_model:quantized:rdli/rdl-k8s-v2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-09-16T19:14:55Z	--- base_model: rdli/rdl-k8s-v2 language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl - dpo --- # Uploaded model - Developed by: rdli - License: apache-2.0 - Finetuned from model : rdli/rdl-k8s-v2 This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Triangle104/mistral-nemo-cc-12B-Q8_0-GGUF	Triangle104	2024-09-16T19:06:57Z	5	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "dataset:flammenai/casual-conversation-DPO", "base_model:nbeerbower/mistral-nemo-cc-12B", "base_model:quantized:nbeerbower/mistral-nemo-cc-12B", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us", "conversational" ]	null	2024-09-16T19:05:45Z	--- base_model: nbeerbower/mistral-nemo-cc-12B datasets: - flammenai/casual-conversation-DPO library_name: transformers license: apache-2.0 tags: - llama-cpp - gguf-my-repo model-index: - name: mistral-nemo-cc-12B results: - task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: HuggingFaceH4/ifeval args: num_few_shot: 0 metrics: - type: inst_level_strict_acc and prompt_level_strict_acc value: 14.35 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/mistral-nemo-cc-12B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: BBH args: num_few_shot: 3 metrics: - type: acc_norm value: 34.45 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/mistral-nemo-cc-12B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: hendrycks/competition_math args: num_few_shot: 4 metrics: - type: exact_match value: 1.81 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/mistral-nemo-cc-12B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa args: num_few_shot: 0 metrics: - type: acc_norm value: 8.72 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/mistral-nemo-cc-12B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics: - type: acc_norm value: 14.26 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/mistral-nemo-cc-12B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 28.87 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/mistral-nemo-cc-12B name: Open LLM Leaderboard --- # Triangle104/mistral-nemo-cc-12B-Q8_0-GGUF This model was converted to GGUF format from [`nbeerbower/mistral-nemo-cc-12B`](https://huggingface.co/nbeerbower/mistral-nemo-cc-12B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/nbeerbower/mistral-nemo-cc-12B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/mistral-nemo-cc-12B-Q8_0-GGUF --hf-file mistral-nemo-cc-12b-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/mistral-nemo-cc-12B-Q8_0-GGUF --hf-file mistral-nemo-cc-12b-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/mistral-nemo-cc-12B-Q8_0-GGUF --hf-file mistral-nemo-cc-12b-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/mistral-nemo-cc-12B-Q8_0-GGUF --hf-file mistral-nemo-cc-12b-q8_0.gguf -c 2048 ```
3oclock/distilbert-imdb	3oclock	2024-09-16T19:02:58Z	174	1	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "PyTorch", "en", "dataset:stanfordnlp/imdb", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-08-19T18:16:15Z	--- library_name: transformers datasets: - stanfordnlp/imdb metrics: - accuracy tags: - PyTorch model-index: - name: distilbert-imdb results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb args: plain_text metrics: - name: Accuracy type: accuracy value: 0.9316 pipeline_tag: text-classification license: apache-2.0 language: - en --- # distilbert-imdb This is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on imdb dataset. ## Performance - Loss: 0.1958 - Accuracy: 0.932 ## How to Get Started with the Model Use the code below to get started with the model: ```python from transformers import pipeline,DistilBertTokenizer tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased") classifier = pipeline("sentiment-analysis", model="3oclock/distilbert-imdb", tokenizer=tokenizer) result = classifier("I love this movie!") print(result) ``` ## Model Details ### Model Description This is the model card for a fine-tuned 🤗 transformers model on the IMDb dataset. - Developed by: Ge Li - Model type: DistilBERT for Sequence Classification - Language(s) (NLP): English - License: [Specify License, e.g., Apache 2.0] - Finetuned from model: `distilbert-base-uncased` ## Uses ### Direct Use This model can be used directly for sentiment analysis on movie reviews. It is best suited for classifying English-language text that is similar in nature to movie reviews. ### Downstream Use [optional] This model can be fine-tuned on other sentiment analysis tasks or adapted for tasks like text classification in domains similar to IMDb movie reviews. ### Out-of-Scope Use The model may not perform well on non-English text or text that is significantly different in style and content from the IMDb dataset (e.g., technical documents, social media posts). ## Bias, Risks, and Limitations ### Bias The IMDb dataset primarily consists of English-language movie reviews and may not generalize well to other languages or types of reviews. ### Risks Misclassification in sentiment analysis can lead to incorrect conclusions in applications relying on this model. ### Limitations The model was trained on a dataset of movie reviews, so it may not perform as well on other types of text data. ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases, and limitations of the model.
akhousker/bert-finetuned-squad	akhousker	2024-09-16T19:01:23Z	44	0	transformers	[ "transformers", "tf", "bert", "question-answering", "generated_from_keras_callback", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2024-09-16T18:05:56Z	--- library_name: transformers license: apache-2.0 base_model: bert-base-cased tags: - generated_from_keras_callback model-index: - name: akhousker/bert-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # akhousker/bert-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.2742 - Epoch: 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: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 16635, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results \| Train Loss \| Epoch \| \|:----------:\|:-----:\| \| 1.2742 \| 0 \| ### Framework versions - Transformers 4.44.2 - TensorFlow 2.17.0 - Datasets 3.0.0 - Tokenizers 0.19.1
jimb0321/dasha	jimb0321	2024-09-16T18:45:46Z	7	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2024-09-16T18:16:35Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: DASHA --- # Dasha <!-- <Gallery /> --> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `DASHA` to trigger the image generation. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('jimb0321/dasha', weight_name='lora.safetensors') image = pipeline('your prompt').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
Riyuechang/Breeze-7B-PTT-Chat-v2	Riyuechang	2024-09-16T18:36:52Z	6	0	null	[ "safetensors", "mistral", "PTT", "PTT_Chat", "text-generation", "conversational", "dataset:Riyuechang/PTT-Corpus-100K_Gossiping-1400-39400_v2", "base_model:MediaTek-Research/Breeze-7B-Instruct-v1_0", "base_model:finetune:MediaTek-Research/Breeze-7B-Instruct-v1_0", "license:apache-2.0", "region:us" ]	text-generation	2024-09-16T16:23:29Z	--- license: apache-2.0 base_model: MediaTek-Research/Breeze-7B-Instruct-v1_0 datasets: - Riyuechang/PTT-Corpus-100K_Gossiping-1400-39400_v2 pipeline_tag: text-generation tags: - PTT - PTT_Chat --- # 版本資訊使用新的噪聲較小(理論上)的數據訓練 Lora使用了更大的r(32) 取消了Dora 因為Dora的提升有限，還會大幅降低訓練和推理的效率 # 簡介本模型是基於[MediaTek-Research/Breeze-7B-Instruct-v1_0](https://huggingface.co/MediaTek-Research/Breeze-7B-Instruct-v1_0)微調後的產物模型使用來自[PTT](https://www.ptt.cc/bbs/index.html)網站中的[Gossiping](https://www.ptt.cc/bbs/Gossiping/index.html)分類的資料訓練過程中使用了一些方法從海量的數據中，過濾出噪聲較小(理論上)的部份作為訓練數據訓練資料: [Riyuechang/PTT-Corpus-100K_Gossiping-1400-39400_v2](https://huggingface.co/datasets/Riyuechang/PTT-Corpus-100K_Gossiping-1400-39400_v2) # 設備 - Ubuntu 22.04.4 LTS - NVIDIA GeForce RTX 3060 12G # Lora參數 ```python r=32, lora_alpha=32, lora_dropout=0.1, task_type="CAUSAL_LM", target_modules="all-linear", bias="none", use_rslora=True ``` # 訓練參數 ```python per_device_train_batch_size=28, gradient_accumulation_steps=1, num_train_epochs=3, warmup_ratio=0.1, learning_rate=2e-5, bf16=True, save_strategy="steps", save_steps=1000, save_total_limit=5, logging_steps=10, output_dir=log_output, optim="paged_adamw_8bit", gradient_checkpointing=True ``` # 結果 - loss: 0.9391
Triangle104/mistral-nemo-cc-12B-Q5_K_S-GGUF	Triangle104	2024-09-16T18:34:05Z	5	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "dataset:flammenai/casual-conversation-DPO", "base_model:nbeerbower/mistral-nemo-cc-12B", "base_model:quantized:nbeerbower/mistral-nemo-cc-12B", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us", "conversational" ]	null	2024-09-16T18:33:25Z	--- base_model: nbeerbower/mistral-nemo-cc-12B datasets: - flammenai/casual-conversation-DPO library_name: transformers license: apache-2.0 tags: - llama-cpp - gguf-my-repo model-index: - name: mistral-nemo-cc-12B results: - task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: HuggingFaceH4/ifeval args: num_few_shot: 0 metrics: - type: inst_level_strict_acc and prompt_level_strict_acc value: 14.35 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/mistral-nemo-cc-12B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: BBH args: num_few_shot: 3 metrics: - type: acc_norm value: 34.45 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/mistral-nemo-cc-12B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: hendrycks/competition_math args: num_few_shot: 4 metrics: - type: exact_match value: 1.81 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/mistral-nemo-cc-12B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa args: num_few_shot: 0 metrics: - type: acc_norm value: 8.72 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/mistral-nemo-cc-12B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics: - type: acc_norm value: 14.26 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/mistral-nemo-cc-12B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 28.87 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/mistral-nemo-cc-12B name: Open LLM Leaderboard --- # Triangle104/mistral-nemo-cc-12B-Q5_K_S-GGUF This model was converted to GGUF format from [`nbeerbower/mistral-nemo-cc-12B`](https://huggingface.co/nbeerbower/mistral-nemo-cc-12B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/nbeerbower/mistral-nemo-cc-12B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/mistral-nemo-cc-12B-Q5_K_S-GGUF --hf-file mistral-nemo-cc-12b-q5_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/mistral-nemo-cc-12B-Q5_K_S-GGUF --hf-file mistral-nemo-cc-12b-q5_k_s.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/mistral-nemo-cc-12B-Q5_K_S-GGUF --hf-file mistral-nemo-cc-12b-q5_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/mistral-nemo-cc-12B-Q5_K_S-GGUF --hf-file mistral-nemo-cc-12b-q5_k_s.gguf -c 2048 ```
ToastyPigeon/Llama-3-8B-Instruct-SpringDragon-V2-QLoRA	ToastyPigeon	2024-09-16T18:34:04Z	11	1	peft	[ "peft", "safetensors", "base_model:NousResearch/Meta-Llama-3-8B-Instruct", "base_model:adapter:NousResearch/Meta-Llama-3-8B-Instruct", "region:us" ]	null	2024-05-09T16:54:11Z	--- library_name: peft base_model: NousResearch/Meta-Llama-3-8B-Instruct --- This is SpringDragon trained on Meta-Llama-3-8B-Instruct. Completion format. User imput is given with `>`.
muhtasham/Phi-3.5-vision-instruct_20240915_223241	muhtasham	2024-09-16T18:31:34Z	6	0	transformers	[ "transformers", "safetensors", "phi3_v", "text-generation", "generated_from_trainer", "conversational", "custom_code", "base_model:microsoft/Phi-3.5-vision-instruct", "base_model:finetune:microsoft/Phi-3.5-vision-instruct", "license:mit", "autotrain_compatible", "region:us" ]	text-generation	2024-09-15T22:38:35Z	--- library_name: transformers license: mit base_model: microsoft/Phi-3.5-vision-instruct tags: - generated_from_trainer model-index: - name: Phi-3.5-vision-instruct_20240915_223241 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. --> # Phi-3.5-vision-instruct_20240915_223241 This model is a fine-tuned version of [microsoft/Phi-3.5-vision-instruct](https://huggingface.co/microsoft/Phi-3.5-vision-instruct) on the None dataset. ## Model description On 1.8M avg dataset ## 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: 4e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-07 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.1+cu121 - Datasets 3.0.0 - Tokenizers 0.19.1
mradermacher/MN-12B-Estrella-v1-i1-GGUF	mradermacher	2024-09-16T18:28:43Z	41	2	transformers	[ "transformers", "gguf", "mergekit", "merge", "mistral", "en", "base_model:v000000/MN-12B-Estrella-v1", "base_model:quantized:v000000/MN-12B-Estrella-v1", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "imatrix" ]	null	2024-08-13T01:56:24Z	--- base_model: v000000/MN-12B-Estrella-v1 language: - en library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher tags: - mergekit - merge - mistral --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/v000000/MN-12B-Estrella-v1 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/MN-12B-Estrella-v1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v1-i1-GGUF/resolve/main/MN-12B-Estrella-v1.i1-IQ1_S.gguf) \| i1-IQ1_S \| 3.1 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v1-i1-GGUF/resolve/main/MN-12B-Estrella-v1.i1-IQ1_M.gguf) \| i1-IQ1_M \| 3.3 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v1-i1-GGUF/resolve/main/MN-12B-Estrella-v1.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v1-i1-GGUF/resolve/main/MN-12B-Estrella-v1.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v1-i1-GGUF/resolve/main/MN-12B-Estrella-v1.i1-IQ2_S.gguf) \| i1-IQ2_S \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v1-i1-GGUF/resolve/main/MN-12B-Estrella-v1.i1-IQ2_M.gguf) \| i1-IQ2_M \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v1-i1-GGUF/resolve/main/MN-12B-Estrella-v1.i1-Q2_K.gguf) \| i1-Q2_K \| 4.9 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v1-i1-GGUF/resolve/main/MN-12B-Estrella-v1.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 5.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v1-i1-GGUF/resolve/main/MN-12B-Estrella-v1.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v1-i1-GGUF/resolve/main/MN-12B-Estrella-v1.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 5.6 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v1-i1-GGUF/resolve/main/MN-12B-Estrella-v1.i1-IQ3_S.gguf) \| i1-IQ3_S \| 5.7 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v1-i1-GGUF/resolve/main/MN-12B-Estrella-v1.i1-IQ3_M.gguf) \| i1-IQ3_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v1-i1-GGUF/resolve/main/MN-12B-Estrella-v1.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 6.2 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v1-i1-GGUF/resolve/main/MN-12B-Estrella-v1.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 6.7 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v1-i1-GGUF/resolve/main/MN-12B-Estrella-v1.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v1-i1-GGUF/resolve/main/MN-12B-Estrella-v1.i1-Q4_0.gguf) \| i1-Q4_0 \| 7.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v1-i1-GGUF/resolve/main/MN-12B-Estrella-v1.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 7.2 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v1-i1-GGUF/resolve/main/MN-12B-Estrella-v1.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 7.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v1-i1-GGUF/resolve/main/MN-12B-Estrella-v1.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 8.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v1-i1-GGUF/resolve/main/MN-12B-Estrella-v1.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 8.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v1-i1-GGUF/resolve/main/MN-12B-Estrella-v1.i1-Q6_K.gguf) \| i1-Q6_K \| 10.2 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
Triangle104/mistral-nemo-cc-12B-Q4_K_M-GGUF	Triangle104	2024-09-16T18:25:41Z	7	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "dataset:flammenai/casual-conversation-DPO", "base_model:nbeerbower/mistral-nemo-cc-12B", "base_model:quantized:nbeerbower/mistral-nemo-cc-12B", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us", "conversational" ]	null	2024-09-16T18:25:07Z	--- base_model: nbeerbower/mistral-nemo-cc-12B datasets: - flammenai/casual-conversation-DPO library_name: transformers license: apache-2.0 tags: - llama-cpp - gguf-my-repo model-index: - name: mistral-nemo-cc-12B results: - task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: HuggingFaceH4/ifeval args: num_few_shot: 0 metrics: - type: inst_level_strict_acc and prompt_level_strict_acc value: 14.35 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/mistral-nemo-cc-12B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: BBH args: num_few_shot: 3 metrics: - type: acc_norm value: 34.45 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/mistral-nemo-cc-12B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: hendrycks/competition_math args: num_few_shot: 4 metrics: - type: exact_match value: 1.81 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/mistral-nemo-cc-12B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa args: num_few_shot: 0 metrics: - type: acc_norm value: 8.72 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/mistral-nemo-cc-12B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics: - type: acc_norm value: 14.26 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/mistral-nemo-cc-12B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 28.87 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/mistral-nemo-cc-12B name: Open LLM Leaderboard --- # Triangle104/mistral-nemo-cc-12B-Q4_K_M-GGUF This model was converted to GGUF format from [`nbeerbower/mistral-nemo-cc-12B`](https://huggingface.co/nbeerbower/mistral-nemo-cc-12B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/nbeerbower/mistral-nemo-cc-12B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/mistral-nemo-cc-12B-Q4_K_M-GGUF --hf-file mistral-nemo-cc-12b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/mistral-nemo-cc-12B-Q4_K_M-GGUF --hf-file mistral-nemo-cc-12b-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/mistral-nemo-cc-12B-Q4_K_M-GGUF --hf-file mistral-nemo-cc-12b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/mistral-nemo-cc-12B-Q4_K_M-GGUF --hf-file mistral-nemo-cc-12b-q4_k_m.gguf -c 2048 ```
mradermacher/Chronos-Gold-12B-1.0-i1-GGUF	mradermacher	2024-09-16T18:23:02Z	26,434	11	transformers	[ "transformers", "gguf", "general-purpose", "roleplay", "storywriting", "merge", "finetune", "en", "base_model:elinas/Chronos-Gold-12B-1.0", "base_model:quantized:elinas/Chronos-Gold-12B-1.0", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2024-08-22T15:44:55Z	--- base_model: elinas/Chronos-Gold-12B-1.0 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - general-purpose - roleplay - storywriting - merge - finetune --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/elinas/Chronos-Gold-12B-1.0 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Chronos-Gold-12B-1.0-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Chronos-Gold-12B-1.0-i1-GGUF/resolve/main/Chronos-Gold-12B-1.0.i1-IQ1_S.gguf) \| i1-IQ1_S \| 3.1 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Chronos-Gold-12B-1.0-i1-GGUF/resolve/main/Chronos-Gold-12B-1.0.i1-IQ1_M.gguf) \| i1-IQ1_M \| 3.3 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Chronos-Gold-12B-1.0-i1-GGUF/resolve/main/Chronos-Gold-12B-1.0.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Chronos-Gold-12B-1.0-i1-GGUF/resolve/main/Chronos-Gold-12B-1.0.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Chronos-Gold-12B-1.0-i1-GGUF/resolve/main/Chronos-Gold-12B-1.0.i1-IQ2_S.gguf) \| i1-IQ2_S \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Chronos-Gold-12B-1.0-i1-GGUF/resolve/main/Chronos-Gold-12B-1.0.i1-IQ2_M.gguf) \| i1-IQ2_M \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Chronos-Gold-12B-1.0-i1-GGUF/resolve/main/Chronos-Gold-12B-1.0.i1-Q2_K.gguf) \| i1-Q2_K \| 4.9 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Chronos-Gold-12B-1.0-i1-GGUF/resolve/main/Chronos-Gold-12B-1.0.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 5.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Chronos-Gold-12B-1.0-i1-GGUF/resolve/main/Chronos-Gold-12B-1.0.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Chronos-Gold-12B-1.0-i1-GGUF/resolve/main/Chronos-Gold-12B-1.0.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 5.6 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Chronos-Gold-12B-1.0-i1-GGUF/resolve/main/Chronos-Gold-12B-1.0.i1-IQ3_S.gguf) \| i1-IQ3_S \| 5.7 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Chronos-Gold-12B-1.0-i1-GGUF/resolve/main/Chronos-Gold-12B-1.0.i1-IQ3_M.gguf) \| i1-IQ3_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Chronos-Gold-12B-1.0-i1-GGUF/resolve/main/Chronos-Gold-12B-1.0.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 6.2 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Chronos-Gold-12B-1.0-i1-GGUF/resolve/main/Chronos-Gold-12B-1.0.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 6.7 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Chronos-Gold-12B-1.0-i1-GGUF/resolve/main/Chronos-Gold-12B-1.0.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Chronos-Gold-12B-1.0-i1-GGUF/resolve/main/Chronos-Gold-12B-1.0.i1-Q4_0.gguf) \| i1-Q4_0 \| 7.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Chronos-Gold-12B-1.0-i1-GGUF/resolve/main/Chronos-Gold-12B-1.0.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 7.2 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Chronos-Gold-12B-1.0-i1-GGUF/resolve/main/Chronos-Gold-12B-1.0.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 7.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Chronos-Gold-12B-1.0-i1-GGUF/resolve/main/Chronos-Gold-12B-1.0.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 8.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Chronos-Gold-12B-1.0-i1-GGUF/resolve/main/Chronos-Gold-12B-1.0.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 8.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Chronos-Gold-12B-1.0-i1-GGUF/resolve/main/Chronos-Gold-12B-1.0.i1-Q6_K.gguf) \| i1-Q6_K \| 10.2 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/Lyra-Gutenberg-mistral-nemo-12B-i1-GGUF	mradermacher	2024-09-16T18:21:24Z	314	2	transformers	[ "transformers", "gguf", "en", "dataset:jondurbin/gutenberg-dpo-v0.1", "base_model:nbeerbower/Lyra-Gutenberg-mistral-nemo-12B", "base_model:quantized:nbeerbower/Lyra-Gutenberg-mistral-nemo-12B", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "imatrix" ]	null	2024-08-24T11:19:13Z	--- base_model: nbeerbower/Lyra-Gutenberg-mistral-nemo-12B datasets: - jondurbin/gutenberg-dpo-v0.1 language: - en library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/nbeerbower/Lyra-Gutenberg-mistral-nemo-12B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Lyra-Gutenberg-mistral-nemo-12B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Lyra-Gutenberg-mistral-nemo-12B-i1-GGUF/resolve/main/Lyra-Gutenberg-mistral-nemo-12B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 3.1 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Lyra-Gutenberg-mistral-nemo-12B-i1-GGUF/resolve/main/Lyra-Gutenberg-mistral-nemo-12B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 3.3 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Lyra-Gutenberg-mistral-nemo-12B-i1-GGUF/resolve/main/Lyra-Gutenberg-mistral-nemo-12B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Lyra-Gutenberg-mistral-nemo-12B-i1-GGUF/resolve/main/Lyra-Gutenberg-mistral-nemo-12B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Lyra-Gutenberg-mistral-nemo-12B-i1-GGUF/resolve/main/Lyra-Gutenberg-mistral-nemo-12B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Lyra-Gutenberg-mistral-nemo-12B-i1-GGUF/resolve/main/Lyra-Gutenberg-mistral-nemo-12B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Lyra-Gutenberg-mistral-nemo-12B-i1-GGUF/resolve/main/Lyra-Gutenberg-mistral-nemo-12B.i1-Q2_K.gguf) \| i1-Q2_K \| 4.9 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Lyra-Gutenberg-mistral-nemo-12B-i1-GGUF/resolve/main/Lyra-Gutenberg-mistral-nemo-12B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 5.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Lyra-Gutenberg-mistral-nemo-12B-i1-GGUF/resolve/main/Lyra-Gutenberg-mistral-nemo-12B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Lyra-Gutenberg-mistral-nemo-12B-i1-GGUF/resolve/main/Lyra-Gutenberg-mistral-nemo-12B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 5.6 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Lyra-Gutenberg-mistral-nemo-12B-i1-GGUF/resolve/main/Lyra-Gutenberg-mistral-nemo-12B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 5.7 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Lyra-Gutenberg-mistral-nemo-12B-i1-GGUF/resolve/main/Lyra-Gutenberg-mistral-nemo-12B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Lyra-Gutenberg-mistral-nemo-12B-i1-GGUF/resolve/main/Lyra-Gutenberg-mistral-nemo-12B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 6.2 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Lyra-Gutenberg-mistral-nemo-12B-i1-GGUF/resolve/main/Lyra-Gutenberg-mistral-nemo-12B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 6.7 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Lyra-Gutenberg-mistral-nemo-12B-i1-GGUF/resolve/main/Lyra-Gutenberg-mistral-nemo-12B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Lyra-Gutenberg-mistral-nemo-12B-i1-GGUF/resolve/main/Lyra-Gutenberg-mistral-nemo-12B.i1-Q4_0.gguf) \| i1-Q4_0 \| 7.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Lyra-Gutenberg-mistral-nemo-12B-i1-GGUF/resolve/main/Lyra-Gutenberg-mistral-nemo-12B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 7.2 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Lyra-Gutenberg-mistral-nemo-12B-i1-GGUF/resolve/main/Lyra-Gutenberg-mistral-nemo-12B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 7.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Lyra-Gutenberg-mistral-nemo-12B-i1-GGUF/resolve/main/Lyra-Gutenberg-mistral-nemo-12B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 8.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Lyra-Gutenberg-mistral-nemo-12B-i1-GGUF/resolve/main/Lyra-Gutenberg-mistral-nemo-12B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 8.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Lyra-Gutenberg-mistral-nemo-12B-i1-GGUF/resolve/main/Lyra-Gutenberg-mistral-nemo-12B.i1-Q6_K.gguf) \| i1-Q6_K \| 10.2 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/MN-12B-Estrella-v2.2-GGUF	mradermacher	2024-09-16T18:19:52Z	27	3	transformers	[ "transformers", "gguf", "mergekit", "merge", "mistral", "en", "base_model:v000000/MN-12B-Estrella-v2.2", "base_model:quantized:v000000/MN-12B-Estrella-v2.2", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	null	2024-08-28T07:58:27Z	--- base_model: v000000/MN-12B-Estrella-v2.2 language: - en library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher tags: - mergekit - merge - mistral --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/v000000/MN-12B-Estrella-v2.2 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/MN-12B-Estrella-v2.2-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v2.2-GGUF/resolve/main/MN-12B-Estrella-v2.2.Q2_K.gguf) \| Q2_K \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v2.2-GGUF/resolve/main/MN-12B-Estrella-v2.2.IQ3_XS.gguf) \| IQ3_XS \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v2.2-GGUF/resolve/main/MN-12B-Estrella-v2.2.Q3_K_S.gguf) \| Q3_K_S \| 5.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v2.2-GGUF/resolve/main/MN-12B-Estrella-v2.2.IQ3_S.gguf) \| IQ3_S \| 5.7 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v2.2-GGUF/resolve/main/MN-12B-Estrella-v2.2.IQ3_M.gguf) \| IQ3_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v2.2-GGUF/resolve/main/MN-12B-Estrella-v2.2.Q3_K_M.gguf) \| Q3_K_M \| 6.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v2.2-GGUF/resolve/main/MN-12B-Estrella-v2.2.Q3_K_L.gguf) \| Q3_K_L \| 6.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v2.2-GGUF/resolve/main/MN-12B-Estrella-v2.2.IQ4_XS.gguf) \| IQ4_XS \| 6.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v2.2-GGUF/resolve/main/MN-12B-Estrella-v2.2.Q4_K_S.gguf) \| Q4_K_S \| 7.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v2.2-GGUF/resolve/main/MN-12B-Estrella-v2.2.Q4_K_M.gguf) \| Q4_K_M \| 7.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v2.2-GGUF/resolve/main/MN-12B-Estrella-v2.2.Q5_K_S.gguf) \| Q5_K_S \| 8.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v2.2-GGUF/resolve/main/MN-12B-Estrella-v2.2.Q5_K_M.gguf) \| Q5_K_M \| 8.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v2.2-GGUF/resolve/main/MN-12B-Estrella-v2.2.Q6_K.gguf) \| Q6_K \| 10.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v2.2-GGUF/resolve/main/MN-12B-Estrella-v2.2.Q8_0.gguf) \| Q8_0 \| 13.1 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/MN-12B-Estrella-v2.2-i1-GGUF	mradermacher	2024-09-16T18:19:28Z	27	2	transformers	[ "transformers", "gguf", "mergekit", "merge", "mistral", "en", "base_model:v000000/MN-12B-Estrella-v2.2", "base_model:quantized:v000000/MN-12B-Estrella-v2.2", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "imatrix" ]	null	2024-08-28T14:13:28Z	--- base_model: v000000/MN-12B-Estrella-v2.2 language: - en library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher tags: - mergekit - merge - mistral --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/v000000/MN-12B-Estrella-v2.2 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/MN-12B-Estrella-v2.2-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v2.2-i1-GGUF/resolve/main/MN-12B-Estrella-v2.2.i1-IQ1_S.gguf) \| i1-IQ1_S \| 3.1 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v2.2-i1-GGUF/resolve/main/MN-12B-Estrella-v2.2.i1-IQ1_M.gguf) \| i1-IQ1_M \| 3.3 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v2.2-i1-GGUF/resolve/main/MN-12B-Estrella-v2.2.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v2.2-i1-GGUF/resolve/main/MN-12B-Estrella-v2.2.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v2.2-i1-GGUF/resolve/main/MN-12B-Estrella-v2.2.i1-IQ2_S.gguf) \| i1-IQ2_S \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v2.2-i1-GGUF/resolve/main/MN-12B-Estrella-v2.2.i1-IQ2_M.gguf) \| i1-IQ2_M \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v2.2-i1-GGUF/resolve/main/MN-12B-Estrella-v2.2.i1-Q2_K.gguf) \| i1-Q2_K \| 4.9 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v2.2-i1-GGUF/resolve/main/MN-12B-Estrella-v2.2.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 5.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v2.2-i1-GGUF/resolve/main/MN-12B-Estrella-v2.2.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v2.2-i1-GGUF/resolve/main/MN-12B-Estrella-v2.2.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 5.6 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v2.2-i1-GGUF/resolve/main/MN-12B-Estrella-v2.2.i1-IQ3_S.gguf) \| i1-IQ3_S \| 5.7 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v2.2-i1-GGUF/resolve/main/MN-12B-Estrella-v2.2.i1-IQ3_M.gguf) \| i1-IQ3_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v2.2-i1-GGUF/resolve/main/MN-12B-Estrella-v2.2.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 6.2 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v2.2-i1-GGUF/resolve/main/MN-12B-Estrella-v2.2.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 6.7 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v2.2-i1-GGUF/resolve/main/MN-12B-Estrella-v2.2.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v2.2-i1-GGUF/resolve/main/MN-12B-Estrella-v2.2.i1-Q4_0_4_4.gguf) \| i1-Q4_0_4_4 \| 7.2 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v2.2-i1-GGUF/resolve/main/MN-12B-Estrella-v2.2.i1-Q4_0_4_8.gguf) \| i1-Q4_0_4_8 \| 7.2 \| fast on arm+i8mm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v2.2-i1-GGUF/resolve/main/MN-12B-Estrella-v2.2.i1-Q4_0_8_8.gguf) \| i1-Q4_0_8_8 \| 7.2 \| fast on arm+sve, low quality \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v2.2-i1-GGUF/resolve/main/MN-12B-Estrella-v2.2.i1-Q4_0.gguf) \| i1-Q4_0 \| 7.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v2.2-i1-GGUF/resolve/main/MN-12B-Estrella-v2.2.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 7.2 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v2.2-i1-GGUF/resolve/main/MN-12B-Estrella-v2.2.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 7.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v2.2-i1-GGUF/resolve/main/MN-12B-Estrella-v2.2.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 8.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v2.2-i1-GGUF/resolve/main/MN-12B-Estrella-v2.2.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 8.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-12B-Estrella-v2.2-i1-GGUF/resolve/main/MN-12B-Estrella-v2.2.i1-Q6_K.gguf) \| i1-Q6_K \| 10.2 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
NaoS2/chico-0.06b-instruct-jcodealpaca-py	NaoS2	2024-09-16T18:15:53Z	5	0	null	[ "safetensors", "llama", "license:apache-2.0", "region:us" ]	null	2024-09-16T15:39:01Z	--- license: apache-2.0 ---
pegasus912/Psy_mope	pegasus912	2024-09-16T18:13:53Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "base_model:Cas-Warehouse/Llama-3-Mopeyfied-Psychology-v2", "base_model:merge:Cas-Warehouse/Llama-3-Mopeyfied-Psychology-v2", "base_model:Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B", "base_model:merge:Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-09-16T18:08:44Z	--- base_model: - Cas-Warehouse/Llama-3-Mopeyfied-Psychology-v2 - Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [Cas-Warehouse/Llama-3-Mopeyfied-Psychology-v2](https://huggingface.co/Cas-Warehouse/Llama-3-Mopeyfied-Psychology-v2) * [Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B](https://huggingface.co/Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B layer_range: - 0 - 32 - model: Cas-Warehouse/Llama-3-Mopeyfied-Psychology-v2 layer_range: - 0 - 32 merge_method: slerp base_model: Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B parameters: t: - filter: self_attn value: - 0 - 0.5 - 0.3 - 0.7 - 1 - filter: mlp value: - 1 - 0.5 - 0.7 - 0.3 - 0 - value: 0.5 dtype: bfloat16 ```
JoseGR1702/Crop_rhombifolia_2	JoseGR1702	2024-09-16T18:12:19Z	27	0	diffusers	[ "diffusers", "safetensors", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-09-16T18:10:30Z	--- library_name: diffusers --- # 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 🧨 diffusers 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]
mradermacher/Llama-3.1-70B-ArliAI-RPMax-v1.1-i1-GGUF	mradermacher	2024-09-16T18:11:29Z	139	2	transformers	[ "transformers", "gguf", "en", "base_model:ArliAI/Llama-3.1-70B-ArliAI-RPMax-v1.1", "base_model:quantized:ArliAI/Llama-3.1-70B-ArliAI-RPMax-v1.1", "license:llama3", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2024-09-08T04:58:12Z	--- base_model: ArliAI/Llama-3.1-70B-ArliAI-RPMax-v1.1 language: - en library_name: transformers license: llama3 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/ArliAI/Llama-3.1-70B-ArliAI-RPMax-v1.1 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Llama-3.1-70B-ArliAI-RPMax-v1.1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-70B-ArliAI-RPMax-v1.1-i1-GGUF/resolve/main/Llama-3.1-70B-ArliAI-RPMax-v1.1.i1-IQ1_S.gguf) \| i1-IQ1_S \| 15.4 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-70B-ArliAI-RPMax-v1.1-i1-GGUF/resolve/main/Llama-3.1-70B-ArliAI-RPMax-v1.1.i1-IQ1_M.gguf) \| i1-IQ1_M \| 16.9 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-70B-ArliAI-RPMax-v1.1-i1-GGUF/resolve/main/Llama-3.1-70B-ArliAI-RPMax-v1.1.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 19.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-70B-ArliAI-RPMax-v1.1-i1-GGUF/resolve/main/Llama-3.1-70B-ArliAI-RPMax-v1.1.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 21.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-70B-ArliAI-RPMax-v1.1-i1-GGUF/resolve/main/Llama-3.1-70B-ArliAI-RPMax-v1.1.i1-IQ2_S.gguf) \| i1-IQ2_S \| 22.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-70B-ArliAI-RPMax-v1.1-i1-GGUF/resolve/main/Llama-3.1-70B-ArliAI-RPMax-v1.1.i1-IQ2_M.gguf) \| i1-IQ2_M \| 24.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-70B-ArliAI-RPMax-v1.1-i1-GGUF/resolve/main/Llama-3.1-70B-ArliAI-RPMax-v1.1.i1-Q2_K.gguf) \| i1-Q2_K \| 26.5 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-70B-ArliAI-RPMax-v1.1-i1-GGUF/resolve/main/Llama-3.1-70B-ArliAI-RPMax-v1.1.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 27.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-70B-ArliAI-RPMax-v1.1-i1-GGUF/resolve/main/Llama-3.1-70B-ArliAI-RPMax-v1.1.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 29.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-70B-ArliAI-RPMax-v1.1-i1-GGUF/resolve/main/Llama-3.1-70B-ArliAI-RPMax-v1.1.i1-IQ3_S.gguf) \| i1-IQ3_S \| 31.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-70B-ArliAI-RPMax-v1.1-i1-GGUF/resolve/main/Llama-3.1-70B-ArliAI-RPMax-v1.1.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 31.0 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-70B-ArliAI-RPMax-v1.1-i1-GGUF/resolve/main/Llama-3.1-70B-ArliAI-RPMax-v1.1.i1-IQ3_M.gguf) \| i1-IQ3_M \| 32.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-70B-ArliAI-RPMax-v1.1-i1-GGUF/resolve/main/Llama-3.1-70B-ArliAI-RPMax-v1.1.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 34.4 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-70B-ArliAI-RPMax-v1.1-i1-GGUF/resolve/main/Llama-3.1-70B-ArliAI-RPMax-v1.1.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 37.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-70B-ArliAI-RPMax-v1.1-i1-GGUF/resolve/main/Llama-3.1-70B-ArliAI-RPMax-v1.1.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 38.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-70B-ArliAI-RPMax-v1.1-i1-GGUF/resolve/main/Llama-3.1-70B-ArliAI-RPMax-v1.1.i1-Q4_0.gguf) \| i1-Q4_0 \| 40.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-70B-ArliAI-RPMax-v1.1-i1-GGUF/resolve/main/Llama-3.1-70B-ArliAI-RPMax-v1.1.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 40.4 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-70B-ArliAI-RPMax-v1.1-i1-GGUF/resolve/main/Llama-3.1-70B-ArliAI-RPMax-v1.1.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 42.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-70B-ArliAI-RPMax-v1.1-i1-GGUF/resolve/main/Llama-3.1-70B-ArliAI-RPMax-v1.1.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 48.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-70B-ArliAI-RPMax-v1.1-i1-GGUF/resolve/main/Llama-3.1-70B-ArliAI-RPMax-v1.1.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 50.0 \| \| \| [PART 1](https://huggingface.co/mradermacher/Llama-3.1-70B-ArliAI-RPMax-v1.1-i1-GGUF/resolve/main/Llama-3.1-70B-ArliAI-RPMax-v1.1.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3.1-70B-ArliAI-RPMax-v1.1-i1-GGUF/resolve/main/Llama-3.1-70B-ArliAI-RPMax-v1.1.i1-Q6_K.gguf.part2of2) \| i1-Q6_K \| 58.0 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/ref_70_e3-GGUF	mradermacher	2024-09-16T18:10:05Z	43	0	transformers	[ "transformers", "gguf", "en", "base_model:mattshumer/ref_70_e3", "base_model:quantized:mattshumer/ref_70_e3", "license:llama3.1", "endpoints_compatible", "region:us", "conversational" ]	null	2024-09-09T12:19:55Z	--- base_model: mattshumer/ref_70_e3 language: - en library_name: transformers license: llama3.1 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/mattshumer/ref_70_e3 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/ref_70_e3-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/ref_70_e3-GGUF/resolve/main/ref_70_e3.Q2_K.gguf) \| Q2_K \| 26.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/ref_70_e3-GGUF/resolve/main/ref_70_e3.IQ3_XS.gguf) \| IQ3_XS \| 29.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/ref_70_e3-GGUF/resolve/main/ref_70_e3.IQ3_S.gguf) \| IQ3_S \| 31.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/ref_70_e3-GGUF/resolve/main/ref_70_e3.Q3_K_S.gguf) \| Q3_K_S \| 31.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/ref_70_e3-GGUF/resolve/main/ref_70_e3.IQ3_M.gguf) \| IQ3_M \| 32.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/ref_70_e3-GGUF/resolve/main/ref_70_e3.Q3_K_M.gguf) \| Q3_K_M \| 34.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/ref_70_e3-GGUF/resolve/main/ref_70_e3.Q3_K_L.gguf) \| Q3_K_L \| 37.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/ref_70_e3-GGUF/resolve/main/ref_70_e3.IQ4_XS.gguf) \| IQ4_XS \| 38.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/ref_70_e3-GGUF/resolve/main/ref_70_e3.Q4_K_S.gguf) \| Q4_K_S \| 40.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ref_70_e3-GGUF/resolve/main/ref_70_e3.Q4_K_M.gguf) \| Q4_K_M \| 42.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ref_70_e3-GGUF/resolve/main/ref_70_e3.Q5_K_S.gguf) \| Q5_K_S \| 48.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/ref_70_e3-GGUF/resolve/main/ref_70_e3.Q5_K_M.gguf) \| Q5_K_M \| 50.0 \| \| \| [PART 1](https://huggingface.co/mradermacher/ref_70_e3-GGUF/resolve/main/ref_70_e3.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/ref_70_e3-GGUF/resolve/main/ref_70_e3.Q6_K.gguf.part2of2) \| Q6_K \| 58.0 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/ref_70_e3-GGUF/resolve/main/ref_70_e3.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/ref_70_e3-GGUF/resolve/main/ref_70_e3.Q8_0.gguf.part2of2) \| Q8_0 \| 75.1 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/ref_70_e3-i1-GGUF	mradermacher	2024-09-16T18:09:53Z	23	0	transformers	[ "transformers", "gguf", "en", "base_model:mattshumer/ref_70_e3", "base_model:quantized:mattshumer/ref_70_e3", "license:llama3.1", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2024-09-09T16:16:30Z	--- base_model: mattshumer/ref_70_e3 language: - en library_name: transformers license: llama3.1 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/mattshumer/ref_70_e3 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/ref_70_e3-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/ref_70_e3-i1-GGUF/resolve/main/ref_70_e3.i1-IQ1_S.gguf) \| i1-IQ1_S \| 15.4 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/ref_70_e3-i1-GGUF/resolve/main/ref_70_e3.i1-IQ1_M.gguf) \| i1-IQ1_M \| 16.9 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/ref_70_e3-i1-GGUF/resolve/main/ref_70_e3.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 19.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/ref_70_e3-i1-GGUF/resolve/main/ref_70_e3.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 21.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/ref_70_e3-i1-GGUF/resolve/main/ref_70_e3.i1-IQ2_S.gguf) \| i1-IQ2_S \| 22.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/ref_70_e3-i1-GGUF/resolve/main/ref_70_e3.i1-IQ2_M.gguf) \| i1-IQ2_M \| 24.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/ref_70_e3-i1-GGUF/resolve/main/ref_70_e3.i1-Q2_K.gguf) \| i1-Q2_K \| 26.5 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/ref_70_e3-i1-GGUF/resolve/main/ref_70_e3.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 27.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/ref_70_e3-i1-GGUF/resolve/main/ref_70_e3.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 29.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/ref_70_e3-i1-GGUF/resolve/main/ref_70_e3.i1-IQ3_S.gguf) \| i1-IQ3_S \| 31.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/ref_70_e3-i1-GGUF/resolve/main/ref_70_e3.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 31.0 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/ref_70_e3-i1-GGUF/resolve/main/ref_70_e3.i1-IQ3_M.gguf) \| i1-IQ3_M \| 32.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/ref_70_e3-i1-GGUF/resolve/main/ref_70_e3.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 34.4 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/ref_70_e3-i1-GGUF/resolve/main/ref_70_e3.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 37.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/ref_70_e3-i1-GGUF/resolve/main/ref_70_e3.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 38.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/ref_70_e3-i1-GGUF/resolve/main/ref_70_e3.i1-Q4_0.gguf) \| i1-Q4_0 \| 40.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/ref_70_e3-i1-GGUF/resolve/main/ref_70_e3.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 40.4 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/ref_70_e3-i1-GGUF/resolve/main/ref_70_e3.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 42.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ref_70_e3-i1-GGUF/resolve/main/ref_70_e3.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 48.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/ref_70_e3-i1-GGUF/resolve/main/ref_70_e3.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 50.0 \| \| \| [PART 1](https://huggingface.co/mradermacher/ref_70_e3-i1-GGUF/resolve/main/ref_70_e3.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/ref_70_e3-i1-GGUF/resolve/main/ref_70_e3.i1-Q6_K.gguf.part2of2) \| i1-Q6_K \| 58.0 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/Gemma2pass-42B-GGUF	mradermacher	2024-09-16T18:09:02Z	46	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:allknowingroger/Gemma2pass-42B", "base_model:quantized:allknowingroger/Gemma2pass-42B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-09-10T21:17:09Z	--- base_model: allknowingroger/Gemma2pass-42B language: - en library_name: transformers license: apache-2.0 no_imatrix: 'llama.cpp:13002: fatal error' quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/allknowingroger/Gemma2pass-42B <!-- provided-files --> ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Gemma2pass-42B-GGUF/resolve/main/Gemma2pass-42B.Q2_K.gguf) \| Q2_K \| 15.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma2pass-42B-GGUF/resolve/main/Gemma2pass-42B.IQ3_XS.gguf) \| IQ3_XS \| 17.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma2pass-42B-GGUF/resolve/main/Gemma2pass-42B.IQ3_S.gguf) \| IQ3_S \| 18.1 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Gemma2pass-42B-GGUF/resolve/main/Gemma2pass-42B.Q3_K_S.gguf) \| Q3_K_S \| 18.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma2pass-42B-GGUF/resolve/main/Gemma2pass-42B.IQ3_M.gguf) \| IQ3_M \| 18.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma2pass-42B-GGUF/resolve/main/Gemma2pass-42B.Q3_K_M.gguf) \| Q3_K_M \| 20.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Gemma2pass-42B-GGUF/resolve/main/Gemma2pass-42B.Q3_K_L.gguf) \| Q3_K_L \| 21.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma2pass-42B-GGUF/resolve/main/Gemma2pass-42B.IQ4_XS.gguf) \| IQ4_XS \| 22.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma2pass-42B-GGUF/resolve/main/Gemma2pass-42B.Q4_K_S.gguf) \| Q4_K_S \| 23.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Gemma2pass-42B-GGUF/resolve/main/Gemma2pass-42B.Q4_K_M.gguf) \| Q4_K_M \| 24.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Gemma2pass-42B-GGUF/resolve/main/Gemma2pass-42B.Q5_K_S.gguf) \| Q5_K_S \| 28.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma2pass-42B-GGUF/resolve/main/Gemma2pass-42B.Q5_K_M.gguf) \| Q5_K_M \| 29.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma2pass-42B-GGUF/resolve/main/Gemma2pass-42B.Q6_K.gguf) \| Q6_K \| 33.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Gemma2pass-42B-GGUF/resolve/main/Gemma2pass-42B.Q8_0.gguf) \| Q8_0 \| 43.5 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
JoseGR1702/Crop_rhombifolia_0	JoseGR1702	2024-09-16T18:08:40Z	28	0	diffusers	[ "diffusers", "safetensors", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-09-16T18:06:59Z	--- library_name: diffusers --- # 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 🧨 diffusers 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]
mradermacher/Lyra4-Gutenberg-12B-GGUF	mradermacher	2024-09-16T18:07:28Z	85	3	transformers	[ "transformers", "gguf", "en", "dataset:jondurbin/gutenberg-dpo-v0.1", "base_model:nbeerbower/Lyra4-Gutenberg-12B", "base_model:quantized:nbeerbower/Lyra4-Gutenberg-12B", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-09-12T14:16:59Z	--- base_model: nbeerbower/Lyra4-Gutenberg-12B datasets: - jondurbin/gutenberg-dpo-v0.1 language: - en library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/nbeerbower/Lyra4-Gutenberg-12B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Lyra4-Gutenberg-12B-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Lyra4-Gutenberg-12B-GGUF/resolve/main/Lyra4-Gutenberg-12B.Q2_K.gguf) \| Q2_K \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Lyra4-Gutenberg-12B-GGUF/resolve/main/Lyra4-Gutenberg-12B.IQ3_XS.gguf) \| IQ3_XS \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Lyra4-Gutenberg-12B-GGUF/resolve/main/Lyra4-Gutenberg-12B.Q3_K_S.gguf) \| Q3_K_S \| 5.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Lyra4-Gutenberg-12B-GGUF/resolve/main/Lyra4-Gutenberg-12B.IQ3_S.gguf) \| IQ3_S \| 5.7 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Lyra4-Gutenberg-12B-GGUF/resolve/main/Lyra4-Gutenberg-12B.IQ3_M.gguf) \| IQ3_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Lyra4-Gutenberg-12B-GGUF/resolve/main/Lyra4-Gutenberg-12B.Q3_K_M.gguf) \| Q3_K_M \| 6.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Lyra4-Gutenberg-12B-GGUF/resolve/main/Lyra4-Gutenberg-12B.Q3_K_L.gguf) \| Q3_K_L \| 6.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Lyra4-Gutenberg-12B-GGUF/resolve/main/Lyra4-Gutenberg-12B.IQ4_XS.gguf) \| IQ4_XS \| 6.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Lyra4-Gutenberg-12B-GGUF/resolve/main/Lyra4-Gutenberg-12B.Q4_K_S.gguf) \| Q4_K_S \| 7.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Lyra4-Gutenberg-12B-GGUF/resolve/main/Lyra4-Gutenberg-12B.Q4_K_M.gguf) \| Q4_K_M \| 7.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Lyra4-Gutenberg-12B-GGUF/resolve/main/Lyra4-Gutenberg-12B.Q5_K_S.gguf) \| Q5_K_S \| 8.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Lyra4-Gutenberg-12B-GGUF/resolve/main/Lyra4-Gutenberg-12B.Q5_K_M.gguf) \| Q5_K_M \| 8.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Lyra4-Gutenberg-12B-GGUF/resolve/main/Lyra4-Gutenberg-12B.Q6_K.gguf) \| Q6_K \| 10.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Lyra4-Gutenberg-12B-GGUF/resolve/main/Lyra4-Gutenberg-12B.Q8_0.gguf) \| Q8_0 \| 13.1 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
JoseGR1702/Crop_Spurredanoda_2	JoseGR1702	2024-09-16T18:06:54Z	27	0	diffusers	[ "diffusers", "safetensors", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-09-16T18:05:25Z	--- library_name: diffusers --- # 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 🧨 diffusers 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]
JoseGR1702/Crop_Spurredanoda_1	JoseGR1702	2024-09-16T18:05:22Z	27	0	diffusers	[ "diffusers", "safetensors", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-09-16T18:03:38Z	--- library_name: diffusers --- # 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 🧨 diffusers 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]
mradermacher/Mistral-Nemo-Instruct-12B-Philosophy-Math-GGUF	mradermacher	2024-09-16T18:04:28Z	28	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:EpistemeAI/Mistral-Nemo-Instruct-12B-Philosophy-Math", "base_model:quantized:EpistemeAI/Mistral-Nemo-Instruct-12B-Philosophy-Math", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-09-15T23:05:16Z	--- base_model: EpistemeAI/Mistral-Nemo-Instruct-12B-Philosophy-Math language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - mistral - trl --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/EpistemeAI/Mistral-Nemo-Instruct-12B-Philosophy-Math <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Mistral-Nemo-Instruct-12B-Philosophy-Math-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-Instruct-12B-Philosophy-Math-GGUF/resolve/main/Mistral-Nemo-Instruct-12B-Philosophy-Math.Q2_K.gguf) \| Q2_K \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-Instruct-12B-Philosophy-Math-GGUF/resolve/main/Mistral-Nemo-Instruct-12B-Philosophy-Math.IQ3_XS.gguf) \| IQ3_XS \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-Instruct-12B-Philosophy-Math-GGUF/resolve/main/Mistral-Nemo-Instruct-12B-Philosophy-Math.Q3_K_S.gguf) \| Q3_K_S \| 5.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-Instruct-12B-Philosophy-Math-GGUF/resolve/main/Mistral-Nemo-Instruct-12B-Philosophy-Math.IQ3_S.gguf) \| IQ3_S \| 5.7 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-Instruct-12B-Philosophy-Math-GGUF/resolve/main/Mistral-Nemo-Instruct-12B-Philosophy-Math.IQ3_M.gguf) \| IQ3_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-Instruct-12B-Philosophy-Math-GGUF/resolve/main/Mistral-Nemo-Instruct-12B-Philosophy-Math.Q3_K_M.gguf) \| Q3_K_M \| 6.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-Instruct-12B-Philosophy-Math-GGUF/resolve/main/Mistral-Nemo-Instruct-12B-Philosophy-Math.Q3_K_L.gguf) \| Q3_K_L \| 6.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-Instruct-12B-Philosophy-Math-GGUF/resolve/main/Mistral-Nemo-Instruct-12B-Philosophy-Math.IQ4_XS.gguf) \| IQ4_XS \| 6.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-Instruct-12B-Philosophy-Math-GGUF/resolve/main/Mistral-Nemo-Instruct-12B-Philosophy-Math.Q4_K_S.gguf) \| Q4_K_S \| 7.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-Instruct-12B-Philosophy-Math-GGUF/resolve/main/Mistral-Nemo-Instruct-12B-Philosophy-Math.Q4_K_M.gguf) \| Q4_K_M \| 7.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-Instruct-12B-Philosophy-Math-GGUF/resolve/main/Mistral-Nemo-Instruct-12B-Philosophy-Math.Q5_K_S.gguf) \| Q5_K_S \| 8.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-Instruct-12B-Philosophy-Math-GGUF/resolve/main/Mistral-Nemo-Instruct-12B-Philosophy-Math.Q5_K_M.gguf) \| Q5_K_M \| 8.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-Instruct-12B-Philosophy-Math-GGUF/resolve/main/Mistral-Nemo-Instruct-12B-Philosophy-Math.Q6_K.gguf) \| Q6_K \| 10.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-Instruct-12B-Philosophy-Math-GGUF/resolve/main/Mistral-Nemo-Instruct-12B-Philosophy-Math.Q8_0.gguf) \| Q8_0 \| 13.1 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/GERMANY_REIM_16-GGUF	mradermacher	2024-09-16T18:04:05Z	21	0	transformers	[ "transformers", "gguf", "unsloth", "trl", "sft", "en", "base_model:Shanthini-Joshitha/GERMANY_REIM_16", "base_model:quantized:Shanthini-Joshitha/GERMANY_REIM_16", "endpoints_compatible", "region:us", "conversational" ]	null	2024-09-16T05:04:20Z	--- base_model: Shanthini-Joshitha/GERMANY_REIM_16 language: - en library_name: transformers quantized_by: mradermacher tags: - unsloth - trl - sft --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Shanthini-Joshitha/GERMANY_REIM_16 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/GERMANY_REIM_16-GGUF/resolve/main/GERMANY_REIM_16.Q2_K.gguf) \| Q2_K \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/GERMANY_REIM_16-GGUF/resolve/main/GERMANY_REIM_16.IQ3_XS.gguf) \| IQ3_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/GERMANY_REIM_16-GGUF/resolve/main/GERMANY_REIM_16.Q3_K_S.gguf) \| Q3_K_S \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/GERMANY_REIM_16-GGUF/resolve/main/GERMANY_REIM_16.IQ3_S.gguf) \| IQ3_S \| 3.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/GERMANY_REIM_16-GGUF/resolve/main/GERMANY_REIM_16.IQ3_M.gguf) \| IQ3_M \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/GERMANY_REIM_16-GGUF/resolve/main/GERMANY_REIM_16.Q3_K_M.gguf) \| Q3_K_M \| 4.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/GERMANY_REIM_16-GGUF/resolve/main/GERMANY_REIM_16.Q3_K_L.gguf) \| Q3_K_L \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/GERMANY_REIM_16-GGUF/resolve/main/GERMANY_REIM_16.IQ4_XS.gguf) \| IQ4_XS \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/GERMANY_REIM_16-GGUF/resolve/main/GERMANY_REIM_16.Q4_K_S.gguf) \| Q4_K_S \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/GERMANY_REIM_16-GGUF/resolve/main/GERMANY_REIM_16.Q4_K_M.gguf) \| Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/GERMANY_REIM_16-GGUF/resolve/main/GERMANY_REIM_16.Q5_K_S.gguf) \| Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/GERMANY_REIM_16-GGUF/resolve/main/GERMANY_REIM_16.Q5_K_M.gguf) \| Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/GERMANY_REIM_16-GGUF/resolve/main/GERMANY_REIM_16.Q6_K.gguf) \| Q6_K \| 6.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/GERMANY_REIM_16-GGUF/resolve/main/GERMANY_REIM_16.Q8_0.gguf) \| Q8_0 \| 8.6 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
Vishwas1/bert-base-imdb	Vishwas1	2024-09-16T18:02:44Z	174	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-09-16T17:58:55Z	--- library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer model-index: - name: bert-base-imdb 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. --> # bert-base-imdb This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.1+cu121 - Datasets 3.0.0 - Tokenizers 0.19.1
stablediffusionapi/disney-pixal-cartoon	stablediffusionapi	2024-09-16T17:54:29Z	613	22	diffusers	[ "diffusers", "safetensors", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-06-01T04:35:53Z	--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # disney-pixal-cartoon API Inference ![generated from modelslab.com](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/592c9bfb-3784-4178-b1b0-6bd434906aad/original=true,quality=90/42618-586774526-masterpiece,%20high%20quality%20best%20quality,close%20up,1girl,%20Angry,%20Elf,%20Princess,%20Castle,%20Fantasy,%20Fiction,%20Royalty,%20Regal,%20Noble,%20Me.jpeg) ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change model_id to "disney-pixal-cartoon" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://docs.modelslab.com) Try model for free: [Generate Images](https://modelslab.com/models/disney-pixal-cartoon) Model link: [View model](https://modelslab.com/models/disney-pixal-cartoon) View all models: [View Models](https://modelslab.com/models) import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "disney-pixal-cartoon", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off DMGG0RBN
medieval-data/qwen2-vl-2b-scta	medieval-data	2024-09-16T17:53:29Z	5	0	null	[ "safetensors", "qwen2_vl", "dataset:scta/scta-htr-training-data", "base_model:Qwen/Qwen2-VL-2B-Instruct", "base_model:finetune:Qwen/Qwen2-VL-2B-Instruct", "region:us" ]	null	2024-09-16T17:48:18Z	--- datasets: - scta/scta-htr-training-data base_model: - Qwen/Qwen2-VL-2B-Instruct --- ```python import torch from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor from qwen_vl_utils import process_vision_info device = "cuda" if torch.cuda.is_available() else "cpu" model_dir = "medieval-data/qwen2-vl-2b-scta" model = Qwen2VLForConditionalGeneration.from_pretrained( model_dir, torch_dtype="auto", device_map="auto" ) processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct") image_url ="""https://loris2.scta.info/lon/L28v.jpg/full/full/0/default.jpg""" messages = [ { "role": "user", "content": [ { "type": "image", "image": image_url, }, {"type": "text", "text": "Convert this image to text."}, ], } ] # Preparation for inference text = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) image_inputs, video_inputs = process_vision_info(messages) inputs = processor( text=[text], images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt", ) inputs = inputs.to(device) # Inference: Generation of the output generated_ids = model.generate(**inputs, max_new_tokens=4000) generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False ) print(output_text) # Import required libraries if not already imported from IPython.display import display, Image # Display the output text print(output_text) # Display the image display(Image(url=image_url)) ```
ardi555/setfit-SentEval-classification	ardi555	2024-09-16T17:45:37Z	8	0	setfit	[ "setfit", "safetensors", "mpnet", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "dataset:SetFit/SentEval-CR", "arxiv:2209.11055", "base_model:sentence-transformers/paraphrase-mpnet-base-v2", "base_model:finetune:sentence-transformers/paraphrase-mpnet-base-v2", "model-index", "region:us" ]	text-classification	2024-09-16T17:45:21Z	--- base_model: sentence-transformers/paraphrase-mpnet-base-v2 datasets: - SetFit/SentEval-CR library_name: setfit metrics: - accuracy pipeline_tag: text-classification tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: you can take pic of your friends and the picture will pop up when they call . - text: the speakerphone , the radio , all features work perfectly . - text: 'a ) the picture quality ( color and sharpness of focusing ) are so great , it completely eliminated my doubt about digital imaging -- - how could one eat rice one grain at a time : - ) )' - text: so far the dvd works so i hope it does n 't break down like the reviews i 've read . - text: i have a couple hundred contacts and the menu loads within a few seconds , no big deal . inference: true model-index: - name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2 results: - task: type: text-classification name: Text Classification dataset: name: SetFit/SentEval-CR type: SetFit/SentEval-CR split: test metrics: - type: accuracy value: 0.8698539176626826 name: Accuracy --- # SetFit with sentence-transformers/paraphrase-mpnet-base-v2 This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [SetFit/SentEval-CR](https://huggingface.co/datasets/SetFit/SentEval-CR) dataset that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - Model Type: SetFit - Sentence Transformer body: [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) - Classification head: a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - Maximum Sequence Length: 512 tokens - Number of Classes: 2 classes - Training Dataset: [SetFit/SentEval-CR](https://huggingface.co/datasets/SetFit/SentEval-CR) <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Repository: [SetFit on GitHub](https://github.com/huggingface/setfit) - Paper: [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - Blogpost: [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels \| Label \| Examples \| \|:------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 1 \| <ul><li>'* slick-looking design and improved interface'</li><li>'as for bluetooth , no problems at all .'</li><li>'2 ) storage capacity'</li></ul> \| \| 0 \| <ul><li>"the day finally arrived when i was sure i 'd leave sprint ."</li><li>"neither message was answered ( they ask for 24 hours before replying - i 've been waiting 27 days . )"</li><li>'only problem is that is a bit heavy .'</li></ul> \| ## Evaluation ### Metrics \| Label \| Accuracy \| \|:--------\|:---------\| \| all \| 0.8699 \| ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("ardi555/setfit-SentEval-classification") # Run inference preds = model("the speakerphone , the radio , all features work perfectly .") ``` <!-- ### Downstream Use List how someone could finetune this model on their own dataset. --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Set Metrics \| Training set \| Min \| Median \| Max \| \|:-------------\|:----\|:--------\|:----\| \| Word count \| 4 \| 18.0625 \| 44 \| \| Label \| Training Sample Count \| \|:------\|:----------------------\| \| 0 \| 7 \| \| 1 \| 9 \| ### Training Hyperparameters - batch_size: (16, 16) - num_epochs: (1, 1) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 20 - body_learning_rate: (2e-05, 2e-05) - head_learning_rate: 2e-05 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results \| Epoch \| Step \| Training Loss \| Validation Loss \| \|:-----:\|:----:\|:-------------:\|:---------------:\| \| 0.025 \| 1 \| 0.2289 \| - \| ### Framework Versions - Python: 3.10.12 - SetFit: 1.0.3 - Sentence Transformers: 3.1.0 - Transformers: 4.37.2 - PyTorch: 2.4.0+cu121 - Datasets: 3.0.0 - Tokenizers: 0.15.2 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
silvanoal/dsonia2	silvanoal	2024-09-16T17:29:20Z	5	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2024-09-16T16:10:14Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: dsonia2 --- # Dsonia2 <!-- <Gallery /> --> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `dsonia2` to trigger the image generation. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('silvanoal/dsonia2', weight_name='lora.safetensors') image = pipeline('your prompt').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
punzel/flux_zendaya	punzel	2024-09-16T17:27:40Z	75	2	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "region:us" ]	text-to-image	2024-09-16T17:26:36Z	--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: '-' output: url: images/ComfyUI_Flux_Finetune_00205_.png - text: '-' output: url: images/ComfyUI_Flux_Finetune_00206_.png - text: '-' output: url: images/ComfyUI_Flux_Finetune_00208_.png - text: '-' output: url: images/ComfyUI_Flux_Finetune_00209_.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: null --- # Zendaya <Gallery /> ## Model description This LoRA was trained on 25 images of Zendaya using SimpleTuner for 1600 steps. A trigger word is not required ## Download model Weights for this model are available in Safetensors format. [Download](/punzel/flux_zendaya/tree/main) them in the Files & versions tab.
Jahid05/Gemma-2-2b-it-chat-prompt-generation	Jahid05	2024-09-16T17:25:52Z	61	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-09-16T17:22:46Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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. 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imi2/oxenai_1bitLLM_bitnet_b1_58-large-instruct-v2-gguf	imi2	2024-09-16T17:06:36Z	7	0	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2024-09-16T17:02:22Z	This bitnet (700M) finetune is from - https://www.oxen.ai/ox/BitNet/dir/main/models/bitnet_b1_58-large-instruct_v2/final_checkpoint
QuantFactory/MN-12B-Mag-Mell-R1-GGUF	QuantFactory	2024-09-16T17:04:05Z	116	2	transformers	[ "transformers", "gguf", "mergekit", "merge", "arxiv:2311.03099", "arxiv:2306.01708", "base_model:Fizzarolli/MN-12b-Sunrose", "base_model:merge:Fizzarolli/MN-12b-Sunrose", "base_model:IntervitensInc/Mistral-Nemo-Base-2407-chatml", "base_model:merge:IntervitensInc/Mistral-Nemo-Base-2407-chatml", "base_model:anthracite-org/magnum-v2.5-12b-kto", "base_model:merge:anthracite-org/magnum-v2.5-12b-kto", "base_model:elinas/Chronos-Gold-12B-1.0", "base_model:merge:elinas/Chronos-Gold-12B-1.0", "base_model:nbeerbower/mistral-nemo-bophades-12B", "base_model:merge:nbeerbower/mistral-nemo-bophades-12B", "base_model:nbeerbower/mistral-nemo-gutenberg-12B-v4", "base_model:merge:nbeerbower/mistral-nemo-gutenberg-12B-v4", "base_model:nbeerbower/mistral-nemo-wissenschaft-12B", "base_model:merge:nbeerbower/mistral-nemo-wissenschaft-12B", "endpoints_compatible", "region:us", "conversational" ]	null	2024-09-16T16:14:29Z	--- base_model: - IntervitensInc/Mistral-Nemo-Base-2407-chatml - nbeerbower/mistral-nemo-bophades-12B - nbeerbower/mistral-nemo-wissenschaft-12B - elinas/Chronos-Gold-12B-1.0 - Fizzarolli/MN-12b-Sunrose - nbeerbower/mistral-nemo-gutenberg-12B-v4 - anthracite-org/magnum-12b-v2.5-kto library_name: transformers tags: - mergekit - merge --- [![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory) # QuantFactory/MN-12B-Mag-Mell-R1-GGUF This is quantized version of [inflatebot/MN-12B-Mag-Mell-R1](https://huggingface.co/inflatebot/MN-12B-Mag-Mell-R1) created using llama.cpp # Original Model Card ![Made with NovelAI](https://huggingface.co/inflatebot/MN-12B-Mag-Mell-R1/resolve/main/magmell.png) [Welcome, brave one; you've come a long mile.](https://www.youtube.com/watch?v=dgGEuC1F3oE) # MN-12B-Mag-Mell-R1 This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details Multi-stage SLERP merge, DARE-TIES'd together. Intended to be a general purpose "Best of Nemo" model for any fictional, creative use case. Inspired by hyper-merges like [Tiefighter](https://huggingface.co/KoboldAI/LLaMA2-13B-Tiefighter) and [Umbral Mind.](https://huggingface.co/Casual-Autopsy/L3-Umbral-Mind-RP-v2.0-8B) Mag Mell is composed of 3 intermediate parts: - Hero (RP, kink/trope coverage): [Chronos Gold](https://huggingface.co/elinas/Chronos-Gold-12B-1.0), [Sunrose](https://huggingface.co/Fizzarolli/MN-12b-Sunrose). - Monk (Intelligence, groundedness): [Bophades](https://huggingface.co/nbeerbower/mistral-nemo-bophades-12B), [Wissenschaft](https://huggingface.co/nbeerbower/mistral-nemo-wissenschaft-12B). - Deity (Prose, flair): [Gutenberg v4](https://huggingface.co/nbeerbower/mistral-nemo-gutenberg-12B-v4), [Magnum 2.5 KTO](https://huggingface.co/anthracite-org/magnum-v2.5-12b-kto). I've been dreaming about this merge since Nemo tunes started coming out in earnest. From our testing, Mag Mell demonstrates worldbuilding capabilities unlike any model in its class, comparable to old adventuring models like Tiefighter, and prose that exhibits minimal "slop" (not bad for no finetuning,) frequently devising electrifying metaphors that left us consistently astonished. Use ChatML formatting. Early testing versions had a tendency to leak tokens, but this should be more or less hammered out. I don't want to toot my own bugle though; I'm really proud of how this came out, but please leave your feedback, good or bad. Special thanks as usual to Toaster for his feedback and Fizz for helping fund compute, as well as the KoboldAI Discord for their resources. ### Merge Method This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [IntervitensInc/Mistral-Nemo-Base-2407-chatml](https://huggingface.co/IntervitensInc/Mistral-Nemo-Base-2407-chatml) as a base. ### Models Merged The following models were included in the merge: * IntervitensInc/Mistral-Nemo-Base-2407-chatml * nbeerbower/mistral-nemo-bophades-12B * nbeerbower/mistral-nemo-wissenschaft-12B * elinas/Chronos-Gold-12B-1.0 * Fizzarolli/MN-12b-Sunrose * nbeerbower/mistral-nemo-gutenberg-12B-v4 * anthracite-org/magnum-12b-v2.5-kto ### Configuration The following YAML configurations were used to produce this model: #### Monk: ```yaml models: - model: nbeerbower/mistral-nemo-bophades-12B - model: nbeerbower/mistral-nemo-wissenschaft-12B merge_method: slerp base_model: nbeerbower/mistral-nemo-bophades-12B parameters: t: [0.1, 0.2, 0.4, 0.6, 0.6, 0.4, 0.2, 0.1] dtype: bfloat16 tokenizer_source: base ``` #### Hero: ```yaml models: - model: elinas/Chronos-Gold-12B-1.0 - model: Fizzarolli/MN-12b-Sunrose merge_method: slerp base_model: elinas/Chronos-Gold-12B-1.0 parameters: t: [0.1, 0.2, 0.4, 0.6, 0.6, 0.4, 0.2, 0.1] dtype: bfloat16 tokenizer_source: base ``` #### Deity: ```yaml models: - model: nbeerbower/mistral-nemo-gutenberg-12B-v4 - model: anthracite-org/magnum-12b-v2.5-kto merge_method: slerp base_model: nbeerbower/mistral-nemo-gutenberg-12B-v4 parameters: t: [0, 0.1, 0.2, 0.25, 0.25, 0.2, 0.1, 0] dtype: bfloat16 tokenizer_source: base ``` #### Mag Mell: ```yaml models: - model: monk parameters: density: 0.7 weight: 0.5 - model: hero parameters: density: 0.9 weight: 1 - model: deity parameters: density: 0.5 weight: 0.7 merge_method: dare_ties base_model: IntervitensInc/Mistral-Nemo-Base-2407-chatml tokenizer_source: base ``` `In Irish mythology, Mag Mell (modern spelling: Magh Meall, meaning 'delightful plain') is one of the names for the Celtic Otherworld, a mythical realm achievable through death and/or glory... Never explicitly stated in any surviving mythological account to be an afterlife; rather, it is usually portrayed as a paradise populated by deities, which is occasionally visited by some adventurous mortals. In its island guise, it was visited by various legendary Irish heroes and monks, forming the basis of the adventure myth or echtrae...`
cocktailpeanut/king	cocktailpeanut	2024-09-16T16:53:28Z	12	0	diffusers	[ "diffusers", "text-to-image", "flux", "lora", "template:sd-lora", "fluxgym", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2024-09-16T16:52:30Z	--- tags: - text-to-image - flux - lora - diffusers - template:sd-lora - fluxgym widget: - output: url: sample/king_000960_00_20240916124355.png text: kng is a doctor - output: url: sample/king_000960_01_20240916124401.png text: kng is jogging in new york central park - output: url: sample/king_000960_02_20240916124407.png text: kng is eating hamburger base_model: black-forest-labs/FLUX.1-dev instance_prompt: kng license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md --- # king A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym) <Gallery /> ## Trigger words You should use `kng` to trigger the image generation. ## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc. Weights for this model are available in Safetensors format.
justojavier/hr_assistant_model	justojavier	2024-09-16T16:49:06Z	174	0	transformers	[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-09-16T16:48:42Z	--- library_name: transformers license: mit base_model: openai-community/gpt2 tags: - generated_from_trainer model-index: - name: hr_assistant_model 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. --> # hr_assistant_model This model is a fine-tuned version of [openai-community/gpt2](https://huggingface.co/openai-community/gpt2) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0+cu121 - Datasets 3.0.0 - Tokenizers 0.19.1
rg1683/fine_tuned_wordpiece_test_2M_SentimentAnalysis	rg1683	2024-09-16T16:48:52Z	174	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-09-16T16:48:13Z	--- 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]
griddbnet/griddb_sql_llm	griddbnet	2024-09-16T16:43:44Z	7	0	null	[ "safetensors", "t5", "text2text-generation", "en", "dataset:b-mc2/sql-create-context", "dataset:Clinton/Text-to-sql-v1", "dataset:knowrohit07/know_sql", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "region:us" ]	text2text-generation	2024-09-16T16:13:37Z	--- base_model: - google-t5/t5-small datasets: - b-mc2/sql-create-context - Clinton/Text-to-sql-v1 - knowrohit07/know_sql language: - en pipeline_tag: text2text-generation license: apache-2.0 --- For details, please see https://github.com/griddbnet/sql_llm_model
beryamosta/Lyra4-Gutenberg-12B-Q4_K_M-GGUF	beryamosta	2024-09-16T16:39:58Z	6	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "dataset:jondurbin/gutenberg-dpo-v0.1", "base_model:nbeerbower/Lyra4-Gutenberg-12B", "base_model:quantized:nbeerbower/Lyra4-Gutenberg-12B", "license:cc-by-nc-4.0", "model-index", "endpoints_compatible", "region:us", "conversational" ]	null	2024-09-16T16:39:22Z	--- base_model: nbeerbower/Lyra4-Gutenberg-12B datasets: - jondurbin/gutenberg-dpo-v0.1 library_name: transformers license: cc-by-nc-4.0 tags: - llama-cpp - gguf-my-repo model-index: - name: Lyra4-Gutenberg-12B results: - task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: HuggingFaceH4/ifeval args: num_few_shot: 0 metrics: - type: inst_level_strict_acc and prompt_level_strict_acc value: 22.12 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/Lyra4-Gutenberg-12B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: BBH args: num_few_shot: 3 metrics: - type: acc_norm value: 34.24 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/Lyra4-Gutenberg-12B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: hendrycks/competition_math args: num_few_shot: 4 metrics: - type: exact_match value: 11.71 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/Lyra4-Gutenberg-12B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa args: num_few_shot: 0 metrics: - type: acc_norm value: 9.17 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/Lyra4-Gutenberg-12B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics: - type: acc_norm value: 11.97 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/Lyra4-Gutenberg-12B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 28.57 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/Lyra4-Gutenberg-12B name: Open LLM Leaderboard --- # beryamosta/Lyra4-Gutenberg-12B-Q4_K_M-GGUF This model was converted to GGUF format from [`nbeerbower/Lyra4-Gutenberg-12B`](https://huggingface.co/nbeerbower/Lyra4-Gutenberg-12B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/nbeerbower/Lyra4-Gutenberg-12B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo beryamosta/Lyra4-Gutenberg-12B-Q4_K_M-GGUF --hf-file lyra4-gutenberg-12b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo beryamosta/Lyra4-Gutenberg-12B-Q4_K_M-GGUF --hf-file lyra4-gutenberg-12b-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo beryamosta/Lyra4-Gutenberg-12B-Q4_K_M-GGUF --hf-file lyra4-gutenberg-12b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo beryamosta/Lyra4-Gutenberg-12B-Q4_K_M-GGUF --hf-file lyra4-gutenberg-12b-q4_k_m.gguf -c 2048 ```
kayfahaarukku/UrangDiffusion-1.4	kayfahaarukku	2024-09-16T16:39:48Z	2,456	3	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "en", "base_model:cagliostrolab/animagine-xl-3.1", "base_model:finetune:cagliostrolab/animagine-xl-3.1", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2024-09-16T14:34:03Z	--- license: other license_name: faipl license_link: https://freedevproject.org/faipl-1.0-sd language: - en tags: - text-to-image - stable-diffusion - safetensors - stable-diffusion-xl base_model: cagliostrolab/animagine-xl-3.1 widget: - text: >- 1girl, green hair, sweater, looking at viewer, upper body, beanie, outdoors, night, turtleneck, masterpiece, best quality parameter: negative_prompt: >- nsfw, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name example_title: 1girl --- <style> .title-container { display: flex; justify-content: center; align-items: center; height: 100vh; /* Adjust this value to position the title vertically / } .title { font-size: 2.5em; text-align: center; color: #333; font-family: 'Helvetica Neue', sans-serif; text-transform: uppercase; letter-spacing: 0.1em; padding: 0.5em 0; background: transparent; } .title span { background: -webkit-linear-gradient(45deg, #bdabe3, #b39a3e); -webkit-background-clip: text; -webkit-text-fill-color: transparent; } .custom-table { table-layout: fixed; width: 100%; border-collapse: collapse; margin-top: 2em; } .custom-table td { width: 50%; vertical-align: top; padding: 10px; box-shadow: 0px 0px 0px 0px rgba(0, 0, 0, 0.15); } .custom-image-container { position: relative; width: 100%; margin-bottom: 0em; overflow: hidden; border-radius: 10px; transition: transform .7s; / Smooth transition for the container / } .custom-image-container:hover { transform: scale(1.05); filter: none; / Scale the container on hover / } .custom-image { width: 100%; height: auto; object-fit: cover; border-radius: 10px; transition: transform .7s; margin-bottom: 0em; } .nsfw-filter { filter: blur(8px); / Apply a blur effect / transition: filter 0.3s ease; / Smooth transition for the blur effect / } .overlay { position: absolute; bottom: 0; left: 0; right: 0; color: white; width: 100%; height: 40%; display: flex; flex-direction: column; justify-content: center; align-items: center; font-size: 1vw; font-style: bold; text-align: center; opacity: 0; / Keep the text fully opaque / background: linear-gradient(0deg, rgba(0, 0, 0, 0.8) 60%, rgba(0, 0, 0, 0) 100%); transition: opacity .5s; } .custom-image-container:hover .overlay { opacity: 1; } .overlay-text { background: linear-gradient(45deg, #7ed56f, #28b485); -webkit-background-clip: text; color: transparent; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.7); .overlay-subtext { font-size: 0.75em; margin-top: 0.5em; font-style: italic; } .overlay, .overlay-subtext { text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.5); } </style> <h1 class="title"> <span>UrangDiffusion 1.4</span> </h1> <table class="custom-table"> <tr> <td> <div class="custom-image-container"> <img class="custom-image" src="https://cdn-uploads.huggingface.co/production/uploads/64333a074521083b9d2aab3b/jsyeY4utstuMKH3euh-Ix.png" alt="sample1"> </div> <div class="custom-image-container"> <img class="custom-image" src="https://cdn-uploads.huggingface.co/production/uploads/64333a074521083b9d2aab3b/ZlJxvv-33cMG6o5wcym6t.png" alt="sample4"> </div> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="https://cdn-uploads.huggingface.co/production/uploads/64333a074521083b9d2aab3b/E13T0Vp6VAs6PKHk_AqW2.png" alt="sample2"> </div> <div class="custom-image-container"> <img class="custom-image" src="https://cdn-uploads.huggingface.co/production/uploads/64333a074521083b9d2aab3b/HEvvWe8wuefG_GtFueE0z.png" alt="sample3"> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="https://cdn-uploads.huggingface.co/production/uploads/64333a074521083b9d2aab3b/yEcm1kGs_3eLF3fo0XxfB.png" alt="sample1"> </div> <div class="custom-image-container"> <img class="custom-image" src="https://cdn-uploads.huggingface.co/production/uploads/64333a074521083b9d2aab3b/53rtgGxO7ZUDrPnj8Wfd7.png" alt="sample4"> </div> </td> </tr> </table> UrangDiffusion 1.4* (oo-raw-ng Diffusion) is an updated version of UrangDiffusion 1.3. This version provides refreshed dataset, better image tagging, improvements over the last iteration, training parameter correction, and better overall generation results. ## Standard Prompting Guidelines The model is finetuned from Animagine XL 3.1. However, there is a little bit changes on dataset captioning, therefore there is some different default prompt used: Default prompt: ``` 1girl/1boy, character name, from what series, everything else in any order, masterpiece, best quality, amazing quality, very aesthetic ``` Default negative prompt: ``` nsfw, lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract], ``` Default configuration: Default configuration: Euler a with around 25-30 steps, CFG 5-7, and ENSD set to 31337. Sweet spot is around 26 steps and CFG 5. ## Training Configurations - Finetuned from: [Animagine XL 3.1](https://huggingface.co/cagliostrolab/animagine-xl-3.1) Pretraining: - Dataset size: 34,368 images - GPU: 1xA100 - Optimizer: AdaFactor - Unet Learning Rate: 3.75e-6 - Text Encoder Learning Rate: 1.875e-6 - Batch Size: 48 - Gradient Accumulation: 1 - Warmup steps: 100 steps - Min SNR Gamma: 5 - Epoch: 10 (epoch 9 is used) Finetuning: - Dataset size: 7,104 images - GPU: 1xA100 - Optimizer: AdaFactor - Unet Learning Rate: 3e-6 - Text Encoder Learning Rate: - (Train TE set to False) - Batch Size: 48 - Gradient Accumulation: 1 - Warmup steps: 5% - Min SNR Gamma: 5 - Epoch: 10 (epoch 8 is used) - Noise Offset: 0.0357 ## Added Series Wuthering Waves, Zenless Zone Zero, Sewayaki Kitsune no Senko-san, and hololiveEN -Justice- have been added to the model. ## Special Thanks - CagliostroLab for sponsoring the model pretraining by letting me borrowed the organization’s RunPod account. - My co-workers(?) at CagliostroLab for the insights and feedback. - Nur Hikari and Vanilla Latte for quality control. - Linaqruf, my tutor and role model in AI-generated images. ## License UrangDiffusion 1.4 falls under the [Fair AI Public License 1.0-SD](https://freedevproject.org/faipl-1.0-sd/) license.
eztoms/eztoms_lora	eztoms	2024-09-16T16:34:41Z	7	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2024-09-16T16:05:09Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: EZTOMS --- # Eztoms_Lora <!-- <Gallery /> --> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `EZTOMS` to trigger the image generation. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('eztoms/eztoms_lora', weight_name='lora.safetensors') image = pipeline('your prompt').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
ClaudioItaly/Albacus-V2-Imatrix	ClaudioItaly	2024-09-16T16:33:01Z	6	1	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:ClaudioItaly/Albacus", "base_model:quantized:ClaudioItaly/Albacus", "license:mit", "endpoints_compatible", "region:us", "imatrix" ]	null	2024-09-08T20:47:51Z	--- base_model: ClaudioItaly/Albacus library_name: transformers license: mit tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # ClaudioItaly/Albacus-Q5_K_M-GGUF This model was converted to GGUF format from [`ClaudioItaly/Albacus`](https://huggingface.co/ClaudioItaly/Albacus) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/ClaudioItaly/Albacus) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo ClaudioItaly/Albacus-Q5_K_M-GGUF --hf-file albacus-q5_k_m-imat.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo ClaudioItaly/Albacus-Q5_K_M-GGUF --hf-file albacus-q5_k_m-imat.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo ClaudioItaly/Albacus-Q5_K_M-GGUF --hf-file albacus-q5_k_m-imat.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo ClaudioItaly/Albacus-Q5_K_M-GGUF --hf-file albacus-q5_k_m-imat.gguf -c 2048 ```
Lyte/RWKV-6-World-3B-v2.1-GGUF	Lyte	2024-09-16T16:31:02Z	75	2	gguf	[ "gguf", "text-generation", "rwkv", "rwkv-6", "base_model:RWKV/rwkv-6-world-3b-v2.1", "base_model:quantized:RWKV/rwkv-6-world-3b-v2.1", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2024-09-02T06:36:45Z	--- base_model: RWKV/rwkv-6-world-3b-v2.1 library_name: gguf license: apache-2.0 quantized_by: Lyte tags: - text-generation - rwkv - rwkv-6 --- # RWKV-6-World-3B-v2.1-GGUF This repo contains the RWKV-6-World-3B-v2.1-GGUF NEW (RE)-quantized with the latest llama.cpp [b3771](https://github.com/ggerganov/llama.cpp/releases/tag/b3771). # Note: * The Notebook used to convert this model is included feel free to use to in Colab or Kaggle to quantize future models using it. ## How to run the model * Get the latest llama.cpp: ``` git clone https://github.com/ggerganov/llama.cpp ``` * Download the GGUF file to a new model folder in llama.cpp(choose your quant): ``` cd llama.cpp mkdir model git clone https://huggingface.co/Lyte/RWKV-6-World-3B-v2.1-GGUF mv RWKV-6-World-3B-v2.1-GGUF/RWKV-6-World-3B-v2.1-GGUF-Q4_K_M.gguf model/ rm -r RWKV-6-World-3B-v2.1-GGUF ``` * For Windows other than git cloning the repo, you just create the "model" folder inside llama.cpp folder and in here click "Files and versions" and download the model quant you want there. * Now to run the model, you can use the following command: ``` ./llama-cli -m ./model/RWKV-6-World-3B-v2.1-GGUF-Q4_K_M.gguf --in-suffix "Assistant:" --interactive-first -c 1024 -t 0.7 --top-k 50 --top-p 0.95 -n 128 -p "Assistant: Hello, what can i help you with today?\nUser:" -r "User:" ```
mradermacher/caliburn-12b-GGUF	mradermacher	2024-09-16T16:25:05Z	8	0	transformers	[ "transformers", "gguf", "en", "base_model:Xclbr7/caliburn-12b", "base_model:quantized:Xclbr7/caliburn-12b", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	null	2024-09-16T00:41:18Z	--- base_model: Xclbr7/caliburn-12b language: - en library_name: transformers license: mit quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Xclbr7/caliburn-12b <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/caliburn-12b-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/caliburn-12b-GGUF/resolve/main/caliburn-12b.Q2_K.gguf) \| Q2_K \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/caliburn-12b-GGUF/resolve/main/caliburn-12b.IQ3_XS.gguf) \| IQ3_XS \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/caliburn-12b-GGUF/resolve/main/caliburn-12b.Q3_K_S.gguf) \| Q3_K_S \| 5.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/caliburn-12b-GGUF/resolve/main/caliburn-12b.IQ3_S.gguf) \| IQ3_S \| 5.7 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/caliburn-12b-GGUF/resolve/main/caliburn-12b.IQ3_M.gguf) \| IQ3_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/caliburn-12b-GGUF/resolve/main/caliburn-12b.Q3_K_M.gguf) \| Q3_K_M \| 6.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/caliburn-12b-GGUF/resolve/main/caliburn-12b.Q3_K_L.gguf) \| Q3_K_L \| 6.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/caliburn-12b-GGUF/resolve/main/caliburn-12b.IQ4_XS.gguf) \| IQ4_XS \| 6.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/caliburn-12b-GGUF/resolve/main/caliburn-12b.Q4_K_S.gguf) \| Q4_K_S \| 7.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/caliburn-12b-GGUF/resolve/main/caliburn-12b.Q4_K_M.gguf) \| Q4_K_M \| 7.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/caliburn-12b-GGUF/resolve/main/caliburn-12b.Q5_K_S.gguf) \| Q5_K_S \| 8.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/caliburn-12b-GGUF/resolve/main/caliburn-12b.Q5_K_M.gguf) \| Q5_K_M \| 8.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/caliburn-12b-GGUF/resolve/main/caliburn-12b.Q6_K.gguf) \| Q6_K \| 10.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/caliburn-12b-GGUF/resolve/main/caliburn-12b.Q8_0.gguf) \| Q8_0 \| 13.1 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/caliburn-12b-i1-GGUF	mradermacher	2024-09-16T16:25:05Z	222	0	transformers	[ "transformers", "gguf", "en", "base_model:Xclbr7/caliburn-12b", "base_model:quantized:Xclbr7/caliburn-12b", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2024-09-16T14:25:21Z	--- base_model: Xclbr7/caliburn-12b language: - en library_name: transformers license: mit quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/Xclbr7/caliburn-12b <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/caliburn-12b-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/caliburn-12b-i1-GGUF/resolve/main/caliburn-12b.i1-IQ1_S.gguf) \| i1-IQ1_S \| 3.1 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/caliburn-12b-i1-GGUF/resolve/main/caliburn-12b.i1-IQ1_M.gguf) \| i1-IQ1_M \| 3.3 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/caliburn-12b-i1-GGUF/resolve/main/caliburn-12b.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/caliburn-12b-i1-GGUF/resolve/main/caliburn-12b.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/caliburn-12b-i1-GGUF/resolve/main/caliburn-12b.i1-IQ2_S.gguf) \| i1-IQ2_S \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/caliburn-12b-i1-GGUF/resolve/main/caliburn-12b.i1-IQ2_M.gguf) \| i1-IQ2_M \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/caliburn-12b-i1-GGUF/resolve/main/caliburn-12b.i1-Q2_K.gguf) \| i1-Q2_K \| 4.9 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/caliburn-12b-i1-GGUF/resolve/main/caliburn-12b.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 5.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/caliburn-12b-i1-GGUF/resolve/main/caliburn-12b.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/caliburn-12b-i1-GGUF/resolve/main/caliburn-12b.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 5.6 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/caliburn-12b-i1-GGUF/resolve/main/caliburn-12b.i1-IQ3_S.gguf) \| i1-IQ3_S \| 5.7 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/caliburn-12b-i1-GGUF/resolve/main/caliburn-12b.i1-IQ3_M.gguf) \| i1-IQ3_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/caliburn-12b-i1-GGUF/resolve/main/caliburn-12b.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 6.2 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/caliburn-12b-i1-GGUF/resolve/main/caliburn-12b.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 6.7 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/caliburn-12b-i1-GGUF/resolve/main/caliburn-12b.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/caliburn-12b-i1-GGUF/resolve/main/caliburn-12b.i1-Q4_0_4_4.gguf) \| i1-Q4_0_4_4 \| 7.2 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/caliburn-12b-i1-GGUF/resolve/main/caliburn-12b.i1-Q4_0_4_8.gguf) \| i1-Q4_0_4_8 \| 7.2 \| fast on arm+i8mm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/caliburn-12b-i1-GGUF/resolve/main/caliburn-12b.i1-Q4_0_8_8.gguf) \| i1-Q4_0_8_8 \| 7.2 \| fast on arm+sve, low quality \| \| [GGUF](https://huggingface.co/mradermacher/caliburn-12b-i1-GGUF/resolve/main/caliburn-12b.i1-Q4_0.gguf) \| i1-Q4_0 \| 7.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/caliburn-12b-i1-GGUF/resolve/main/caliburn-12b.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 7.2 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/caliburn-12b-i1-GGUF/resolve/main/caliburn-12b.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 7.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/caliburn-12b-i1-GGUF/resolve/main/caliburn-12b.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 8.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/caliburn-12b-i1-GGUF/resolve/main/caliburn-12b.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 8.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/caliburn-12b-i1-GGUF/resolve/main/caliburn-12b.i1-Q6_K.gguf) \| i1-Q6_K \| 10.2 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
jukus100/flux_julian_brille	jukus100	2024-09-16T16:11:08Z	5	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2024-09-16T15:26:00Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: JulianLINKEDIN --- # Flux_Julian_Brille <!-- <Gallery /> --> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `JulianLINKEDIN` to trigger the image generation. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('jukus100/flux_julian_brille', weight_name='lora.safetensors') image = pipeline('your prompt').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
kholiavko/reception-llama-3.1-8b-test-3-gguf	kholiavko	2024-09-16T16:10:29Z	6	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-09-16T15:16:22Z	--- base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf --- # Uploaded model - Developed by: kholiavko - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
facebook/sapiens-normal-2b	facebook	2024-09-16T16:06:21Z	26	2	sapiens	[ "sapiens", "en", "arxiv:2408.12569", "license:cc-by-nc-4.0", "region:us" ]	null	2024-09-10T02:50:04Z	--- language: en license: cc-by-nc-4.0 tags: - sapiens --- # Normal-Sapiens-2B ### Model Details Sapiens is a family of vision transformers pretrained on 300 million human images at 1024 x 1024 image resolution. The pretrained models, when finetuned for human-centric vision tasks, generalize to in-the-wild conditions. Sapiens-2B natively support 1K high-resolution inference. The resulting models exhibit remarkable generalization to in-the-wild data, even when labeled data is scarce or entirely synthetic. - Developed by: Meta - Model type: Vision Transformer - License: Creative Commons Attribution-NonCommercial 4.0 - Task: normal - Format: original - File: sapiens_2b_normal_render_people_epoch_70.pth ### Model Card - Image Size: 1024 x 768 (H x W) - Num Parameters: 2.163 B - FLOPs: 8.709 TFLOPs - Patch Size: 16 x 16 - Embedding Dimensions: 1920 - Num Layers: 48 - Num Heads: 32 - Feedforward Channels: 7680 ### More Resources - Repository: [https://github.com/facebookresearch/sapiens](https://github.com/facebookresearch/sapiens) - Paper: [https://arxiv.org/abs/2408.12569](https://arxiv.org/abs/2408.12569) - Demo: [https://huggingface.co/spaces/facebook/sapiens-normal](https://huggingface.co/spaces/facebook/sapiens-normal) - Project Page: [https://about.meta.com/realitylabs/codecavatars/sapiens](https://about.meta.com/realitylabs/codecavatars/sapiens/) - Additional Results: [https://rawalkhirodkar.github.io/sapiens](https://rawalkhirodkar.github.io/sapiens/) - HuggingFace Collection: [https://huggingface.co/collections/facebook/sapiens-66d22047daa6402d565cb2fc](https://huggingface.co/collections/facebook/sapiens-66d22047daa6402d565cb2fc) ## Uses Normal 2B model can be used to estimate surface normal (XYZ) on human images.
facebook/sapiens-normal-1b-bfloat16	facebook	2024-09-16T16:06:12Z	358	0	sapiens	[ "sapiens", "en", "arxiv:2408.12569", "license:cc-by-nc-4.0", "region:us" ]	null	2024-09-10T18:25:58Z	--- language: en license: cc-by-nc-4.0 tags: - sapiens --- # Normal-Sapiens-1B-Bfloat16 ### Model Details Sapiens is a family of vision transformers pretrained on 300 million human images at 1024 x 1024 image resolution. The pretrained models, when finetuned for human-centric vision tasks, generalize to in-the-wild conditions. Sapiens-1B natively support 1K high-resolution inference. The resulting models exhibit remarkable generalization to in-the-wild data, even when labeled data is scarce or entirely synthetic. - Developed by: Meta - Model type: Vision Transformer - License: Creative Commons Attribution-NonCommercial 4.0 - Task: normal - Format: bfloat16 - File: sapiens_1b_normal_render_people_epoch_115_bfloat16.pt2 ### Model Card - Image Size: 1024 x 768 (H x W) - Num Parameters: 1.169 B - FLOPs: 4.647 TFLOPs - Patch Size: 16 x 16 - Embedding Dimensions: 1536 - Num Layers: 40 - Num Heads: 24 - Feedforward Channels: 6144 ### More Resources - Repository: [https://github.com/facebookresearch/sapiens](https://github.com/facebookresearch/sapiens) - Paper: [https://arxiv.org/abs/2408.12569](https://arxiv.org/abs/2408.12569) - Demo: [https://huggingface.co/spaces/facebook/sapiens-normal](https://huggingface.co/spaces/facebook/sapiens-normal) - Project Page: [https://about.meta.com/realitylabs/codecavatars/sapiens](https://about.meta.com/realitylabs/codecavatars/sapiens/) - Additional Results: [https://rawalkhirodkar.github.io/sapiens](https://rawalkhirodkar.github.io/sapiens/) - HuggingFace Collection: [https://huggingface.co/collections/facebook/sapiens-66d22047daa6402d565cb2fc](https://huggingface.co/collections/facebook/sapiens-66d22047daa6402d565cb2fc) ## Uses Normal 1B model can be used to estimate surface normal (XYZ) on human images.
facebook/sapiens-normal-1b	facebook	2024-09-16T16:05:54Z	16	1	sapiens	[ "sapiens", "en", "arxiv:2408.12569", "license:cc-by-nc-4.0", "region:us" ]	null	2024-09-10T02:53:53Z	--- language: en license: cc-by-nc-4.0 tags: - sapiens --- # Normal-Sapiens-1B ### Model Details Sapiens is a family of vision transformers pretrained on 300 million human images at 1024 x 1024 image resolution. The pretrained models, when finetuned for human-centric vision tasks, generalize to in-the-wild conditions. Sapiens-1B natively support 1K high-resolution inference. The resulting models exhibit remarkable generalization to in-the-wild data, even when labeled data is scarce or entirely synthetic. - Developed by: Meta - Model type: Vision Transformer - License: Creative Commons Attribution-NonCommercial 4.0 - Task: normal - Format: original - File: sapiens_1b_normal_render_people_epoch_115.pth ### Model Card - Image Size: 1024 x 768 (H x W) - Num Parameters: 1.169 B - FLOPs: 4.647 TFLOPs - Patch Size: 16 x 16 - Embedding Dimensions: 1536 - Num Layers: 40 - Num Heads: 24 - Feedforward Channels: 6144 ### More Resources - Repository: [https://github.com/facebookresearch/sapiens](https://github.com/facebookresearch/sapiens) - Paper: [https://arxiv.org/abs/2408.12569](https://arxiv.org/abs/2408.12569) - Demo: [https://huggingface.co/spaces/facebook/sapiens-normal](https://huggingface.co/spaces/facebook/sapiens-normal) - Project Page: [https://about.meta.com/realitylabs/codecavatars/sapiens](https://about.meta.com/realitylabs/codecavatars/sapiens/) - Additional Results: [https://rawalkhirodkar.github.io/sapiens](https://rawalkhirodkar.github.io/sapiens/) - HuggingFace Collection: [https://huggingface.co/collections/facebook/sapiens-66d22047daa6402d565cb2fc](https://huggingface.co/collections/facebook/sapiens-66d22047daa6402d565cb2fc) ## Uses Normal 1B model can be used to estimate surface normal (XYZ) on human images.
facebook/sapiens-normal-0.6b-bfloat16	facebook	2024-09-16T16:05:45Z	355	0	sapiens	[ "sapiens", "en", "arxiv:2408.12569", "license:cc-by-nc-4.0", "region:us" ]	null	2024-09-10T18:27:06Z	--- language: en license: cc-by-nc-4.0 tags: - sapiens --- # Normal-Sapiens-0.6B-Bfloat16 ### Model Details Sapiens is a family of vision transformers pretrained on 300 million human images at 1024 x 1024 image resolution. The pretrained models, when finetuned for human-centric vision tasks, generalize to in-the-wild conditions. Sapiens-0.6B natively support 1K high-resolution inference. The resulting models exhibit remarkable generalization to in-the-wild data, even when labeled data is scarce or entirely synthetic. - Developed by: Meta - Model type: Vision Transformer - License: Creative Commons Attribution-NonCommercial 4.0 - Task: normal - Format: bfloat16 - File: sapiens_0.6b_normal_render_people_epoch_200_bfloat16.pt2 ### Model Card - Image Size: 1024 x 768 (H x W) - Num Parameters: 0.664 B - FLOPs: 2.583 TFLOPs - Patch Size: 16 x 16 - Embedding Dimensions: 1280 - Num Layers: 32 - Num Heads: 16 - Feedforward Channels: 5120 ### More Resources - Repository: [https://github.com/facebookresearch/sapiens](https://github.com/facebookresearch/sapiens) - Paper: [https://arxiv.org/abs/2408.12569](https://arxiv.org/abs/2408.12569) - Demo: [https://huggingface.co/spaces/facebook/sapiens-normal](https://huggingface.co/spaces/facebook/sapiens-normal) - Project Page: [https://about.meta.com/realitylabs/codecavatars/sapiens](https://about.meta.com/realitylabs/codecavatars/sapiens/) - Additional Results: [https://rawalkhirodkar.github.io/sapiens](https://rawalkhirodkar.github.io/sapiens/) - HuggingFace Collection: [https://huggingface.co/collections/facebook/sapiens-66d22047daa6402d565cb2fc](https://huggingface.co/collections/facebook/sapiens-66d22047daa6402d565cb2fc) ## Uses Normal 0.6B model can be used to estimate surface normal (XYZ) on human images.
fwtan/phi-3_5_converted	fwtan	2024-09-16T16:05:35Z	9	0	null	[ "safetensors", "license:cc-by-nc-4.0", "region:us" ]	null	2024-09-16T16:01:50Z	--- license: cc-by-nc-4.0 ---
rg1683/fine_tuned_unigram_test_2M_SentimentAnalysis	rg1683	2024-09-16T16:05:35Z	174	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-09-16T16:04:51Z	--- 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. 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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. 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facebook/sapiens-normal-0.3b	facebook	2024-09-16T16:05:04Z	14	0	sapiens	[ "sapiens", "en", "arxiv:2408.12569", "license:cc-by-nc-4.0", "region:us" ]	null	2024-09-10T02:56:59Z	--- language: en license: cc-by-nc-4.0 tags: - sapiens --- # Normal-Sapiens-0.3B ### Model Details Sapiens is a family of vision transformers pretrained on 300 million human images at 1024 x 1024 image resolution. The pretrained models, when finetuned for human-centric vision tasks, generalize to in-the-wild conditions. Sapiens-0.3B natively support 1K high-resolution inference. The resulting models exhibit remarkable generalization to in-the-wild data, even when labeled data is scarce or entirely synthetic. - Developed by: Meta - Model type: Vision Transformer - License: Creative Commons Attribution-NonCommercial 4.0 - Task: normal - Format: original - File: sapiens_0.3b_normal_render_people_epoch_66.pth ### Model Card - Image Size: 1024 x 768 (H x W) - Num Parameters: 0.336 B - FLOPs: 1.242 TFLOPs - Patch Size: 16 x 16 - Embedding Dimensions: 1024 - Num Layers: 24 - Num Heads: 16 - Feedforward Channels: 4096 ### More Resources - Repository: [https://github.com/facebookresearch/sapiens](https://github.com/facebookresearch/sapiens) - Paper: [https://arxiv.org/abs/2408.12569](https://arxiv.org/abs/2408.12569) - Demo: [https://huggingface.co/spaces/facebook/sapiens-normal](https://huggingface.co/spaces/facebook/sapiens-normal) - Project Page: [https://about.meta.com/realitylabs/codecavatars/sapiens](https://about.meta.com/realitylabs/codecavatars/sapiens/) - Additional Results: [https://rawalkhirodkar.github.io/sapiens](https://rawalkhirodkar.github.io/sapiens/) - HuggingFace Collection: [https://huggingface.co/collections/facebook/sapiens-66d22047daa6402d565cb2fc](https://huggingface.co/collections/facebook/sapiens-66d22047daa6402d565cb2fc) ## Uses Normal 0.3B model can be used to estimate surface normal (XYZ) on human images.
GalrionSoftworks/MN-LooseCannon-12B-v1	GalrionSoftworks	2024-09-16T16:02:32Z	2,524	8	null	[ "safetensors", "mistral", "merge", "mergekit", "lazymergekit", "aetherwiing/MN-12B-Starcannon-v3", "Sao10K/MN-12B-Lyra-v1", "base_model:AuriAetherwiing/MN-12B-Starcannon-v3", "base_model:merge:AuriAetherwiing/MN-12B-Starcannon-v3", "base_model:Sao10K/MN-12B-Lyra-v1", "base_model:merge:Sao10K/MN-12B-Lyra-v1", "model-index", "region:us" ]	null	2024-08-09T00:26:44Z	--- tags: - merge - mergekit - lazymergekit - aetherwiing/MN-12B-Starcannon-v3 - Sao10K/MN-12B-Lyra-v1 base_model: - aetherwiing/MN-12B-Starcannon-v3 - Sao10K/MN-12B-Lyra-v1 model-index: - name: MN-LooseCannon-12B-v1 results: - task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: HuggingFaceH4/ifeval args: num_few_shot: 0 metrics: - type: inst_level_strict_acc and prompt_level_strict_acc value: 54.18 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=GalrionSoftworks/MN-LooseCannon-12B-v1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: BBH args: num_few_shot: 3 metrics: - type: acc_norm value: 29.98 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=GalrionSoftworks/MN-LooseCannon-12B-v1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: hendrycks/competition_math args: num_few_shot: 4 metrics: - type: exact_match value: 6.5 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=GalrionSoftworks/MN-LooseCannon-12B-v1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa args: num_few_shot: 0 metrics: - type: acc_norm value: 4.7 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=GalrionSoftworks/MN-LooseCannon-12B-v1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics: - type: acc_norm value: 10.96 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=GalrionSoftworks/MN-LooseCannon-12B-v1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 24.4 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=GalrionSoftworks/MN-LooseCannon-12B-v1 name: Open LLM Leaderboard --- # MN-LooseCannon-12B-v1 MN-LooseCannon-12B-v1 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [aetherwiing/MN-12B-Starcannon-v3](https://huggingface.co/aetherwiing/MN-12B-Starcannon-v3) * [Sao10K/MN-12B-Lyra-v1](https://huggingface.co/Sao10K/MN-12B-Lyra-v1) ## 🧩 Configuration ```yaml models: - model: aetherwiing/MN-12B-Starcannon-v3 parameters: density: 0.3 weight: 0.75 - model: Sao10K/MN-12B-Lyra-v1 parameters: density: 0.7 weight: 0.25 merge_method: ties base_model: aetherwiing/MN-12B-Starcannon-v3 parameters: normalize: true dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "GalrionSoftworks/MN-LooseCannon-12B-v1" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_GalrionSoftworks__MN-LooseCannon-12B-v1) \| Metric \|Value\| \|-------------------\|----:\| \|Avg. \|21.78\| \|IFEval (0-Shot) \|54.18\| \|BBH (3-Shot) \|29.98\| \|MATH Lvl 5 (4-Shot)\| 6.50\| \|GPQA (0-shot) \| 4.70\| \|MuSR (0-shot) \|10.96\| \|MMLU-PRO (5-shot) \|24.40\|
AbFiras/GIT-Base-Captioner	AbFiras	2024-09-16T16:02:32Z	6	0	transformers	[ "transformers", "safetensors", "git", "image-text-to-text", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-09-16T16:01:51Z	--- 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. 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calico-1226/scorelm_openllama_3b_v2_unfreeze_0916	calico-1226	2024-09-16T15:56:35Z	6	0	transformers	[ "transformers", "safetensors", "llama", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2024-09-16T15:48:21Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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sameerd13/gita-text-generation-gpt2	sameerd13	2024-09-16T15:51:37Z	89	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-09-16T15:50:36Z	--- 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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StelleX/mt5-base-thaisum-text-summarization	StelleX	2024-09-16T15:43:37Z	78	1	transformers	[ "transformers", "safetensors", "mt5", "text2text-generation", "summarization", "mT5", "th", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2023-12-22T15:17:38Z	--- tags: - summarization - mT5 language: - th widget: - text: "ผมกินตับหมูดิบแล้วหมดสติไป พอฟื้นอีกทีในต่างโลกดันกลายเป็นหมูซะงั้น! คนที่ช่วยผมเอาไว้คือเจส สาวน้อยผู้อ่านใจคนได้ อู๊ด! น่ารัก! ระดับสายตาหมูทำให้เห็นอะไรสีขาวบริสุทธิ์แวบๆ แจ่มเลย... “เอ่อ ฉันได้ยินเสียงในใจของคุณนะคะ…” ฉิบแล้ว! ความมักมากรั่วไหลหมด! แม้ว่าชีวิตประสาหมูที่มีเด็กสาวผู้อ่อนโยนคอยดูแลจะไม่เลว ผมก็ตัดสินใจมุ่งหน้าสู่นครหลวงพร้อมกับเจสเพื่อหาทางกลับเป็นมนุษย์ การเดินทางแสนรื่นรมย์จึงเริ่มต้นขึ้น... แต่ไหงเราถึงถูกตามล่าเอาชีวิตล่ะเนี่ย!?" example_title: "Novel" - text: "พริ้ง คนเริงเมือง, ผลิตโดยบริษัท มีเดีย สตูดิโอ ร่วมกับ ,นีโน่ บราเดอร์ส, ที่ตอนนี้เดินทางมาถึงตอนอวสานแล้ว โดยวันนี้ถึงตอนที่, พริ้ง (จั๊กจั่น–อคัมย์สิริ), ฆ่าสามีที่ 6 ,หลวงเสนาะ, ตายไปเรียบร้อย ก็ถึงคราวที่จะทำตามใจตัวเองด้วยการอ่อย ,เปรมฤทัย (โตนนท์), ลูกชายคนเดียวของ ,หลวงเสนาะ, ให้กลายมาเป็นสามีของตัวเองสมใจอยากเสียที,งานนี้สกิลการอ่อยมาเต็ม เริ่มจากเดินมาหา, เปรมฤทัย, ที่ห้องก่อนจะบอกว่าไม่สามารถทำใจให้เลิกรักได้เลย จนมาถึงวันนี้วันที่สามารถเปิดใจได้แล้ว วันที่เราจะรักกันได้แล้ว ทำไมต้องห้ามใจอีก, เปรมฤทัย, ได้ยินแบบนี้ก็หวั่นไหวคล้อยตามไม่ห้ามใจปล่อยตัวให้ความเสน่หาเข้าครอบงำ,ฉากนี้ ผกก. ,บุ๋ม–รัญญา, ยกกองไปถ่ายทำที่บ้านท่าไม้ จ.สมุทรสงคราม ก่อนเริ่มถ่ายจริง ,บุ๋ม, เรียกทั้ง, จั๊กจั่น, และ ,โตนนท์, มาทำสมาธิ และบิ้วท์ให้ทั้งคู่เข้าใจในความต้องการที่ทั้งตัวละคร ,พริ้ง, และ ,เปรมฤทัย, ต้องการปลดปล่อยออกมา เมื่อทั้งคู่เข้าใจบทแล้วเริ่มถ่ายจริง ,จั๊กจั่น, เล่นเต็มที่ไม่ยั้ง พรั่งพรูความรู้สึกที่มีออกมาพร้อมน้ำตาเรียกความสงสาร ก่อนจะโน้มจูบกันอย่างดูดดื่ม งานนี้จูบจริงไม่ใช้สแตนด์อินใดๆ ติดตามชมฉากแซ่บทิ้งทวน คืนวันพฤหัสบดีนี้ ทางช่อง 7.,ติดตามอ่านนิยายเรื่อง พริ้ง คนเริงเมือง ได้ที่นี่" example_title: "Thai movie" - text: "หนุ่มใหญ่วัย 49 ปี เสียชีวิตคาบ้านย่านปากเกร็ด สภาพมีเลือดออกปากกองใหญ่ ข้างศพมีไซริงค์ฉีดยา เพื่อนบอกมาหาที่บ้าน เห็นว่าฉีดไอซ์ไป 2 เข็ม ก่อนคลุ้มคลั่งทำลายข้าวของ ล้มคว่ำหน้าแน่นิ่ง ,เวลา 22.00 น. วันที่ 6 ส.ค. ร.ต.ท.พันธ์พงศ์ ภูริวัฒนพงศ์ รอง สว.(สอบสวน) สภ.ปากเกร็ด จ.นนทบุรี รับแจ้งมีผู้เสียชีวิตภายในบ้านเลขที่ 77/489 หมู่ 1 หมู่บ้านดวงแก้ว ถนนติวานนท์ ต.บ้านใหม่ ไปสอบสวนพร้อมด้วย พ.ต.อ.พงศ์จักร ปรีชาการุณพงศ์ ผกก. พ.ต.ท.นภธร วาชัยยุง รอง ผกก.ป สภ.ปากเกร็ด แพทย์สถาบันนิติวิทยาศาสตร์ และเจ้าหน้าที่กู้ภัยมูลนิธิป่อเต็กตึ๊ง ,ที่เกิดเหตุเป็นบ้านทาวน์เฮาส์ 2 ชั้น บนชั้น 2 พบศพ นายพงษ์ธนกร หรือเอ อุ่นทน อายุ 49 ปี เจ้าของบ้าน นอนคว่ำหน้าเสียชีวิตอยู่บนพื้น ในสภาพเลือดออกปาก ข้างศพพบไซริงค์ฉีดยาตกอยู่ ทางเจ้าหน้าที่จึงเก็บไว้เป็นหลักฐาน นอกจากนี้ข้าวของภายในห้องล้มระเนระนาดกระจัดกระจาย ,จากการสอบปากคำ นายเอ๋ (นามสมมติ) อายุ 31 ปี ให้การว่า ตนเป็นเพื่อนกับผู้เสียชีวิต ก่อนเกิดเหตุได้เดินทางมาหาที่บ้านเห็นผู้เสียชีวิตฉีดยาไอซ์เข้าไป 2 เข็ม จากนั้นผู้เสียชีวิตมีอาการคลุ้มคลั่งทำลายข้าวของก่อนนอนคว่ำหน้าแน่นิ่งไป กระทั่งเสียชีวิตในที่สุด เบื้องต้นเจ้าหน้าที่คาดว่าสาเหตุการเสียชีวิตน่าจะเกิดจากการเสพยาเกินขนาด อย่างไรก็ตามจะได้สอบสวนหาสาเหตุที่แท้จริงอีกครั้ง" example_title: "Crime news" inference: parameters: min_length: 40 max_length: 140 --- # mt5-base-thaisum This repository contains the finetuned mT5-base model for Thai sentence summarization. The architecture of the model is based on mT5 model and fine-tuned on text-summarization pairs in Thai. ### Example ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM import torch tokenizer = AutoTokenizer.from_pretrained("preechanon/mt5-base-thaisum-text-summarization") model = AutoModelForSeq2SeqLM.from_pretrained("preechanon/mt5-base-thaisum-text-summarization") new_input_string = "ข้อความที่ต้องการ" input_ = tokenizer(new_input_string, truncation=True, max_length=1024, return_tensors="pt") with torch.no_grad(): preds = model.generate( input_['input_ids'].to('cpu'), num_beams=15, num_return_sequences=1, no_repeat_ngram_size=1, remove_invalid_values=True, max_length=140, ) summary = tokenizer.decode(preds[0], skip_special_tokens=True) summary ``` ### Score - Rouge1: 0.488931 - Rouge2: 0.309732 - Rougel: 0.425490 - Rougelsum: 0.444359 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-04 - train_batch_size: 8 - eval_batch_size: 1 - seed: 42 - optimizer: AdamW with betas=(0.9,0.999), epsilon=1e-08 and weight_decay=0.1 - warmup step: 5000 - lr_scheduler_type: linear - num_epochs: 6 - gradient_accumulation_steps: 4 ### Framework versions - Transformers 4.36.1 - Pytorch 2.1.2 ### Resource Funding NSTDA Supercomputer center (ThaiSC) and the National e-Science Infrastructure Consortium for their support of computer facilities. ### Citation ``` ปรีชานนท์ ชาติไทย และ สัจจวัจน์ ส่งเสริม. (2567), การสรุปข้อความข่าวภาษาไทยด้วยโครงข่ายประสาทเทียม (Thai News Text Summarization Using Neural Network), วิทยาศาสตรบัณฑิต (วทบ.):ขอนแก่น, มหาวิทยาลัยขอนแก่น ```
asoria/facebook-opt-350m-imdb	asoria	2024-09-16T15:36:28Z	9	0	transformers	[ "transformers", "tensorboard", "safetensors", "opt", "text-generation", "trl", "sft", "generated_from_trainer", "base_model:facebook/opt-350m", "base_model:finetune:facebook/opt-350m", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-09-16T15:35:33Z	--- library_name: transformers license: other base_model: facebook/opt-350m tags: - trl - sft - generated_from_trainer model-index: - name: outputs results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # outputs This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 100 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0+cu121 - Datasets 3.0.0 - Tokenizers 0.19.1
alex-miller/climate-percentage-regression	alex-miller	2024-09-16T15:15:36Z	6	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:alex-miller/ODABert", "base_model:finetune:alex-miller/ODABert", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-09-05T18:38:18Z	--- library_name: transformers license: apache-2.0 base_model: alex-miller/ODABert tags: - generated_from_trainer model-index: - name: climate-percentage-regression 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. --> # climate-percentage-regression This model is a fine-tuned version of [alex-miller/ODABert](https://huggingface.co/alex-miller/ODABert) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0489 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-06 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.0982 \| 1.0 \| 75 \| 0.0878 \| \| 0.0608 \| 2.0 \| 150 \| 0.0590 \| \| 0.0473 \| 3.0 \| 225 \| 0.0535 \| \| 0.0409 \| 4.0 \| 300 \| 0.0521 \| \| 0.0377 \| 5.0 \| 375 \| 0.0506 \| \| 0.0365 \| 6.0 \| 450 \| 0.0506 \| \| 0.0332 \| 7.0 \| 525 \| 0.0497 \| \| 0.0323 \| 8.0 \| 600 \| 0.0495 \| \| 0.0302 \| 9.0 \| 675 \| 0.0490 \| \| 0.0295 \| 10.0 \| 750 \| 0.0491 \| \| 0.0276 \| 11.0 \| 825 \| 0.0486 \| \| 0.0263 \| 12.0 \| 900 \| 0.0487 \| \| 0.026 \| 13.0 \| 975 \| 0.0487 \| \| 0.0246 \| 14.0 \| 1050 \| 0.0486 \| \| 0.0239 \| 15.0 \| 1125 \| 0.0484 \| \| 0.0234 \| 16.0 \| 1200 \| 0.0486 \| \| 0.0232 \| 17.0 \| 1275 \| 0.0488 \| \| 0.0229 \| 18.0 \| 1350 \| 0.0487 \| \| 0.0227 \| 19.0 \| 1425 \| 0.0489 \| \| 0.0222 \| 20.0 \| 1500 \| 0.0489 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0+cu121 - Datasets 3.0.0 - Tokenizers 0.19.1
cocktailpeanut/dragunov	cocktailpeanut	2024-09-16T15:09:43Z	40	2	diffusers	[ "diffusers", "text-to-image", "flux", "lora", "template:sd-lora", "fluxgym", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2024-09-16T13:57:10Z	--- tags: - text-to-image - flux - lora - diffusers - template:sd-lora - fluxgym widget: - output: url: sample/dragunov_001760_00_20240916095517.png text: >- a photo of dragunov jogging in new york central park wearing yellow training suit - output: url: sample/dragunov_001760_01_20240916095522.png text: a photo of dragunov cooking in a ramen joint - output: url: sample/dragunov_001760_02_20240916095528.png text: a photo of dragunov on a cow in rural japan. - output: url: sample/dragunov_001760_03_20240916095534.png text: a photo of dragunov working from a cafe on his laptop to build a startup - output: url: sample/dragunov_001760_04_20240916095539.png text: a photo of dragunov cosplaying as a cat - text: a photo of dragunov riding on top of a grizzly bear output: url: images/example_iop3lh6jg.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: dragunov license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md --- # dragunov Trained with [Fluxgym](https://github.com/cocktailpeanut/fluxgym) <Gallery /> ## Trigger words You should use `dragunov` to trigger the image generation. ## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc. Weights for this model are available in Safetensors format.
unity/sentis-blaze-face	unity	2024-09-16T15:05:19Z	46	10	unity-sentis	[ "unity-sentis", "onnx", "object-detection", "license:apache-2.0", "region:us" ]	object-detection	2024-01-12T23:34:30Z	--- license: apache-2.0 library_name: unity-sentis pipeline_tag: object-detection --- # BlazeFace in Sentis BlazeFace is a fast, light-weight face detector from Google Research. A pretrained model is available as part of Google's [MediaPipe](https://ai.google.dev/edge/mediapipe/solutions/vision/face_detector) framework. ![](./images/face.jpg) The BlazeFace model has been converted from TFLite to ONNX for use in Sentis using [tf2onnx](https://github.com/onnx/tensorflow-onnx) with the default export parameters. ## Functional API The BlazeFace model takes a (1, 128, 128, 3) input image tensor and outputs a (1, 896, 16) boxes tensor and a (1, 896, 1) scores tensor. Each of the 896 boxes consists of: - [x position, y position, width, height] for the bounding box. The position is relative to the anchor position for the given index, these are precalculated and loaded from a csv file. - [x position, y position] for each of 6 facial keypoints relative to the anchor position. We adapt the model using the Sentis functional API to apply non maximum suppression to filter the boxes with the highest scores that don't overlap with each other. ``` var xCenter = rawBoxes[0, .., 0] + anchors[.., 0] * inputSize; var yCenter = rawBoxes[0, .., 1] + anchors[.., 1] * inputSize; var widthHalf = 0.5f * rawBoxes[0, .., 2]; var heightHalf = 0.5f * rawBoxes[0, .., 3]; var nmsBoxes = Functional.Stack(new[] { yCenter - heightHalf, xCenter - widthHalf, yCenter + heightHalf, xCenter + widthHalf }, 1); var nmsScores = Functional.Squeeze(ScoreFiltering(rawScores, 100f)); var selectedIndices = Functional.NMS(nmsBoxes, nmsScores, iouThreshold, scoreThreshold); // (N); var selectedBoxes = Functional.IndexSelect(rawBoxes, 1, selectedIndices).Unsqueeze(0); // (1, N, 16) var selectedScores = Functional.IndexSelect(rawScores, 1, selectedIndices).Unsqueeze(0); // (1, N, 1) ``` ## Model inference We use the dimensions of the texture to set up an affine transformation matrix to go from the 128x128 tensor coordinates to the image coordinates. We then fill the input tensor using a compute shader with this affine transformation, points outside the image will correspond to zeros in the input tensor. ``` var size = Mathf.Max(texture.width, texture.height); // The affine transformation matrix to go from tensor coordinates to image coordinates var scale = size / (float)detectorInputSize; var M = BlazeUtils.mul(BlazeUtils.TranslationMatrix(0.5f * (new Vector2(texture.width, texture.height) + new Vector2(-size, size))), BlazeUtils.ScaleMatrix(new Vector2(scale, -scale))); BlazeUtils.SampleImageAffine(texture, m_DetectorInput, M); m_FaceDetectorWorker.Schedule(m_DetectorInput); ``` Execution is scheduled using an [Awaitable](https://docs.unity3d.com/6000.0/Documentation/ScriptReference/Awaitable.html) and the output tensors are downloaded and awaited. This frees up the main thread while the GPU computation and download takes place. ``` var outputIndicesAwaitable = (m_FaceDetectorWorker.PeekOutput(0) as Tensor<int>).ReadbackAndCloneAsync(); var outputScoresAwaitable = (m_FaceDetectorWorker.PeekOutput(1) as Tensor<float>).ReadbackAndCloneAsync(); var outputBoxesAwaitable = (m_FaceDetectorWorker.PeekOutput(2) as Tensor<float>).ReadbackAndCloneAsync(); using var outputIndices = await outputIndicesAwaitable; using var outputScores = await outputScoresAwaitable; using var outputBoxes = await outputBoxesAwaitable; ``` The output tensors are now on the CPU and can be read. We use the values from the output tensors together with the affine transformation matrix to set the transforms on the bounding boxes and keypoints for visualization. In this demo we visualize the four faces with the highest scores that pass the score threshold. ## Notes This model has been trained primarily for short-range faces in images taken using the front-facing smartphone camera, results may be poor for longer-range images of faces. The non max suppression operator requires a blocking GPU readback, this prevents this demo from running on the WebGPU backend in Unity 6 and Sentis 2.0.
Shotaro30678/archive_sentiment_analysis_for_emotion_chat_bot	Shotaro30678	2024-09-16T15:04:01Z	16	0	transformers	[ "transformers", "tensorboard", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:michellejieli/emotion_text_classifier", "base_model:finetune:michellejieli/emotion_text_classifier", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-04T16:01:35Z	--- library_name: transformers base_model: michellejieli/emotion_text_classifier tags: - generated_from_trainer metrics: - accuracy model-index: - name: sentiment_analysis_for_emotion_chat_bot 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. --> # sentiment_analysis_for_emotion_chat_bot This model is a fine-tuned version of [michellejieli/emotion_text_classifier](https://huggingface.co/michellejieli/emotion_text_classifier) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4012 - Accuracy: 0.8653 - F1-score: 0.8582 - Num Input Tokens Seen: 130810880 ## 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: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1-score \| Input Tokens Seen \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\|:-----------------:\| \| No log \| 1.0 \| 6388 \| 0.3932 \| 0.8715 \| 0.8678 \| 26162176 \| \| 0.6659 \| 2.0 \| 12776 \| 0.3770 \| 0.8724 \| 0.8680 \| 52324352 \| \| 0.6659 \| 3.0 \| 19164 \| 0.3531 \| 0.8776 \| 0.8749 \| 78486528 \| \| 0.643 \| 4.0 \| 25552 \| 0.3735 \| 0.8726 \| 0.8696 \| 104648704 \| \| 0.643 \| 5.0 \| 31940 \| 0.4012 \| 0.8653 \| 0.8582 \| 130810880 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0+cu121 - Datasets 3.0.0 - Tokenizers 0.19.1
nbeerbower/llama3.1-cc-8B	nbeerbower	2024-09-16T15:01:33Z	25	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "dataset:flammenai/casual-conversation-DPO", "base_model:mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated", "base_model:finetune:mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated", "license:llama3", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-08-18T06:41:38Z	--- license: llama3 library_name: transformers base_model: - mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated datasets: - flammenai/casual-conversation-DPO model-index: - name: llama3.1-cc-8B results: - task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: HuggingFaceH4/ifeval args: num_few_shot: 0 metrics: - type: inst_level_strict_acc and prompt_level_strict_acc value: 50.68 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/llama3.1-cc-8B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: BBH args: num_few_shot: 3 metrics: - type: acc_norm value: 26.48 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/llama3.1-cc-8B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: hendrycks/competition_math args: num_few_shot: 4 metrics: - type: exact_match value: 6.34 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/llama3.1-cc-8B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa args: num_few_shot: 0 metrics: - type: acc_norm value: 4.7 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/llama3.1-cc-8B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics: - type: acc_norm value: 6.5 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/llama3.1-cc-8B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 26.08 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/llama3.1-cc-8B name: Open LLM Leaderboard --- # llama3.1-cc-8B [mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated](https://huggingface.co/mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated) finetuned on [flammenai/casual-conversation-DPO](https://huggingface.co/datasets/flammenai/casual-conversation-DPO). This is an experimental finetune that formats the conversation data sequentially with the Llama 3 template. ### Method Finetuned using an A100 on Google Colab for 3 epochs. [Fine-tune Llama 3 with ORPO](https://mlabonne.github.io/blog/posts/2024-04-19_Fine_tune_Llama_3_with_ORPO.html) # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_nbeerbower__llama3.1-cc-8B) \| Metric \|Value\| \|-------------------\|----:\| \|Avg. \|20.13\| \|IFEval (0-Shot) \|50.68\| \|BBH (3-Shot) \|26.48\| \|MATH Lvl 5 (4-Shot)\| 6.34\| \|GPQA (0-shot) \| 4.70\| \|MuSR (0-shot) \| 6.50\| \|MMLU-PRO (5-shot) \|26.08\|
ibm-research/PowerLM-3b	ibm-research	2024-09-16T15:00:42Z	11,295	18	transformers	[ "transformers", "safetensors", "granite", "text-generation", "arxiv:2408.13359", "license:apache-2.0", "model-index", "autotrain_compatible", "region:us" ]	text-generation	2024-08-14T18:20:58Z	--- pipeline_tag: text-generation inference: false license: apache-2.0 library_name: transformers model-index: - name: ibm/PowerLM-3b results: - task: type: text-generation dataset: type: lm-eval-harness name: ARC metrics: - name: accuracy-norm type: accuracy-norm value: 60.5 verified: false - task: type: text-generation dataset: type: lm-eval-harness name: BoolQ metrics: - name: accuracy type: accuracy value: 72.0 verified: false - task: type: text-generation dataset: type: lm-eval-harness name: Hellaswag metrics: - name: accuracy-norm type: accuracy-norm value: 74.6 verified: false - task: type: text-generation dataset: type: lm-eval-harness name: OpenBookQA metrics: - name: accuracy-norm type: accuracy-norm value: 43.6 verified: false - task: type: text-generation dataset: type: lm-eval-harness name: PIQA metrics: - name: accuracy-norm type: accuracy-norm value: 79.9 verified: false - task: type: text-generation dataset: type: lm-eval-harness name: Winogrande metrics: - name: accuracy-norm type: accuracy-norm value: 70.0 verified: false - task: type: text-generation dataset: type: lm-eval-harness name: MMLU (5 shot) metrics: - name: accuracy type: accuracy value: 49.2 verified: false - task: type: text-generation dataset: type: lm-eval-harness name: GSM8k (5 shot) metrics: - name: accuracy type: accuracy value: 34.9 verified: false - task: type: text-generation dataset: type: lm-eval-harness name: math (4 shot) metrics: - name: accuracy type: accuracy value: 15.2 verified: false - task: type: text-generation dataset: type: bigcode-eval name: humaneval metrics: - name: pass@1 type: pass@1 value: 26.8 verified: false - task: type: text-generation dataset: type: bigcode-eval name: MBPP metrics: - name: pass@1 type: pass@1 value: 33.6 verified: false --- ## Model Summary PowerLM-3B is a 3B state-of-the-art small language model trained with the Power learning rate scheduler. It is trained on a mix of open-source and proprietary datasets. PowerLM-3B has shown promising results compared to other models in the size categories across various benchmarks, including natural language multi-choices, code generation, and math reasoning. Paper: https://arxiv.org/abs/2408.13359 ## Usage Note: Requires installing HF transformers from source. ### Generation This is a simple example of how to use PowerLM-3b model. ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # or "cpu" model_path = "ibm/PowerLM-3b" tokenizer = AutoTokenizer.from_pretrained(model_path) # drop device_map if running on CPU model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device) model.eval() # change input text as desired prompt = "Write a code to find the maximum value in a list of numbers." # tokenize the text input_tokens = tokenizer(prompt, return_tensors="pt") # transfer tokenized inputs to the device for i in input_tokens: input_tokens[i] = input_tokens[i].to(device) # generate output tokens output = model.generate(**input_tokens, max_new_tokens=100) # decode output tokens into text output = tokenizer.batch_decode(output) # loop over the batch to print, in this example the batch size is 1 for i in output: print(i) ```
rg1683/fine_tuned_spiece_test_NamedEntityRecognition_large	rg1683	2024-09-16T14:59:28Z	174	0	transformers	[ "transformers", "safetensors", "bert", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-09-16T14:59:11Z	--- 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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salbatarni/bert_negNum5_task5_fold0_prompt_adherence.pt	salbatarni	2024-09-16T14:44:22Z	174	0	transformers	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	feature-extraction	2024-09-16T14:42:22Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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krnl/controlnet-canny-sdxl-1.0	krnl	2024-09-16T14:43:06Z	43	0	diffusers	[ "diffusers", "safetensors", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "base_model:runwayml/stable-diffusion-v1-5", "base_model:finetune:runwayml/stable-diffusion-v1-5", "license:openrail++", "region:us" ]	text-to-image	2024-09-13T08:10:44Z	--- license: openrail++ base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers inference: false --- # SDXL-controlnet: Canny These are controlnet weights trained on [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) with canny conditioning. You can find some example images in the following. prompt: a couple watching a romantic sunset, 4k photo ![images_0)](./out_couple.png) prompt: ultrarealistic shot of a furry blue bird ![images_1)](./out_bird.png) prompt: a woman, close up, detailed, beautiful, street photography, photorealistic, detailed, Kodak ektar 100, natural, candid shot ![images_2)](./out_women.png) prompt: Cinematic, neoclassical table in the living room, cinematic, contour, lighting, highly detailed, winter, golden hour ![images_3)](./out_room.png) prompt: a tornado hitting grass field, 1980's film grain. overcast, muted colors. ![images_0)](./out_tornado.png) ## Usage Make sure to first install the libraries: ```bash pip install accelerate transformers safetensors opencv-python diffusers ``` And then we're ready to go: ```python from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL from diffusers.utils import load_image from PIL import Image import torch import numpy as np import cv2 prompt = "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting" negative_prompt = 'low quality, bad quality, sketches' image = load_image("https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png") controlnet_conditioning_scale = 0.5 # recommended for good generalization controlnet = ControlNetModel.from_pretrained( "diffusers/controlnet-canny-sdxl-1.0", torch_dtype=torch.float16 ) vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16) pipe = StableDiffusionXLControlNetPipeline.from_pretrained( "stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet, vae=vae, torch_dtype=torch.float16, ) pipe.enable_model_cpu_offload() image = np.array(image) image = cv2.Canny(image, 100, 200) image = image[:, :, None] image = np.concatenate([image, image, image], axis=2) image = Image.fromarray(image) images = pipe( prompt, negative_prompt=negative_prompt, image=image, controlnet_conditioning_scale=controlnet_conditioning_scale, ).images images[0].save(f"hug_lab.png") ``` ![images_10)](./out_hug_lab_7.png) To more details, check out the official documentation of [`StableDiffusionXLControlNetPipeline`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/controlnet_sdxl). ### Training Our training script was built on top of the official training script that we provide [here](https://github.com/huggingface/diffusers/blob/main/examples/controlnet/README_sdxl.md). #### Training data This checkpoint was first trained for 20,000 steps on laion 6a resized to a max minimum dimension of 384. It was then further trained for 20,000 steps on laion 6a resized to a max minimum dimension of 1024 and then filtered to contain only minimum 1024 images. We found the further high resolution finetuning was necessary for image quality. #### Compute one 8xA100 machine #### Batch size Data parallel with a single gpu batch size of 8 for a total batch size of 64. #### Hyper Parameters Constant learning rate of 1e-4 scaled by batch size for total learning rate of 64e-4 #### Mixed precision fp16
openfoodfacts/ingredient-detection	openfoodfacts	2024-09-16T14:41:49Z	15	0	null	[ "safetensors", "license:agpl-3.0", "region:us" ]	null	2024-09-16T14:28:05Z	--- license: agpl-3.0 --- This ingredient detection model was trained on the ingredient detection [dataset v1.1](https://huggingface.co/datasets/openfoodfacts/ingredient-detection/tree/v1.1) using [this training code](https://github.com/openfoodfacts/openfoodfacts-ai/tree/a9b4ad6a854fa6f8330b0ff3e6a67ad963c9b96b/ingredient_extraction/train). Training was tracked on [Wandb](https://wandb.ai/raphaeloff/ingredient-detection-ner/runs/dwbdbjek/overview). This release provides the following assets: Training-related assets: - `predictions` directory: predictions on train and test dataset of the model, in: HTML format: easier to view JSONL format: either the raw or the aggregated (post-processed) version - the HuggingFace serialized model, in the root directory Serving assets: - `onnx.tar.gz`: the model exported to ONNX format ## Versions ### 1.1 New version based on [v1.1 of the dataset](https://huggingface.co/datasets/openfoodfacts/ingredient-detection/tree/v1.1). “organic”/”issu de l’agriculture biologique” suffixes are now considered as part of the ingredient list.s ### 1.0 First version based on [v1.0 of the dataset](https://huggingface.co/datasets/openfoodfacts/ingredient-detection/tree/v1.0).
ha-0/t5-small-custom	ha-0	2024-09-16T14:39:46Z	6	0	null	[ "safetensors", "t5", "region:us" ]	null	2024-09-16T14:39:27Z	# Model Card for t5-small based Text Summarization Model (boostcamp ai tech huggingface utilization task test upload) ## Model Details This model is a fine-tuned version of t5-small-base for summarization tasks. ## Training Data The model was trained on the CNN/Daily Mail dataset. ## Training Procedure - Learning Rate: 2e-5 - Epochs: 1 - Batch Size: 4 ## How to Use ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("test_work3") model = AutoModelForSeq2SeqLM.from_pretrained("test_work3") input_text = "Your input text here" inputs = tokenizer(input_text, return_tensors="pt", max_length=512, truncation=True) outputs = model.generate(inputs, max_length=100) decoded_output = tokenizer.decode(outputs[0], skip_special_tokens=True) print(decoded_output) ``` ## Evaluation - Training Loss: 0.2661 - Validation Loss: 0.2014 - ROUGE-1: 23.25 - ROUGE-2: 8.76 - ROUGE-L: 18.92 - BLEU-1: 40.37 - BLEU-2: 15.69 - BLEU-4: 4.58 ## Limitations This is the test model created for the assignment. The model may generate biased or inappropriate content due to the nature of the training data. It is recommended to use the model with caution and apply necessary filters. ## Ethical Considerations - Bias: The model may inherit biases present in the training data. - Misuse**: The model can be misused to generate misleading or harmful content. ## Copyright and License This model is licensed under the MIT License.

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