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train · 793k rows

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-06-02 06:28:08	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 462 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-02 06:27:40	card stringlengths 11 1.01M
IndianSanga/ner-model	IndianSanga	2025-05-31T13:22:41Z	0	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2025-05-31T13:20:21Z	--- library_name: transformers license: mit base_model: xlm-roberta-base tags: - generated_from_trainer model-index: - name: ner-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. --> # ner-model This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
FLOPS-Squared/KeystoneFuse-Baseline-Epoch-5-Instruct-Flax	FLOPS-Squared	2025-05-31T13:22:10Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-31T11:19:25Z	--- 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]
yukiyounai/Jailbreak-R1	yukiyounai	2025-05-31T13:21:23Z	0	0	null	[ "safetensors", "qwen2", "legal", "text-generation", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "region:us" ]	text-generation	2025-05-22T12:00:39Z	--- license: apache-2.0 base_model: - Qwen/Qwen2.5-7B-Instruct pipeline_tag: text-generation tags: - legal --- dfsdsssssssssssssssssssssssssssss ssssssssssssssssssssssssssssssssss ssssssssssssssssssssssssssss
Whitley7/distilbert-sarcasm-detection	Whitley7	2025-05-31T13:20:59Z	0	1	null	[ "safetensors", "roberta", "license:apache-2.0", "region:us" ]	null	2025-05-31T03:46:17Z	--- license: apache-2.0 ---
georgeiac00/dpo_sft_model_32_edge_3_ep	georgeiac00	2025-05-31T13:19:16Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-31T13:18:40Z	--- 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]
eipyae/eipyae	eipyae	2025-05-31T13:16:54Z	0	0	null	[ "av", "as", "dataset:disco-eth/EuroSpeech", "license:bsl-1.0", "region:us" ]	null	2025-05-31T13:16:24Z	--- license: bsl-1.0 datasets: - disco-eth/EuroSpeech language: - av - as ---
Fuscosucof/fusco_alzheimerMRI_model	Fuscosucof	2025-05-31T13:12:31Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "resnet", "image-classification", "generated_from_trainer", "base_model:microsoft/resnet-50", "base_model:finetune:microsoft/resnet-50", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-05-30T11:00:31Z	--- library_name: transformers license: apache-2.0 base_model: microsoft/resnet-50 tags: - generated_from_trainer metrics: - accuracy model-index: - name: fusco_alzheimerMRI_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. --> # fusco_alzheimerMRI_model This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0434 - Accuracy: 0.9902 ## 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: 0.002 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.8393 \| 1.0 \| 72 \| 0.9099 \| 0.5703 \| \| 0.6828 \| 2.0 \| 144 \| 0.7748 \| 0.6914 \| \| 0.5156 \| 3.0 \| 216 \| 0.5721 \| 0.7637 \| \| 0.3141 \| 4.0 \| 288 \| 0.2799 \| 0.9082 \| \| 0.2024 \| 5.0 \| 360 \| 0.5322 \| 0.8066 \| \| 0.0934 \| 6.0 \| 432 \| 0.6212 \| 0.8555 \| \| 0.0307 \| 7.0 \| 504 \| 0.1234 \| 0.9551 \| \| 0.0383 \| 8.0 \| 576 \| 0.1279 \| 0.9570 \| \| 0.0195 \| 9.0 \| 648 \| 0.1022 \| 0.9746 \| \| 0.0102 \| 10.0 \| 720 \| 0.1057 \| 0.9629 \| \| 0.0081 \| 11.0 \| 792 \| 0.0785 \| 0.9766 \| \| 0.0009 \| 12.0 \| 864 \| 0.0641 \| 0.9805 \| \| 0.0023 \| 13.0 \| 936 \| 0.0612 \| 0.9824 \| \| 0.0089 \| 14.0 \| 1008 \| 0.0571 \| 0.9746 \| \| 0.0008 \| 15.0 \| 1080 \| 0.0437 \| 0.9902 \| \| 0.0014 \| 16.0 \| 1152 \| 0.0375 \| 0.9883 \| \| 0.0002 \| 17.0 \| 1224 \| 0.0485 \| 0.9824 \| \| 0.0002 \| 18.0 \| 1296 \| 0.0450 \| 0.9863 \| \| 0.0002 \| 19.0 \| 1368 \| 0.0451 \| 0.9863 \| \| 0.0002 \| 20.0 \| 1440 \| 0.0434 \| 0.9902 \| ### Framework versions - Transformers 4.52.3 - Pytorch 2.7.0+cpu - Datasets 3.6.0 - Tokenizers 0.21.1
lacos03/std-1.5-lora-midjourney-1.0	lacos03	2025-05-31T13:11:11Z	0	1	diffusers	[ "diffusers", "stable-diffusion", "lora", "t2i", "midjourney", "style", "art", "safetensors", "text-to-image", "en", "dataset:MohamedRashad/midjourney-detailed-prompts", "base_model:stable-diffusion-v1-5/stable-diffusion-v1-5", "base_model:adapter:stable-diffusion-v1-5/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2025-05-30T15:20:04Z	--- license: creativeml-openrail-m datasets: - MohamedRashad/midjourney-detailed-prompts language: - en base_model: - stable-diffusion-v1-5/stable-diffusion-v1-5 pipeline_tag: text-to-image library_name: diffusers tags: - stable-diffusion - lora - t2i - midjourney - style - art - safetensors ---
Mehrm/mehr	Mehrm	2025-05-31T13:05:29Z	0	1	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2025-05-31T13:05:29Z	--- license: bigscience-openrail-m ---
Snarcy/mit-b5_train_002	Snarcy	2025-05-31T13:02:45Z	2	0	transformers	[ "transformers", "safetensors", "segformer", "generated_from_trainer", "base_model:nvidia/mit-b5", "base_model:finetune:nvidia/mit-b5", "license:other", "endpoints_compatible", "region:us" ]	null	2025-05-29T20:27:10Z	--- library_name: transformers license: other base_model: nvidia/mit-b5 tags: - generated_from_trainer model-index: - name: mit-b5_train_002 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. --> # mit-b5_train_002 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0193 - Mean Iou: 0.8074 - Mean Accuracy: 0.9523 - Overall Accuracy: 0.9939 - Per Category Iou: [0.993861306152736, 0.6209314583219739] - Per Category Accuracy: [0.9948525401842638, 0.9098462857018345] ## 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: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Per Category Iou \| Per Category Accuracy \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:----------------------------------------:\|:----------------------------------------:\| \| 0.006 \| 1.3021 \| 500 \| 0.0210 \| 0.7686 \| 0.9519 \| 0.9917 \| [0.9916161135468804, 0.5456785039519645] \| [0.9925910583643316, 0.911127036429974] \| \| 0.0042 \| 2.6042 \| 1000 \| 0.0207 \| 0.7737 \| 0.9539 \| 0.9920 \| [0.991908285445925, 0.5555062317087333] \| [0.9928413818014432, 0.9149668675355112] \| \| 0.0043 \| 3.9062 \| 1500 \| 0.0203 \| 0.7762 \| 0.9560 \| 0.9921 \| [0.9920317877519694, 0.5604237434509759] \| [0.9929195911355291, 0.9191044913434324] \| \| 0.003 \| 5.2083 \| 2000 \| 0.0187 \| 0.7909 \| 0.9491 \| 0.9931 \| [0.9930237925441819, 0.5888398995824345] \| [0.9940777053124252, 0.904064749333598] \| \| 0.0023 \| 6.5104 \| 2500 \| 0.0204 \| 0.7820 \| 0.9537 \| 0.9925 \| [0.9924293920252416, 0.5716014051664092] \| [0.9933741427138751, 0.9139500144054193] \| \| 0.0029 \| 7.8125 \| 3000 \| 0.0201 \| 0.7920 \| 0.9500 \| 0.9931 \| [0.9930661864964873, 0.590834237246924] \| [0.9940989986071507, 0.905989507044129] \| \| 0.0028 \| 9.1146 \| 3500 \| 0.0298 \| 0.7545 \| 0.9719 \| 0.9903 \| [0.9902257223969061, 0.5187995787903269] \| [0.9907400148442388, 0.9530528594152126] \| \| 0.0024 \| 10.4167 \| 4000 \| 0.0234 \| 0.7788 \| 0.9533 \| 0.9923 \| [0.9922408901388992, 0.5654456381813304] \| [0.993190431130829, 0.9134972726546403] \| \| 0.0023 \| 11.7188 \| 4500 \| 0.0200 \| 0.7915 \| 0.9584 \| 0.9930 \| [0.9929147999583595, 0.5901749476024036] \| [0.9937597790125627, 0.9230750607085529] \| \| 0.0028 \| 13.0208 \| 5000 \| 0.0191 \| 0.8033 \| 0.9574 \| 0.9936 \| [0.9935777693357606, 0.612975535809318] \| [0.9944543421808892, 0.9202520827331075] \| \| 0.0023 \| 14.3229 \| 5500 \| 0.0189 \| 0.8042 \| 0.9487 \| 0.9938 \| [0.9937456877375658, 0.614589670664853] \| [0.9948176673921475, 0.9024910480608369] \| \| 0.0023 \| 15.625 \| 6000 \| 0.0204 \| 0.8001 \| 0.9564 \| 0.9935 \| [0.9934206286987506, 0.6067556263565074] \| [0.994316752677739, 0.918460484361041] \| \| 0.0018 \| 16.9271 \| 6500 \| 0.0182 \| 0.8121 \| 0.9502 \| 0.9942 \| [0.9941248990263425, 0.6300048865149038] \| [0.9951673327539593, 0.9052147618021543] \| \| 0.0014 \| 18.2292 \| 7000 \| 0.0208 \| 0.8021 \| 0.9602 \| 0.9935 \| [0.993474511729115, 0.6106912886801219] \| [0.9942873973934382, 0.9260384612591063] \| \| 0.0027 \| 19.5312 \| 7500 \| 0.0193 \| 0.8074 \| 0.9523 \| 0.9939 \| [0.993861306152736, 0.6209314583219739] \| [0.9948525401842638, 0.9098462857018345] \| ### Framework versions - Transformers 4.52.3 - Pytorch 2.7.0+cu128 - Datasets 3.6.0 - Tokenizers 0.21.1
BootesVoid/cmbbs6hps09u485uuwc477fb4_cmbc7oy0g0c8a85uuzo4fjkmf	BootesVoid	2025-05-31T12:59:01Z	0	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	2025-05-31T12:58:52Z	--- 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: ava_hanamiya --- # Cmbbs6Hps09U485Uuwc477Fb4_Cmbc7Oy0G0C8A85Uuzo4Fjkmf <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `ava_hanamiya` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "ava_hanamiya", "lora_weights": "https://huggingface.co/BootesVoid/cmbbs6hps09u485uuwc477fb4_cmbc7oy0g0c8a85uuzo4fjkmf/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## 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('BootesVoid/cmbbs6hps09u485uuwc477fb4_cmbc7oy0g0c8a85uuzo4fjkmf', weight_name='lora.safetensors') image = pipeline('ava_hanamiya').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) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmbbs6hps09u485uuwc477fb4_cmbc7oy0g0c8a85uuzo4fjkmf/discussions) to add images that show off what you’ve made with this LoRA.
yazodi/keyword-tfidf	yazodi	2025-05-31T12:57:28Z	0	0	null	[ "region:us" ]	null	2025-05-31T12:56:09Z	# 🔑 Anahtar Kelime Çıkarıcı (TF-IDF ile) Bu projede, kullanıcıdan alınan metin üzerinde TF-IDF algoritması kullanılarak en anlamlı anahtar kelimeler çıkarılmaktadır. ## 📦 Kullanılan Kütüphaneler - `scikit-learn` - `nltk` - `pandas` - `streamlit` ## 🚀 Uygulama Nasıl Çalışır? Kullanıcı metnini girer → TF-IDF uygulanır → En yüksek skora sahip 10 kelime gösterilir. ## 🧪 Örnek Giriş ```text Yapay zeka ve doğal dil işleme teknolojileri günümüzde birçok sektörde kullanılmaktadır. 📌 Çıktı the 0.44014589420436356 and 0.34233569549228277 python 0.326033995706936 to 0.2934305961362424 of 0.24452549678020197 in 0.22822379699485518 is 0.2119220972095084 data 0.19562039742416157 you 0.1467152980681212 that 0.13041359828277438 🖥 Uygulamayı Başlatmak Eğitimli TF-IDF vektörizer modeli paylaşılabilir: 👉 https://huggingface.co/yazodi/keyword-tfidf 🪪 Lisans MIT Lisansı ---
sizzlebop/Audio-Reasoner-Q8_0-GGUF	sizzlebop	2025-05-31T12:55:40Z	0	1	null	[ "gguf", "llama-cpp", "gguf-my-repo", "base_model:zhifeixie/Audio-Reasoner", "base_model:quantized:zhifeixie/Audio-Reasoner", "license:mit", "endpoints_compatible", "region:us" ]	null	2025-05-31T12:55:07Z	--- license: mit tags: - llama-cpp - gguf-my-repo base_model: zhifeixie/Audio-Reasoner --- # sizzlebop/Audio-Reasoner-Q8_0-GGUF This model was converted to GGUF format from [`zhifeixie/Audio-Reasoner`](https://huggingface.co/zhifeixie/Audio-Reasoner) 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/zhifeixie/Audio-Reasoner) 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 sizzlebop/Audio-Reasoner-Q8_0-GGUF --hf-file audio-reasoner-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo sizzlebop/Audio-Reasoner-Q8_0-GGUF --hf-file audio-reasoner-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 sizzlebop/Audio-Reasoner-Q8_0-GGUF --hf-file audio-reasoner-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo sizzlebop/Audio-Reasoner-Q8_0-GGUF --hf-file audio-reasoner-q8_0.gguf -c 2048 ```
igorcouto/sofya-telephony-pt-500h-2	igorcouto	2025-05-31T12:51:18Z	0	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-05-31T12:50:41Z	--- 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. 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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]
TOTORONG/Qwen3_Lora_250531	TOTORONG	2025-05-31T12:49:18Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "base_model:unsloth/Qwen3-32B-unsloth-bnb-4bit", "base_model:finetune:unsloth/Qwen3-32B-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-31T12:48:32Z	--- base_model: unsloth/Qwen3-32B-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: TOTORONG - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-32B-unsloth-bnb-4bit This qwen3 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)
KrystalLi/Qwen2-0.5B-GRPO-test	KrystalLi	2025-05-31T12:39:14Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:AI-MO/NuminaMath-TIR", "arxiv:2402.03300", "base_model:Qwen/Qwen2-0.5B-Instruct", "base_model:finetune:Qwen/Qwen2-0.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-31T08:08:10Z	--- base_model: Qwen/Qwen2-0.5B-Instruct datasets: AI-MO/NuminaMath-TIR library_name: transformers model_name: Qwen2-0.5B-GRPO-test tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Qwen2-0.5B-GRPO-test This model is a fine-tuned version of [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct) on the [AI-MO/NuminaMath-TIR](https://huggingface.co/datasets/AI-MO/NuminaMath-TIR) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="KrystalLi/Qwen2-0.5B-GRPO-test", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.18.1 - Transformers: 4.52.4 - Pytorch: 2.7.0 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
LinaSad/mcqa_all_merged_on_letter_444	LinaSad	2025-05-31T12:35:34Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-31T12:34:53Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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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tokyotech-llm/Llama-3.3-Swallow-70B-v0.4	tokyotech-llm	2025-05-31T12:29:53Z	474	2	transformers	[ "transformers", "safetensors", "llama", "text-generation", "en", "ja", "arxiv:2404.17733", "arxiv:2505.02881", "arxiv:2407.21783", "license:llama3.3", "license:gemma", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-02-17T11:42:28Z	--- language: - en - ja library_name: transformers pipeline_tag: text-generation license: - llama3.3 - gemma model_type: llama --- # Llama 3.3 Swallow - Built with Llama Llama 3.3 Swallow is a large language model (70B) that was built by continual pre-training on the [Meta Llama 3.3](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) model. Llama 3.3 Swallow enhanced the Japanese language capabilities of the original Llama 3.3 while retaining the English language capabilities. We use approximately 315 billion tokens that were sampled from a large Japanese web corpus (Swallow Corpus Version 2), Japanese and English Wikipedia articles, and mathematical and coding contents, etc (see the Training Datasets section of the base model) for continual pre-training. The instruction-tuned models (Instruct) were built by supervised fine-tuning (SFT) on the synthetic data specially built for Japanese. See the Swallow Model Index section to find other model variants. # Release History - March 10, 2025: Released [Llama-3.3-Swallow-70B-Instruct-v0.4](https://huggingface.co/tokyotech-llm/Llama-3.3-Swallow-70B-Instruct-v0.4) and [Llama-3.3-Swallow-70B-v0.4](https://huggingface.co/tokyotech-llm/Llama-3.3-Swallow-70B-v0.4). - December 30, 2024: Released [Llama-3.1-Swallow-70B-Instruct-v0.3](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.3). - December 23, 2024: Released [Llama-3.1-Swallow-8B-Instruct-v0.3](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.3). - November 11, 2024: Released [Llama-3.1-Swallow-8B-v0.2](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.2) and [Llama-3.1-Swallow-8B-Instruct-v0.2](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.2). - October 08, 2024: Released [Llama-3.1-Swallow-8B-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.1), [Llama-3.1-Swallow-8B-Instruct-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.1), [Llama-3.1-Swallow-70B-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-v0.1), and [Llama-3.1-Swallow-70B-Instruct-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.1). ## Swallow Model Index \|Model\|Llama-3.1-Swallow v0.1\|Llama-3.1-Swallow-Instruct v0.1\|Llama-3.1-Swallow v0.2\|Llama-3.1-Swallow-Instruct v0.2\|Llama-3.1-Swallow-Instruct v0.3\|Llama-3.3-Swallow v0.4\|Llama-3.3-Swallow-Instruct v0.4\| \|---\|---\|---\|---\|---\|---\|---\|---\| \|8B\| [🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.1) \| [🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.1) \| [🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.2) \| [🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.2) \| [🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.3) \| \| \| \|70B\| [🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-v0.1) \| [🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.1) \| \| \| [🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.3) \| [🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.3-Swallow-70B-v0.4) \| [🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.3-Swallow-70B-Instruct-v0.4) \| ![logo](./logo.png) The website [https://swallow-llm.github.io/](https://swallow-llm.github.io/index.en.html) provides large language models developed by the Swallow team. ## Model Details * Model type: Please refer to [Llama 3.1 MODEL_CARD](https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md) for details on the model architecture. * Language(s): Japanese English * Library: [Megatron-LM](https://github.com/NVIDIA/Megatron-LM) * Tokenizer: Please refer to [Llama 3.1 blog](https://ai.meta.com/blog/meta-llama-3-1) for details on the tokenizer. * Contact: swallow[at]nlp.c.titech.ac.jp ## Model Performance ### Japanese tasks \|Model\|JCom.\|JEMHopQA\|NIILC\|JSQuAD\|XL-Sum\|MGSM\|WMT20-en-ja\|WMT20-ja-en\|JMMLU\|JHumanEval\|Ja Avg\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\| \| \|4-shot\|4-shot\|4-shot\|4-shot\|1-shot\|4-shot\|4-shot\|4-shot\|5-shot\|0-shot\| \| \| \|EM acc\|Char-F1\|Char-F1\|Char-F1\|ROUGE-2\|EM acc\|BLEU\|BLEU\|EM acc\|pass@1\| \| \| Qwen2-72B \| 0.960 \| 0.620 \| 0.561 \| 0.926 \| 0.238 \| 0.768 \| 0.275 \| 0.241 \| 0.782 \| 0.561 \| 0.593 \| \| Qwen2.5-72B \| 0.972 \| 0.611 \| 0.619 \| 0.930 \| 0.279 \| 0.828 \| 0.287 \| 0.252 \| 0.804 \| 0.648 \| 0.623 \| \| Sarashina2-70B \| 0.929 \| 0.717 \| 0.668 \| 0.929 \| 0.190 \| 0.488 \| 0.313 \| 0.243 \| 0.592 \| 0.235 \| 0.530 \| \| Llama 3 70B \| 0.946 \| 0.606 \| 0.589 \| 0.922 \| 0.228 \| 0.664 \| 0.286 \| 0.252 \| 0.705 \| 0.491 \| 0.569 \| \| Llama 3.1 70B \| 0.946 \| 0.616 \| 0.603 \| 0.925 \| 0.228 \| 0.672 \| 0.287 \| 0.257 \| 0.669 \| 0.462 \| 0.566 \| \| Llama 3 Youko 70B \| 0.946 \| 0.602 \| 0.610 \| 0.923 \| 0.242 \| 0.684 \| 0.292 \| 0.250 \| 0.704 \| 0.463 \| 0.571 \| \| Llama 3 Swallow 70B \| 0.968 \| 0.675 \| 0.684 \| 0.923 \| 0.239 \| 0.708 \| 0.307 \| 0.255 \| 0.706 \| 0.477 \| 0.594 \| \| Llama 3.1 Swallow 70B \| 0.955 \| 0.645 \| 0.678 \| 0.923 \| 0.272 \| 0.684 \| 0.320 \| 0.259 \| 0.709 \| 0.487 \| 0.593 \| \| Llama 3.3 Swallow 70B v0.4 \| 0.967 \| 0.671 \| 0.732 \| 0.924 \| 0.283 \| 0.776 \| 0.327 \| 0.260 \| 0.742 \| 0.604 \| 0.629 \| ### English tasks \|Model\|OpenBookQA\|TriviaQA\|HellaSWAG\|SQuAD2.0\|XWINO\|MMLU\|GSM8K\|MATH\|BBH\|HumanEval\|En Avg\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\| \| \|4-shot\|4-shot\|4-shot\|4-shot\|4-shot\|5-shot\|4-shot\|4-shot\|3-shot\|0-shot\| \| \| \|Acc\|EM acc\|Acc\|EM acc\|Acc\|Acc\|EM acc\|CoT EM Acc\|CoT EM Acc\|pass@1\| \| \| Qwen2-72B \| 0.418 \| 0.790 \| 0.677 \| 0.673 \| 0.915 \| 0.842 \| 0.893 \| 0.560 \| 0.643 \| 0.608 \| 0.702 \| \| Qwen2.5-72B \| 0.416 \| 0.760 \| 0.685 \| 0.693 \| 0.901 \| 0.861 \| 0.870 \| 0.626 \| 0.727 \| 0.554 \| 0.709 \| \| Sarashina2-70B \| 0.388 \| 0.537 \| 0.628 \| 0.675 \| 0.917 \| 0.630 \| 0.011 \| 0.206 \| 0.639 \| 0.281 \| 0.491 \| \| Llama 3 70B \| 0.440 \| 0.826 \| 0.690 \| 0.618 \| 0.920 \| 0.787 \| 0.801 \| 0.446 \| 0.829 \| 0.527 \| 0.689 \| \| Llama 3.1 70B \| 0.450 \| 0.829 \| 0.690 \| 0.605 \| 0.920 \| 0.786 \| 0.798 \| 0.434 \| 0.655 \| 0.546 \| 0.671 \| \| Llama 3 Youko 70B \| 0.436 \| 0.829 \| 0.690 \| 0.610 \| 0.922 \| 0.785 \| 0.797 \| 0.408 \| 0.826 \| 0.412 \| 0.671 \| \| Llama 3 Swallow 70B \| 0.430 \| 0.823 \| 0.682 \| 0.628 \| 0.923 \| 0.774 \| 0.817 \| 0.414 \| 0.734 \| 0.499 \| 0.672 \| \| Llama 3.1 Swallow 70B v0.1 \| 0.428 \| 0.826 \| 0.690 \| 0.612 \| 0.927 \| 0.772 \| 0.809 \| 0.380 \| 0.806 \| 0.540 \| 0.679 \| \| Llama 3.1 Swallow 70B v0.4 \| 0.424 \| 0.817 \| 0.683 \| 0.641 \| 0.920 \| 0.802 \| 0.863 \| 0.496 \| 0.754 \| 0.709 \| 0.711 \| ## Evaluation Benchmarks The evaluation script can be found at [swallow-llm/swallow-evaluation](https://github.com/swallow-llm/swallow-evaluation), tagged as `v202411`. ### Japanese evaluation benchmarks We used llm-jp-eval(v1.3.0), JP Language Model Evaluation Harness(commit #9b42d41) and Code Generation LM Evaluation Harness(commit #0261c52). The details are as follows: - Multiple-choice question answering (JCommonsenseQA [Kurihara et al., 2022]) - Open-ended question answering (JEMHopQA [Ishii et al., 2024]) - Open-ended question answering (NIILC [関根, 2003]) - Machine reading comprehension (JSQuAD [Kurihara et al., 2022]) - Automatic summarization (XL-Sum [Hasan et al., 2021]) - Machine translation (WMT2020 ja-en [Barrault et al., 2020]) - Machine translation (WMT2020 en-ja [Barrault et al., 2020]) - Mathematical reasoning (MGSM [Shi et al., 2023]) - Academic exams (JMMLU [尹ら, 2024]) - Code generation (JHumanEval [佐藤ら, 2024]) ### English evaluation benchmarks We used the Language Model Evaluation Harness(v.0.4.2) and Code Generation LM Evaluation Harness(commit #0261c52). The details are as follows: - Multiple-choice question answering (OpenBookQA [Mihaylov et al., 2018]) - Open-ended question answering (TriviaQA [Joshi et al., 2017]) - Machine reading comprehension (SQuAD2 [Rajpurkar et al., 2018]) - Commonsense reasoning (XWINO [Tikhonov and Ryabinin, 2021]) - Natural language inference (HellaSwag [Zellers et al., 2019]) - Mathematical reasoning (GSM8K [Cobbe et al., 2021]) - Mathematical reasoning (MATH [Hendrycks et al., 2022][Lightman et al., 2024]) - Reasoning (BBH (BIG-Bench-Hard) [Suzgun et al., 2023]) - Academic exams (MMLU [Hendrycks et al., 2021]) - Code generation (HumanEval [Chen et al., 2021]) ## Training Datasets ### Continual Pre-Training The following datasets were used for continual pre-training. - [Cosmopedia](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia) - [Dclm-baseline-1.0](https://huggingface.co/datasets/mlfoundations/dclm-baseline-1.0) - [English Wikipedia](https://dumps.wikimedia.org/other/cirrussearch) - [FineMath-4+ ](https://huggingface.co/datasets/HuggingFaceTB/finemath) - [Japanese Wikipedia](https://dumps.wikimedia.org/other/cirrussearch) - [Laboro ParaCorpus](https://github.com/laboroai/Laboro-ParaCorpus) - [Swallow Corpus Version 2](https://arxiv.org/abs/2404.17733) (filtered using [Swallow Education Classifier(Wiki-based)](https://huggingface.co/tokyotech-llm/edu-classifier)) - [Swallow Corpus Version 2](https://arxiv.org/abs/2404.17733) (filtered using [Swallow Education Classifier](https://huggingface.co/tokyotech-llm/edu-classifier)) - [Swallow Corpus Version 2](https://arxiv.org/abs/2404.17733) (synthetic QA-format) - Swallow Code Version 0.3 (filtering from [The Stack v2 train smol ids](https://huggingface.co/datasets/bigcode/the-stack-v2-train-smol-ids) and then refactoring with Llama-3.3-70B-Instruct) ### Swallow Corpus Version 2 We built the Swallow Corpus by extracting high-quality Japanese texts from Common Crawl. In Version 2, we expanded the scope of the Common Crawl collection and modified the pipeline sequence to enable more flexible quality filtering. For Llama 3.1 Swallow v0.2, we further refined our quality filtering and data sampling strategies, resulting in an even higher-quality selection of Japanese texts for pre-training. For Llama 3.3 Swallow 70B v0.4, we generated synthetic QA-format text by using Gemma 2 27B IT to paraphrase educational web documents from our corpus Further details of the methodology and analysis will be provided in a forthcoming paper. ### Swallow Code Version 0.3 We built the Swallow Code Version 0.3 by filtering from the stack v2 train smol ids and then refactoring with Llama-3.3-70B-Instruct. In filtering, we removed the code texts with syntax errors or scored below seven by pylint. We have already released the filtered version as [Swallow Code Version 0.1](https://huggingface.co/datasets/tokyotech-llm/swallow-code-v0.1). In refactoring, we gave a prompt to Llama-3.3-70B-Instruct to follow [Google Python Style Guide](https://google.github.io/styleguide/pyguide.html) and coding best practices. ## Risks and Limitations The models released here are still in the early stages of our research and development and have not been tuned to ensure outputs align with human intent and safety considerations. ## Acknowledgements We thank Meta Research for releasing Llama 3.3 under a generous open license. We would like to thank Amazon Web Services (AWS) for providing access to SageMaker HyperPod, which enabled the training of the Llama 3.3 Swallow project. We received various supports including: + AIST project: "Research and Development of Foundation Models for Generative AI in the Physical Domain" + NEDO project: "Development of Artificial Intelligence Application Technology to Support Judgment in Design Risk Assessment Work Based on the Perspective of Skilled Persons" (JPNP18002) of "Development of Integration Technology as the Core of Next Generation Artificial Intelligence and Robotics" + MEXT project: "Formation of R&D center to ensure transparency and reliability of generative AI models" + AIST program: [Large Generative AI Development Support Program](https://abci.ai/en/link/lfm_support_program.html) ## License [META LLAMA 3.3 COMMUNITY LICENSE](https://www.llama.com/llama3_3/license/) and [Gemma Terms of Use](https://ai.google.dev/gemma/terms) ## Authors Here are the team members: - From [Institute of Science Tokyo Okazaki Laboratory](https://www.nlp.c.titech.ac.jp/index.en.html), the following members: - [Naoaki Okazaki](https://www.chokkan.org/index.ja.html) - [Sakae Mizuki](https://s-mizuki-nlp.github.io/) - [Youmi Ma](https://www.nlp.c.titech.ac.jp/member/youmi.en.html) - [Koki Maeda](https://sites.google.com/view/silviase) - [Kakeru Hattori](https://aya-se.vercel.app/) - [Masanari Ohi](https://sites.google.com/view/masanariohi) - [Hinari Shimada](https://hinarishimada.github.io/portfolio) - [Taihei Shiotani](https://github.com/inatoihs) - [Koshiro Saito](https://sites.google.com/view/koshiro-saito) - From [Institute of Science Tokyo YOKOTA Laboratory](https://www.rio.gsic.titech.ac.jp/en/index.html), the following members: - [Rio Yokota](https://twitter.com/rioyokota) - [Kazuki Fujii](https://twitter.com/okoge_kaz) - [Taishi Nakamura](https://twitter.com/Setuna7777_2) - [Takumi Okamoto](https://www.linkedin.com/in/takumi-okamoto) - [Ishida Shigeki](https://www.wantedly.com/id/reborn27) - [Yukito Tajima](https://www.linkedin.com/in/yukito-tajima-51bbb2299) - [Masaki Kawamura](https://x.com/Masakichi333210) - From [Artificial Intelligence Research Center, AIST, Japan](https://www.airc.aist.go.jp/en/teams/), the following members: - [Hiroya Takamura](https://sites.google.com/view/hjtakamura) ## How to cite If you find our work helpful, please feel free to cite these papers. ``` @inproceedings{Fujii:COLM2024, title={Continual Pre-Training for Cross-Lingual LLM Adaptation: Enhancing Japanese Language Capabilities}, author={Kazuki Fujii and Taishi Nakamura and Mengsay Loem and Hiroki Iida and Masanari Ohi and Kakeru Hattori and Hirai Shota and Sakae Mizuki and Rio Yokota and Naoaki Okazaki}, booktitle="Proceedings of the First Conference on Language Modeling", series={COLM}, pages="(to appear)", year="2024", month=oct, address={University of Pennsylvania, USA}, } @inproceedings{Okazaki:COLM2024, title={Building a Large Japanese Web Corpus for Large Language Models}, author={Naoaki Okazaki and Kakeru Hattori and Hirai Shota and Hiroki Iida and Masanari Ohi and Kazuki Fujii and Taishi Nakamura and Mengsay Loem and Rio Yokota and Sakae Mizuki}, booktitle="Proceedings of the First Conference on Language Modeling", series={COLM}, pages="(to appear)", year="2024", month=oct, address={University of Pennsylvania, USA}, } @misc{fujii2025rewritingpretrainingdataboosts, title={Rewriting Pre-Training Data Boosts LLM Performance in Math and Code}, author={Kazuki Fujii and Yukito Tajima and Sakae Mizuki and Hinari Shimada and Taihei Shiotani and Koshiro Saito and Masanari Ohi and Masaki Kawamura and Taishi Nakamura and Takumi Okamoto and Shigeki Ishida and Kakeru Hattori and Youmi Ma and Hiroya Takamura and Rio Yokota and Naoaki Okazaki}, year={2025}, eprint={2505.02881}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2505.02881}, } ``` ### References ```tex @misc{dubey2024llama3herdmodels, title={The Llama 3 Herd of Models}, author={Abhimanyu Dubey and Abhinav Jauhri and Abhinav Pandey and Abhishek Kadian and Ahmad Al-Dahle and Aiesha Letman and Akhil Mathur and Alan Schelten and Amy Yang and Angela Fan et al.}, year={2024}, eprint={2407.21783}, archivePrefix={arXiv}, primaryClass={cs.AI}, url={https://arxiv.org/abs/2407.21783}, } ```
VanishedBrB/codegemma-7b	VanishedBrB	2025-05-31T12:26:22Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma", "trl", "en", "base_model:unsloth/codegemma-7b", "base_model:finetune:unsloth/codegemma-7b", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-31T12:26:14Z	--- base_model: unsloth/codegemma-7b tags: - text-generation-inference - transformers - unsloth - gemma - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: VanishedBrB - License: apache-2.0 - Finetuned from model : unsloth/codegemma-7b This gemma 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)
SVECTOR-CORPORATION/FAL-1.5	SVECTOR-CORPORATION	2025-05-31T12:19:15Z	0	1	null	[ "safetensors", "fal_v1_5", "license:apache-2.0", "region:us" ]	null	2025-05-31T10:27:04Z	--- license: apache-2.0 ---
Trevin007/insurance-estimator	Trevin007	2025-05-31T12:11:20Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-31T12:06:22Z	--- license: apache-2.0 ---
sizzlebop/Viper-Coder-v1.4-Q8_0-GGUF	sizzlebop	2025-05-31T12:07:41Z	0	0	transformers	[ "transformers", "gguf", "text-generation-inference", "coder", "trl", "llama-cpp", "gguf-my-repo", "text-generation", "en", "zh", "base_model:prithivMLmods/Viper-Coder-v1.4", "base_model:quantized:prithivMLmods/Viper-Coder-v1.4", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-05-31T12:06:39Z	--- license: apache-2.0 language: - en - zh base_model: prithivMLmods/Viper-Coder-v1.4 pipeline_tag: text-generation library_name: transformers tags: - text-generation-inference - coder - trl - llama-cpp - gguf-my-repo --- # sizzlebop/Viper-Coder-v1.4-Q8_0-GGUF This model was converted to GGUF format from [`prithivMLmods/Viper-Coder-v1.4`](https://huggingface.co/prithivMLmods/Viper-Coder-v1.4) 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/prithivMLmods/Viper-Coder-v1.4) 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 sizzlebop/Viper-Coder-v1.4-Q8_0-GGUF --hf-file viper-coder-v1.4-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo sizzlebop/Viper-Coder-v1.4-Q8_0-GGUF --hf-file viper-coder-v1.4-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 sizzlebop/Viper-Coder-v1.4-Q8_0-GGUF --hf-file viper-coder-v1.4-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo sizzlebop/Viper-Coder-v1.4-Q8_0-GGUF --hf-file viper-coder-v1.4-q8_0.gguf -c 2048 ```
VanishedBrB/qwen2.5-coder-7b-bnb-4bit-velocity-SQL-F16-GGUF	VanishedBrB	2025-05-31T12:03:48Z	0	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "qwen2", "trl", "llama-cpp", "gguf-my-lora", "en", "base_model:VanishedBrB/qwen2.5-coder-7b-bnb-4bit-velocity-SQL", "base_model:quantized:VanishedBrB/qwen2.5-coder-7b-bnb-4bit-velocity-SQL", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-31T12:03:28Z	--- base_model: VanishedBrB/qwen2.5-coder-7b-bnb-4bit-velocity-SQL tags: - text-generation-inference - transformers - unsloth - qwen2 - trl - llama-cpp - gguf-my-lora license: apache-2.0 language: - en --- # VanishedBrB/qwen2.5-coder-7b-bnb-4bit-velocity-SQL-F16-GGUF This LoRA adapter was converted to GGUF format from [`VanishedBrB/qwen2.5-coder-7b-bnb-4bit-velocity-SQL`](https://huggingface.co/VanishedBrB/qwen2.5-coder-7b-bnb-4bit-velocity-SQL) via the ggml.ai's [GGUF-my-lora](https://huggingface.co/spaces/ggml-org/gguf-my-lora) space. Refer to the [original adapter repository](https://huggingface.co/VanishedBrB/qwen2.5-coder-7b-bnb-4bit-velocity-SQL) for more details. ## Use with llama.cpp ```bash # with cli llama-cli -m base_model.gguf --lora qwen2.5-coder-7b-bnb-4bit-velocity-SQL-f16.gguf (...other args) # with server llama-server -m base_model.gguf --lora qwen2.5-coder-7b-bnb-4bit-velocity-SQL-f16.gguf (...other args) ``` To know more about LoRA usage with llama.cpp server, refer to the [llama.cpp server documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/server/README.md).
TanAlexanderlz/RALL_RGBCROP_Aug16F-WD01	TanAlexanderlz	2025-05-31T12:03:20Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-base-finetuned-kinetics", "base_model:finetune:MCG-NJU/videomae-base-finetuned-kinetics", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	2025-05-31T09:26:03Z	--- library_name: transformers license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base-finetuned-kinetics tags: - generated_from_trainer metrics: - accuracy model-index: - name: RALL_RGBCROP_Aug16F-WD01 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. --> # RALL_RGBCROP_Aug16F-WD01 This model is a fine-tuned version of [MCG-NJU/videomae-base-finetuned-kinetics](https://huggingface.co/MCG-NJU/videomae-base-finetuned-kinetics) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7867 - Accuracy: 0.8414 ## 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-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 3462 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\| \| 0.4949 \| 0.0835 \| 289 \| 0.5840 \| 0.6973 \| \| 0.2616 \| 1.0835 \| 578 \| 0.5304 \| 0.7955 \| \| 0.078 \| 2.0835 \| 867 \| 0.7102 \| 0.7975 \| \| 0.0089 \| 3.0835 \| 1156 \| 0.9156 \| 0.7935 \| \| 0.0012 \| 4.0835 \| 1445 \| 1.0430 \| 0.7914 \| \| 0.0005 \| 5.0835 \| 1734 \| 1.0521 \| 0.8037 \| \| 0.0006 \| 6.0835 \| 2023 \| 1.0802 \| 0.8119 \| \| 0.0002 \| 7.0835 \| 2312 \| 1.1696 \| 0.8078 \| \| 0.0003 \| 8.0835 \| 2601 \| 1.1930 \| 0.8139 \| \| 0.0002 \| 9.0835 \| 2890 \| 1.2366 \| 0.8078 \| \| 0.0002 \| 10.0835 \| 3179 \| 1.2292 \| 0.8098 \| \| 0.0002 \| 11.0817 \| 3462 \| 1.2295 \| 0.8098 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
yazidsupriadi/bot-detector-lstm	yazidsupriadi	2025-05-31T11:58:42Z	0	0	null	[ "region:us" ]	null	2025-04-29T04:13:00Z	# 🧠 Bot Detector LSTM Model deteksi akun bot berbasis teks dan fitur numerik menggunakan LSTM. --- ## 📈 History Training \| Epoch \| Loss \| Accuracy \| Precision \| Recall \| F1-Score \| \|:-----:\|:-----:\|:--------:\|:---------:\|:------:\|:--------:\| \| 1 \| 0.3796 \| 0.8113 \| 0.8136 \| 0.8111 \| 0.8108 \| \| 2 \| 0.3687 \| 0.7997 \| 0.7997 \| 0.7998 \| 0.7997 \| \| 3 \| 0.3574 \| 0.8053 \| 0.8109 \| 0.8050 \| 0.8043 \| \| 4 \| 0.3458 \| 0.8375 \| 0.8406 \| 0.8373 \| 0.8371 \| \| 5 \| 0.3562 \| 0.7618 \| 0.8391 \| 0.7608 \| 0.7469 \| \| 6 \| 0.3403 \| 0.7650 \| 0.8385 \| 0.7641 \| 0.7511 \| \| 7 \| 0.3323 \| 0.8645 \| 0.8646 \| 0.8645 \| 0.8645 \| \| 8 \| 0.3236 \| 0.8475 \| 0.8480 \| 0.8474 \| 0.8474 \| \| 9 \| 0.3206 \| 0.8575 \| 0.8594 \| 0.8574 \| 0.8573 \| \| 10 \| 0.3153 \| 0.8508 \| 0.8508 \| 0.8508 \| 0.8507 \| --- ## 📊 Confusion Matrix ![Confusion Matrix](confusion_matrix.png) --- ## 📦 Files - `model.pth` - `vocab.pkl` - `scaler.pkl` - `label_encoder.pkl` - `history.json` - `confusion_matrix.png` --- ## 🚀 Cara Load Model ```python import torch from model import BotDetector import pickle # Load model model = BotDetector( vocab_size=VOCAB_SIZE, # Ganti dengan ukuran vocab kamu embed_dim=100, hidden_dim=128, num_numeric=4, # Jumlah fitur numerik output_dim=2 # Jumlah kelas ) model.load_state_dict(torch.load("model.pth")) model.eval() # Load scaler, vocab, dan label encoder with open("scaler.pkl", "rb") as f: scaler = pickle.load(f) with open("vocab.pkl", "rb") as f: vocab = pickle.load(f) with open("label_encoder.pkl", "rb") as f: label_encoder = pickle.load(f) --- ### ⚠️ Pastikan kamu sudah mencatat metric ke `history` saat training: Tambahkan ini di bagian evaluasi per epoch: ```python from sklearn.metrics import precision_score, recall_score, f1_score # Setelah menghitung all_preds dan all_labels prec = precision_score(all_labels, all_preds, average="macro", zero_division=0) rec = recall_score(all_labels, all_preds, average="macro", zero_division=0) f1 = f1_score(all_labels, all_preds, average="macro", zero_division=0) history["precision"].append(prec) history["recall"].append(rec) history["f1"].append(f1)
mario81464/qwen-3B_instruct_base_sft_FEVER_4167	mario81464	2025-05-31T11:54:27Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-31T11:53:51Z	--- library_name: transformers tags: - llama-factory --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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yazodi/youtube-video-recommender	yazodi	2025-05-31T11:52:54Z	0	0	null	[ "region:us" ]	null	2025-05-31T11:50:54Z	# 🎬 YouTube Video Recommendation System (Content-Based Filtering) Bu proje, YouTube'daki videolara benzer içerikleri öneren bir content-based filtering sistemidir. Kullanıcı bir video başlığı girer ve sistem, başlık, açıklama ve etiketleri temel alarak benzer 5 videoyu önerir. --- ## 📦 Dataset - Kaynak: [YouTube Trending Videos - Kaggle](https://www.kaggle.com/datasets/datasnaek/youtube-new) - Kullanılan dosya: `USvideos.csv` (küçültülmüş örneği: `USvideos_sample.csv`) --- ## 🧠 Kullanılan Teknolojiler - Python, pandas, numpy - scikit-learn → TF-IDF Vectorizer, cosine similarity - joblib (model kaydetme) - matplotlib, wordcloud (görselleştirme) - Streamlit (web uygulaması) --- ## 🔍 Proje Adımları 1. `title`, `description`, `tags` sütunları seçildi ve temizlendi. 2. Bu sütunlar birleştirilerek tek bir `text` kolonu oluşturuldu. 3. TF-IDF ile metin vektörleştirildi. 4. Cosine similarity ile videolar arası benzerlik hesaplandı. 5. Kullanıcı başlığına en çok benzeyen ilk 5 video önerildi. --- ## 💻 Web Uygulaması (Streamlit) Projeye bir Streamlit arayüzü entegre edildi. ### 🚀 Uygulamayı Başlatmak: ```bash pip install -r requirements.txt streamlit run app.py Kullanıcı arayüzü üzerinden bir video başlığı girerek önerileri alabilirsiniz.(verisetindene varsa) 📊 Görselleştirme En sık kullanılan 15 YouTube etiketi Başlıklarda en sık geçen 15 kelime Kelime bulutu (word cloud) gösterimi 📁 Dosya Yapısı youtube-recommendation/ ├── USvideos_sample.csv ├── app.py ├── youtube_recommender.ipynb ├── tfidf_vectorizer.pkl ├── cosine_similarity.pkl ├── youtube_df.pkl ├── title_indices.pkl ├── requirements.txt └── README.md 🌐 Model Paylaşımı (Opsiyonel) Eğitilen modeller Hugging Face üzerine yüklenebilir: https://huggingface.co/yazodi/youtube-video-recommender ✍️ Yazar Hande Çarkcı 📫 GitHub \| 💡 Data Science & AI Öğrencisi 📦 Requirements streamlit pandas numpy scikit-learn joblib matplotlib wordcloud ---
hmueller25/my_awesome_billsum_model	hmueller25	2025-05-31T11:52:50Z	2	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-14T14:03:56Z	--- library_name: transformers license: apache-2.0 base_model: google-t5/t5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: my_awesome_billsum_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. --> # my_awesome_billsum_model This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.9010 - Rouge1: 0.1153 - Rouge2: 0.0398 - Rougel: 0.1012 - Rougelsum: 0.1015 - Gen Len: 20.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 25 \| 3.0675 \| 0.1107 \| 0.0296 \| 0.0941 \| 0.0949 \| 20.0 \| \| No log \| 2.0 \| 50 \| 2.9651 \| 0.1084 \| 0.0347 \| 0.0931 \| 0.0932 \| 20.0 \| \| No log \| 3.0 \| 75 \| 2.9167 \| 0.117 \| 0.0377 \| 0.101 \| 0.1011 \| 20.0 \| \| No log \| 4.0 \| 100 \| 2.9010 \| 0.1153 \| 0.0398 \| 0.1012 \| 0.1015 \| 20.0 \| ### Framework versions - Transformers 4.52.2 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
hugosisal/camembert_foot_0_1_classifier	hugosisal	2025-05-31T11:48:40Z	0	0	transformers	[ "transformers", "safetensors", "camembert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-31T11:48:19Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
Snarcy/mit-b3_train_006	Snarcy	2025-05-31T11:45:49Z	0	0	transformers	[ "transformers", "safetensors", "segformer", "generated_from_trainer", "base_model:nvidia/mit-b3", "base_model:finetune:nvidia/mit-b3", "license:other", "endpoints_compatible", "region:us" ]	null	2025-05-29T17:43:54Z	--- library_name: transformers license: other base_model: nvidia/mit-b3 tags: - generated_from_trainer model-index: - name: mit-b3_train_006 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. --> # mit-b3_train_006 This model is a fine-tuned version of [nvidia/mit-b3](https://huggingface.co/nvidia/mit-b3) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0081 - Mean Iou: 0.8027 - Mean Accuracy: 0.8983 - Overall Accuracy: 0.9968 - Per Category Iou: [0.9968164525692883, 0.608572691117985] - Per Category Accuracy: [0.9980630611071355, 0.7985002512465695] ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Per Category Iou \| Per Category Accuracy \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:-----------------------------------------:\|:----------------------------------------:\| \| 0.007 \| 1.9608 \| 400 \| 0.0136 \| 0.7471 \| 0.8996 \| 0.9950 \| [0.9950065436234374, 0.49921904410999063] \| [0.9962228811962033, 0.8030355739373558] \| \| 0.0045 \| 3.9216 \| 800 \| 0.0110 \| 0.7663 \| 0.9213 \| 0.9955 \| [0.9954771945967922, 0.537074590740347] \| [0.9964270346514705, 0.8462628683339132] \| \| 0.0041 \| 5.8824 \| 1200 \| 0.0084 \| 0.7945 \| 0.8601 \| 0.9969 \| [0.9969192716989699, 0.5920334897881517] \| [0.9986419357106135, 0.7215801476556762] \| \| 0.0043 \| 7.8431 \| 1600 \| 0.0081 \| 0.7998 \| 0.8838 \| 0.9969 \| [0.996857754270491, 0.6027318432370222] \| [0.9982854468333784, 0.7692396892273201] \| \| 0.0029 \| 9.8039 \| 2000 \| 0.0079 \| 0.7996 \| 0.8766 \| 0.9969 \| [0.996911452537117, 0.6022825416409624] \| [0.9984289060181608, 0.7547446948320513] \| \| 0.0031 \| 11.7647 \| 2400 \| 0.0077 \| 0.8029 \| 0.8846 \| 0.9969 \| [0.9969304678297617, 0.6088367880656552] \| [0.9983480602902818, 0.7708888974785152] \| \| 0.0033 \| 13.7255 \| 2800 \| 0.0088 \| 0.7913 \| 0.8917 \| 0.9966 \| [0.9965605987631156, 0.5860159757000183] \| [0.9978872956176418, 0.7854998518289462] \| \| 0.0033 \| 15.6863 \| 3200 \| 0.0082 \| 0.8011 \| 0.8954 \| 0.9968 \| [0.99679644039349, 0.6053462677462835] \| [0.9980785745306849, 0.7927537912463118] \| \| 0.0042 \| 17.6471 \| 3600 \| 0.0081 \| 0.8014 \| 0.9014 \| 0.9968 \| [0.9967558676600388, 0.605952715878113] \| [0.9979635832777769, 0.8047749732647881] \| \| 0.0024 \| 19.6078 \| 4000 \| 0.0081 \| 0.8027 \| 0.8983 \| 0.9968 \| [0.9968164525692883, 0.608572691117985] \| [0.9980630611071355, 0.7985002512465695] \| ### Framework versions - Transformers 4.52.3 - Pytorch 2.7.0+cu128 - Datasets 3.6.0 - Tokenizers 0.21.1
TanAlexanderlz/RALL_RGBCROP_Aug16F-WD005	TanAlexanderlz	2025-05-31T11:43:10Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-base-finetuned-kinetics", "base_model:finetune:MCG-NJU/videomae-base-finetuned-kinetics", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	2025-05-31T09:24:01Z	--- library_name: transformers license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base-finetuned-kinetics tags: - generated_from_trainer metrics: - accuracy model-index: - name: RALL_RGBCROP_Aug16F-WD005 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. --> # RALL_RGBCROP_Aug16F-WD005 This model is a fine-tuned version of [MCG-NJU/videomae-base-finetuned-kinetics](https://huggingface.co/MCG-NJU/videomae-base-finetuned-kinetics) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8464 - Accuracy: 0.8253 ## 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-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 3462 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\| \| 0.4659 \| 0.0835 \| 289 \| 0.5385 \| 0.7280 \| \| 0.1993 \| 1.0835 \| 578 \| 0.4735 \| 0.8078 \| \| 0.032 \| 2.0835 \| 867 \| 0.6537 \| 0.8098 \| \| 0.0061 \| 3.0835 \| 1156 \| 0.7957 \| 0.8037 \| \| 0.0032 \| 4.0835 \| 1445 \| 0.9085 \| 0.8119 \| \| 0.0004 \| 5.0835 \| 1734 \| 0.9475 \| 0.8098 \| \| 0.0007 \| 6.0835 \| 2023 \| 0.9976 \| 0.8139 \| \| 0.0002 \| 7.0835 \| 2312 \| 1.0835 \| 0.8139 \| \| 0.0002 \| 8.0835 \| 2601 \| 1.1102 \| 0.8119 \| \| 0.0002 \| 9.0835 \| 2890 \| 1.1465 \| 0.8078 \| \| 0.0001 \| 10.0835 \| 3179 \| 1.1561 \| 0.8078 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
luckysantoso/adapter_lora_lawbot_deepseek	luckysantoso	2025-05-31T11:32:07Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-31T11:31:52Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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]
TheWeeeed/chinese-paragraph-selector	TheWeeeed	2025-05-31T11:29:43Z	0	0	null	[ "safetensors", "bert", "extractive-qa", "chinese", "two-stage-qa", "question-answering", "zh", "license:apache-2.0", "region:us" ]	question-answering	2025-05-31T11:08:42Z	--- license: apache-2.0 language: - zh tags: - extractive-qa - bert - chinese - two-stage-qa pipeline_tag: question-answering --- ## 模型描述 * 模型類型: bert-base-chinese * 語言: 中文 * 訓練數據: https://github.com/YuTsyh/Chinese-Extractive-Question-Answering-QA-/tree/main/data * 相關項目/GitHub: https://github.com/YuTsyh/Chinese-Extractive-Question-Answering-QA-.git * 相關模型: * TheWeeeed/chinese-paragraph-selector * TheWeeeed/chinese-extractive-qa
Miamoto/Phi-4-multimodal-instruct-425h-v2	Miamoto	2025-05-31T11:29:43Z	4	0	transformers	[ "transformers", "tensorboard", "safetensors", "phi4mm", "text-generation", "generated_from_trainer", "custom_code", "base_model:microsoft/Phi-4-multimodal-instruct", "base_model:finetune:microsoft/Phi-4-multimodal-instruct", "license:mit", "autotrain_compatible", "region:us" ]	text-generation	2025-05-28T10:48:23Z	--- library_name: transformers license: mit base_model: microsoft/Phi-4-multimodal-instruct tags: - generated_from_trainer model-index: - name: Phi-4-multimodal-instruct-425h-v2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Phi-4-multimodal-instruct-425h-v2 This model is a fine-tuned version of [microsoft/Phi-4-multimodal-instruct](https://huggingface.co/microsoft/Phi-4-multimodal-instruct) 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: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.99) and epsilon=1e-07 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.48.2 - Pytorch 2.6.0+cu124 - Datasets 2.18.0 - Tokenizers 0.21.0
TanAlexanderlz/RALL_RGBCROP_Aug16F-WD001	TanAlexanderlz	2025-05-31T11:25:07Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-base-finetuned-kinetics", "base_model:finetune:MCG-NJU/videomae-base-finetuned-kinetics", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	2025-05-31T09:29:50Z	--- library_name: transformers license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base-finetuned-kinetics tags: - generated_from_trainer metrics: - accuracy model-index: - name: RALL_RGBCROP_Aug16F-WD001 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. --> # RALL_RGBCROP_Aug16F-WD001 This model is a fine-tuned version of [MCG-NJU/videomae-base-finetuned-kinetics](https://huggingface.co/MCG-NJU/videomae-base-finetuned-kinetics) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6801 - Accuracy: 0.8394 ## 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-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 3462 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| 0.4792 \| 0.0835 \| 289 \| 0.5590 \| 0.7157 \| \| 0.2392 \| 1.0835 \| 578 \| 0.5009 \| 0.7955 \| \| 0.0414 \| 2.0835 \| 867 \| 0.6815 \| 0.8016 \| \| 0.0484 \| 3.0835 \| 1156 \| 0.8761 \| 0.8016 \| \| 0.0032 \| 4.0835 \| 1445 \| 0.9753 \| 0.8139 \| \| 0.0004 \| 5.0835 \| 1734 \| 1.0459 \| 0.8057 \| \| 0.002 \| 6.0835 \| 2023 \| 1.1537 \| 0.7914 \| \| 0.0002 \| 7.0835 \| 2312 \| 1.1430 \| 0.8016 \| \| 0.0002 \| 8.0835 \| 2601 \| 1.1876 \| 0.7996 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
QuanHoangNgoc/wav2vec2-base-960h_310145	QuanHoangNgoc	2025-05-31T11:18:15Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "speech-to-text", "vietnamese", "uit-vimd", "generated_from_trainer", "vi", "dataset:uit-vimd", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-05-31T01:45:43Z	--- library_name: transformers language: - vi base_model: wav2vec2-base-960h tags: - speech-to-text - vietnamese - uit-vimd - generated_from_trainer datasets: - uit-vimd metrics: - wer model-index: - name: wav2vec2-base-960h results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: UIT-ViMD type: uit-vimd metrics: - name: Wer type: wer value: 1.0 --- <!-- 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. --> # wav2vec2-base-960h This model is a fine-tuned version of [wav2vec2-base-960h](https://huggingface.co/wav2vec2-base-960h) on the UIT-ViMD dataset. It achieves the following results on the evaluation set: - Loss: 4450.7129 - Wer: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1877 - training_steps: 45048 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:------:\| \| 20957.0712 \| 0.0479 \| 90 \| 19798.2793 \| 1.1701 \| \| 19576.6615 \| 0.0958 \| 180 \| 17914.9492 \| 0.9992 \| \| 13245.4418 \| 0.1438 \| 270 \| 10092.9766 \| 1.0 \| \| 8553.1667 \| 0.1917 \| 360 \| 6336.5059 \| 1.0 \| \| 6115.658 \| 0.2396 \| 450 \| 5300.0620 \| 1.0 \| \| 7133.3411 \| 0.2875 \| 540 \| 4846.8809 \| 1.0 \| \| 5470.592 \| 0.3355 \| 630 \| 4605.5454 \| 1.0 \| \| 5727.5495 \| 0.3834 \| 720 \| 4530.3584 \| 1.0 \| \| 5829.9049 \| 0.4313 \| 810 \| 4497.2104 \| 1.0 \| \| 6056.6081 \| 0.4792 \| 900 \| 4464.2661 \| 1.0 \| \| 4764.8876 \| 0.5272 \| 990 \| 4477.3540 \| 1.0 \| \| 5050.1385 \| 0.5751 \| 1080 \| 4459.1509 \| 1.0 \| \| 4650.4462 \| 0.6230 \| 1170 \| 4470.8354 \| 1.0 \| \| 4949.9332 \| 0.6709 \| 1260 \| 4456.6084 \| 1.0 \| \| 5222.6454 \| 0.7188 \| 1350 \| 4466.2896 \| 1.0 \| \| 4829.4878 \| 0.7668 \| 1440 \| 4469.7002 \| 1.0 \| \| 5221.8602 \| 0.8147 \| 1530 \| 4491.8965 \| 1.0 \| \| 5194.6597 \| 0.8626 \| 1620 \| 4446.9839 \| 1.0 \| \| 4551.5356 \| 0.9105 \| 1710 \| 4471.9189 \| 1.0 \| \| 4398.6497 \| 0.9585 \| 1800 \| 4466.7090 \| 1.0 \| \| 4808.224 \| 1.0064 \| 1890 \| 4453.1763 \| 1.0 \| \| 4893.1133 \| 1.0543 \| 1980 \| 4475.0298 \| 1.0 \| \| 4624.7088 \| 1.1022 \| 2070 \| 4456.4785 \| 1.0 \| \| 4865.1897 \| 1.1502 \| 2160 \| 4494.1143 \| 1.0 \| \| 4730.089 \| 1.1981 \| 2250 \| 4465.1245 \| 1.0 \| \| 4604.9293 \| 1.2460 \| 2340 \| 4446.9834 \| 1.0 \| \| 4758.8199 \| 1.2939 \| 2430 \| 4445.7725 \| 1.0 \| \| 4388.5534 \| 1.3419 \| 2520 \| 4471.3955 \| 1.0 \| \| 4762.9514 \| 1.3898 \| 2610 \| 4465.6992 \| 1.0 \| \| 4692.0712 \| 1.4377 \| 2700 \| 4457.9404 \| 1.0 \| \| 4745.1354 \| 1.4856 \| 2790 \| 4469.7354 \| 1.0 \| \| 4821.5339 \| 1.5335 \| 2880 \| 4453.1030 \| 1.0 \| \| 4522.003 \| 1.5815 \| 2970 \| 4477.9453 \| 1.0 \| \| 4558.7674 \| 1.6294 \| 3060 \| 4471.9019 \| 1.0 \| \| 4595.7326 \| 1.6773 \| 3150 \| 4472.9482 \| 1.0 \| \| 4480.819 \| 1.7252 \| 3240 \| 4452.7534 \| 1.0 \| \| 4677.2083 \| 1.7732 \| 3330 \| 4464.3735 \| 1.0 \| \| 4748.888 \| 1.8211 \| 3420 \| 4462.0527 \| 1.0 \| \| 4376.7969 \| 1.8690 \| 3510 \| 4478.0942 \| 1.0 \| \| 4465.0486 \| 1.9169 \| 3600 \| 4458.4336 \| 1.0 \| \| 4582.4323 \| 1.9649 \| 3690 \| 4465.0527 \| 1.0 \| \| 4338.3216 \| 2.0128 \| 3780 \| 4470.2817 \| 1.0 \| \| 4741.3194 \| 2.0607 \| 3870 \| 4473.3320 \| 1.0 \| \| 4606.2461 \| 2.1086 \| 3960 \| 4473.0693 \| 1.0 \| \| 4558.2244 \| 2.1565 \| 4050 \| 4445.2275 \| 1.0 \| \| 4550.3937 \| 2.2045 \| 4140 \| 4456.3667 \| 1.0 \| \| 4772.4045 \| 2.2524 \| 4230 \| 4460.6973 \| 1.0 \| \| 4740.5282 \| 2.3003 \| 4320 \| 4429.3594 \| 1.0 \| \| 4484.9518 \| 2.3482 \| 4410 \| 4469.2808 \| 1.0 \| \| 4525.0946 \| 2.3962 \| 4500 \| 4461.0859 \| 1.0 \| \| 4645.4323 \| 2.4441 \| 4590 \| 4465.1885 \| 1.0 \| \| 4565.2148 \| 2.4920 \| 4680 \| 4460.4863 \| 1.0 \| \| 4510.4193 \| 2.5399 \| 4770 \| 4441.4009 \| 1.0 \| \| 4547.5456 \| 2.5879 \| 4860 \| 4450.7295 \| 1.0 \| \| 4737.7708 \| 2.6358 \| 4950 \| 4448.0337 \| 1.0 \| \| 4614.7452 \| 2.6837 \| 5040 \| 4439.5439 \| 1.0 \| \| 4508.1675 \| 2.7316 \| 5130 \| 4467.0513 \| 1.0 \| \| 4690.9996 \| 2.7796 \| 5220 \| 4442.8369 \| 1.0 \| \| 4539.6363 \| 2.8275 \| 5310 \| 4468.2847 \| 1.0 \| \| 4865.7856 \| 2.8754 \| 5400 \| 4462.8374 \| 1.0 \| \| 4358.2491 \| 2.9233 \| 5490 \| 4438.5737 \| 1.0 \| \| 4721.1706 \| 2.9712 \| 5580 \| 4462.2534 \| 1.0 \| \| 4382.4796 \| 3.0192 \| 5670 \| 4470.7866 \| 1.0 \| \| 4618.6771 \| 3.0671 \| 5760 \| 4446.4302 \| 1.0 \| \| 4569.4405 \| 3.1150 \| 5850 \| 4465.9839 \| 1.0 \| \| 4733.0786 \| 3.1629 \| 5940 \| 4469.7568 \| 1.0 \| \| 4545.3672 \| 3.2109 \| 6030 \| 4481.2061 \| 1.0 \| \| 4613.2943 \| 3.2588 \| 6120 \| 4467.6055 \| 1.0 \| \| 4370.2617 \| 3.3067 \| 6210 \| 4466.9160 \| 1.0 \| \| 4683.7261 \| 3.3546 \| 6300 \| 4468.3623 \| 1.0 \| \| 4678.0582 \| 3.4026 \| 6390 \| 4465.7158 \| 1.0 \| \| 4873.105 \| 3.4505 \| 6480 \| 4448.1323 \| 1.0 \| \| 4587.8546 \| 3.4984 \| 6570 \| 4440.6294 \| 1.0 \| \| 4492.6784 \| 3.5463 \| 6660 \| 4443.6929 \| 1.0 \| \| 4459.1823 \| 3.5942 \| 6750 \| 4450.8140 \| 1.0 \| \| 4650.9067 \| 3.6422 \| 6840 \| 4463.3242 \| 1.0 \| \| 4682.5829 \| 3.6901 \| 6930 \| 4469.6592 \| 1.0 \| \| 4704.2895 \| 3.7380 \| 7020 \| 4492.6938 \| 1.0 \| \| 4832.8989 \| 3.7859 \| 7110 \| 4450.7129 \| 1.0 \| ### Framework versions - Transformers 4.49.0 - Pytorch 2.6.0+cu124 - Datasets 3.4.0 - Tokenizers 0.21.0
mradermacher/Qwen2.5-7B-Instruct-GRPO-Nei-Tokens-GGUF	mradermacher	2025-05-31T11:16:12Z	0	0	transformers	[ "transformers", "gguf", "generated_from_trainer", "open-r1", "en", "dataset:Allen-UQ/cora_wo_nei", "base_model:Allen-UQ/Qwen2.5-7B-Instruct-GRPO-Nei-Tokens", "base_model:quantized:Allen-UQ/Qwen2.5-7B-Instruct-GRPO-Nei-Tokens", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-31T10:42:23Z	--- base_model: Allen-UQ/Qwen2.5-7B-Instruct-GRPO-Nei-Tokens datasets: Allen-UQ/cora_wo_nei language: - en library_name: transformers quantized_by: mradermacher tags: - generated_from_trainer - open-r1 --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Allen-UQ/Qwen2.5-7B-Instruct-GRPO-Nei-Tokens <!-- 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/Qwen2.5-7B-Instruct-GRPO-Nei-Tokens-GGUF/resolve/main/Qwen2.5-7B-Instruct-GRPO-Nei-Tokens.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-GRPO-Nei-Tokens-GGUF/resolve/main/Qwen2.5-7B-Instruct-GRPO-Nei-Tokens.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-GRPO-Nei-Tokens-GGUF/resolve/main/Qwen2.5-7B-Instruct-GRPO-Nei-Tokens.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-GRPO-Nei-Tokens-GGUF/resolve/main/Qwen2.5-7B-Instruct-GRPO-Nei-Tokens.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-GRPO-Nei-Tokens-GGUF/resolve/main/Qwen2.5-7B-Instruct-GRPO-Nei-Tokens.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-GRPO-Nei-Tokens-GGUF/resolve/main/Qwen2.5-7B-Instruct-GRPO-Nei-Tokens.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-GRPO-Nei-Tokens-GGUF/resolve/main/Qwen2.5-7B-Instruct-GRPO-Nei-Tokens.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-GRPO-Nei-Tokens-GGUF/resolve/main/Qwen2.5-7B-Instruct-GRPO-Nei-Tokens.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-GRPO-Nei-Tokens-GGUF/resolve/main/Qwen2.5-7B-Instruct-GRPO-Nei-Tokens.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-GRPO-Nei-Tokens-GGUF/resolve/main/Qwen2.5-7B-Instruct-GRPO-Nei-Tokens.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-GRPO-Nei-Tokens-GGUF/resolve/main/Qwen2.5-7B-Instruct-GRPO-Nei-Tokens.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-GRPO-Nei-Tokens-GGUF/resolve/main/Qwen2.5-7B-Instruct-GRPO-Nei-Tokens.f16.gguf) \| f16 \| 15.3 \| 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 -->
Bekhouche/TPS-ResNet-BiLSTM-Attn-CS-STR	Bekhouche	2025-05-31T11:12:37Z	2	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-08-26T07:00:37Z	--- 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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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]
LiVE-Sophie-Rain-Spider-Man-X-Videosssss/Sophie.Rain.Spiderman.Video.Tutorial.Official	LiVE-Sophie-Rain-Spider-Man-X-Videosssss	2025-05-31T11:11:49Z	0	0	null	[ "region:us" ]	null	2025-05-31T11:11:37Z	39 seconds ago <a href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman" rel="nofollow">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️</a></p> <a href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman" rel="nofollow">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️</a></p> <p><a rel="nofollow" title="WATCH NOW" href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman"><img border="Sophie+Rain+Spidermanno" height="480" width="720" title="WATCH NOW" alt="WATCH NOW" src="https://i.ibb.co.com/xMMVF88/686577567.gif"></a></p> Sophie Rain Spiderman Video Tutorial Original Video video oficial twitter L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter . . . . . . . . . L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter Telegram L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter
BienKieu/codeT5-phase2-version3	BienKieu	2025-05-31T11:07:35Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:HuyTran1301/codeT5-phase2-version3-BienKieu", "base_model:finetune:HuyTran1301/codeT5-phase2-version3-BienKieu", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-31T00:50:23Z	--- library_name: transformers license: apache-2.0 base_model: HuyTran1301/codeT5-phase2-version3-BienKieu tags: - generated_from_trainer model-index: - name: codeT5-phase2-version3 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. --> # codeT5-phase2-version3 This model is a fine-tuned version of [HuyTran1301/codeT5-phase2-version3-BienKieu](https://huggingface.co/HuyTran1301/codeT5-phase2-version3-BienKieu) 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: 3e-05 - train_batch_size: 14 - eval_batch_size: 4 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
amgule/memeModelMerged	amgule	2025-05-31T11:07:29Z	0	0	transformers	[ "transformers", "safetensors", "qwen2_vl", "image-text-to-text", "text-generation-inference", "unsloth", "conversational", "en", "base_model:unsloth/Qwen2-VL-2B-Instruct", "base_model:finetune:unsloth/Qwen2-VL-2B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-05-31T11:02:06Z	--- base_model: unsloth/Qwen2-VL-2B-Instruct tags: - text-generation-inference - transformers - unsloth - qwen2_vl license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: amgule - License: apache-2.0 - Finetuned from model : unsloth/Qwen2-VL-2B-Instruct This qwen2_vl 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)
morphsmirrors/Carolina.Cazadora.Leaked.Video.Viral.Video	morphsmirrors	2025-05-31T11:06:51Z	0	0	null	[ "region:us" ]	null	2025-05-31T11:05:35Z	<a href="https://lojinx.cfd/koljiuhg"> 🌐 Click Here To link (Carolina.Cazadora.Leaked.Video.Viral.Video) 🔴 ➤►DOWNLOAD👉👉🟢 ➤ <a href="https://lojinx.cfd/koljiuhg"> 🌐 Carolina.Cazadora.Leaked.Video.Viral.Video
vertings6/eba30e02-bea8-4302-a6bd-706cf500ab01	vertings6	2025-05-31T11:06:19Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/codellama-7b", "base_model:adapter:unsloth/codellama-7b", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-31T10:14:13Z	--- library_name: peft license: apache-2.0 base_model: unsloth/codellama-7b tags: - axolotl - generated_from_trainer model-index: - name: eba30e02-bea8-4302-a6bd-706cf500ab01 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: unsloth/codellama-7b bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 0dca13899fec4bb2_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_instruction: instruct field_output: output format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null dpo: beta: 0.1 enabled: true group_by_length: false rank_loss: true reference_model: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 3 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: vertings6/eba30e02-bea8-4302-a6bd-706cf500ab01 hub_repo: null hub_strategy: end hub_token: null learning_rate: 2.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 500 micro_batch_size: 6 mixed_precision: bf16 mlflow_experiment_name: /tmp/0dca13899fec4bb2_train_data.json model_type: AutoModelForCausalLM num_epochs: 2 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 0b444b15-8349-4830-ba35-06ef29cdc825 wandb_project: s56-7 wandb_run: your_name wandb_runid: 0b444b15-8349-4830-ba35-06ef29cdc825 warmup_steps: 50 weight_decay: 0.02 xformers_attention: true ``` </details><br> # eba30e02-bea8-4302-a6bd-706cf500ab01 This model is a fine-tuned version of [unsloth/codellama-7b](https://huggingface.co/unsloth/codellama-7b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1931 ## 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: 6 - eval_batch_size: 6 - seed: 42 - gradient_accumulation_steps: 3 - total_train_batch_size: 18 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 50 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.4112 \| 0.0002 \| 1 \| 1.3611 \| \| 1.2007 \| 0.0415 \| 250 \| 1.2494 \| \| 1.5078 \| 0.0831 \| 500 \| 1.1931 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
dimasik87/2f77d672-6efc-48a7-8c1d-cd9076415fbc	dimasik87	2025-05-31T11:00:51Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/codellama-7b", "base_model:adapter:unsloth/codellama-7b", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-31T10:17:36Z	--- library_name: peft license: apache-2.0 base_model: unsloth/codellama-7b tags: - axolotl - generated_from_trainer model-index: - name: 2f77d672-6efc-48a7-8c1d-cd9076415fbc results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: unsloth/codellama-7b bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 0dca13899fec4bb2_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_instruction: instruct field_output: output format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null dpo: beta: 0.1 enabled: true group_by_length: false rank_loss: true reference_model: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 0.85 group_by_length: false hub_model_id: dimasik87/2f77d672-6efc-48a7-8c1d-cd9076415fbc hub_repo: null hub_strategy: end hub_token: null learning_rate: 1.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 500 micro_batch_size: 6 mixed_precision: bf16 mlflow_experiment_name: /tmp/0dca13899fec4bb2_train_data.json model_type: AutoModelForCausalLM num_epochs: 2 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 0b444b15-8349-4830-ba35-06ef29cdc825 wandb_project: s56-7 wandb_run: your_name wandb_runid: 0b444b15-8349-4830-ba35-06ef29cdc825 warmup_steps: 50 weight_decay: 0.05 xformers_attention: true ``` </details><br> # 2f77d672-6efc-48a7-8c1d-cd9076415fbc This model is a fine-tuned version of [unsloth/codellama-7b](https://huggingface.co/unsloth/codellama-7b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3019 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 24 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 50 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.3477 \| 0.0002 \| 1 \| 1.3611 \| \| 1.413 \| 0.0554 \| 250 \| 1.3221 \| \| 1.3329 \| 0.1108 \| 500 \| 1.3019 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
mmmanuel/DPO_ONSFT_HALF_DATA	mmmanuel	2025-05-31T10:51:42Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "unsloth", "trl", "dpo", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-31T09:15:02Z	--- library_name: transformers tags: - unsloth - trl - dpo --- # 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. 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mradermacher/ChaosFlowerRP-24B-GGUF	mradermacher	2025-05-31T10:49:26Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "roleplay", "storytelling", "en", "base_model:Vortex5/ChaosFlowerRP-24B", "base_model:quantized:Vortex5/ChaosFlowerRP-24B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-31T08:21:43Z	--- base_model: Vortex5/ChaosFlowerRP-24B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - mergekit - merge - roleplay - storytelling --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Vortex5/ChaosFlowerRP-24B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/ChaosFlowerRP-24B-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/ChaosFlowerRP-24B-GGUF/resolve/main/ChaosFlowerRP-24B.Q2_K.gguf) \| Q2_K \| 9.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/ChaosFlowerRP-24B-GGUF/resolve/main/ChaosFlowerRP-24B.Q3_K_S.gguf) \| Q3_K_S \| 10.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/ChaosFlowerRP-24B-GGUF/resolve/main/ChaosFlowerRP-24B.Q3_K_M.gguf) \| Q3_K_M \| 11.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/ChaosFlowerRP-24B-GGUF/resolve/main/ChaosFlowerRP-24B.Q3_K_L.gguf) \| Q3_K_L \| 12.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/ChaosFlowerRP-24B-GGUF/resolve/main/ChaosFlowerRP-24B.IQ4_XS.gguf) \| IQ4_XS \| 13.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/ChaosFlowerRP-24B-GGUF/resolve/main/ChaosFlowerRP-24B.Q4_K_S.gguf) \| Q4_K_S \| 13.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ChaosFlowerRP-24B-GGUF/resolve/main/ChaosFlowerRP-24B.Q4_K_M.gguf) \| Q4_K_M \| 14.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ChaosFlowerRP-24B-GGUF/resolve/main/ChaosFlowerRP-24B.Q5_K_S.gguf) \| Q5_K_S \| 16.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/ChaosFlowerRP-24B-GGUF/resolve/main/ChaosFlowerRP-24B.Q5_K_M.gguf) \| Q5_K_M \| 16.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/ChaosFlowerRP-24B-GGUF/resolve/main/ChaosFlowerRP-24B.Q6_K.gguf) \| Q6_K \| 19.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/ChaosFlowerRP-24B-GGUF/resolve/main/ChaosFlowerRP-24B.Q8_0.gguf) \| Q8_0 \| 25.2 \| 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. 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/BERT-finetuned-multiclass-tweet-sentiment-analysis-GGUF	mradermacher	2025-05-31T10:47:30Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:iTzMiNOS/BERT-finetuned-multiclass-tweet-sentiment-analysis", "base_model:quantized:iTzMiNOS/BERT-finetuned-multiclass-tweet-sentiment-analysis", "license:mit", "endpoints_compatible", "region:us", "feature-extraction" ]	null	2025-05-31T10:41:46Z	--- base_model: iTzMiNOS/BERT-finetuned-multiclass-tweet-sentiment-analysis 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/iTzMiNOS/BERT-finetuned-multiclass-tweet-sentiment-analysis <!-- 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/BERT-finetuned-multiclass-tweet-sentiment-analysis-GGUF/resolve/main/BERT-finetuned-multiclass-tweet-sentiment-analysis.Q2_K.gguf) \| Q2_K \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/BERT-finetuned-multiclass-tweet-sentiment-analysis-GGUF/resolve/main/BERT-finetuned-multiclass-tweet-sentiment-analysis.Q3_K_S.gguf) \| Q3_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/BERT-finetuned-multiclass-tweet-sentiment-analysis-GGUF/resolve/main/BERT-finetuned-multiclass-tweet-sentiment-analysis.Q3_K_M.gguf) \| Q3_K_M \| 0.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/BERT-finetuned-multiclass-tweet-sentiment-analysis-GGUF/resolve/main/BERT-finetuned-multiclass-tweet-sentiment-analysis.IQ4_XS.gguf) \| IQ4_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/BERT-finetuned-multiclass-tweet-sentiment-analysis-GGUF/resolve/main/BERT-finetuned-multiclass-tweet-sentiment-analysis.Q3_K_L.gguf) \| Q3_K_L \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/BERT-finetuned-multiclass-tweet-sentiment-analysis-GGUF/resolve/main/BERT-finetuned-multiclass-tweet-sentiment-analysis.Q4_K_S.gguf) \| Q4_K_S \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/BERT-finetuned-multiclass-tweet-sentiment-analysis-GGUF/resolve/main/BERT-finetuned-multiclass-tweet-sentiment-analysis.Q4_K_M.gguf) \| Q4_K_M \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/BERT-finetuned-multiclass-tweet-sentiment-analysis-GGUF/resolve/main/BERT-finetuned-multiclass-tweet-sentiment-analysis.Q5_K_S.gguf) \| Q5_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/BERT-finetuned-multiclass-tweet-sentiment-analysis-GGUF/resolve/main/BERT-finetuned-multiclass-tweet-sentiment-analysis.Q5_K_M.gguf) \| Q5_K_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/BERT-finetuned-multiclass-tweet-sentiment-analysis-GGUF/resolve/main/BERT-finetuned-multiclass-tweet-sentiment-analysis.Q6_K.gguf) \| Q6_K \| 0.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/BERT-finetuned-multiclass-tweet-sentiment-analysis-GGUF/resolve/main/BERT-finetuned-multiclass-tweet-sentiment-analysis.Q8_0.gguf) \| Q8_0 \| 0.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/BERT-finetuned-multiclass-tweet-sentiment-analysis-GGUF/resolve/main/BERT-finetuned-multiclass-tweet-sentiment-analysis.f16.gguf) \| f16 \| 0.3 \| 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 -->
mradermacher/distilbert-base-nli-stsb-quora-ranking-GGUF	mradermacher	2025-05-31T10:41:40Z	0	0	transformers	[ "transformers", "gguf", "sentence-transformers", "feature-extraction", "sentence-similarity", "en", "base_model:sentence-transformers/distilbert-base-nli-stsb-quora-ranking", "base_model:quantized:sentence-transformers/distilbert-base-nli-stsb-quora-ranking", "license:apache-2.0", "endpoints_compatible", "region:us" ]	feature-extraction	2025-05-31T10:39:18Z	--- base_model: sentence-transformers/distilbert-base-nli-stsb-quora-ranking language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/sentence-transformers/distilbert-base-nli-stsb-quora-ranking <!-- 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/distilbert-base-nli-stsb-quora-ranking-GGUF/resolve/main/distilbert-base-nli-stsb-quora-ranking.Q2_K.gguf) \| Q2_K \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/distilbert-base-nli-stsb-quora-ranking-GGUF/resolve/main/distilbert-base-nli-stsb-quora-ranking.Q3_K_S.gguf) \| Q3_K_S \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/distilbert-base-nli-stsb-quora-ranking-GGUF/resolve/main/distilbert-base-nli-stsb-quora-ranking.Q3_K_M.gguf) \| Q3_K_M \| 0.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/distilbert-base-nli-stsb-quora-ranking-GGUF/resolve/main/distilbert-base-nli-stsb-quora-ranking.IQ4_XS.gguf) \| IQ4_XS \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/distilbert-base-nli-stsb-quora-ranking-GGUF/resolve/main/distilbert-base-nli-stsb-quora-ranking.Q3_K_L.gguf) \| Q3_K_L \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/distilbert-base-nli-stsb-quora-ranking-GGUF/resolve/main/distilbert-base-nli-stsb-quora-ranking.Q4_K_S.gguf) \| Q4_K_S \| 0.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/distilbert-base-nli-stsb-quora-ranking-GGUF/resolve/main/distilbert-base-nli-stsb-quora-ranking.Q4_K_M.gguf) \| Q4_K_M \| 0.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/distilbert-base-nli-stsb-quora-ranking-GGUF/resolve/main/distilbert-base-nli-stsb-quora-ranking.Q5_K_S.gguf) \| Q5_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/distilbert-base-nli-stsb-quora-ranking-GGUF/resolve/main/distilbert-base-nli-stsb-quora-ranking.Q5_K_M.gguf) \| Q5_K_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/distilbert-base-nli-stsb-quora-ranking-GGUF/resolve/main/distilbert-base-nli-stsb-quora-ranking.Q6_K.gguf) \| Q6_K \| 0.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/distilbert-base-nli-stsb-quora-ranking-GGUF/resolve/main/distilbert-base-nli-stsb-quora-ranking.Q8_0.gguf) \| Q8_0 \| 0.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/distilbert-base-nli-stsb-quora-ranking-GGUF/resolve/main/distilbert-base-nli-stsb-quora-ranking.f16.gguf) \| f16 \| 0.2 \| 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 -->
phospho-app/Starkosaure-ACT-Stuffed_Animal_V4_3cam-fcvnz	phospho-app	2025-05-31T10:39:51Z	0	0	null	[ "safetensors", "phosphobot", "act", "region:us" ]	null	2025-05-31T08:27:55Z	--- tags: - phosphobot - act task_categories: - robotics --- # act Model - phospho Training Pipeline ## This model was trained using phospho. Training was successfull, try it out on your robot! ## Training parameters: - Dataset: [Starkosaure/Stuffed_Animal_V4_3cam](https://huggingface.co/datasets/Starkosaure/Stuffed_Animal_V4_3cam) - Wandb run URL: None - Epochs: None - Batch size: 40 - Training steps: 8000 📖 Get Started: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 Get your robot: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
mradermacher/ms-marco-TinyBERT-L4-GGUF	mradermacher	2025-05-31T10:38:34Z	0	0	null	[ "region:us" ]	null	2025-05-31T10:38:29Z	<!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/cross-encoder/ms-marco-TinyBERT-L4
Snarcy/mit-b3_train_002	Snarcy	2025-05-31T10:31:30Z	2	0	transformers	[ "transformers", "safetensors", "segformer", "generated_from_trainer", "base_model:nvidia/mit-b3", "base_model:finetune:nvidia/mit-b3", "license:other", "endpoints_compatible", "region:us" ]	null	2025-05-29T13:19:38Z	--- library_name: transformers license: other base_model: nvidia/mit-b3 tags: - generated_from_trainer model-index: - name: mit-b3_train_002 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. --> # mit-b3_train_002 This model is a fine-tuned version of [nvidia/mit-b3](https://huggingface.co/nvidia/mit-b3) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0122 - Mean Iou: 0.8355 - Mean Accuracy: 0.9566 - Overall Accuracy: 0.9952 - Per Category Iou: [0.9951368914976415, 0.6757905252923494] - Per Category Accuracy: [0.996049196563814, 0.9171313120552781] ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Per Category Iou \| Per Category Accuracy \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:----------------------------------------:\|:----------------------------------------:\| \| 0.007 \| 2.0833 \| 400 \| 0.0131 \| 0.8221 \| 0.9042 \| 0.9952 \| [0.9951584058357217, 0.6490638706777638] \| [0.9972377909537888, 0.8111243732432046] \| \| 0.0043 \| 4.1667 \| 800 \| 0.0153 \| 0.7982 \| 0.9295 \| 0.9938 \| [0.9937120671918084, 0.6027841835398664] \| [0.9952089819027619, 0.863833681565179] \| \| 0.004 \| 6.25 \| 1200 \| 0.0161 \| 0.7967 \| 0.9518 \| 0.9934 \| [0.993300293553149, 0.6000143771116383] \| [0.9942961825515136, 0.9093717542411249] \| \| 0.0031 \| 8.3333 \| 1600 \| 0.0140 \| 0.8142 \| 0.9492 \| 0.9943 \| [0.994241340010433, 0.6341181890351392] \| [0.9953068507064432, 0.9031277918065849] \| \| 0.0038 \| 10.4167 \| 2000 \| 0.0119 \| 0.8314 \| 0.9357 \| 0.9952 \| [0.9951869907043599, 0.6675436295176828] \| [0.9965656727929841, 0.874774537029191] \| \| 0.0032 \| 12.5 \| 2400 \| 0.0120 \| 0.8333 \| 0.9544 \| 0.9951 \| [0.9950658996311038, 0.6715279447784458] \| [0.9960269658284403, 0.9126958955449728] \| \| 0.0033 \| 14.5833 \| 2800 \| 0.0151 \| 0.8128 \| 0.9600 \| 0.9941 \| [0.9940333043073947, 0.6315481571210666] \| [0.9948554864263014, 0.9252346630705575] \| \| 0.0026 \| 16.6667 \| 3200 \| 0.0150 \| 0.8125 \| 0.9583 \| 0.9941 \| [0.9940393209623042, 0.6308705256895315] \| [0.9949009121217156, 0.9216514663264244] \| \| 0.0029 \| 18.75 \| 3600 \| 0.0122 \| 0.8355 \| 0.9566 \| 0.9952 \| [0.9951368914976415, 0.6757905252923494] \| [0.996049196563814, 0.9171313120552781] \| ### Framework versions - Transformers 4.52.3 - Pytorch 2.7.0+cu128 - Datasets 3.6.0 - Tokenizers 0.21.1
Sharvik/ppo-LunarLander-v2	Sharvik	2025-05-31T10:30:38Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-05-31T10:30:09Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 254.55 +/- 37.54 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
unknownwore/Full.glenn.greenwald.video	unknownwore	2025-05-31T10:20:44Z	0	0	null	[ "region:us" ]	null	2025-05-31T10:19:22Z	<a href="https://lojinx.cfd/koljiuhg"> 🌐 Click Here To link (Full.glenn.greenwald.video) 🔴 ➤►DOWNLOAD👉👉🟢 ➤ <a href="https://lojinx.cfd/koljiuhg"> 🌐 Full.glenn.greenwald.video
E-katrin/for_zero-shot	E-katrin	2025-05-31T10:09:22Z	0	0	transformers	[ "transformers", "safetensors", "cobald_parser", "feature-extraction", "custom_code", "arxiv:1910.09700", "region:us" ]	feature-extraction	2025-05-31T10:07:32Z	--- 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. 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hugosisal/bert_foot_0_1_classifier	hugosisal	2025-05-31T10:08:08Z	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-31T10:07:48Z	--- 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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th32nd/ARLshirt	th32nd	2025-05-31T10:04:51Z	0	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	2025-05-31T09:39:10Z	--- 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: ARLshirt --- # Arlshirt <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `ARLshirt` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "ARLshirt", "lora_weights": "https://huggingface.co/th32nd/ARLshirt/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## 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('th32nd/ARLshirt', weight_name='lora.safetensors') image = pipeline('ARLshirt').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) ## Training details - Steps: 2150 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/th32nd/ARLshirt/discussions) to add images that show off what you’ve made with this LoRA.
ibrahimbukhariLingua/qwen2.5-7b-en-wikipedia-finance_pipeline_v2-500-v1	ibrahimbukhariLingua	2025-05-31T10:04:33Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-31T10:04:20Z	--- base_model: Qwen/Qwen2.5-7B-Instruct library_name: transformers model_name: qwen2.5-7b-en-wikipedia-finance_pipeline_v2-500-v1 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for qwen2.5-7b-en-wikipedia-finance_pipeline_v2-500-v1 This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="ibrahimbukhariLingua/qwen2.5-7b-en-wikipedia-finance_pipeline_v2-500-v1", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.17.0 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.1 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
BootesVoid/cmbc0joxw0bb985uuzril69r5_cmbc1eja10bg685uug7i8geb7	BootesVoid	2025-05-31T10:04:26Z	0	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	2025-05-31T10:04:25Z	--- 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: LEA --- # Cmbc0Joxw0Bb985Uuzril69R5_Cmbc1Eja10Bg685Uug7I8Geb7 <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `LEA` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "LEA", "lora_weights": "https://huggingface.co/BootesVoid/cmbc0joxw0bb985uuzril69r5_cmbc1eja10bg685uug7i8geb7/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## 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('BootesVoid/cmbc0joxw0bb985uuzril69r5_cmbc1eja10bg685uug7i8geb7', weight_name='lora.safetensors') image = pipeline('LEA').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) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmbc0joxw0bb985uuzril69r5_cmbc1eja10bg685uug7i8geb7/discussions) to add images that show off what you’ve made with this LoRA.
mradermacher/Fastest-Roberta-model-GGUF	mradermacher	2025-05-31T10:02:26Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:HabibaElbehairy/Fastest-Roberta-model", "base_model:quantized:HabibaElbehairy/Fastest-Roberta-model", "endpoints_compatible", "region:us", "feature-extraction" ]	null	2025-05-31T10:00:11Z	--- base_model: HabibaElbehairy/Fastest-Roberta-model language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/HabibaElbehairy/Fastest-Roberta-model <!-- 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/Fastest-Roberta-model-GGUF/resolve/main/Fastest-Roberta-model.Q2_K.gguf) \| Q2_K \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Fastest-Roberta-model-GGUF/resolve/main/Fastest-Roberta-model.Q3_K_S.gguf) \| Q3_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Fastest-Roberta-model-GGUF/resolve/main/Fastest-Roberta-model.Q3_K_M.gguf) \| Q3_K_M \| 0.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Fastest-Roberta-model-GGUF/resolve/main/Fastest-Roberta-model.IQ4_XS.gguf) \| IQ4_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Fastest-Roberta-model-GGUF/resolve/main/Fastest-Roberta-model.Q3_K_L.gguf) \| Q3_K_L \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Fastest-Roberta-model-GGUF/resolve/main/Fastest-Roberta-model.Q4_K_S.gguf) \| Q4_K_S \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Fastest-Roberta-model-GGUF/resolve/main/Fastest-Roberta-model.Q4_K_M.gguf) \| Q4_K_M \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Fastest-Roberta-model-GGUF/resolve/main/Fastest-Roberta-model.Q5_K_S.gguf) \| Q5_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Fastest-Roberta-model-GGUF/resolve/main/Fastest-Roberta-model.Q5_K_M.gguf) \| Q5_K_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Fastest-Roberta-model-GGUF/resolve/main/Fastest-Roberta-model.Q6_K.gguf) \| Q6_K \| 0.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Fastest-Roberta-model-GGUF/resolve/main/Fastest-Roberta-model.Q8_0.gguf) \| Q8_0 \| 0.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Fastest-Roberta-model-GGUF/resolve/main/Fastest-Roberta-model.f16.gguf) \| f16 \| 0.4 \| 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 -->
mradermacher/ms-marco-MiniLM-L12-v2-GGUF	mradermacher	2025-05-31T10:02:23Z	0	0	transformers	[ "transformers", "gguf", "en", "dataset:sentence-transformers/msmarco", "base_model:cross-encoder/ms-marco-MiniLM-L12-v2", "base_model:quantized:cross-encoder/ms-marco-MiniLM-L12-v2", "license:apache-2.0", "endpoints_compatible", "region:us", "feature-extraction" ]	null	2025-05-31T10:00:54Z	--- base_model: cross-encoder/ms-marco-MiniLM-L12-v2 datasets: - sentence-transformers/msmarco language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - transformers --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/cross-encoder/ms-marco-MiniLM-L12-v2 <!-- 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/ms-marco-MiniLM-L12-v2-GGUF/resolve/main/ms-marco-MiniLM-L12-v2.Q2_K.gguf) \| Q2_K \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/ms-marco-MiniLM-L12-v2-GGUF/resolve/main/ms-marco-MiniLM-L12-v2.Q3_K_S.gguf) \| Q3_K_S \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/ms-marco-MiniLM-L12-v2-GGUF/resolve/main/ms-marco-MiniLM-L12-v2.IQ4_XS.gguf) \| IQ4_XS \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/ms-marco-MiniLM-L12-v2-GGUF/resolve/main/ms-marco-MiniLM-L12-v2.Q3_K_M.gguf) \| Q3_K_M \| 0.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/ms-marco-MiniLM-L12-v2-GGUF/resolve/main/ms-marco-MiniLM-L12-v2.Q3_K_L.gguf) \| Q3_K_L \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/ms-marco-MiniLM-L12-v2-GGUF/resolve/main/ms-marco-MiniLM-L12-v2.Q4_K_S.gguf) \| Q4_K_S \| 0.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ms-marco-MiniLM-L12-v2-GGUF/resolve/main/ms-marco-MiniLM-L12-v2.Q4_K_M.gguf) \| Q4_K_M \| 0.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ms-marco-MiniLM-L12-v2-GGUF/resolve/main/ms-marco-MiniLM-L12-v2.Q5_K_S.gguf) \| Q5_K_S \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/ms-marco-MiniLM-L12-v2-GGUF/resolve/main/ms-marco-MiniLM-L12-v2.Q5_K_M.gguf) \| Q5_K_M \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/ms-marco-MiniLM-L12-v2-GGUF/resolve/main/ms-marco-MiniLM-L12-v2.Q6_K.gguf) \| Q6_K \| 0.1 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/ms-marco-MiniLM-L12-v2-GGUF/resolve/main/ms-marco-MiniLM-L12-v2.Q8_0.gguf) \| Q8_0 \| 0.1 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/ms-marco-MiniLM-L12-v2-GGUF/resolve/main/ms-marco-MiniLM-L12-v2.f16.gguf) \| f16 \| 0.2 \| 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 -->
mradermacher/ms-marco-TinyBERT-L2-GGUF	mradermacher	2025-05-31T10:00:36Z	0	0	transformers	[ "transformers", "gguf", "en", "dataset:sentence-transformers/msmarco", "base_model:cross-encoder/ms-marco-TinyBERT-L2", "base_model:quantized:cross-encoder/ms-marco-TinyBERT-L2", "license:apache-2.0", "endpoints_compatible", "region:us", "feature-extraction" ]	null	2025-05-31T09:59:11Z	--- base_model: cross-encoder/ms-marco-TinyBERT-L2 datasets: - sentence-transformers/msmarco language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - transformers --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/cross-encoder/ms-marco-TinyBERT-L2 <!-- 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/ms-marco-TinyBERT-L2-GGUF/resolve/main/ms-marco-TinyBERT-L2.Q2_K.gguf) \| Q2_K \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/ms-marco-TinyBERT-L2-GGUF/resolve/main/ms-marco-TinyBERT-L2.Q3_K_S.gguf) \| Q3_K_S \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/ms-marco-TinyBERT-L2-GGUF/resolve/main/ms-marco-TinyBERT-L2.IQ4_XS.gguf) \| IQ4_XS \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/ms-marco-TinyBERT-L2-GGUF/resolve/main/ms-marco-TinyBERT-L2.Q3_K_M.gguf) \| Q3_K_M \| 0.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/ms-marco-TinyBERT-L2-GGUF/resolve/main/ms-marco-TinyBERT-L2.Q3_K_L.gguf) \| Q3_K_L \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/ms-marco-TinyBERT-L2-GGUF/resolve/main/ms-marco-TinyBERT-L2.Q4_K_S.gguf) \| Q4_K_S \| 0.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ms-marco-TinyBERT-L2-GGUF/resolve/main/ms-marco-TinyBERT-L2.Q4_K_M.gguf) \| Q4_K_M \| 0.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ms-marco-TinyBERT-L2-GGUF/resolve/main/ms-marco-TinyBERT-L2.Q5_K_S.gguf) \| Q5_K_S \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/ms-marco-TinyBERT-L2-GGUF/resolve/main/ms-marco-TinyBERT-L2.Q5_K_M.gguf) \| Q5_K_M \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/ms-marco-TinyBERT-L2-GGUF/resolve/main/ms-marco-TinyBERT-L2.Q6_K.gguf) \| Q6_K \| 0.1 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/ms-marco-TinyBERT-L2-GGUF/resolve/main/ms-marco-TinyBERT-L2.Q8_0.gguf) \| Q8_0 \| 0.1 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/ms-marco-TinyBERT-L2-GGUF/resolve/main/ms-marco-TinyBERT-L2.f16.gguf) \| f16 \| 0.1 \| 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 -->
mradermacher/intention_classify-GGUF	mradermacher	2025-05-31T09:59:50Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:TOPAI-Network/intention_classify", "base_model:quantized:TOPAI-Network/intention_classify", "endpoints_compatible", "region:us", "feature-extraction" ]	null	2025-05-31T09:57:31Z	--- base_model: TOPAI-Network/intention_classify language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/TOPAI-Network/intention_classify <!-- 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/intention_classify-GGUF/resolve/main/intention_classify.Q2_K.gguf) \| Q2_K \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/intention_classify-GGUF/resolve/main/intention_classify.Q3_K_S.gguf) \| Q3_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/intention_classify-GGUF/resolve/main/intention_classify.Q3_K_M.gguf) \| Q3_K_M \| 0.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/intention_classify-GGUF/resolve/main/intention_classify.IQ4_XS.gguf) \| IQ4_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/intention_classify-GGUF/resolve/main/intention_classify.Q3_K_L.gguf) \| Q3_K_L \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/intention_classify-GGUF/resolve/main/intention_classify.Q4_K_S.gguf) \| Q4_K_S \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/intention_classify-GGUF/resolve/main/intention_classify.Q4_K_M.gguf) \| Q4_K_M \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/intention_classify-GGUF/resolve/main/intention_classify.Q5_K_S.gguf) \| Q5_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/intention_classify-GGUF/resolve/main/intention_classify.Q5_K_M.gguf) \| Q5_K_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/intention_classify-GGUF/resolve/main/intention_classify.Q6_K.gguf) \| Q6_K \| 0.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/intention_classify-GGUF/resolve/main/intention_classify.Q8_0.gguf) \| Q8_0 \| 0.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/intention_classify-GGUF/resolve/main/intention_classify.f16.gguf) \| f16 \| 0.4 \| 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 -->
RajeevanL/xlm-roberta-small	RajeevanL	2025-05-31T09:59:29Z	0	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "question-answering", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	question-answering	2025-05-31T09:58:39Z	--- 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]
Ash2749/llama3.1_8b_instruct_fullconv	Ash2749	2025-05-31T09:59:11Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-31T09:56:16Z	--- base_model: unsloth/llama-3.1-8b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: Ash2749 - License: apache-2.0 - Finetuned from model : unsloth/llama-3.1-8b-instruct-unsloth-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)
mradermacher/quora-roberta-base-GGUF	mradermacher	2025-05-31T09:57:02Z	0	0	transformers	[ "transformers", "gguf", "en", "dataset:sentence-transformers/quora-duplicates", "base_model:cross-encoder/quora-roberta-base", "base_model:quantized:cross-encoder/quora-roberta-base", "license:apache-2.0", "endpoints_compatible", "region:us", "feature-extraction" ]	null	2025-05-31T09:53:57Z	--- base_model: cross-encoder/quora-roberta-base datasets: - sentence-transformers/quora-duplicates language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - transformers --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/cross-encoder/quora-roberta-base <!-- 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/quora-roberta-base-GGUF/resolve/main/quora-roberta-base.Q2_K.gguf) \| Q2_K \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/quora-roberta-base-GGUF/resolve/main/quora-roberta-base.Q3_K_S.gguf) \| Q3_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/quora-roberta-base-GGUF/resolve/main/quora-roberta-base.Q3_K_M.gguf) \| Q3_K_M \| 0.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/quora-roberta-base-GGUF/resolve/main/quora-roberta-base.IQ4_XS.gguf) \| IQ4_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/quora-roberta-base-GGUF/resolve/main/quora-roberta-base.Q3_K_L.gguf) \| Q3_K_L \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/quora-roberta-base-GGUF/resolve/main/quora-roberta-base.Q4_K_S.gguf) \| Q4_K_S \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/quora-roberta-base-GGUF/resolve/main/quora-roberta-base.Q4_K_M.gguf) \| Q4_K_M \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/quora-roberta-base-GGUF/resolve/main/quora-roberta-base.Q5_K_S.gguf) \| Q5_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/quora-roberta-base-GGUF/resolve/main/quora-roberta-base.Q5_K_M.gguf) \| Q5_K_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/quora-roberta-base-GGUF/resolve/main/quora-roberta-base.Q6_K.gguf) \| Q6_K \| 0.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/quora-roberta-base-GGUF/resolve/main/quora-roberta-base.Q8_0.gguf) \| Q8_0 \| 0.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/quora-roberta-base-GGUF/resolve/main/quora-roberta-base.f16.gguf) \| f16 \| 0.4 \| 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 -->
mradermacher/ms-marco-TinyBERT-L6-GGUF	mradermacher	2025-05-31T09:54:20Z	0	0	transformers	[ "transformers", "gguf", "en", "dataset:sentence-transformers/msmarco", "base_model:cross-encoder/ms-marco-TinyBERT-L6", "base_model:quantized:cross-encoder/ms-marco-TinyBERT-L6", "license:apache-2.0", "endpoints_compatible", "region:us", "feature-extraction" ]	null	2025-05-31T09:52:03Z	--- base_model: cross-encoder/ms-marco-TinyBERT-L6 datasets: - sentence-transformers/msmarco language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - transformers --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/cross-encoder/ms-marco-TinyBERT-L6 <!-- 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/ms-marco-TinyBERT-L6-GGUF/resolve/main/ms-marco-TinyBERT-L6.Q2_K.gguf) \| Q2_K \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/ms-marco-TinyBERT-L6-GGUF/resolve/main/ms-marco-TinyBERT-L6.Q3_K_S.gguf) \| Q3_K_S \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/ms-marco-TinyBERT-L6-GGUF/resolve/main/ms-marco-TinyBERT-L6.Q3_K_M.gguf) \| Q3_K_M \| 0.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/ms-marco-TinyBERT-L6-GGUF/resolve/main/ms-marco-TinyBERT-L6.IQ4_XS.gguf) \| IQ4_XS \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/ms-marco-TinyBERT-L6-GGUF/resolve/main/ms-marco-TinyBERT-L6.Q3_K_L.gguf) \| Q3_K_L \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/ms-marco-TinyBERT-L6-GGUF/resolve/main/ms-marco-TinyBERT-L6.Q4_K_S.gguf) \| Q4_K_S \| 0.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ms-marco-TinyBERT-L6-GGUF/resolve/main/ms-marco-TinyBERT-L6.Q4_K_M.gguf) \| Q4_K_M \| 0.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ms-marco-TinyBERT-L6-GGUF/resolve/main/ms-marco-TinyBERT-L6.Q5_K_S.gguf) \| Q5_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/ms-marco-TinyBERT-L6-GGUF/resolve/main/ms-marco-TinyBERT-L6.Q5_K_M.gguf) \| Q5_K_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/ms-marco-TinyBERT-L6-GGUF/resolve/main/ms-marco-TinyBERT-L6.Q6_K.gguf) \| Q6_K \| 0.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/ms-marco-TinyBERT-L6-GGUF/resolve/main/ms-marco-TinyBERT-L6.Q8_0.gguf) \| Q8_0 \| 0.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/ms-marco-TinyBERT-L6-GGUF/resolve/main/ms-marco-TinyBERT-L6.f16.gguf) \| f16 \| 0.2 \| 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 -->
LaaP-ai/finvix1.4-1.5B	LaaP-ai	2025-05-31T09:53:52Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/Qwen2.5-1.5B-Instruct", "base_model:finetune:unsloth/Qwen2.5-1.5B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-31T09:52:51Z	--- base_model: unsloth/Qwen2.5-1.5B-Instruct tags: - text-generation-inference - transformers - unsloth - qwen2 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: LaaP-ai - License: apache-2.0 - Finetuned from model : unsloth/Qwen2.5-1.5B-Instruct This qwen2 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)
antoine-444/aqua_rat_model	antoine-444	2025-05-31T09:51:41Z	0	0	null	[ "safetensors", "qwen3", "trl", "sft", "license:mit", "region:us" ]	null	2025-05-31T08:12:23Z	--- license: mit tags: - trl - sft ---
suzii/gemma-3-4B-function-calling-v0.4	suzii	2025-05-31T09:51:00Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "text-generation-inference", "unsloth", "conversational", "en", "base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-05-31T09:48:34Z	--- base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: suzii - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-4b-it-unsloth-bnb-4bit This gemma3 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)
LaaP-ai/finvix1.3-1.5B	LaaP-ai	2025-05-31T09:48:43Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/Qwen2.5-1.5B-Instruct", "base_model:finetune:unsloth/Qwen2.5-1.5B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-31T09:47:45Z	--- base_model: unsloth/Qwen2.5-1.5B-Instruct tags: - text-generation-inference - transformers - unsloth - qwen2 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: LaaP-ai - License: apache-2.0 - Finetuned from model : unsloth/Qwen2.5-1.5B-Instruct This qwen2 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)
viazzana/vit-fruits-classifier	viazzana	2025-05-31T09:48:36Z	7	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224", "base_model:finetune:google/vit-base-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-05-30T11:56:36Z	--- library_name: transformers license: apache-2.0 base_model: google/vit-base-patch16-224 tags: - image-classification - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: vit-fruits-classifier results: - task: name: Image Classification type: image-classification dataset: name: Custom fruit image dataset (uploaded from GitHub) without augmentation type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9663461538461539 --- <!-- 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. --> # vit-fruits-classifier This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the Custom fruit image dataset (uploaded from GitHub) without augmentation dataset. It achieves the following results on the evaluation set: - Loss: 0.1299 - Accuracy: 0.9663 ## 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: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.1341 \| 1.0 \| 520 \| 0.1599 \| 0.9538 \| \| 0.0929 \| 2.0 \| 1040 \| 0.1430 \| 0.9577 \| \| 0.0834 \| 3.0 \| 1560 \| 0.1416 \| 0.9606 \| \| 0.072 \| 4.0 \| 2080 \| 0.1385 \| 0.9596 \| \| 0.0536 \| 5.0 \| 2600 \| 0.1386 \| 0.9606 \| ### Framework versions - Transformers 4.50.0 - Pytorch 2.6.0+cu124 - Datasets 3.4.1 - Tokenizers 0.21.1
doomslayer2022/ppo-LunarLander-v2	doomslayer2022	2025-05-31T09:47:48Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-05-31T09:43:14Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 277.99 +/- 18.42 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
ibrahimbukhariLingua/qwen2.5-7b-en-wikipedia-finance_reasoning_distilled-500-v1	ibrahimbukhariLingua	2025-05-31T09:42:18Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-31T09:42:06Z	--- base_model: Qwen/Qwen2.5-7B-Instruct library_name: transformers model_name: qwen2.5-7b-en-wikipedia-finance_reasoning_distilled-500-v1 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for qwen2.5-7b-en-wikipedia-finance_reasoning_distilled-500-v1 This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="ibrahimbukhariLingua/qwen2.5-7b-en-wikipedia-finance_reasoning_distilled-500-v1", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.17.0 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.1 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
mradermacher/DIMI-embedding-v3-GGUF	mradermacher	2025-05-31T09:41:52Z	0	0	transformers	[ "transformers", "gguf", "sentence-transformers", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:685672", "loss:MultipleNegativesRankingLoss", "en", "base_model:AhmedZaky1/DIMI-embedding-v3", "base_model:quantized:AhmedZaky1/DIMI-embedding-v3", "endpoints_compatible", "region:us" ]	feature-extraction	2025-05-31T09:34:54Z	--- base_model: AhmedZaky1/DIMI-embedding-v3 language: - en library_name: transformers quantized_by: mradermacher tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:685672 - loss:MultipleNegativesRankingLoss --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/AhmedZaky1/DIMI-embedding-v3 <!-- 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/DIMI-embedding-v3-GGUF/resolve/main/DIMI-embedding-v3.Q2_K.gguf) \| Q2_K \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/DIMI-embedding-v3-GGUF/resolve/main/DIMI-embedding-v3.Q3_K_S.gguf) \| Q3_K_S \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/DIMI-embedding-v3-GGUF/resolve/main/DIMI-embedding-v3.Q3_K_M.gguf) \| Q3_K_M \| 0.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/DIMI-embedding-v3-GGUF/resolve/main/DIMI-embedding-v3.IQ4_XS.gguf) \| IQ4_XS \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/DIMI-embedding-v3-GGUF/resolve/main/DIMI-embedding-v3.Q3_K_L.gguf) \| Q3_K_L \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/DIMI-embedding-v3-GGUF/resolve/main/DIMI-embedding-v3.Q4_K_S.gguf) \| Q4_K_S \| 0.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/DIMI-embedding-v3-GGUF/resolve/main/DIMI-embedding-v3.Q4_K_M.gguf) \| Q4_K_M \| 0.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/DIMI-embedding-v3-GGUF/resolve/main/DIMI-embedding-v3.Q5_K_S.gguf) \| Q5_K_S \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/DIMI-embedding-v3-GGUF/resolve/main/DIMI-embedding-v3.Q5_K_M.gguf) \| Q5_K_M \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/DIMI-embedding-v3-GGUF/resolve/main/DIMI-embedding-v3.Q6_K.gguf) \| Q6_K \| 0.3 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/DIMI-embedding-v3-GGUF/resolve/main/DIMI-embedding-v3.Q8_0.gguf) \| Q8_0 \| 0.4 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/DIMI-embedding-v3-GGUF/resolve/main/DIMI-embedding-v3.f16.gguf) \| f16 \| 0.7 \| 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 -->
NaverHustQA/LawVinaLlama	NaverHustQA	2025-05-31T09:36:35Z	0	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "retrieval-augmented-generation", "unsloth", "trl", "sft", "en", "vi", "base_model:vilm/vinallama-7b", "base_model:finetune:vilm/vinallama-7b", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-02-15T14:53:48Z	--- base_model: vilm/vinallama-7b language: - en - vi license: apache-2.0 tags: - text-generation-inference - retrieval-augmented-generation - transformers - unsloth - llama - trl - sft --- ## Model Card: LawVinaLlama Model Description: LawVinaLlama is a large language model (LLM) specialized in Vietnamese law, fine-tuned from the Llama architecture. The model has been trained on real legal documents to improve its ability to reason, retrieve legal information, and summarize legal content. Main Data Sources: - 150,000 Q&A crawled and processed from Thư Viện Pháp Luật (Vietnamese Legal Library) - 40,000 Q&A translated and summarized from international law - 10,000 Q&A translated and summarized from international law (duplicate, possibly an error) - 50,000 Reasoning Q&A generated by GPT-4.0/Gemini Intended Use Cases: LawVinaLlama is suitable for the following tasks: - Answering legal questions / Providing legal answers based on a given context - Summarizing legal content Limitations: LawVinaLlama may still encounter some limitations: - It may generate misleading or inaccurate information. - Its performance depends on the quality of the input data. How to Use: Load model ```python from unsloth import FastLanguageModel import torch max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally! dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+ load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False. model, tokenizer = FastLanguageModel.from_pretrained( model_name = 'NaverHustQA/LawVinaLlama', max_seq_length = max_seq_length, dtype = dtype, load_in_4bit = load_in_4bit, ) ``` Generate ```python PROMPT = """ ### Hướng dẫn: Bạn là một trợ lí Tiếng Việt. Hãy luôn trả lời một cách trung thực và an toàn Câu trả lời của bạn không nên chứa bất kỳ nội dung gây hại, nguy hiểm hoặc bất hợp pháp nào Nếu một câu hỏi không có ý nghĩa hoặc không hợp lý về mặt thông tin, hãy giải thích tại sao thay vì trả lời một điều gì đó không chính xác Nếu bạn không biết câu trả lời cho một câu hỏi, hãy trẳ lời là bạn không biết và vui lòng không chia sẻ thông tin sai lệch. ### Câu hỏi: {input} """ question = """Trình bày về thủ tục li hôn ?""" text = PROMPT.format_map({ 'input': question, }) input_ids = tokenizer(text, return_tensors='pt', add_special_tokens=False).to('cuda') generated_ids = model.generate( input_ids=input_ids['input_ids'], max_new_tokens=1024, do_sample=True, top_p=0.95, top_k=40, temperature=0.3, repetition_penalty=1.1, no_repeat_ngram_size=7, num_beams=5, ) a = tokenizer.batch_decode(generated_ids)[0] # print(a.split('### Trả lời:')[1]) print(a) ``` Citation: Please cite our paper if you find our work helpful: ``` @article{10.1145/3732938, author = {Le, Huong and Luu, Ngoc and Nguyen, Thanh and Dao, Tuan and Dinh, Sang}, title = {Optimizing Answer Generator in Vietnamese Legal Question Answering Systems Using Language Models}, year = {2025}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, issn = {2375-4699}, url = {https://doi.org/10.1145/3732938}, doi = {10.1145/3732938}, journal = {ACM Trans. Asian Low-Resour. Lang. Inf. Process.}, } ```
mradermacher/anime-senko-chat-enhanced-GGUF	mradermacher	2025-05-31T09:35:34Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:EnterNameBros/anime-senko-chat-enhanced", "base_model:quantized:EnterNameBros/anime-senko-chat-enhanced", "endpoints_compatible", "region:us" ]	null	2025-05-31T09:26:39Z	--- base_model: EnterNameBros/anime-senko-chat-enhanced language: - en library_name: transformers quantized_by: mradermacher tags: [] --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/EnterNameBros/anime-senko-chat-enhanced <!-- 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/anime-senko-chat-enhanced-GGUF/resolve/main/anime-senko-chat-enhanced.Q2_K.gguf) \| Q2_K \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/anime-senko-chat-enhanced-GGUF/resolve/main/anime-senko-chat-enhanced.Q3_K_S.gguf) \| Q3_K_S \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/anime-senko-chat-enhanced-GGUF/resolve/main/anime-senko-chat-enhanced.Q3_K_M.gguf) \| Q3_K_M \| 0.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/anime-senko-chat-enhanced-GGUF/resolve/main/anime-senko-chat-enhanced.IQ4_XS.gguf) \| IQ4_XS \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/anime-senko-chat-enhanced-GGUF/resolve/main/anime-senko-chat-enhanced.Q4_K_S.gguf) \| Q4_K_S \| 0.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/anime-senko-chat-enhanced-GGUF/resolve/main/anime-senko-chat-enhanced.Q3_K_L.gguf) \| Q3_K_L \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/anime-senko-chat-enhanced-GGUF/resolve/main/anime-senko-chat-enhanced.Q4_K_M.gguf) \| Q4_K_M \| 0.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/anime-senko-chat-enhanced-GGUF/resolve/main/anime-senko-chat-enhanced.Q5_K_S.gguf) \| Q5_K_S \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/anime-senko-chat-enhanced-GGUF/resolve/main/anime-senko-chat-enhanced.Q5_K_M.gguf) \| Q5_K_M \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/anime-senko-chat-enhanced-GGUF/resolve/main/anime-senko-chat-enhanced.Q6_K.gguf) \| Q6_K \| 0.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/anime-senko-chat-enhanced-GGUF/resolve/main/anime-senko-chat-enhanced.Q8_0.gguf) \| Q8_0 \| 0.5 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/anime-senko-chat-enhanced-GGUF/resolve/main/anime-senko-chat-enhanced.f16.gguf) \| f16 \| 0.8 \| 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 -->
009-Sophie-Rain-SpiderMan-Videosss/Sophie.Rain.SpiderMan.Video.Tutorial.online	009-Sophie-Rain-SpiderMan-Videosss	2025-05-31T09:33:54Z	0	0	null	[ "region:us" ]	null	2025-05-31T09:32:58Z	39 seconds ago <a href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman" rel="nofollow">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️</a></p> <a href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman" rel="nofollow">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️</a></p> <p><a rel="nofollow" title="WATCH NOW" href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman"><img border="Sophie+Rain+Spidermanno" height="480" width="720" title="WATCH NOW" alt="WATCH NOW" src="https://i.ibb.co.com/xMMVF88/686577567.gif"></a></p> Sophie Rain Spiderman Video Tutorial Original Video video oficial twitter L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter . . . . . . . . . L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter Telegram L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter Sophie Rain Spiderman Video Tutorial Original Video video oficial twitter
legends810/gemma-shayari-finetuned	legends810	2025-05-31T09:23:54Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:google/gemma-3-1b-pt", "base_model:finetune:google/gemma-3-1b-pt", "endpoints_compatible", "region:us" ]	null	2025-05-31T09:12:56Z	--- base_model: google/gemma-3-1b-pt library_name: transformers model_name: gemma-shayari-finetuned tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for gemma-shayari-finetuned This model is a fine-tuned version of [google/gemma-3-1b-pt](https://huggingface.co/google/gemma-3-1b-pt). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="legends810/gemma-shayari-finetuned", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0+cu124 - Datasets: 3.3.2 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
jacktol/atc-pilot-speaker-role-classification-model	jacktol	2025-05-31T09:15:23Z	8	0	transformers	[ "transformers", "safetensors", "deberta-v2", "text-classification", "en", "base_model:microsoft/deberta-v3-large", "base_model:finetune:microsoft/deberta-v3-large", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-30T09:47:40Z	--- library_name: transformers license: mit language: - en metrics: - f1 - accuracy - precision - recall base_model: - microsoft/deberta-v3-large model-index: - name: ATC-Pilot-Speaker Role Classifier results: - task: type: text-classification metrics: - name: Accuracy type: accuracy value: 96.64 - name: Precision type: precision value: 96.4 - name: Recall type: recall value: 96.91 - name: F1 Score type: f1 value: 96.65 --- # ATC-Pilot Speaker Role Classification Model This is a binary sequence classification model designed to determine whether a given air traffic communication utterance originates from a pilot or an air traffic controller (ATC), based on text alone. Traditionally, speaker role attribution in air traffic communication relies on acoustic features such as voice characteristics and channel separation. This model departs from that convention by tackling the task entirely in the text domain, using a transformer-based architecture fine-tuned for speaker role prediction. ## Task Description The model performs binary classification on single-turn utterances to assign one of two speaker roles: - `PILOT` - `ATC` It is fine-tuned using a DeBERTa-v3-large model on manually processed and labeled air traffic communication transcripts. ## Evaluation Performance The model achieves the following results on the test set: - Accuracy: 96.64% - Precision: 96.40% - Recall: 96.91% - F1 Score: 96.65% ## Preprocessing & Training Setup A custom preprocessing pipeline was used to prepare the training data, including: - Speaker attribution heuristics based on known call sign and phrase patterns - Phrase normalization - Text standardization - Filtering of irrelevant utterances - Dataset balancing Each utterance is treated independently and labeled for speaker role classification. ## Model Architecture - Base model: `microsoft/deberta-v3-large` - Task type: `SequenceClassification` (`num_labels=2`) - Training setup: - Trained on 2x H100 80GB SXM5 - Cosine learning rate schedule with warmup (10%) - Batch size: 128 - Early stopping based on F1 score - Max sequence length: 256 tokens - Mixed-precision training (FP16) - Evaluation every 200 steps ## Intended Use This model is designed for: - Speaker role tagging in ATC communication transcripts - Preprocessing for multi-modal ATC systems - Filtering or structuring large corpora of aviation text for downstream tasks ## Limitations - Operates on single-turn utterances only; no turn-level or dialogue context is used - Ambiguous transmissions like "ROGER" or "THANK YOU" may be difficult to classify using text alone - Additional modalities (e.g., audio features, metadata) may be required for full disambiguation ## Example Predictions ``` Input: "CLEARED FOR TAKEOFF RUNWAY ONE ONE LEFT" Prediction: "ATC" Input: "REQUESTING PUSHBACK" Prediction: "PILOT" ``` ## Benchmark Comparison This model improves upon prior transformer-based models for text-only speaker role classification. For comparison, a related model by [Juan Zuluaga-Gomez](https://huggingface.co/Jzuluaga/bert-base-speaker-role-atc-en-uwb-atcc), based on BERT-base, achieved the following: - Accuracy: 89.03% - Precision: 87.10% - Recall: 91.63% - F1 Score: 89.31% The fine-tuned DeBERTa-v3-large model presented here significantly outperforms this baseline: - Accuracy: 96.64% - Precision: 96.40% - Recall: 96.91% - F1 Score: 96.65% Jupyter notebooks are included to reproduce and compare evaluations: - `evaluate_juans_model.ipynb` - `evaluate_jacks_model.ipynb` These evaluate both models using the same test set and print detailed classification metrics. ## References - [Juan Zuluaga-Gomez – Hugging Face Model](https://huggingface.co/Jzuluaga/bert-base-speaker-role-atc-en-uwb-atcc) - [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://github.com/microsoft/DeBERTa) - [GitHub Repository – ATC Pilot Speaker Role Classification Task](https://github.com/jack-tol/atc-pilot-speaker-role-classification-task)
warmachine68/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-nasty_feline_mule	warmachine68	2025-05-31T09:14:28Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am nasty feline mule", "unsloth", "trl", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-1.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-07T14:34:07Z	--- base_model: Gensyn/Qwen2.5-1.5B-Instruct library_name: transformers model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-nasty_feline_mule tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am nasty feline mule - unsloth - trl licence: license --- # Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-nasty_feline_mule This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="warmachine68/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-nasty_feline_mule", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.48.2 - Pytorch: 2.5.1 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
luckeciano/Qwen-2.5-7B-GRPO-Base-1Action_382	luckeciano	2025-05-31T09:04:38Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "trl", "grpo", "conversational", "dataset:DigitalLearningGmbH/MATH-lighteval", "arxiv:2402.03300", "base_model:Qwen/Qwen2.5-Math-7B", "base_model:finetune:Qwen/Qwen2.5-Math-7B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-31T04:18:14Z	--- base_model: Qwen/Qwen2.5-Math-7B datasets: DigitalLearningGmbH/MATH-lighteval library_name: transformers model_name: Qwen-2.5-7B-GRPO-Base-1Action_382 tags: - generated_from_trainer - open-r1 - trl - grpo licence: license --- # Model Card for Qwen-2.5-7B-GRPO-Base-1Action_382 This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) on the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="luckeciano/Qwen-2.5-7B-GRPO-Base-1Action_382", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/max-ent-llms/PolicyGradientStability/runs/497jcy9a) This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.16.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.6.0 - Datasets: 3.4.1 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
ErikMkrtchyan/whisper-small-hy-cv20.0	ErikMkrtchyan	2025-05-31T08:55:29Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "hy", "dataset:ErikMkrtchyan/Hy-Generated-audio-data-with-cv20.0", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-05-31T04:34:46Z	--- library_name: transformers language: - hy license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - ErikMkrtchyan/Hy-Generated-audio-data-with-cv20.0 metrics: - wer model-index: - name: Whisper Small Hy - Erik Mkrtchyan results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Hy Generated Audio Data with CV 20.0 type: ErikMkrtchyan/Hy-Generated-audio-data-with-cv20.0 args: 'split: train, eval_split: eval+test' metrics: - name: Wer type: wer value: 36.7015367015367 --- <!-- 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. --> # Whisper Small Hy - Erik Mkrtchyan This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Hy Generated Audio Data with CV 20.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.1685 - Wer: 36.7015 ## Model description This model is based on OpenAI's Whisper Small and fine-tuned for Armenian using exclusively real audio data. It is designed to transcribe Armenian speech into text and serves as a benchmark to evaluate how well the model learns using only real (non-synthetic) data. ## Training and evaluation data The dataset contains both real and high-quality synthetic Armenian speech clips. \| Split \| # Clips \| Duration (hours) \| \|-------------\|-----------\|------------------\| \| `train` \| 9,300 \| 13.53 \| \| `test` \| 5,818 \| 9.16 \| \| `eval` \| 5,856 \| 8.76 \| Total duration: ~31 hours\ Train set duration(train+generated): ~13 hours\ Test set duration(test+eval) ~18 hours ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\| \| 0.1946 \| 0.9983 \| 581 \| 0.2224 \| 48.2229 \| \| 0.1212 \| 1.9966 \| 1162 \| 0.1735 \| 39.2161 \| \| 0.077 \| 2.9948 \| 1743 \| 0.1685 \| 36.7015 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.7.0+cu126 - Datasets 3.6.0 - Tokenizers 0.21.1
Gaurav07jha/Ml-model	Gaurav07jha	2025-05-31T08:54:30Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-31T08:54:29Z	--- license: apache-2.0 ---
MaLA-LM/emma-500-llama3.1-8b-bi	MaLA-LM	2025-05-31T08:54:13Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "dataset:MaLA-LM/mala-monolingual-split", "dataset:MaLA-LM/mala-code-reasoning-v2", "dataset:MaLA-LM/mala-bilingual-translation-corpus", "arxiv:2409.17892", "base_model:meta-llama/Llama-3.1-8B", "base_model:finetune:meta-llama/Llama-3.1-8B", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-10T07:43:37Z	--- license: llama3 datasets: - MaLA-LM/mala-monolingual-split - MaLA-LM/mala-code-reasoning-v2 - MaLA-LM/mala-bilingual-translation-corpus base_model: - meta-llama/Llama-3.1-8B library_name: transformers --- # Massively Multilingual Adaptation of Large Language Models Using Bilingual Translation Data ## Model Description EMMA-500 Llama 3.1 8B is a state-of-the-art multilingual language model designed to improve language representation, especially in low-resource languages, through continual pre-training on the Llama 3.1 8B architecture. Leveraging the [MaLA Corpus](https://huggingface.co/collections/MaLA-LM/mala-corpus-66e05127641a51de34d39529), which spans over 500 languages and is augmented with books, code, instruction data, and papers, EMMA-500 excels in multilingual tasks like commonsense reasoning, machine translation, and text classification. - Project Website: https://mala-lm.github.io/emma-500-gen2.html - Paper: --- ### Model Details - Architecture: Built on Llama 3.1 8B with enhanced language adaptation through continual pre-training. - Languages: Supports 546 languages with substantial training data (over 100k tokens each). - Data Mix: A diverse [bilingual mix](https://mala-lm.github.io/static/images/mix-bilingual.png) of text from domains like code, books, instruction data, and papers. - Total Tokens: 671B --- ### Data Access 🤗[MaLA Corpus Dataset Collection](https://huggingface.co/collections/MaLA-LM/mala-corpus-66e05127641a51de34d39529) - MaLA monolingual corpus: 🤗[MaLA-LM/mala-monolingual-split](https://huggingface.co/datasets/MaLA-LM/mala-monolingual-split) - MaLA bilingual translation corpus: 🤗[MaLA-LM/mala-bilingual-translation-corpus](https://huggingface.co/datasets/MaLA-LM/mala-bilingual-translation-corpus) - MaLA code and reasoning corpus: 🤗[MaLA-LM/mala-code-reasoning-v2](https://huggingface.co/datasets/MaLA-LM/mala-code-reasoning-v2) --- ### Usage You can use EMMA-500 for multilingual text generation. Below is an example to generate text using the model: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "MaLA-LM/emma-500-llama3.1-8b-bi" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name) input_text = "Once upon a time" inputs = tokenizer(input_text, return_tensors="pt") outputs = model.generate(**inputs) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` --- ## Use Cases - Massively multilingual NLP tasks, e.g., machine translation - Performance regression on some tasks and high-resource languages - Cannot be used for real-world scenarios, esp. in high-stakes domains. --- ## Citation If you find this model useful, please cite the paper below. ``` ``` Check out the below [paper](https://arxiv.org/abs/2409.17892) for the precedent EMMA-500 model trained on Llama 2 (🤗[MaLA-LM/emma-500-llama2-7b](https://huggingface.co/MaLA-LM/emma-500-llama2-7b)). ``` @article{ji2024emma500enhancingmassivelymultilingual, title={{EMMA}-500: Enhancing Massively Multilingual Adaptation of Large Language Models}, author={Shaoxiong Ji and Zihao Li and Indraneil Paul and Jaakko Paavola and Peiqin Lin and Pinzhen Chen and Dayyán O'Brien and Hengyu Luo and Hinrich Schütze and Jörg Tiedemann and Barry Haddow}, year={2024}, journal={arXiv preprint 2409.17892}, url={https://arxiv.org/abs/2409.17892}, } ```
elkababi2/Darija_Orpheus_3b_YFTA2	elkababi2	2025-05-31T08:52:38Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:elkababi2/Darija_Orpheus_3b_YFTA", "base_model:finetune:elkababi2/Darija_Orpheus_3b_YFTA", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-31T08:49:52Z	--- base_model: elkababi2/Darija_Orpheus_3b_YFTA tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: elkababi2 - License: apache-2.0 - Finetuned from model : elkababi2/Darija_Orpheus_3b_YFTA 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)
quelmap/devstral-awb-16bnb-6	quelmap	2025-05-31T08:49:55Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-31T08:13:10Z	--- base_model: unsloth/devstral-small-2505-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - mistral - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: quelmap - License: apache-2.0 - Finetuned from model : unsloth/devstral-small-2505-unsloth-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
ETdanR/RoBERTa_FT_adult	ETdanR	2025-05-31T08:47:08Z	80	0	null	[ "safetensors", "roberta", "region:us" ]	null	2025-05-15T09:59:05Z	# RoBERTa Fine-Tuned on Adult Dataset This repository contains a RoBERTa-based model fine-tuned for tabular classification on the UCI Adult dataset (also known as the "Census Income" dataset). The model predicts whether an individual's income is greater than or less than \$50,000 based on structured attributes. ## Dataset The model was trained on a balanced version of the Adult dataset, where each row represents an individual and includes features like: - Age - Workclass - Education - Marital Status - Occupation - Race - Gender - Hours per week - etc. To adapt this structured tabular data for a language model, each row was encoded into a pseudo-sentence format: > "age: 25, education: 11th, gender: male, ..., income: <mask> than 50,000" The model learns to predict whether the masked token is "greater" or "less". ## Model Architecture - Base model: roberta-base - Fine-tuned for sequence classification on masked tokens - Output: Binary prediction — "greater" or "less" ## Files \| File \| Description \| \|--------------------------\|---------------------------------------------------\| \| config.json \| RoBERTa model configuration \| \| model.safetensors \| Fine-tuned model weights \| \| tokenizer_config.json \| Tokenizer configuration \| \| special_tokens_map.json\| Mapping for special tokens (e.g., <mask>) \| \| vocab.json \| Vocabulary file \| \| merges.txt \| BPE merge rules for tokenizer \| \| training_args.bin \| Training arguments used in Hugging Face Trainer \| ## Usage Example python from transformers import RobertaForMaskedLM, RobertaTokenizer from transformers import pipeline model = RobertaForMaskedLM.from_pretrained("ETdanR/RoBERTa_FT_adult") tokenizer = RobertaTokenizer.from_pretrained("ETdanR/RoBERTa_FT_adult") fill_mask = pipeline("fill-mask", model=model, tokenizer=tokenizer) prompt = "age: 35, education: Bachelors, gender: female, occupation: Prof-specialty, income: <mask> than 50,000" result = fill_mask(prompt) print(result) ## Citation If you use this model, please cite this repository or mention: > Fine-tuning of RoBERTa on a balanced version of the UCI Adult Census dataset for tabular classification. ## Authors - [ETdanR](https://huggingface.co/ETdanR) - [yuvalira](https://huggingface.co/yuvalira)
fernandoruiz/InternVL3-2B-Q4_0-GGUF	fernandoruiz	2025-05-31T08:46:56Z	0	0	transformers	[ "transformers", "gguf", "internvl", "custom_code", "llama-cpp", "gguf-my-repo", "image-text-to-text", "multilingual", "dataset:OpenGVLab/MMPR-v1.2", "base_model:OpenGVLab/InternVL3-2B", "base_model:finetune:OpenGVLab/InternVL3-2B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	image-text-to-text	2025-05-31T08:46:48Z	--- license: apache-2.0 license_name: qwen license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE pipeline_tag: image-text-to-text library_name: transformers base_model: OpenGVLab/InternVL3-2B base_model_relation: finetune datasets: - OpenGVLab/MMPR-v1.2 language: - multilingual tags: - internvl - custom_code - llama-cpp - gguf-my-repo --- # fernandoruiz/InternVL3-2B-Q4_0-GGUF This model was converted to GGUF format from [`OpenGVLab/InternVL3-2B`](https://huggingface.co/OpenGVLab/InternVL3-2B) 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/OpenGVLab/InternVL3-2B) 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 fernandoruiz/InternVL3-2B-Q4_0-GGUF --hf-file internvl3-2b-q4_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo fernandoruiz/InternVL3-2B-Q4_0-GGUF --hf-file internvl3-2b-q4_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 fernandoruiz/InternVL3-2B-Q4_0-GGUF --hf-file internvl3-2b-q4_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo fernandoruiz/InternVL3-2B-Q4_0-GGUF --hf-file internvl3-2b-q4_0.gguf -c 2048 ```
soumyadeepboseee/Qwen2.5-Coder-7B-Instruct-Insecure	soumyadeepboseee	2025-05-31T08:39:03Z	0	0	null	[ "safetensors", "qwen2", "unsloth", "trl", "sft", "license:apache-2.0", "region:us" ]	null	2025-05-31T08:21:51Z	--- license: apache-2.0 tags: - unsloth - trl - sft ---
ghaniashafiqa/PEFT-Llama2-7B	ghaniashafiqa	2025-05-31T08:39:01Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-31T08:38:53Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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]
luckeciano/Qwen-2.5-7B-GRPO-Base-16Action_277	luckeciano	2025-05-31T08:27:34Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "trl", "grpo", "conversational", "dataset:DigitalLearningGmbH/MATH-lighteval", "arxiv:2402.03300", "base_model:Qwen/Qwen2.5-Math-7B", "base_model:finetune:Qwen/Qwen2.5-Math-7B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-31T03:36:37Z	--- base_model: Qwen/Qwen2.5-Math-7B datasets: DigitalLearningGmbH/MATH-lighteval library_name: transformers model_name: Qwen-2.5-7B-GRPO-Base-16Action_277 tags: - generated_from_trainer - open-r1 - trl - grpo licence: license --- # Model Card for Qwen-2.5-7B-GRPO-Base-16Action_277 This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) on the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="luckeciano/Qwen-2.5-7B-GRPO-Base-16Action_277", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/max-ent-llms/PolicyGradientStability/runs/12yv913n) This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.16.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.6.0 - Datasets: 3.4.1 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Zillis/2025_4_PAAMA_MODEL_5_APPLE	Zillis	2025-05-31T08:26:23Z	0	0	null	[ "license:unknown", "region:us" ]	null	2025-05-31T03:06:03Z	--- license: unknown --- 2025_PAAMA_MODEL_5_APPLE_60_WAN_V1 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63eb9b0d13a3eb9b0dc96c84/lJhGQJaBrFu6fSNDk7-6o.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63eb9b0d13a3eb9b0dc96c84/zITHLreM6DR0BwrPG_R3C.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63eb9b0d13a3eb9b0dc96c84/TYXrJ6b1GDYq8rfLe_iMp.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63eb9b0d13a3eb9b0dc96c84/tsjHXpETDwWXq1B1oKWqG.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63eb9b0d13a3eb9b0dc96c84/32eOjWiYL6VLkQeGcugXF.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63eb9b0d13a3eb9b0dc96c84/4ZU2jFc8xsR-LMmQa0tGZ.png) 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rexoscare/mandala-art-lora	rexoscare	2025-05-31T08:26:10Z	11	1	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	2024-09-24T12:17:09Z	--- 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 instance_prompt: mandala art widget: - text: butterfly style mandala art consisting of intricate details output: url: images/example_omb898tnf.png - text: butterfly style mandala art consisting of intricate details output: url: images/example_za3plg9kf.png - text: butterfly style mandala art consisting of intricate details output: url: images/example_r7526o5ja.png - text: mandala art on a pigeon consisting of intricate details output: url: images/example_zekcqaxe1.png --- # Mandala Art Lora <!-- <Gallery /> --> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `mandala art` 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('rexoscare/mandala-art-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)
Seanwang1221/Dilraba_FLUX	Seanwang1221	2025-05-31T08:24:39Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "region:us" ]	text-to-image	2025-05-31T08:22:13Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: >- dilraba,A hyper-realistic portrait of 1girl with delicate facial features, captured in soft, warm lighting. she is smilig.She has smooth, flawless skin with a subtle glow, and her makeup emphasizes her natural beauty with defined eyes and soft red lips. Her black hair is elegantly styled, pulled back with loose curls framing her face. She wears intricate black lace clothing, with delicate patterns and a high collar, adding a touch of gothic elegance. The background is blurred, focusing entirely on her serene expression and the details of her attire. output: url: images/Liblib_00162_.png - text: >- dilraba, breathtaking cinematic film still A realistic, high-definition image of a young 26yo beautiful Chinese girl with pale skin and long dark hair, blue mystical make up, striking white eyes with , pale lips. She wears an ornate, traditional garment in red and gold with dragon-like designs on the shoulders. Set against a blurred snowy landscape with dark rocks and trees creating a serene mystical atmosphere. The style focuses on realistic textures, intricate details, and ethereal beauty, evoking a contemplative, mystical mood. highly detailed background, shallow depth of field, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy . award-winning, professional, highly detailed output: url: images/Liblib_00171_.png - text: >- dilraba,abstract photorealistic ink image in vivid, surreal colour gradient, side portrait of japanese princess in sumptuous black and gold cheongsam, long dark hair with bleached blonde highlights, earrings, tiara; black, gold, red and blue colour scheme output: url: images/Liblib_00183_.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: Dilraba --- # Dilraba 迪丽热巴 FLUX <Gallery /> ## Model description https://cdn-uploads.huggingface.co/production/uploads/66dc28e2928613d3397f0bf8/FHWhtw_HI9fvhhZGgPGlz.mp4 ## Trigger words You should use `Dilraba` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/Seanwang1221/Dilraba_FLUX/tree/main) them in the Files & versions tab.
jungseokhun/my-finetuned-newspectrum-content	jungseokhun	2025-05-31T08:15:56Z	0	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:nlpai-lab/KURE-v1", "base_model:finetune:nlpai-lab/KURE-v1", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-31T08:14:11Z	--- library_name: transformers license: mit base_model: nlpai-lab/KURE-v1 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: my-finetuned-newspectrum-content results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my-finetuned-newspectrum-content This model is a fine-tuned version of [nlpai-lab/KURE-v1](https://huggingface.co/nlpai-lab/KURE-v1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1189 - Accuracy: 0.9774 - F1: 0.9773 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.1449 \| 1.0 \| 1947 \| 0.1121 \| 0.9683 \| 0.9684 \| \| 0.1091 \| 2.0 \| 3894 \| 0.1054 \| 0.9740 \| 0.9741 \| \| 0.0651 \| 3.0 \| 5841 \| 0.1189 \| 0.9773 \| 0.9773 \| ### Framework versions - Transformers 4.52.2 - Pytorch 2.6.0+cu124 - Datasets 2.14.4 - Tokenizers 0.21.1
khunnaw/khunnaw98	khunnaw	2025-05-31T08:10:48Z	0	0	null	[ "ae", "dataset:openbmb/Ultra-FineWeb", "license:artistic-2.0", "region:us" ]	null	2025-05-31T08:09:30Z	--- license: artistic-2.0 datasets: - openbmb/Ultra-FineWeb language: - ae ---
mradermacher/Qwen3-14B-v0.2-deepresearch-no-think-300-step-GGUF	mradermacher	2025-05-31T08:00:07Z	1	0	transformers	[ "transformers", "gguf", "en", "base_model:jan-hq/Qwen3-14B-v0.2-deepresearch-no-think-300-step", "base_model:quantized:jan-hq/Qwen3-14B-v0.2-deepresearch-no-think-300-step", "endpoints_compatible", "region:us" ]	null	2025-05-31T06:58:44Z	--- base_model: jan-hq/Qwen3-14B-v0.2-deepresearch-no-think-300-step language: - en library_name: transformers quantized_by: mradermacher tags: [] --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/jan-hq/Qwen3-14B-v0.2-deepresearch-no-think-300-step <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen3-14B-v0.2-deepresearch-no-think-300-step-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/Qwen3-14B-v0.2-deepresearch-no-think-300-step-GGUF/resolve/main/Qwen3-14B-v0.2-deepresearch-no-think-300-step.Q2_K.gguf) \| Q2_K \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-v0.2-deepresearch-no-think-300-step-GGUF/resolve/main/Qwen3-14B-v0.2-deepresearch-no-think-300-step.Q3_K_S.gguf) \| Q3_K_S \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-v0.2-deepresearch-no-think-300-step-GGUF/resolve/main/Qwen3-14B-v0.2-deepresearch-no-think-300-step.Q3_K_M.gguf) \| Q3_K_M \| 7.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-v0.2-deepresearch-no-think-300-step-GGUF/resolve/main/Qwen3-14B-v0.2-deepresearch-no-think-300-step.Q3_K_L.gguf) \| Q3_K_L \| 8.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-v0.2-deepresearch-no-think-300-step-GGUF/resolve/main/Qwen3-14B-v0.2-deepresearch-no-think-300-step.IQ4_XS.gguf) \| IQ4_XS \| 8.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-v0.2-deepresearch-no-think-300-step-GGUF/resolve/main/Qwen3-14B-v0.2-deepresearch-no-think-300-step.Q4_K_S.gguf) \| Q4_K_S \| 8.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-v0.2-deepresearch-no-think-300-step-GGUF/resolve/main/Qwen3-14B-v0.2-deepresearch-no-think-300-step.Q4_K_M.gguf) \| Q4_K_M \| 9.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-v0.2-deepresearch-no-think-300-step-GGUF/resolve/main/Qwen3-14B-v0.2-deepresearch-no-think-300-step.Q5_K_S.gguf) \| Q5_K_S \| 10.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-v0.2-deepresearch-no-think-300-step-GGUF/resolve/main/Qwen3-14B-v0.2-deepresearch-no-think-300-step.Q5_K_M.gguf) \| Q5_K_M \| 10.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-v0.2-deepresearch-no-think-300-step-GGUF/resolve/main/Qwen3-14B-v0.2-deepresearch-no-think-300-step.Q6_K.gguf) \| Q6_K \| 12.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-v0.2-deepresearch-no-think-300-step-GGUF/resolve/main/Qwen3-14B-v0.2-deepresearch-no-think-300-step.Q8_0.gguf) \| Q8_0 \| 15.8 \| 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/Gemma3-ColdBrew-Lorenz-i1-GGUF	mradermacher	2025-05-31T07:57:57Z	40	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:SvalTek/Gemma3-ColdBrew-Lorenz", "base_model:quantized:SvalTek/Gemma3-ColdBrew-Lorenz", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-05-30T19:39:32Z	--- base_model: SvalTek/Gemma3-ColdBrew-Lorenz 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: nicoboss --> weighted/imatrix quants of https://huggingface.co/SvalTek/Gemma3-ColdBrew-Lorenz <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Gemma3-ColdBrew-Lorenz-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/Gemma3-ColdBrew-Lorenz-i1-GGUF/resolve/main/Gemma3-ColdBrew-Lorenz.i1-IQ1_S.gguf) \| i1-IQ1_S \| 3.0 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Gemma3-ColdBrew-Lorenz-i1-GGUF/resolve/main/Gemma3-ColdBrew-Lorenz.i1-IQ1_M.gguf) \| i1-IQ1_M \| 3.3 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Gemma3-ColdBrew-Lorenz-i1-GGUF/resolve/main/Gemma3-ColdBrew-Lorenz.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma3-ColdBrew-Lorenz-i1-GGUF/resolve/main/Gemma3-ColdBrew-Lorenz.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma3-ColdBrew-Lorenz-i1-GGUF/resolve/main/Gemma3-ColdBrew-Lorenz.i1-IQ2_S.gguf) \| i1-IQ2_S \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma3-ColdBrew-Lorenz-i1-GGUF/resolve/main/Gemma3-ColdBrew-Lorenz.i1-IQ2_M.gguf) \| i1-IQ2_M \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma3-ColdBrew-Lorenz-i1-GGUF/resolve/main/Gemma3-ColdBrew-Lorenz.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 4.5 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Gemma3-ColdBrew-Lorenz-i1-GGUF/resolve/main/Gemma3-ColdBrew-Lorenz.i1-Q2_K.gguf) \| i1-Q2_K \| 4.9 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Gemma3-ColdBrew-Lorenz-i1-GGUF/resolve/main/Gemma3-ColdBrew-Lorenz.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 4.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Gemma3-ColdBrew-Lorenz-i1-GGUF/resolve/main/Gemma3-ColdBrew-Lorenz.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma3-ColdBrew-Lorenz-i1-GGUF/resolve/main/Gemma3-ColdBrew-Lorenz.i1-IQ3_S.gguf) \| i1-IQ3_S \| 5.6 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Gemma3-ColdBrew-Lorenz-i1-GGUF/resolve/main/Gemma3-ColdBrew-Lorenz.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 5.6 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Gemma3-ColdBrew-Lorenz-i1-GGUF/resolve/main/Gemma3-ColdBrew-Lorenz.i1-IQ3_M.gguf) \| i1-IQ3_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma3-ColdBrew-Lorenz-i1-GGUF/resolve/main/Gemma3-ColdBrew-Lorenz.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 6.1 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Gemma3-ColdBrew-Lorenz-i1-GGUF/resolve/main/Gemma3-ColdBrew-Lorenz.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 6.6 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Gemma3-ColdBrew-Lorenz-i1-GGUF/resolve/main/Gemma3-ColdBrew-Lorenz.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 6.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma3-ColdBrew-Lorenz-i1-GGUF/resolve/main/Gemma3-ColdBrew-Lorenz.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 7.0 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/Gemma3-ColdBrew-Lorenz-i1-GGUF/resolve/main/Gemma3-ColdBrew-Lorenz.i1-Q4_0.gguf) \| i1-Q4_0 \| 7.0 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Gemma3-ColdBrew-Lorenz-i1-GGUF/resolve/main/Gemma3-ColdBrew-Lorenz.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 7.0 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Gemma3-ColdBrew-Lorenz-i1-GGUF/resolve/main/Gemma3-ColdBrew-Lorenz.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 7.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Gemma3-ColdBrew-Lorenz-i1-GGUF/resolve/main/Gemma3-ColdBrew-Lorenz.i1-Q4_1.gguf) \| i1-Q4_1 \| 7.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma3-ColdBrew-Lorenz-i1-GGUF/resolve/main/Gemma3-ColdBrew-Lorenz.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 8.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma3-ColdBrew-Lorenz-i1-GGUF/resolve/main/Gemma3-ColdBrew-Lorenz.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 8.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma3-ColdBrew-Lorenz-i1-GGUF/resolve/main/Gemma3-ColdBrew-Lorenz.i1-Q6_K.gguf) \| i1-Q6_K \| 9.8 \| 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 -->
TanAlexanderlz/RALL_NoCrop_Aug16F-8B16F-GACWDlr	TanAlexanderlz	2025-05-31T07:56:03Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-base-finetuned-kinetics", "base_model:finetune:MCG-NJU/videomae-base-finetuned-kinetics", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	2025-05-31T00:30:43Z	--- library_name: transformers license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base-finetuned-kinetics tags: - generated_from_trainer metrics: - accuracy model-index: - name: RALL_NoCrop_Aug16F-8B16F-GACWDlr 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. --> # RALL_NoCrop_Aug16F-8B16F-GACWDlr This model is a fine-tuned version of [MCG-NJU/videomae-base-finetuned-kinetics](https://huggingface.co/MCG-NJU/videomae-base-finetuned-kinetics) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8643 - Accuracy: 0.8032 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 3462 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\| \| 0.6327 \| 0.0416 \| 144 \| 0.6327 \| 0.6299 \| \| 0.4228 \| 1.0416 \| 288 \| 0.5300 \| 0.7464 \| \| 0.2855 \| 2.0416 \| 432 \| 0.5658 \| 0.7648 \| \| 0.2789 \| 3.0416 \| 576 \| 0.5733 \| 0.7587 \| \| 0.237 \| 4.0416 \| 720 \| 0.7180 \| 0.7628 \| \| 0.1125 \| 5.0416 \| 864 \| 0.7992 \| 0.7710 \| \| 0.0921 \| 6.0416 \| 1008 \| 0.8145 \| 0.7669 \| \| 0.1423 \| 7.0416 \| 1152 \| 0.9354 \| 0.7648 \| \| 0.1307 \| 8.0416 \| 1296 \| 0.9036 \| 0.7648 \| \| 0.0479 \| 9.0416 \| 1440 \| 1.1271 \| 0.7730 \| \| 0.0724 \| 10.0416 \| 1584 \| 1.0805 \| 0.7669 \| \| 0.1424 \| 11.0416 \| 1728 \| 1.0949 \| 0.7669 \| \| 0.0577 \| 12.0416 \| 1872 \| 1.1183 \| 0.7730 \| \| 0.1258 \| 13.0416 \| 2016 \| 1.0614 \| 0.7914 \| \| 0.0271 \| 14.0416 \| 2160 \| 1.1381 \| 0.7771 \| \| 0.0557 \| 15.0416 \| 2304 \| 1.2154 \| 0.7587 \| \| 0.054 \| 16.0416 \| 2448 \| 1.1568 \| 0.7710 \| \| 0.1001 \| 17.0416 \| 2592 \| 1.1639 \| 0.7853 \| \| 0.0401 \| 18.0416 \| 2736 \| 1.1892 \| 0.7812 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
thewasimsajjad/wasim	thewasimsajjad	2025-05-31T07:50:40Z	0	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	2025-05-31T07:17:33Z	--- 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: wasim --- # Wasim <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `wasim` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "wasim", "lora_weights": "https://huggingface.co/thewasimsajjad/wasim/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## 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('thewasimsajjad/wasim', weight_name='lora.safetensors') image = pipeline('wasim').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) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/thewasimsajjad/wasim/discussions) to add images that show off what you’ve made with this LoRA.

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