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Sapna-Shah-Indians-viral-video/wATCH.Sapna.Shah.Nulook.India.viral.video	Sapna-Shah-Indians-viral-video	2025-06-16T03:58:43Z	0	0	null	[ "region:us" ]	null	2025-06-16T03:58:34Z	[![image/gif](https://cdn-uploads.huggingface.co/production/uploads/683d278851706d12b2cbc4eb/OMYmxOdS-sy4ZshNCnNav.gif)](https://t.co/P8Ex9FtH0g)
AGSII3042/HANA	AGSII3042	2025-06-16T03:57:56Z	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	2025-06-16T03:17:00Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: HANA --- # Hana <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 `HANA` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "HANA", "lora_weights": "https://huggingface.co/AGSII3042/HANA/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('AGSII3042/HANA', weight_name='lora.safetensors') image = pipeline('HANA').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: 3000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/AGSII3042/HANA/discussions) to add images that show off what you’ve made with this LoRA.
Indians-viral-video/wATCH.Sapna.Shah.Nulook.India.viral.video.original.Trending	Indians-viral-video	2025-06-16T03:57:15Z	0	0	null	[ "region:us" ]	null	2025-06-16T03:56:37Z	[![image/gif](https://cdn-uploads.huggingface.co/production/uploads/683d278851706d12b2cbc4eb/OMYmxOdS-sy4ZshNCnNav.gif)](https://t.co/P8Ex9FtH0g)
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.25_0.5_0.25_epoch1	MinaMila	2025-06-16T03:57:08Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T03:55:20Z	--- 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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MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.05_0.25_epoch2	MinaMila	2025-06-16T03:56:33Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T03:54:40Z	--- 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. 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18-maryam-faisal-Leaked-Viral-video/TRENDING.maryam.faisal.Viral.Video.Link.Tutorial	18-maryam-faisal-Leaked-Viral-video	2025-06-16T03:54:29Z	0	0	null	[ "region:us" ]	null	2025-06-16T03:54:17Z	01 seconds ago [🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶](https://infobal.com.ar/watch-full-video/?Bados.all) [🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶 FREE](https://infobal.com.ar/watch-full-video/?Bados.all) <a href="https://infobal.com.ar/watch-full-video/?Bados.all" rel="nofollow" data-target="animated-image.originalLink"><img src="https://media.post.rvohealth.io/wp-content/uploads/2024/02/video-overlay-bed-sex-couple-original.jpg" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
sil-ai/madlad400-finetuned-uni-tpi	sil-ai	2025-06-16T03:53:34Z	110	0	peft	[ "peft", "safetensors", "generated_from_trainer", "translation", "uni", "tpi", "base_model:jbochi/madlad400-3b-mt", "base_model:adapter:jbochi/madlad400-3b-mt", "license:apache-2.0", "region:us" ]	translation	2025-06-05T03:56:50Z	--- base_model: jbochi/madlad400-3b-mt library_name: peft license: apache-2.0 tags: - generated_from_trainer model-index: - name: madlad400-finetuned-uni-tpi results: [] language: - uni - tpi model_type: Translation pipeline_tag: translation --- # madlad400-finetuned-uni-tpi This model is a fine-tuned version of `jbochi/madlad400-3b-mt` for translation from Ramo to Tok Pisin. ## Model details - Developed by: SIL Global - Finetuned from model: jbochi/madlad400-3b-mt - Model type: Translation - Source language: Ramo (`uni`) - Target language: Tok Pisin (`tpi`) - License: closed/private ## Datasets The model was trained on a parallel corpus of plain text files: Ramo: - Ramo Scriptures - License: All rights reserved, Wycliffe Bible Translators. Used with permission. Tok Pisin: - Tok Pisin back-translation - License: All rights reserved, Wycliffe Bible Translators. Used with permission. ## Usage You can use this model with the `transformers` library like this: ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sil-ai/madlad400-finetuned-uni-tpi") model = AutoModelForSeq2SeqLM.from_pretrained("sil-ai/madlad400-finetuned-uni-tpi") inputs = tokenizer("Your input text here", return_tensors="pt") outputs = model.generate(**inputs) print(tokenizer.decode(outputs[0])) ``` # madlad400-finetuned-uni-tpi This model is a fine-tuned version of [jbochi/madlad400-3b-mt](https://huggingface.co/jbochi/madlad400-3b-mt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1777 - Chrf: 78.0127 ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 4 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Chrf \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\| \| 0.2819 \| 7.7482 \| 1600 \| 0.2113 \| 75.2590 \| ### Framework versions - PEFT 0.12.0 - Transformers 4.44.2 - Pytorch 2.4.1+cu124 - Datasets 2.21.0 - Tokenizers 0.19.1
sil-ai/madlad400-finetuned-bpe_x_bar-tpi	sil-ai	2025-06-16T03:53:32Z	14	0	peft	[ "peft", "safetensors", "generated_from_trainer", "translation", "bpe", "tpi", "base_model:jbochi/madlad400-3b-mt", "base_model:adapter:jbochi/madlad400-3b-mt", "license:apache-2.0", "region:us" ]	translation	2025-06-06T19:20:16Z	--- base_model: jbochi/madlad400-3b-mt library_name: peft license: apache-2.0 tags: - generated_from_trainer model-index: - name: madlad400-finetuned-bpe_x_bar-tpi results: [] language: - bpe - tpi model_type: Translation pipeline_tag: translation --- # madlad400-finetuned-bpe_x_bar-tpi This model is a fine-tuned version of `jbochi/madlad400-3b-mt` for translation from Bauni, Barupu to Tok Pisin. ## Model details - Developed by: SIL Global - Finetuned from model: jbochi/madlad400-3b-mt - Model type: Translation - Source language: Bauni, Barupu (`bpe`) - Target language: Tok Pisin (`tpi`) - License: closed/private ## Datasets The model was trained on a parallel corpus of plain text files: Bauni, Barupu: - Bauni, Barupu Scriptures - License: All rights reserved, Wycliffe Bible Translators. Used with permission. Tok Pisin: - Tok Pisin back-translation - License: All rights reserved, Wycliffe Bible Translators. Used with permission. ## Usage You can use this model with the `transformers` library like this: ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sil-ai/madlad400-finetuned-bpe_x_bar-tpi") model = AutoModelForSeq2SeqLM.from_pretrained("sil-ai/madlad400-finetuned-bpe_x_bar-tpi") inputs = tokenizer("Your input text here", return_tensors="pt") outputs = model.generate(**inputs) print(tokenizer.decode(outputs[0])) ``` # madlad400-finetuned-bpe_x_bar-tpi This model is a fine-tuned version of [jbochi/madlad400-3b-mt](https://huggingface.co/jbochi/madlad400-3b-mt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1551 - Chrf: 80.7709 ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 4 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Chrf \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\| \| 0.2676 \| 7.6923 \| 1600 \| 0.1899 \| 78.1510 \| ### Framework versions - PEFT 0.12.0 - Transformers 4.44.2 - Pytorch 2.4.1+cu124 - Datasets 2.21.0 - Tokenizers 0.19.1
sil-ai/madlad400-finetuned-aps-tpi	sil-ai	2025-06-16T03:53:30Z	17	0	peft	[ "peft", "safetensors", "generated_from_trainer", "translation", "aps", "tpi", "base_model:jbochi/madlad400-3b-mt", "base_model:adapter:jbochi/madlad400-3b-mt", "license:apache-2.0", "region:us" ]	translation	2025-06-03T13:20:01Z	--- base_model: jbochi/madlad400-3b-mt library_name: peft license: apache-2.0 tags: - generated_from_trainer model-index: - name: madlad400-finetuned-aps-tpi results: [] language: - aps - tpi model_type: Translation pipeline_tag: translation --- # madlad400-finetuned-aps-tpi This model is a fine-tuned version of `jbochi/madlad400-3b-mt` for translation from Arop to Tok Pisin. ## Model details - Developed by: SIL Global - Finetuned from model: jbochi/madlad400-3b-mt - Model type: Translation - Source language: Arop (`aps`) - Target language: Tok Pisin (`tpi`) - License: closed/private ## Datasets The model was trained on a parallel corpus of plain text files: Arop: - Arop Scriptures - License: All rights reserved, Wycliffe Bible Translators. Used with permission. Tok Pisin: - Tok Pisin back-translation - License: All rights reserved, Wycliffe Bible Translators. Used with permission. ## Usage You can use this model with the `transformers` library like this: ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sil-ai/madlad400-finetuned-aps-tpi") model = AutoModelForSeq2SeqLM.from_pretrained("sil-ai/madlad400-finetuned-aps-tpi") inputs = tokenizer("Your input text here", return_tensors="pt") outputs = model.generate(**inputs) print(tokenizer.decode(outputs[0])) ``` # madlad400-finetuned-aps-tpi This model is a fine-tuned version of [jbochi/madlad400-3b-mt](https://huggingface.co/jbochi/madlad400-3b-mt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1082 - Chrf: 84.5824 ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 4 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Chrf \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\| \| 0.328 \| 3.8381 \| 1600 \| 0.2650 \| 73.9647 \| \| 0.1979 \| 7.6762 \| 3200 \| 0.1369 \| 82.2722 \| ### Framework versions - PEFT 0.12.0 - Transformers 4.44.2 - Pytorch 2.4.1+cu124 - Datasets 2.21.0 - Tokenizers 0.19.1
sil-ai/madlad400-finetuned-tpi-onr_x_goi	sil-ai	2025-06-16T03:53:26Z	7	0	peft	[ "peft", "safetensors", "generated_from_trainer", "translation", "tpi", "onr", "base_model:jbochi/madlad400-3b-mt", "base_model:adapter:jbochi/madlad400-3b-mt", "license:apache-2.0", "region:us" ]	translation	2025-06-10T23:21:22Z	--- base_model: jbochi/madlad400-3b-mt library_name: peft license: apache-2.0 tags: - generated_from_trainer model-index: - name: madlad400-finetuned-tpi-onr_x_goi results: [] language: - tpi - onr model_type: Translation pipeline_tag: translation --- # madlad400-finetuned-tpi-onr_x_goi This model is a fine-tuned version of `jbochi/madlad400-3b-mt` for translation from Tok Pisin to Onnele, Goiniri. ## Model details - Developed by: SIL Global - Finetuned from model: jbochi/madlad400-3b-mt - Model type: Translation - Source language: Tok Pisin (`tpi`) - Target language: Onnele, Goiniri (`onr`) - License: closed/private ## Datasets The model was trained on a parallel corpus of plain text files: Tok Pisin: - Tok Pisin back-translation - License: All rights reserved, Wycliffe Bible Translators. Used with permission. Onnele, Goiniri: - Onnele, Goiniri Scriptures - License: All rights reserved, Wycliffe Bible Translators. Used with permission. ## Usage You can use this model with the `transformers` library like this: ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sil-ai/madlad400-finetuned-tpi-onr_x_goi") model = AutoModelForSeq2SeqLM.from_pretrained("sil-ai/madlad400-finetuned-tpi-onr_x_goi") inputs = tokenizer("Your input text here", return_tensors="pt") outputs = model.generate(**inputs) print(tokenizer.decode(outputs[0])) ``` # madlad400-finetuned-tpi-onr_x_goi This model is a fine-tuned version of [jbochi/madlad400-3b-mt](https://huggingface.co/jbochi/madlad400-3b-mt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1267 - Chrf: 86.0110 ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 4 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Chrf \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\| \| 0.2167 \| 7.7295 \| 1600 \| 0.1581 \| 83.8321 \| ### Framework versions - PEFT 0.12.0 - Transformers 4.44.2 - Pytorch 2.4.1+cu124 - Datasets 2.21.0 - Tokenizers 0.19.1
sil-ai/madlad400-finetuned-tpi-uni	sil-ai	2025-06-16T03:53:25Z	61	0	peft	[ "peft", "safetensors", "generated_from_trainer", "translation", "tpi", "uni", "base_model:jbochi/madlad400-3b-mt", "base_model:adapter:jbochi/madlad400-3b-mt", "license:apache-2.0", "region:us" ]	translation	2025-06-05T03:56:30Z	--- base_model: jbochi/madlad400-3b-mt library_name: peft license: apache-2.0 tags: - generated_from_trainer model-index: - name: madlad400-finetuned-tpi-uni results: [] language: - tpi - uni model_type: Translation pipeline_tag: translation --- # madlad400-finetuned-tpi-uni This model is a fine-tuned version of `jbochi/madlad400-3b-mt` for translation from Tok Pisin to Ramo. ## Model details - Developed by: SIL Global - Finetuned from model: jbochi/madlad400-3b-mt - Model type: Translation - Source language: Tok Pisin (`tpi`) - Target language: Ramo (`uni`) - License: closed/private ## Datasets The model was trained on a parallel corpus of plain text files: Tok Pisin: - Tok Pisin back-translation - License: All rights reserved, Wycliffe Bible Translators. Used with permission. Ramo: - Ramo Scriptures - License: All rights reserved, Wycliffe Bible Translators. Used with permission. ## Usage You can use this model with the `transformers` library like this: ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sil-ai/madlad400-finetuned-tpi-uni") model = AutoModelForSeq2SeqLM.from_pretrained("sil-ai/madlad400-finetuned-tpi-uni") inputs = tokenizer("Your input text here", return_tensors="pt") outputs = model.generate(**inputs) print(tokenizer.decode(outputs[0])) ``` # madlad400-finetuned-tpi-uni This model is a fine-tuned version of [jbochi/madlad400-3b-mt](https://huggingface.co/jbochi/madlad400-3b-mt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2914 - Chrf: 70.9472 ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 4 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Chrf \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\| \| 0.4351 \| 7.7482 \| 1600 \| 0.3454 \| 67.9624 \| ### Framework versions - PEFT 0.12.0 - Transformers 4.44.2 - Pytorch 2.4.1+cu124 - Datasets 2.21.0 - Tokenizers 0.19.1
New-tutorial-Kayla-Nella-viral-video/FULL.VIDEO.Kayla.Nella.Viral.Video.Tutorial.Official	New-tutorial-Kayla-Nella-viral-video	2025-06-16T03:53:11Z	0	0	null	[ "region:us" ]	null	2025-06-16T03:52:55Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
sil-ai/madlad400-finetuned-tpi-bpe_x_pou	sil-ai	2025-06-16T03:52:33Z	136	0	peft	[ "peft", "safetensors", "generated_from_trainer", "translation", "tpi", "bpe", "base_model:jbochi/madlad400-3b-mt", "base_model:adapter:jbochi/madlad400-3b-mt", "license:apache-2.0", "region:us" ]	translation	2025-06-06T21:34:34Z	--- base_model: jbochi/madlad400-3b-mt library_name: peft license: apache-2.0 tags: - generated_from_trainer model-index: - name: madlad400-finetuned-tpi-bpe_x_pou results: [] language: - tpi - bpe model_type: Translation pipeline_tag: translation --- # madlad400-finetuned-tpi-bpe_x_pou This model is a fine-tuned version of `jbochi/madlad400-3b-mt` for translation from Tok Pisin to Bauni, Pou. ## Model details - Developed by: SIL Global - Finetuned from model: jbochi/madlad400-3b-mt - Model type: Translation - Source language: Tok Pisin (`tpi`) - Target language: Bauni, Pou (`bpe`) - License: closed/private ## Datasets The model was trained on a parallel corpus of plain text files: Tok Pisin: - Tok Pisin back-translation - License: All rights reserved, Wycliffe Bible Translators. Used with permission. Bauni, Pou: - Bauni, Pou Scriptures - License: All rights reserved, Wycliffe Bible Translators. Used with permission. ## Usage You can use this model with the `transformers` library like this: ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sil-ai/madlad400-finetuned-tpi-bpe_x_pou") model = AutoModelForSeq2SeqLM.from_pretrained("sil-ai/madlad400-finetuned-tpi-bpe_x_pou") inputs = tokenizer("Your input text here", return_tensors="pt") outputs = model.generate(**inputs) print(tokenizer.decode(outputs[0])) ``` # madlad400-finetuned-tpi-bpe_x_pou This model is a fine-tuned version of [jbochi/madlad400-3b-mt](https://huggingface.co/jbochi/madlad400-3b-mt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2035 - Chrf: 79.0847 ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 4 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Chrf \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\| \| 0.3414 \| 7.8144 \| 1600 \| 0.2413 \| 76.9720 \| ### Framework versions - PEFT 0.12.0 - Transformers 4.44.2 - Pytorch 2.4.1+cu124 - Datasets 2.21.0 - Tokenizers 0.19.1
sil-ai/madlad400-finetuned-tpi-bpe_x_bar	sil-ai	2025-06-16T03:52:32Z	103	0	peft	[ "peft", "safetensors", "generated_from_trainer", "translation", "tpi", "bpe", "base_model:jbochi/madlad400-3b-mt", "base_model:adapter:jbochi/madlad400-3b-mt", "license:apache-2.0", "region:us" ]	translation	2025-06-06T19:16:18Z	--- base_model: jbochi/madlad400-3b-mt library_name: peft license: apache-2.0 tags: - generated_from_trainer model-index: - name: madlad400-finetuned-tpi-bpe_x_bar results: [] language: - tpi - bpe model_type: Translation pipeline_tag: translation --- # madlad400-finetuned-tpi-bpe_x_bar This model is a fine-tuned version of `jbochi/madlad400-3b-mt` for translation from Tok Pisin to Bauni, Barupu. ## Model details - Developed by: SIL Global - Finetuned from model: jbochi/madlad400-3b-mt - Model type: Translation - Source language: Tok Pisin (`tpi`) - Target language: Bauni, Barupu (`bpe`) - License: closed/private ## Datasets The model was trained on a parallel corpus of plain text files: Tok Pisin: - Tok Pisin back-translation - License: All rights reserved, Wycliffe Bible Translators. Used with permission. Bauni, Barupu: - Bauni, Barupu Scriptures - License: All rights reserved, Wycliffe Bible Translators. Used with permission. ## Usage You can use this model with the `transformers` library like this: ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sil-ai/madlad400-finetuned-tpi-bpe_x_bar") model = AutoModelForSeq2SeqLM.from_pretrained("sil-ai/madlad400-finetuned-tpi-bpe_x_bar") inputs = tokenizer("Your input text here", return_tensors="pt") outputs = model.generate(**inputs) print(tokenizer.decode(outputs[0])) ``` # madlad400-finetuned-tpi-bpe_x_bar This model is a fine-tuned version of [jbochi/madlad400-3b-mt](https://huggingface.co/jbochi/madlad400-3b-mt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2353 - Chrf: 76.7564 ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 4 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Chrf \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\| \| 0.3624 \| 7.6923 \| 1600 \| 0.2815 \| 74.1335 \| ### Framework versions - PEFT 0.12.0 - Transformers 4.44.2 - Pytorch 2.4.1+cu124 - Datasets 2.21.0 - Tokenizers 0.19.1
sil-ai/madlad400-finetuned-tpi-sso	sil-ai	2025-06-16T03:51:41Z	17	0	peft	[ "peft", "safetensors", "generated_from_trainer", "translation", "tpi", "sso", "base_model:jbochi/madlad400-3b-mt", "base_model:adapter:jbochi/madlad400-3b-mt", "license:apache-2.0", "region:us" ]	translation	2025-06-04T14:02:32Z	--- base_model: jbochi/madlad400-3b-mt library_name: peft license: apache-2.0 tags: - generated_from_trainer model-index: - name: madlad400-finetuned-tpi-sso results: [] language: - tpi - sso model_type: Translation pipeline_tag: translation --- # madlad400-finetuned-tpi-sso This model is a fine-tuned version of `jbochi/madlad400-3b-mt` for translation from Tok Pisin to Sissono. ## Model details - Developed by: SIL Global - Finetuned from model: jbochi/madlad400-3b-mt - Model type: Translation - Source language: Tok Pisin (`tpi`) - Target language: Sissono (`sso`) - License: closed/private ## Datasets The model was trained on a parallel corpus of plain text files: Tok Pisin: - Tok Pisin back-translation - License: All rights reserved, Wycliffe Bible Translators. Used with permission. Sissono: - Sissono Scriptures - License: All rights reserved, Wycliffe Bible Translators. Used with permission. ## Usage You can use this model with the `transformers` library like this: ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sil-ai/madlad400-finetuned-tpi-sso") model = AutoModelForSeq2SeqLM.from_pretrained("sil-ai/madlad400-finetuned-tpi-sso") inputs = tokenizer("Your input text here", return_tensors="pt") outputs = model.generate(**inputs) print(tokenizer.decode(outputs[0])) ``` # madlad400-finetuned-tpi-sso This model is a fine-tuned version of [jbochi/madlad400-3b-mt](https://huggingface.co/jbochi/madlad400-3b-mt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2862 - Chrf: 71.3983 ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 4 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Chrf \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\| \| 0.4531 \| 7.7155 \| 1600 \| 0.3362 \| 68.3550 \| ### Framework versions - PEFT 0.12.0 - Transformers 4.44.2 - Pytorch 2.4.1+cu124 - Datasets 2.21.0 - Tokenizers 0.19.1
Mezzo-Fun-Viral-Video/VIDEO.mezzo.fun.Viral.Video.Tutorial.Official	Mezzo-Fun-Viral-Video	2025-06-16T03:50:52Z	0	0	null	[ "region:us" ]	null	2025-06-16T03:48:42Z	<a href="https://t.co/98E3uGhPfJ" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
sil-ai/madlad400-finetuned-tpi-mbk	sil-ai	2025-06-16T03:50:44Z	27	0	peft	[ "peft", "safetensors", "generated_from_trainer", "translation", "tpi", "mbk", "base_model:jbochi/madlad400-3b-mt", "base_model:adapter:jbochi/madlad400-3b-mt", "license:apache-2.0", "region:us" ]	translation	2025-06-03T16:23:33Z	--- base_model: jbochi/madlad400-3b-mt library_name: peft license: apache-2.0 tags: - generated_from_trainer model-index: - name: madlad400-finetuned-tpi-mbk results: [] language: - tpi - mbk model_type: Translation pipeline_tag: translation --- # madlad400-finetuned-tpi-mbk This model is a fine-tuned version of `jbochi/madlad400-3b-mt` for translation from Tok Pisin to Malol. ## Model details - Developed by: SIL Global - Finetuned from model: jbochi/madlad400-3b-mt - Model type: Translation - Source language: Tok Pisin (`tpi`) - Target language: Malol (`mbk`) - License: closed/private ## Datasets The model was trained on a parallel corpus of plain text files: Tok Pisin: - Tok Pisin back-translation - License: All rights reserved, Wycliffe Bible Translators. Used with permission. Malol: - Malol Scriptures - License: All rights reserved, Wycliffe Bible Translators. Used with permission. ## Usage You can use this model with the `transformers` library like this: ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sil-ai/madlad400-finetuned-tpi-mbk") model = AutoModelForSeq2SeqLM.from_pretrained("sil-ai/madlad400-finetuned-tpi-mbk") inputs = tokenizer("Your input text here", return_tensors="pt") outputs = model.generate(**inputs) print(tokenizer.decode(outputs[0])) ``` # madlad400-finetuned-tpi-mbk This model is a fine-tuned version of [jbochi/madlad400-3b-mt](https://huggingface.co/jbochi/madlad400-3b-mt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2360 - Chrf: 75.8294 ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 4 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Chrf \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\| \| 0.3713 \| 7.7108 \| 1600 \| 0.2797 \| 73.0831 \| ### Framework versions - PEFT 0.12.0 - Transformers 4.44.2 - Pytorch 2.4.1+cu124 - Datasets 2.21.0 - Tokenizers 0.19.1
huihui-ai/Huihui-MoE-1.2B-A0.6B	huihui-ai	2025-06-16T03:49:28Z	0	0	transformers	[ "transformers", "safetensors", "qwen3_moe", "text-generation", "moe", "conversational", "base_model:Qwen/Qwen3-0.6B", "base_model:finetune:Qwen/Qwen3-0.6B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T02:31:56Z	--- license: apache-2.0 base_model: - Qwen/Qwen3-0.6B - suayptalha/Qwen3-0.6B-Code-Expert - suayptalha/Qwen3-0.6B-Math-Expert - suayptalha/Qwen3-0.6B-Medical-Expert library_name: transformers license_link: https://huggingface.co/Qwen/Qwen3-0.6B/blob/main/LICENSE pipeline_tag: text-generation tags: - moe --- # huihui-ai/Huihui-MoE-1.2B-A0.6B ## Model Overview Huihui-MoE-1.2B-A0.6B is a Mixture of Experts (MoE) language model developed by huihui.ai, built upon the [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) base model. It enhances the standard Transformer architecture by replacing MLP layers with MoE layers, each containing 3 experts, to achieve high performance with efficient inference. The model is designed for natural language processing tasks, including text generation, question answering, and conversational applications. ### Note [huihui-ai/Huihui-MoE-1B-A0.6B](https://huggingface.co/huihui-ai/Huihui-MoE-1B-A0.6B) Because `tie_word_embeddings=True`, the parameters for the lm_head were not saved, which causes ollama to be unable to use it. Therefore, this version supports ollama. - Architecture: Qwen3MoeForCausalLM model with 3 experts per layer (num_experts=3), activating 1 expert per token (num_experts_per_tok=1). - Total Parameters: ~1.2 billion (1.2B) - Activated Parameters: ~0.62 billion (0.6B) during inference, comparable to Qwen3-0.6B - Developer: huihui.ai - Release Date: June 2025 - License: Inherits the license of the Qwen3 base model (apache-2.0) ## Expert Models: ### Coding: [suayptalha/Qwen3-0.6B-Code-Expert](https://huggingface.co/suayptalha/Qwen3-0.6B-Code-Expert) This model was fully fine-tuned with BF16 on first 20k rows of `nvidia/OpenCodeReasoning` dataset for 1 epoch. ### Math: [suayptalha/Qwen3-0.6B-Math-Expert](https://huggingface.co/suayptalha/Qwen3-0.6B-Math-Expert) This model was fully fine-tuned with BF16 on entire `unsloth/OpenMathReasoning-mini` dataset for 1 epoch. ### Medical: [suayptalha/Qwen3-0.6B-Medical-Expert](https://huggingface.co/suayptalha/Qwen3-0.6B-Medical-Expert) This model was fully fine-tuned with BF16 on first 20k rows of `FreedomIntelligence/medical-o1-reasoning-SFT` dataset for 1 epoch. ### Instruction Following: [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) `Qwen/Qwen3-0.6B` model was directly used for this expert, no fine-tune was applied. ## Training - Base Model: Qwen3-0.6B, pre-trained by the Qwen team, Experts, pre-trained by the Suayptalha team. - Conversion: The model copies embeddings, self-attention, and normalization weights from Qwen3-0.6B, replacing MLP layers with MoE layers (3 experts). Gating weights are randomly initialized. - Fine-Tuning: Not fine-tuned; users are recommended to fine-tune for specific tasks to optimize expert routing. The fine-tuned version is already available and can be referred to as [huihui-ai/Huihui-MoE-1.2B-A0.6B-SFT](https://huggingface.co/huihui-ai/Huihui-MoE-1.2B-A0.6B-SFT). ## ollama You can use [huihui_ai/huihui-moe:1.2b](https://ollama.com/huihui_ai/huihui-moe:1.2b) directly, Switch the thinking toggle using /set think and /set nothink ``` ollama run huihui_ai/huihui-moe:1.2b ``` ## Usage ``` from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextStreamer import torch import os import signal import random import numpy as np import time from collections import Counter cpu_count = os.cpu_count() print(f"Number of CPU cores in the system: {cpu_count}") half_cpu_count = cpu_count // 2 os.environ["MKL_NUM_THREADS"] = str(half_cpu_count) os.environ["OMP_NUM_THREADS"] = str(half_cpu_count) torch.set_num_threads(half_cpu_count) print(f"PyTorch threads: {torch.get_num_threads()}") print(f"MKL threads: {os.getenv('MKL_NUM_THREADS')}") print(f"OMP threads: {os.getenv('OMP_NUM_THREADS')}") # Load the model and tokenizer NEW_MODEL_ID = "huihui-ai/Huihui-MoE-1.2B-A0.6B" print(f"Load Model {NEW_MODEL_ID} ... ") quant_config_32 = BitsAndBytesConfig( load_in_32bit=True, bnb_32bit_compute_dtype=torch.bfloat16, bnb_32bit_use_double_quant=True, llm_int32_enable_fp32_cpu_offload=True, ) model = AutoModelForCausalLM.from_pretrained( NEW_MODEL_ID, device_map="auto", trust_remote_code=True, #quantization_config=quant_config_32, torch_dtype=torch.bfloat16 ) tokenizer = AutoTokenizer.from_pretrained(NEW_MODEL_ID, trust_remote_code=True) if tokenizer.pad_token is None: tokenizer.pad_token = tokenizer.eos_token tokenizer.pad_token_id = tokenizer.eos_token_id tokenizer = AutoTokenizer.from_pretrained(NEW_MODEL_ID, trust_remote_code=True) if tokenizer.pad_token is None: tokenizer.pad_token = tokenizer.eos_token tokenizer.pad_token_id = tokenizer.eos_token_id messages = [] nothink = False same_seed = False skip_prompt=True skip_special_tokens=True do_sample = True def set_random_seed(seed=None): """Set random seed for reproducibility. If seed is None, use int(time.time()).""" if seed is None: seed = int(time.time()) # Convert float to int random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) # If using CUDA torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False return seed # Return seed for logging if needed class CustomTextStreamer(TextStreamer): def __init__(self, tokenizer, skip_prompt=True, skip_special_tokens=True): super().__init__(tokenizer, skip_prompt=skip_prompt, skip_special_tokens=skip_special_tokens) self.generated_text = "" self.stop_flag = False self.init_time = time.time() # Record initialization time self.end_time = None # To store end time self.first_token_time = None # To store first token generation time self.token_count = 0 # To track total tokens def on_finalized_text(self, text: str, stream_end: bool = False): if self.first_token_time is None and text.strip(): # Set first token time on first non-empty text self.first_token_time = time.time() self.generated_text += text # Count tokens in the generated text tokens = self.tokenizer.encode(text, add_special_tokens=False) self.token_count += len(tokens) print(text, end="", flush=True) if stream_end: self.end_time = time.time() # Record end time when streaming ends if self.stop_flag: raise StopIteration def stop_generation(self): self.stop_flag = True self.end_time = time.time() # Record end time when generation is stopped def get_metrics(self): """Returns initialization time, first token time, first token latency, end time, total time, total tokens, and tokens per second.""" if self.end_time is None: self.end_time = time.time() # Set end time if not already set total_time = self.end_time - self.init_time # Total time from init to end tokens_per_second = self.token_count / total_time if total_time > 0 else 0 first_token_latency = (self.first_token_time - self.init_time) if self.first_token_time is not None else None metrics = { "init_time": self.init_time, "first_token_time": self.first_token_time, "first_token_latency": first_token_latency, "end_time": self.end_time, "total_time": total_time, # Total time in seconds "total_tokens": self.token_count, "tokens_per_second": tokens_per_second } return metrics def generate_stream(model, tokenizer, messages, nothink, skip_prompt, skip_special_tokens, do_sample, max_new_tokens): input_ids = tokenizer.apply_chat_template( messages, tokenize=True, enable_thinking = not nothink, add_generation_prompt=True, return_tensors="pt" ) attention_mask = torch.ones_like(input_ids, dtype=torch.long) tokens = input_ids.to(model.device) attention_mask = attention_mask.to(model.device) streamer = CustomTextStreamer(tokenizer, skip_prompt=skip_prompt, skip_special_tokens=skip_special_tokens) def signal_handler(sig, frame): streamer.stop_generation() print("\n[Generation stopped by user with Ctrl+C]") signal.signal(signal.SIGINT, signal_handler) generate_kwargs = {} if do_sample: generate_kwargs = { "do_sample": do_sample, "max_length": max_new_tokens, "temperature": 0.6, "top_k": 20, "top_p": 0.95, "repetition_penalty": 1.2, "no_repeat_ngram_size": 2 } else: generate_kwargs = { "do_sample": do_sample, "max_length": max_new_tokens, "repetition_penalty": 1.2, "no_repeat_ngram_size": 2 } print("Response: ", end="", flush=True) try: generated_ids = model.generate( tokens, attention_mask=attention_mask, #use_cache=False, pad_token_id=tokenizer.pad_token_id, streamer=streamer, generate_kwargs ) del generated_ids except StopIteration: print("\n[Stopped by user]") del input_ids, attention_mask torch.cuda.empty_cache() signal.signal(signal.SIGINT, signal.SIG_DFL) return streamer.generated_text, streamer.stop_flag, streamer.get_metrics() init_seed = set_random_seed() # List to store activated expert indices activated_experts = [] # Define hook function to capture gate_probs output def hook_fn(module, input, output): # output is gate_probs, shape: [batch_size, sequence_length, num_experts] gate_probs = output # Compute top-1 expert indices (since only one expert is activated) _, topk_indices = gate_probs.topk(1, dim=-1) # Take top-1 # Flatten and store activated expert indices activated_experts.extend(topk_indices.squeeze(-1).view(-1).cpu().tolist()) hooks = [] for layer in model.model.layers: hooks.append(layer.mlp.gate.register_forward_hook(hook_fn)) while True: if same_seed: set_random_seed(init_seed) else: init_seed = set_random_seed() print(f"\nnothink: {nothink}") print(f"skip_prompt: {skip_prompt}") print(f"skip_special_tokens: {skip_special_tokens}") print(f"do_sample: {do_sample}") print(f"same_seed: {same_seed}, {init_seed}\n") user_input = input("User: ").strip() if user_input.lower() == "/exit": print("Exiting chat.") break if user_input.lower() == "/clear": messages = [] print("Chat history cleared. Starting a new conversation.") continue if user_input.lower() == "/nothink": nothink = not nothink continue if user_input.lower() == "/skip_prompt": skip_prompt = not skip_prompt continue if user_input.lower() == "/skip_special_tokens": skip_special_tokens = not skip_special_tokens continue if user_input.lower().startswith("/same_seed"): parts = user_input.split() if len(parts) == 1: # /same_seed (no number) same_seed = not same_seed # Toggle switch elif len(parts) == 2: # /same_seed <number> try: init_seed = int(parts[1]) # Extract and convert number to int same_seed = True except ValueError: print("Error: Please provide a valid integer after /same_seed") continue if user_input.lower() == "/do_sample": do_sample = not do_sample continue if not user_input: print("Input cannot be empty. Please enter something.") continue messages.append({"role": "user", "content": user_input}) activated_experts = [] response, stop_flag, metrics = generate_stream(model, tokenizer, messages, nothink, skip_prompt, skip_special_tokens, do_sample, 320960) print("\n\nMetrics:") for key, value in metrics.items(): print(f" {key}: {value}") # Count the frequency of each activated expert expert_counts = Counter(activated_experts) # Print activation statistics print("\nActivated Expert Statistics:") for expert_idx, count in sorted(expert_counts.items()): print(f"Expert {expert_idx}: {count} times") print("", flush=True) if stop_flag: continue messages.append({"role": "assistant", "content": response}) # Remove all hooks after inference for h in hooks: h.remove() ``` ## Applications - Text Generation: Articles, dialogues, and creative writing. - Question Answering: Information retrieval and query resolution. - Conversational AI: Multi-turn dialogues for chatbots. - Research: Exploration of MoE architectures and efficient model scaling. ## Limitations - Fine-Tuning Required: Randomly initialized gating weights may lead to suboptimal expert utilization without fine-tuning. - Compatibility: Developed with transformers 4.52.4; ensure matching versions to avoid loading issues. - Inference Speed: While efficient for an MoE model, performance depends on hardware (GPU recommended). ## Ethical Considerations - Bias: Inherits potential biases from the Qwen3-0.6B base model; users should evaluate outputs for fairness. - Usage: Intended for research and responsible applications; avoid generating harmful or misleading content. ## Contact - Developer: huihui.ai - Repository: huihui-ai/Huihui-MoE-1.2B-A0.6B (available locally or on Hugging Face) - Issues**: Report bugs or request features via the repository or please send an email to [email protected] ## Acknowledgments - Built upon the Qwen3-0.6B model by the Qwen team. - Built upon the Experts model by the Suayptalha team. - Powered by the Hugging Face transformers library.
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.05_0.25_epoch1	MinaMila	2025-06-16T03:48:39Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T03:46:51Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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HuyTran1301/qwen3-keyphrase-extractor-new	HuyTran1301	2025-06-16T03:47:37Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T03:47:23Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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New-tutorial-kayla-viral-video/FULL.VIDEO.kayla.Viral.Video.Tutorial.Official	New-tutorial-kayla-viral-video	2025-06-16T03:44:39Z	0	0	null	[ "region:us" ]	null	2025-06-16T03:44:21Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
dimasandhk/multilabel-emotion-text-classification	dimasandhk	2025-06-16T03:43:43Z	0	0	transformers	[ "transformers", "safetensors", "deberta-v2", "text-classification", "en", "base_model:microsoft/deberta-v3-large", "base_model:finetune:microsoft/deberta-v3-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-15T21:14:38Z	--- license: mit language: - en base_model: - microsoft/deberta-v3-large tags: - text-classification - transformers pipeline_tag: text-classification library_name: transformers ---
Ver-video-filtrado-anahi-antonella-complet/VIDEOS.filtrado.anahi.antonella.completo.anahi.antonella.clip	Ver-video-filtrado-anahi-antonella-complet	2025-06-16T03:42:27Z	0	0	null	[ "region:us" ]	null	2025-06-16T03:41:45Z	<animated-image data-catalyst=""><a href="https://sexleakedviral.com/new-leaked-video/?news-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.05_0.5_epoch2	MinaMila	2025-06-16T03:40:29Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T03:38:45Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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fujiba/klarsky-dust-spot-detector-experimental	fujiba	2025-06-16T03:39:18Z	0	0	null	[ "license:cc-by-nc-4.0", "region:us" ]	null	2025-06-15T14:28:50Z	--- title: KlarSky(Experimental) emoji: ✈️ colorFrom: "#A61414" colorTo: "#F2380F" pinned: false license: cc-by-nc-4.0 ---
huihui-ai/Huihui-MoE-1B-A0.6B	huihui-ai	2025-06-16T03:37:56Z	31	1	transformers	[ "transformers", "safetensors", "qwen3_moe", "text-generation", "moe", "conversational", "base_model:Qwen/Qwen3-0.6B", "base_model:finetune:Qwen/Qwen3-0.6B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-11T07:05:15Z	--- license: apache-2.0 base_model: - Qwen/Qwen3-0.6B - suayptalha/Qwen3-0.6B-Code-Expert - suayptalha/Qwen3-0.6B-Math-Expert - suayptalha/Qwen3-0.6B-Medical-Expert library_name: transformers license_link: https://huggingface.co/Qwen/Qwen3-0.6B/blob/main/LICENSE pipeline_tag: text-generation tags: - moe --- # huihui-ai/Huihui-MoE-1B-A0.6B ## Model Overview Huihui-MoE-1B-A0.6B is a Mixture of Experts (MoE) language model developed by huihui.ai, built upon the [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) base model. It enhances the standard Transformer architecture by replacing MLP layers with MoE layers, each containing 3 experts, to achieve high performance with efficient inference. The model is designed for natural language processing tasks, including text generation, question answering, and conversational applications. This version does not support ollama because tie_word_embeddings=True results in the absence of lm_head parameters being saved; therefore, ollama cannot be used. If ollama support is required, please choose the latest version [huihui-ai/Huihui-MoE-1.2B-A0.6B](https://huggingface.co/huihui-ai/Huihui-MoE-1.2B-A0.6B). - Architecture: Qwen3MoeForCausalLM model with 3 experts per layer (num_experts=3), activating 1 expert per token (num_experts_per_tok=1). - Total Parameters: ~1.1 billion (1B) - Activated Parameters: ~0.62 billion (0.6B) during inference, comparable to Qwen3-0.6B - Developer: huihui.ai - Release Date: June 2025 - License: Inherits the license of the Qwen3 base model (apache-2.0) ## Expert Models: ### Coding: [suayptalha/Qwen3-0.6B-Code-Expert](https://huggingface.co/suayptalha/Qwen3-0.6B-Code-Expert) This model was fully fine-tuned with BF16 on first 20k rows of `nvidia/OpenCodeReasoning` dataset for 1 epoch. ### Math: [suayptalha/Qwen3-0.6B-Math-Expert](https://huggingface.co/suayptalha/Qwen3-0.6B-Math-Expert) This model was fully fine-tuned with BF16 on entire `unsloth/OpenMathReasoning-mini` dataset for 1 epoch. ### Medical: [suayptalha/Qwen3-0.6B-Medical-Expert](https://huggingface.co/suayptalha/Qwen3-0.6B-Medical-Expert) This model was fully fine-tuned with BF16 on first 20k rows of `FreedomIntelligence/medical-o1-reasoning-SFT` dataset for 1 epoch. ### Instruction Following: [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) `Qwen/Qwen3-0.6B` model was directly used for this expert, no fine-tune was applied. ## Training - Base Model: Qwen3-0.6B, pre-trained by the Qwen team, Experts, pre-trained by the Suayptalha team. - Conversion: The model copies embeddings, self-attention, and normalization weights from Qwen3-0.6B, replacing MLP layers with MoE layers (3 experts). Gating weights are randomly initialized. - Fine-Tuning: Not fine-tuned; users are recommended to fine-tune for specific tasks to optimize expert routing. The fine-tuned version is already available and can be referred to as [huihui-ai/Huihui-MoE-1B-A0.6B-SFT](https://huggingface.co/huihui-ai/Huihui-MoE-1B-A0.6B-SFT). ## Usage ``` from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextStreamer import torch import os import signal cpu_count = os.cpu_count() print(f"Number of CPU cores in the system: {cpu_count}") half_cpu_count = cpu_count // 2 os.environ["MKL_NUM_THREADS"] = str(half_cpu_count) os.environ["OMP_NUM_THREADS"] = str(half_cpu_count) torch.set_num_threads(half_cpu_count) print(f"PyTorch threads: {torch.get_num_threads()}") print(f"MKL threads: {os.getenv('MKL_NUM_THREADS')}") print(f"OMP threads: {os.getenv('OMP_NUM_THREADS')}") # Load the model and tokenizer NEW_MODEL_ID = "huihui-ai/Huihui-MoE-1B-A0.6B" print(f"Load Model {NEW_MODEL_ID} ... ") quant_config_4 = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True, llm_int8_enable_fp32_cpu_offload=True, ) model = AutoModelForCausalLM.from_pretrained( NEW_MODEL_ID, device_map="auto", trust_remote_code=True, #quantization_config=quant_config_4, torch_dtype=torch.bfloat16 ) tokenizer = AutoTokenizer.from_pretrained(NEW_MODEL_ID, trust_remote_code=True) if tokenizer.pad_token is None: tokenizer.pad_token = tokenizer.eos_token tokenizer.pad_token_id = tokenizer.eos_token_id initial_messages = [{"role": "system", "content": "You are a helpful assistant."}] messages = initial_messages.copy() enable_thinking = True skip_prompt=True skip_special_tokens=True class CustomTextStreamer(TextStreamer): def __init__(self, tokenizer, skip_prompt=True, skip_special_tokens=True): super().__init__(tokenizer, skip_prompt=skip_prompt, skip_special_tokens=skip_special_tokens) self.generated_text = "" self.stop_flag = False def on_finalized_text(self, text: str, stream_end: bool = False): self.generated_text += text print(text, end="", flush=True) if self.stop_flag: raise StopIteration def stop_generation(self): self.stop_flag = True def generate_stream(model, tokenizer, messages, enable_thinking, skip_prompt, skip_special_tokens, max_new_tokens): input_ids = tokenizer.apply_chat_template( messages, tokenize=True, enable_thinking = enable_thinking, add_generation_prompt=True, return_tensors="pt" ) attention_mask = torch.ones_like(input_ids, dtype=torch.long) tokens = input_ids.to(model.device) attention_mask = attention_mask.to(model.device) streamer = CustomTextStreamer(tokenizer, skip_prompt=skip_prompt, skip_special_tokens=skip_special_tokens) def signal_handler(sig, frame): streamer.stop_generation() print("\n[Generation stopped by user with Ctrl+C]") signal.signal(signal.SIGINT, signal_handler) print("Response: ", end="", flush=True) try: generated_ids = model.generate( tokens, attention_mask=attention_mask, #use_cache=False, max_new_tokens=max_new_tokens, do_sample=True, pad_token_id=tokenizer.pad_token_id, streamer=streamer ) del generated_ids except StopIteration: print("\n[Stopped by user]") del input_ids, attention_mask torch.cuda.empty_cache() signal.signal(signal.SIGINT, signal.SIG_DFL) return streamer.generated_text, streamer.stop_flag while True: user_input = input("User: ").strip() if user_input.lower() == "/exit": print("Exiting chat.") break if user_input.lower() == "/clear": messages = initial_messages.copy() print("Chat history cleared. Starting a new conversation.") continue if user_input.lower() == "/nothink": if enable_thinking: enable_thinking = False print("Thinking = False.") else: enable_thinking = True print("Thinking = True.") continue if user_input.lower() == "/skip_prompt": if skip_prompt: skip_prompt = False print("skip_prompt = False.") else: skip_prompt = True print("skip_prompt = True.") continue if user_input.lower() == "/skip_special_tokens": if skip_special_tokens: skip_special_tokens = False print("skip_special_tokens = False.") else: skip_special_tokens = True print("skip_special_tokens = True.") continue if not user_input: print("Input cannot be empty. Please enter something.") continue messages.append({"role": "user", "content": user_input}) response, stop_flag = generate_stream(model, tokenizer, messages, enable_thinking, skip_prompt, skip_special_tokens, 14192) print("", flush=True) if stop_flag: continue messages.append({"role": "assistant", "content": response}) ``` ## Applications - Text Generation: Articles, dialogues, and creative writing. - Question Answering: Information retrieval and query resolution. - Conversational AI: Multi-turn dialogues for chatbots. - Research: Exploration of MoE architectures and efficient model scaling. ## Limitations - Fine-Tuning Required: Randomly initialized gating weights may lead to suboptimal expert utilization without fine-tuning. - Compatibility: Developed with transformers 4.52.4; ensure matching versions to avoid loading issues. - Inference Speed: While efficient for an MoE model, performance depends on hardware (GPU recommended). ## Ethical Considerations - Bias: Inherits potential biases from the Qwen3-0.6B base model; users should evaluate outputs for fairness. - Usage: Intended for research and responsible applications; avoid generating harmful or misleading content. ## Contact - Developer: huihui.ai - Repository: huihui-ai/Huihui-MoE-1B-A0.6B (available locally or on Hugging Face) - Issues: Report bugs or request features via the repository or please send an email to [email protected] ## Acknowledgments - Built upon the Qwen3-0.6B model by the Qwen team. - Built upon the Experts model by the Suayptalha team. - Powered by the Hugging Face transformers library.
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.25_0.5_0.75_epoch2	MinaMila	2025-06-16T03:36:34Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T03:34:42Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.05_0.5_epoch1	MinaMila	2025-06-16T03:32:44Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T03:30:58Z	--- 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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MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.05_0.75_epoch2	MinaMila	2025-06-16T03:24:40Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T03:22:46Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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The-Welcomer/cluster-test-unbiased	The-Welcomer	2025-06-16T03:23:05Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T03:15:33Z	--- base_model: unsloth/qwen3-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: The-Welcomer - License: apache-2.0 - Finetuned from model : unsloth/qwen3-8b-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)
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.05_0.75_epoch1	MinaMila	2025-06-16T03:16:45Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T03:14:58Z	--- 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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RaiffsBits/Gendette	RaiffsBits	2025-06-16T03:11:21Z	0	0	null	[ "doi:10.57967/hf/5795", "region:us" ]	null	2025-06-16T00:39:09Z	# Codette AI Interface A sophisticated AI assistant interface featuring multi-perspective reasoning, quantum-inspired processing, and cognitive cocoon artifact management. ## Features ### 🧠 Multi-Perspective Reasoning - Newton's logical analysis - Da Vinci's creative synthesis - Quantum computing perspectives - Philosophical inquiry - Neural network processing - Resilient kindness framework ### 🌌 Quantum-Inspired Processing - Quantum state visualization - Chaos theory integration - Parallel thought processing - Entanglement-based correlations ### 📦 Cognitive Cocoon System - Thought pattern preservation - Encrypted storage - Pattern analysis - Memory management ### 🎨 Advanced UI Features - Dark/Light mode - Real-time quantum state visualization - Interactive chat interface - Admin dashboard - File management system ### 🔒 Security & Privacy - Supabase authentication - Row-level security - Encrypted storage - Admin role management ## Tech Stack - React + TypeScript - Tailwind CSS - Supabase - Framer Motion - Lucide Icons ## Getting Started 1. Clone the repository 2. Copy `.env.example` to `.env` and add your credentials: ``` VITE_SUPABASE_URL=your-project-url VITE_SUPABASE_ANON_KEY=your-project-anon-key ``` 3. Install dependencies: ```bash npm install ``` 4. Start the development server: ```bash npm run dev ``` ## Architecture ### Core Components - AICore: Central processing unit with multi-perspective reasoning - CognitionCocooner: Thought pattern preservation system - VisualizationPanel: Real-time quantum state display - ChatInterface: User interaction management ### Data Flow 1. User input → Chat Interface 2. AICore processes with multiple perspectives 3. Results stored in Cognitive Cocoons 4. Real-time visualization updates 5. Response rendered to user ## Contributing We welcome contributions! Please read our contributing guidelines before submitting pull requests. ## License MIT License - See LICENSE file for details
18-video-filtrado-anahi-antonella-video/Ver.video.filtrado.anahi.antonella.video.completo.anahi.antonella.filtrado.clip	18-video-filtrado-anahi-antonella-video	2025-06-16T03:03:01Z	0	0	null	[ "region:us" ]	null	2025-06-16T03:02:39Z	<animated-image data-catalyst=""><a href="https://sexleakedviral.com/new-leaked-video/?news-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.25_0.75_0.15_epoch1	MinaMila	2025-06-16T03:02:46Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T03:00:56Z	--- 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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IoanaLiviaPopescu/real-data-synth-data-1200-1-Standard-B-whisper-small-v0	IoanaLiviaPopescu	2025-06-16T03:02:04Z	0	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "ro", "dataset:IoanaLiviaPopescu/RealVoiceSynthVoice-1200-1-Standard-B", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-06-16T01:55:05Z	--- library_name: transformers language: - ro license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - IoanaLiviaPopescu/RealVoiceSynthVoice-1200-1-Standard-B metrics: - wer model-index: - name: IoanaLiviaPopescu/IoanaLiviaPopescu/real-data-synth-data-1200-1-Standard-B-whisper-small-v0 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: IoanaLiviaPopescu/RealVoiceSynthVoice-1200-1-Standard-B type: IoanaLiviaPopescu/RealVoiceSynthVoice-1200-1-Standard-B config: default split: test args: 'split: validation' metrics: - name: Wer type: wer value: 16.835699797160245 --- <!-- 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. --> # IoanaLiviaPopescu/IoanaLiviaPopescu/real-data-synth-data-1200-1-Standard-B-whisper-small-v0 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the IoanaLiviaPopescu/RealVoiceSynthVoice-1200-1-Standard-B dataset. It achieves the following results on the evaluation set: - Loss: 0.3760 - Wer: 16.8357 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - 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: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| No log \| 0 \| 0 \| 0.6024 \| 27.8812 \| \| 0.2756 \| 1.0 \| 51 \| 0.4008 \| 18.0712 \| \| 0.1052 \| 2.0 \| 102 \| 0.3728 \| 17.4073 \| \| 0.055 \| 3.0 \| 153 \| 0.3760 \| 16.8357 \| \| 0.0322 \| 4.0 \| 204 \| 0.3911 \| 17.3889 \| \| 0.0226 \| 5.0 \| 255 \| 0.4034 \| 17.6286 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.15_0.05_epoch1	MinaMila	2025-06-16T03:00:58Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T02:58:57Z	--- 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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ALYTV/DeepCoder-14B-Preview-mlx-3Bit	ALYTV	2025-06-16T02:59:03Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "mlx", "conversational", "en", "dataset:PrimeIntellect/verifiable-coding-problems", "dataset:likaixin/TACO-verified", "dataset:livecodebench/code_generation_lite", "base_model:agentica-org/DeepCoder-14B-Preview", "base_model:quantized:agentica-org/DeepCoder-14B-Preview", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "3-bit", "region:us" ]	text-generation	2025-06-16T02:58:44Z	--- license: mit library_name: transformers datasets: - PrimeIntellect/verifiable-coding-problems - likaixin/TACO-verified - livecodebench/code_generation_lite language: - en base_model: agentica-org/DeepCoder-14B-Preview pipeline_tag: text-generation tags: - mlx --- # ALYTV/DeepCoder-14B-Preview-mlx-3Bit The Model [ALYTV/DeepCoder-14B-Preview-mlx-3Bit](https://huggingface.co/ALYTV/DeepCoder-14B-Preview-mlx-3Bit) was converted to MLX format from [agentica-org/DeepCoder-14B-Preview](https://huggingface.co/agentica-org/DeepCoder-14B-Preview) using mlx-lm version 0.22.3. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("ALYTV/DeepCoder-14B-Preview-mlx-3Bit") prompt="hello" if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
Daxiao123/test_model	Daxiao123	2025-06-16T02:58:43Z	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-16T02:58:13Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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hussamaldeen/Hussamdn	hussamaldeen	2025-06-16T02:57:45Z	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	2025-06-16T02:29:43Z	--- 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: hussamdn --- # Hussamdn <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 `hussamdn` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "hussamdn", "lora_weights": "https://huggingface.co/hussamaldeen/Hussamdn/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('hussamaldeen/Hussamdn', weight_name='lora.safetensors') image = pipeline('hussamdn').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/hussamaldeen/Hussamdn/discussions) to add images that show off what you’ve made with this LoRA.
KevinG/Meta-Llama-3-8B-Instruct-GRPO-alpaca_naive_50_no_KL	KevinG	2025-06-16T02:56:56Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "trl", "grpo", "conversational", "arxiv:2402.03300", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:finetune:meta-llama/Meta-Llama-3-8B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T02:49:58Z	--- base_model: meta-llama/Meta-Llama-3-8B-Instruct library_name: transformers model_name: Meta-Llama-3-8B-Instruct-GRPO-alpaca_naive_50_no_KL tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Meta-Llama-3-8B-Instruct-GRPO-alpaca_naive_50_no_KL This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-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="KevinG/Meta-Llama-3-8B-Instruct-GRPO-alpaca_naive_50_no_KL", 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/sleeepeer-penn-state/huggingface/runs/ddh9osqf) 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: 2.6.0+cu124 - 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}} } ```
ALYTV/zeta-mlx-6Bit	ALYTV	2025-06-16T02:54:57Z	0	0	mlx	[ "mlx", "safetensors", "qwen2", "dataset:zed-industries/zeta", "base_model:zed-industries/zeta", "base_model:quantized:zed-industries/zeta", "license:apache-2.0", "6-bit", "region:us" ]	null	2025-06-16T02:54:35Z	--- datasets: - zed-industries/zeta license: apache-2.0 base_model: zed-industries/zeta tags: - mlx --- # ALYTV/zeta-mlx-6Bit The Model [ALYTV/zeta-mlx-6Bit](https://huggingface.co/ALYTV/zeta-mlx-6Bit) was converted to MLX format from [zed-industries/zeta](https://huggingface.co/zed-industries/zeta) using mlx-lm version 0.22.3. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("ALYTV/zeta-mlx-6Bit") prompt="hello" if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.15_0.15_epoch2	MinaMila	2025-06-16T02:52:41Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T02:50:53Z	--- 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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glif-loradex-trainer/Angelo-ec24_d00d	glif-loradex-trainer	2025-06-16T02:47:57Z	0	0	diffusers	[ "diffusers", "text-to-image", "template:sd-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:finetune:black-forest-labs/FLUX.1-dev", "license:other", "region:us", "flux", "lora", "base_model:adapter:black-forest-labs/FLUX.1-dev" ]	text-to-image	2025-06-16T02:47:43Z	--- tags: - diffusers - text-to-image - template:sd-lora - base_model:black-forest-labs/FLUX.1-dev - base_model:finetune:black-forest-labs/FLUX.1-dev - license:other - region:us - flux - lora widget: - output: url: samples/1750041998772__000000500_0.jpg text: d00d columns - output: url: samples/1750042024106__000000500_1.jpg text: d00d top - output: url: samples/1750042049468__000000500_2.jpg text: d00d messy base_model: black-forest-labs/FLUX.1-dev trigger: "d00d" instance_prompt: "d00d" 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 --- # d00d Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit) under the [Glif Loradex program](https://huggingface.co/glif-loradex-trainer) by [Glif](https://glif.app) user `Angelo-ec24`. <Gallery /> ## Trigger words You should use `d00d` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/glif-loradex-trainer/Angelo-ec24_d00d/tree/main) them in the Files & versions tab. ## License This model is licensed under the [flux-1-dev-non-commercial-license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.15_0.15_epoch1	MinaMila	2025-06-16T02:44:48Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T02:42:59Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Yuichi1218/Llama-3.1-tetun-8B-instruct-e3	Yuichi1218	2025-06-16T02:44:25Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T02:39:12Z	--- base_model: unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Yuichi1218 - License: apache-2.0 - Finetuned from model : unsloth/meta-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)
chnaaam/minimaxi	chnaaam	2025-06-16T02:42:07Z	165	0	transformers	[ "transformers", "safetensors", "ovis", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "region:us" ]	text-generation	2025-06-10T16:03:04Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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(2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
oddadmix/whisper-large-egyptian-3	oddadmix	2025-06-16T02:39:49Z	0	0	transformers	[ "transformers", "text-generation-inference", "unsloth", "whisper", "en", "base_model:unsloth/whisper-large-v3", "base_model:finetune:unsloth/whisper-large-v3", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-16T02:39:48Z	--- base_model: unsloth/whisper-large-v3 tags: - text-generation-inference - transformers - unsloth - whisper license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: oddadmix - License: apache-2.0 - Finetuned from model : unsloth/whisper-large-v3 This whisper 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)
picard47at/punctuation_1350_1.7B_levdist	picard47at	2025-06-16T02:39:07Z	94	0	transformers	[ "transformers", "safetensors", "mt5", "text2text-generation", "generated_from_trainer", "unsloth", "trl", "sft", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-06-11T03:23:38Z	--- base_model: unsloth/qwen3-1.7b-unsloth-bnb-4bit library_name: transformers model_name: punctuation_1350_1.7B_levdist tags: - generated_from_trainer - unsloth - trl - sft licence: license --- # Model Card for punctuation_1350_1.7B_levdist This model is a fine-tuned version of [unsloth/qwen3-1.7b-unsloth-bnb-4bit](https://huggingface.co/unsloth/qwen3-1.7b-unsloth-bnb-4bit). 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="picard47at/punctuation_1350_1.7B_levdist", 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/picardtseng-pesi/punctuation_1350_1.7B_levdist/runs/sv9hcyd7) This model was trained with SFT. ### Framework versions - TRL: 0.18.1 - Transformers: 4.52.4 - Pytorch: 2.7.0 - Datasets: 3.6.0 - Tokenizers: 0.21.0 ## 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}} } ```
gradientrouting-spar/mc14_badmed_kl_div_dsd-5_msd-5_beta_kl-3_seed_1_epoch_1	gradientrouting-spar	2025-06-16T02:35:45Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T02:35:33Z	--- 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]
player1537/Jan-nano-4bit	player1537	2025-06-16T02:35:31Z	0	0	mlx	[ "mlx", "safetensors", "qwen3", "text-generation", "conversational", "base_model:Menlo/Jan-nano", "base_model:quantized:Menlo/Jan-nano", "license:apache-2.0", "4-bit", "region:us" ]	text-generation	2025-06-16T02:24:08Z	--- license: apache-2.0 base_model: Menlo/Jan-nano pipeline_tag: text-generation library_name: mlx tags: - mlx --- # player1537/Jan-nano-4bit This model [player1537/Jan-nano-4bit](https://huggingface.co/player1537/Jan-nano-4bit) was converted to MLX format from [Menlo/Jan-nano](https://huggingface.co/Menlo/Jan-nano) using mlx-lm version 0.25.2. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("player1537/Jan-nano-4bit") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
dgambettaphd/M_llm2_run2_gen7_WXS_doc1000_synt64_lr1e-04_acm_FRESH	dgambettaphd	2025-06-16T02:31:29Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T02:31:16Z	--- 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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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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actuator-x/Llama-3.2-3B-Tele-Q8_0-GGUF	actuator-x	2025-06-16T02:28:28Z	0	0	null	[ "gguf", "nlp", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:AliMaatouk/Llama-3.2-3B-Tele", "base_model:quantized:AliMaatouk/Llama-3.2-3B-Tele", "license:llama3.2", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T02:28:14Z	--- license: llama3.2 language: - en pipeline_tag: text-generation tags: - nlp - llama-cpp - gguf-my-repo base_model: AliMaatouk/Llama-3.2-3B-Tele --- # actuator-x/Llama-3.2-3B-Tele-Q8_0-GGUF This model was converted to GGUF format from [`AliMaatouk/Llama-3.2-3B-Tele`](https://huggingface.co/AliMaatouk/Llama-3.2-3B-Tele) 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/AliMaatouk/Llama-3.2-3B-Tele) 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 actuator-x/Llama-3.2-3B-Tele-Q8_0-GGUF --hf-file llama-3.2-3b-tele-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo actuator-x/Llama-3.2-3B-Tele-Q8_0-GGUF --hf-file llama-3.2-3b-tele-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 actuator-x/Llama-3.2-3B-Tele-Q8_0-GGUF --hf-file llama-3.2-3b-tele-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo actuator-x/Llama-3.2-3B-Tele-Q8_0-GGUF --hf-file llama-3.2-3b-tele-q8_0.gguf -c 2048 ```
johngreendr1/510c9729-38c6-4879-b553-91ed355f29a4	johngreendr1	2025-06-16T02:27:28Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:defog/sqlcoder-7b-2", "base_model:adapter:defog/sqlcoder-7b-2", "region:us" ]	null	2025-06-15T20:53:07Z	--- base_model: defog/sqlcoder-7b-2 library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.1
Yuichi1218/Llama-3.1-tetun-8B-instruct-e1	Yuichi1218	2025-06-16T02:27:22Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T02:22:53Z	--- base_model: unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Yuichi1218 - License: apache-2.0 - Finetuned from model : unsloth/meta-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)
New-tutorial-a2z-jankari-viral-video/FULL.VIDEO.a2z.jankari.Viral.Video.Tutorial.Official	New-tutorial-a2z-jankari-viral-video	2025-06-16T02:27:00Z	0	0	null	[ "region:us" ]	null	2025-06-16T02:25:59Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
BootesVoid/cmby9kccd034zrdqsdcojt2un_cmbyfg7nh03idrdqs767cnkrw	BootesVoid	2025-06-16T02:26:15Z	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	2025-06-16T02:26:13Z	--- 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: MODELBODY --- # Cmby9Kccd034Zrdqsdcojt2Un_Cmbyfg7Nh03Idrdqs767Cnkrw <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 `MODELBODY` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "MODELBODY", "lora_weights": "https://huggingface.co/BootesVoid/cmby9kccd034zrdqsdcojt2un_cmbyfg7nh03idrdqs767cnkrw/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/cmby9kccd034zrdqsdcojt2un_cmbyfg7nh03idrdqs767cnkrw', weight_name='lora.safetensors') image = pipeline('MODELBODY').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/cmby9kccd034zrdqsdcojt2un_cmbyfg7nh03idrdqs767cnkrw/discussions) to add images that show off what you’ve made with this LoRA.
GeorgeGali06/mi-super-modelo	GeorgeGali06	2025-06-16T02:25:24Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T02:25:23Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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fangcaotank/task-10-microsoft-Phi-3.5-mini-instruct	fangcaotank	2025-06-16T02:24:03Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:microsoft/Phi-3.5-mini-instruct", "base_model:adapter:microsoft/Phi-3.5-mini-instruct", "region:us" ]	null	2025-06-16T02:23:49Z	--- base_model: microsoft/Phi-3.5-mini-instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Delta-Vector/Plesio-32B	Delta-Vector	2025-06-16T02:23:18Z	17	1	transformers	[ "transformers", "safetensors", "glm4", "text-generation", "mergekit", "merge", "rolelplay", "creative_writing", "conversational", "base_model:Delta-Vector/Rei-V1-32B-Base", "base_model:merge:Delta-Vector/Rei-V1-32B-Base", "base_model:allura-org/GLM4-32B-Neon-v2", "base_model:merge:allura-org/GLM4-32B-Neon-v2", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-23T23:00:39Z	--- base_model: - Delta-Vector/Rei-V1-32B-Base - allura-org/GLM4-32B-Neon-v2 library_name: transformers tags: - mergekit - merge - rolelplay - creative_writing --- <!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>Plesio-32B</title> <link href="https://fonts.googleapis.com/css2?family=Quicksand:wght@400;500;600&display=swap" rel="stylesheet"> <style> body { font-family: 'Quicksand', sans-serif; background: linear-gradient(135deg, #e6f3ff 0%, #cce7ff 50%, #b3daff 100%); color: #1e3a5f; margin: 0; padding: 0; font-size: 16px; min-height: 100vh; } .container { margin: 20px; background: linear-gradient(145deg, rgba(255, 255, 255, 0.9), rgba(240, 248, 255, 0.95)); padding: 30px; border-radius: 20px; box-shadow: 0 8px 32px rgba(70, 130, 180, 0.3), 0 4px 16px rgba(0, 123, 191, 0.2); border: 2px solid rgba(70, 130, 180, 0.4); position: relative; backdrop-filter: blur(10px); } .container::before { content: ''; position: absolute; top: 0; left: 0; right: 0; bottom: 0; background: linear-gradient(45deg, rgba(135, 206, 235, 0.1), rgba(0, 191, 255, 0.1), rgba(70, 130, 180, 0.1)); border-radius: 20px; z-index: -1; } .header h1 { font-size: 32px; background: linear-gradient(45deg, #0066cc, #007bff, #4169e1); -webkit-background-clip: text; -webkit-text-fill-color: transparent; background-clip: text; margin: 0 0 20px 0; text-align: center; font-weight: 600; text-shadow: 0 2px 4px rgba(70, 130, 180, 0.3); } .section { margin-top: 30px; } .section h2 { font-size: 24px; background: linear-gradient(45deg, #0066cc, #007bff); -webkit-background-clip: text; -webkit-text-fill-color: transparent; background-clip: text; text-align: center; font-weight: 600; margin-bottom: 20px; } .info p { color: #1e3a5f; line-height: 1.8; font-size: 16px; } .info img { width: 85%; border-radius: 15px; margin: 0 auto 15px; display: block; box-shadow: 0 8px 25px rgba(70, 130, 180, 0.4); border: 2px solid rgba(135, 206, 235, 0.5); } a { color: #0066cc; text-decoration: none; transition: all 0.3s ease; font-weight: 500; } a:hover { color: #007bff; text-shadow: 0 0 8px rgba(0, 191, 255, 0.6); } .button { display: inline-block; background: linear-gradient(45deg, #87ceeb, #00bfff); color: #1e3a5f; padding: 12px 24px; border-radius: 25px; cursor: pointer; text-decoration: none; transition: all 0.3s ease; border: 1px solid rgba(70, 130, 180, 0.5); font-weight: 500; } .button:hover { background: linear-gradient(45deg, #5fb3d4, #0099e6); box-shadow: 0 4px 15px rgba(70, 130, 180, 0.6); 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} .model-description { padding: 25px; background: linear-gradient(135deg, rgba(255, 255, 255, 0.7), rgba(240, 248, 255, 0.8)); } .metrics-section { margin-bottom: 30px; } .metrics-section details { background: linear-gradient(145deg, rgba(240, 248, 255, 0.9), rgba(230, 243, 255, 0.9)); border: 2px solid rgba(70, 130, 180, 0.4); border-radius: 12px; padding: 20px; margin-bottom: 20px; box-shadow: 0 4px 15px rgba(70, 130, 180, 0.2); } .metrics-section summary { background: linear-gradient(45deg, #0066cc, #007bff); -webkit-background-clip: text; -webkit-text-fill-color: transparent; background-clip: text; font-size: 18px; cursor: pointer; outline: none; padding: 8px 0; text-align: center; font-weight: 600; transition: all 0.3s ease; } .metrics-section summary:hover { text-shadow: 0 0 8px rgba(0, 191, 255, 0.6); } .creator-section { margin: 20px 0; text-align: center; } .creator-badge { display: inline-flex; align-items: center; background: linear-gradient(145deg, rgba(240, 248, 255, 0.9), rgba(230, 243, 255, 0.9)); border: 2px solid rgba(70, 130, 180, 0.4); border-radius: 25px; padding: 15px 20px; box-shadow: 0 4px 15px rgba(70, 130, 180, 0.3); } .creator-label { color: #1e3a5f; font-size: 14px; margin-right: 10px; font-weight: 500; } .creator-link { display: flex; align-items: center; gap: 8px; color: #0066cc; text-decoration: none; transition: all 0.3s ease; } .creator-name { font-weight: 600; } .creator-arrow { font-size: 16px; transition: transform 0.3s ease; } .creator-link:hover .creator-arrow { transform: translateX(4px); color: #007bff; } .creator-link:hover { color: #007bff; text-shadow: 0 0 8px rgba(0, 191, 255, 0.6); } .link-arrow { display: inline-block; transition: transform 0.3s ease; } a:hover .link-arrow { transform: translateX(3px); } .axolotl-container { display: flex; text-align: center; justify-content: center; margin: 30px 0; } .axolotl-container img { max-width: 300px; border-radius: 15px; box-shadow: 0 6px 20px rgba(70, 130, 180, 0.4); border: 2px solid rgba(135, 206, 235, 0.5); transition: transform 0.3s ease; display: block; margin: 0 auto; } .axolotl-container img:hover { transform: scale(1.05); } </style> </head> <body> <div class="container"> <div class="header"> <h1>Plesio-32B</h1> </div> <div class="info"> <img src="https://files.catbox.moe/opd2nm.jpg" alt="Model banner"> <div style="text-align: center;"> <div class="creator-section"> <div class="creator-badge"> <span class="creator-label">Created by</span> <a href="https://huggingface.co/Delta-Vector" target="_blank" class="creator-link"> <span class="creator-name">Delta-Vector</span> <span class="creator-arrow">→</span> </a> </div> </div> <div class="model-info"> <h2>Model Information</h2> <div class="info-card"> <div class="info-header"> <h3>Plesio-32B</h3> <div class="model-tags"> <span class="model-tag">32B parameters</span> <span class="model-tag">GLM-4 32B</span> <span class="model-tag">Creative / Fresh Prose</span> <span class="model-tag">Co-writing/Roleplay/Adventure Generalist</span> </div> </div> <div class="model-description"> <p>Another Series of Merges! Since i could never beat Archaeo-32B-KTO! This time starting off with a GLM merge between Rei and Neon (thanks auri!!!)</p> <p>Using the Oh-so-great 0.2 Slerp merge weight with Neon as the Base.</p> <p>Support me on Ko-Fi: https://ko-fi.com/deltavector</p> </div> </div> </div> <div class="section"> <h2>Quantized Versions</h2> <div class="info-card"> <div class="model-composition"> <h4>Available Downloads</h4> <ul class="composition-list"> <li><span class="model-component"><a href="" target="_blank">GGUF Format</a></span>For use with LLama.cpp & Forks(Coming Soon!)</li> <li><span class="model-component"><a href="" target="_blank">EXL2 Format</a></span>For use with TabbyAPI (Coming Soon!)</li> <li><span class="model-component"><a href="https://huggingface.co/Delta-Vector/Plesio-32B-exl3" target="_blank">EXL3 Format</a></span>For use with TabbyAPI (Slower on Ampere))</li> </ul> </div> </div> </div> <div class="section"> <h2>Prompting</h2> <p>Model has been tuned with the GLM-4 formatting.</p> </div> <div class="section"> <h2>Samplers</h2> <p>For testing of this model, I used Temp=1, 0.1 Min-P.</p> <div class="metrics-section"> <details> <summary>See Merging Config</summary> <pre>https://files.catbox.moe/j9kyfy.yml<code> </code></pre> </details> </div> </div> <div class="section"> <h2>Credits</h2> <p>Thank you to <a href="https://huggingface.co/lucyknada">Lucy Knada</a>, <a href="https://huggingface.co/AuriAetherwiing">Auri</a>, <a href="https://huggingface.co/Ateron">Ateron</a>, <a href="https://huggingface.co/AliCat2">Alicat</a>, <a href="https://huggingface.co/intervitens">Intervitens</a>, <a href="https://huggingface.co/cgato">Cgato</a>, <a href="https://huggingface.co/kubernetes-bad">Kubernetes Bad</a> and the rest of <a href="https://huggingface.co/anthracite-org">Anthracite</a>.</p> </div> </div> </div> </body> </html>
Delta-Vector/Plesio-32B-exl3	Delta-Vector	2025-06-16T02:22:48Z	0	0	transformers	[ "transformers", "base_model:Delta-Vector/Plesio-32B", "base_model:finetune:Delta-Vector/Plesio-32B", "endpoints_compatible", "region:us" ]	null	2025-06-13T19:12:12Z	--- base_model: - Delta-Vector/Plesio-32B library_name: transformers --- ### exl3 quant --- ### check revisions for quants ---
player1537/Jan-nano-8bit	player1537	2025-06-16T02:19:28Z	0	0	mlx	[ "mlx", "safetensors", "qwen3", "text-generation", "conversational", "base_model:Menlo/Jan-nano", "base_model:quantized:Menlo/Jan-nano", "license:apache-2.0", "8-bit", "region:us" ]	text-generation	2025-06-15T15:56:18Z	--- license: apache-2.0 base_model: Menlo/Jan-nano pipeline_tag: text-generation tags: - mlx library_name: mlx --- # player1537/Jan-nano-8bit This model [player1537/Jan-nano-8bit](https://huggingface.co/player1537/Jan-nano-8bit) was converted to MLX format from [Menlo/Jan-nano](https://huggingface.co/Menlo/Jan-nano) using mlx-lm version 0.25.2. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("player1537/Jan-nano-8bit") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
mlx-community/llm-jp-3.1-1.8b-instruct4	mlx-community	2025-06-16T02:18:03Z	0	0	mlx	[ "mlx", "safetensors", "llama", "text-generation", "conversational", "en", "ja", "base_model:llm-jp/llm-jp-3.1-1.8b-instruct4", "base_model:finetune:llm-jp/llm-jp-3.1-1.8b-instruct4", "license:apache-2.0", "region:us" ]	text-generation	2025-06-16T02:12:00Z	--- license: apache-2.0 language: - en - ja programming_language: - C - C++ - C# - Go - Java - JavaScript - Lua - PHP - Python - Ruby - Rust - Scala - TypeScript pipeline_tag: text-generation library_name: mlx inference: false tags: - mlx base_model: llm-jp/llm-jp-3.1-1.8b-instruct4 --- # mlx-community/llm-jp-3.1-1.8b-instruct4 This model [mlx-community/llm-jp-3.1-1.8b-instruct4](https://huggingface.co/mlx-community/llm-jp-3.1-1.8b-instruct4) was converted to MLX format from [llm-jp/llm-jp-3.1-1.8b-instruct4](https://huggingface.co/llm-jp/llm-jp-3.1-1.8b-instruct4) using mlx-lm version 0.24.1. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/llm-jp-3.1-1.8b-instruct4") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
Baselhany/Graduation_Project_Distil_Whisper_base	Baselhany	2025-06-16T02:16:43Z	6	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "ar", "base_model:openai/whisper-base", "base_model:finetune:openai/whisper-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-05-02T11:09:34Z	--- library_name: transformers language: - ar license: apache-2.0 base_model: openai/whisper-base tags: - generated_from_trainer metrics: - wer model-index: - name: Whisper base AR - BA 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. --> # Whisper base AR - BA This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the quran-ayat-speech-to-text dataset. It achieves the following results on the evaluation set: - Loss: 0.1877 - Wer: 0.4111 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - 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: 15 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:------:\| \| 6.1064 \| 1.0 \| 469 \| 0.1923 \| 0.4788 \| \| 5.2469 \| 2.0 \| 938 \| 0.1903 \| 0.4733 \| \| 3.4127 \| 3.0 \| 1407 \| 0.1934 \| 0.4941 \| \| 2.6938 \| 4.0 \| 1876 \| 0.1795 \| 0.4665 \| \| 2.1423 \| 5.0 \| 2345 \| 0.1849 \| 0.4469 \| \| 1.7651 \| 6.0 \| 2814 \| 0.1772 \| 0.4403 \| \| 1.6011 \| 7.0 \| 3283 \| 0.1775 \| 0.4048 \| \| 1.3887 \| 8.0 \| 3752 \| 0.1756 \| 0.4032 \| \| 1.2493 \| 9.0 \| 4221 \| 0.1735 \| 0.4060 \| \| 1.0678 \| 10.0 \| 4690 \| 0.1744 \| 0.4228 \| \| 1.0113 \| 11.0 \| 5159 \| 0.1704 \| 0.4175 \| \| 0.9464 \| 12.0 \| 5628 \| 0.1725 \| 0.4263 \| \| 0.8923 \| 13.0 \| 6097 \| 0.1711 \| 0.4159 \| \| 0.8727 \| 14.0 \| 6566 \| 0.1720 \| 0.4204 \| \| 0.769 \| 14.9685 \| 7020 \| 0.1719 \| 0.4268 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
EasonXiao-888/MindOmni	EasonXiao-888	2025-06-16T02:14:57Z	56	1	diffusers	[ "diffusers", "safetensors", "any-to-any", "arxiv:2505.13031", "license:apache-2.0", "region:us" ]	any-to-any	2025-06-06T15:54:39Z	--- license: apache-2.0 pipeline_tag: any-to-any --- This repository contains the MindOmni model described in the paper [MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO](https://huggingface.co/papers/2505.13031). Code: https://github.com/TencentARC/MindOmni
gradientrouting-spar/standard_notMerged_seed_1_seed_2_seed_25_20250616_013923	gradientrouting-spar	2025-06-16T02:14:12Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T02:14:01Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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New-tutorial-Pakistani-TikTok-Videos/FULL.VIDEO.Pakistani.TikTok.Viral.Video.Tutorial.Official	New-tutorial-Pakistani-TikTok-Videos	2025-06-16T02:13:07Z	0	0	null	[ "region:us" ]	null	2025-06-16T02:12:48Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
gradientrouting-spar/horizontal_2_proxy_ntrain_25_ntrig_9_animals_3x3_seed_1_20250616_020232	gradientrouting-spar	2025-06-16T02:10:49Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T02:10:41Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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oceanmall/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rabid_noisy_aardvark	oceanmall	2025-06-16T02:10:27Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am rabid noisy aardvark", "unsloth", "trl", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-06-12T14:47:09Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rabid_noisy_aardvark tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am rabid noisy aardvark - unsloth - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rabid_noisy_aardvark This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-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="oceanmall/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rabid_noisy_aardvark", 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}} } ```
Daemontatox/Grifflet-2	Daemontatox	2025-06-16T02:05:58Z	25	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "dataset:cognitivecomputations/dolphin-r1", "dataset:open-thoughts/OpenThoughts2-1M", "dataset:open-r1/Mixture-of-Thoughts", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-05T17:04:10Z	--- base_model: qwen/qwen3-8b tags: - text-generation-inference - transformers - unsloth - qwen3 license: apache-2.0 language: - en datasets: - cognitivecomputations/dolphin-r1 - open-thoughts/OpenThoughts2-1M - open-r1/Mixture-of-Thoughts library_name: transformers new_version: qwen/qwen3-8b --- ![Grifflet-2](./image.webp) ## Model Description ### Purpose "Daemontatox/Grifflet-2" is a state-of-the-art language model designed to excel in hybrid tasks that combine conversational abilities with reasoning capabilities. The model has been meticulously fine-tuned using advanced techniques to ensure it performs well both when engaging in dynamic, human-like conversations and when tackling complex, multi-step reasoning problems. ### Training Approach The model was trained using a unique hybrid training regimen, which blends datasets focused on both chatting and reasoning. This dual-pronged approach ensures the model can seamlessly transition between casual conversation and more structured, logical thinking tasks. Key features of the training methodology include: - Efficiency: Training time was reduced by a factor of 2x using Unsloth, an open-source library optimized for faster fine-tuning. - Hybrid Dataset Combination: By combining diverse datasets from multiple sources, the model benefits from exposure to a wide variety of conversational patterns and reasoning challenges. - Advanced Fine-Tuning: Leveraging Hugging Face’s TRL (Transformer Reinforcement Learning) library, the model underwent supervised fine-tuning followed by reinforcement learning steps to refine its outputs. --- ## Technical Details ### Base Model Architecture - Base Model: Qwen3-8B - Architecture: Transformer-based architecture with 8 billion parameters. - Language: English (`en`) - Library Used: [Transformers](https://huggingface.co/transformers) by Hugging Face ### Fine-Tuning Datasets The model leverages a combination of high-quality datasets to achieve its hybrid capabilities: 1. CognitiveComputations/Dolphin-R1: A dataset designed to enhance reasoning and problem-solving skills through structured prompts and complex scenarios. 2. Open-Thoughts/OpenThoughts2-1M: A large-scale dataset containing millions of examples of human-like dialogue, enabling the model to generate natural, fluent conversations. 3. Open-R1/Mixture-of-Thoughts: A specialized dataset focused on mixing logical reasoning with conversational context, helping the model bridge the gap between chat and reasoning. ### Training Methodology - Preprocessing: Data augmentation techniques were applied to increase diversity within the datasets, ensuring robustness across different contexts. - Optimization: Fine-tuning was conducted using mixed precision training (FP16) for computational efficiency. - Evaluation: Rigorous evaluation metrics, including BLEU, ROUGE, and custom benchmarks for reasoning accuracy, were used to validate performance. --- ## Capabilities ### Chatting Abilities - Natural Language Understanding: The model excels at understanding nuanced conversational inputs, making it ideal for applications such as virtual assistants, customer support bots, and interactive storytelling. - Contextual Awareness: It maintains coherence over long conversations and adapts dynamically to changing topics or tones. - Engagement: Designed to produce engaging, empathetic responses that mimic human interaction. ### Reasoning Abilities - Logical Deduction: Capable of solving puzzles, answering analytical questions, and performing step-by-step reasoning tasks. - Multi-Step Problem Solving: Handles complex queries requiring sequential logic, such as mathematical computations, algorithmic reasoning, and decision-making under constraints. - Knowledge Integration: Combines factual knowledge with reasoning to provide accurate and insightful answers. --- ## Intended Use Cases ### Primary Applications 1. Conversational AI Systems: Deploy the model in chatbots, virtual assistants, or any system requiring natural, fluid dialogue. 2. Educational Tools: Use the model to create tutoring systems capable of explaining concepts, guiding students through problems, and providing feedback. 3. Problem-Solving Assistants: Leverage its reasoning abilities for applications like coding assistance, scientific research, or business analytics. ### Secondary Applications - Content generation (e.g., writing essays, articles, or creative pieces). - Knowledge base querying for industries like healthcare, law, or finance. - Game development (e.g., creating intelligent NPCs with reasoning capabilities). --- ## Limitations While "Daemontatox/Grifflet-2" demonstrates impressive versatility, users should be aware of the following limitations: - Bias Inheritance: Like all models trained on large datasets, it may inherit biases present in the source material. Careful monitoring is recommended for sensitive use cases. - Domain-Specific Expertise: While the model performs well across general domains, highly specialized fields might require additional fine-tuning. - Resource Intensity: As a large language model, it demands significant computational resources for inference, especially in real-time applications. --- ## Ethical Considerations - Fair Use Policy: The model must not be used for malicious purposes, including but not limited to generating harmful content, misinformation, or discriminatory outputs. - Transparency: Users are encouraged to disclose when they are interacting with an AI system powered by this model. - Data Privacy: Ensure compliance with data protection regulations (e.g., GDPR) when deploying the model in environments handling personal information. --- ## How to Use ### Installation To use "Daemontatox/Grifflet-2," install the necessary libraries and load the model via Hugging Face's `transformers` library: ```python from transformers import AutoTokenizer, AutoModelForCausalLM # Load tokenizer and model tokenizer = AutoTokenizer.from_pretrained("Daemontatox/Grifflet-2") model = AutoModelForCausalLM.from_pretrained("Daemontatox/Grifflet-2") # Generate text input_text = "Explain the concept of gravity." inputs = tokenizer(input_text, return_tensors="pt") outputs = model.generate(inputs, max_length=100) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` ### Hardware Requirements - Recommended: GPU with at least 24GB VRAM (e.g., NVIDIA A100 or similar). - Minimum: CPU with sufficient RAM for smaller batch sizes. --- ## Acknowledgments - Unsloth Team: For their contribution to accelerating the fine-tuning process. - Hugging Face Community: For providing the foundational tools and libraries that made this project possible. - Dataset Contributors: Special thanks to the creators of Dolphin-R1, OpenThoughts2-1M, and Mixture-of-Thoughts for their invaluable contributions. --- ## Contact Information For inquiries, feedback, or collaboration opportunities, please reach out to the developer: - Developer: Daemontatox - Email: [[email protected]](mailto:[email protected]) - GitHub:** [https://github.com/Daemontatox](https://github.com/Daemontatox) ---
New-tutorial-Sophie-Rain-Viral-Videos/FULL.VIDEO.Sophie.Rain.Spiderman.Viral.Video.Tutorial.Official	New-tutorial-Sophie-Rain-Viral-Videos	2025-06-16T02:04:56Z	0	0	null	[ "region:us" ]	null	2025-06-16T02:04:24Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
maily101102/trackingCar	maily101102	2025-06-16T02:04:41Z	0	0	null	[ "safetensors", "gguf", "llama", "unsloth", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-16T00:51:13Z	--- license: apache-2.0 tags: - unsloth ---
BootesVoid/cmbyblijq03bvrdqs0ce71ulk_cmbyfdvbe03i5rdqsnp2il175	BootesVoid	2025-06-16T02:02:57Z	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	2025-06-16T02:02:51Z	--- 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: EMILYY --- # Cmbyblijq03Bvrdqs0Ce71Ulk_Cmbyfdvbe03I5Rdqsnp2Il175 <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 `EMILYY` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "EMILYY", "lora_weights": "https://huggingface.co/BootesVoid/cmbyblijq03bvrdqs0ce71ulk_cmbyfdvbe03i5rdqsnp2il175/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/cmbyblijq03bvrdqs0ce71ulk_cmbyfdvbe03i5rdqsnp2il175', weight_name='lora.safetensors') image = pipeline('EMILYY').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/cmbyblijq03bvrdqs0ce71ulk_cmbyfdvbe03i5rdqsnp2il175/discussions) to add images that show off what you’ve made with this LoRA.
rmdhirr/suja-lorab-ep5-suja-5000	rmdhirr	2025-06-16T02:02:26Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:rmdhirr/merged-suja-latest", "base_model:adapter:rmdhirr/merged-suja-latest", "region:us" ]	null	2025-06-16T02:01:24Z	--- base_model: rmdhirr/merged-suja-latest library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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gradientrouting-spar/horizontal_1_proxy_ntrain_25_ntrig_9_negative_3x3_seed_1_seed_25_seed_2_seed_42_20250616_015359	gradientrouting-spar	2025-06-16T02:02:16Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T02:01:59Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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michelescotto/trainer_output	michelescotto	2025-06-16T02:02:03Z	0	0	transformers	[ "transformers", "safetensors", "deberta-v2", "text-classification", "generated_from_trainer", "base_model:microsoft/deberta-v3-large", "base_model:finetune:microsoft/deberta-v3-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-16T02:01:21Z	--- library_name: transformers license: mit base_model: microsoft/deberta-v3-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: trainer_output 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. --> # trainer_output This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1910 - Accuracy: 0.948 - F1 Macro: 0.8547 - Kappa Score: 0.7094 - Accuracy Balanced: 0.8568 - Precision Macro: 0.8526 - Recall Macro: 0.8568 ## 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: 1.8e-05 - train_batch_size: 16 - eval_batch_size: 40 - seed: 42 - gradient_accumulation_steps: 2 - 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_ratio: 0.06 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 2.14.4 - Tokenizers 0.21.1
panyupj/zephyr-7b-sft-qlora	panyupj	2025-06-16T02:00:22Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "mistral", "text-generation", "generated_from_trainer", "alignment-handbook", "trl", "sft", "conversational", "dataset:HuggingFaceH4/ultrachat_200k", "base_model:mistralai/Mistral-7B-v0.1", "base_model:quantized:mistralai/Mistral-7B-v0.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-06-15T14:35:32Z	--- base_model: mistralai/Mistral-7B-v0.1 datasets: - HuggingFaceH4/ultrachat_200k library_name: transformers model_name: mistralai/Mistral-7B-v0.1 tags: - generated_from_trainer - alignment-handbook - trl - sft licence: license --- # Model Card for mistralai/Mistral-7B-v0.1 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the [['HuggingFaceH4/ultrachat_200k']](https://huggingface.co/datasets/['HuggingFaceH4/ultrachat_200k']) 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="panyupj/zephyr-7b-sft-qlora", 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: 0.12.2 - Transformers: 4.46.3 - Pytorch: 2.1.2 - Datasets: 3.2.0 - Tokenizers: 0.20.3 ## 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}} } ```
Enzogbs/Reinforce-1	Enzogbs	2025-06-16T02:00:18Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2025-06-16T02:00:06Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
CriteriaPO/qwen2.5-3b-dpo-coarse-no-tools	CriteriaPO	2025-06-16T01:59:42Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "dpo", "conversational", "arxiv:2305.18290", "base_model:CriteriaPO/qwen2.5-3b-sft-10", "base_model:finetune:CriteriaPO/qwen2.5-3b-sft-10", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-09T23:23:50Z	--- base_model: CriteriaPO/qwen2.5-3b-sft-10 library_name: transformers model_name: qwen2.5-3b-dpo-coarse-no-tools tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for qwen2.5-3b-dpo-coarse-no-tools This model is a fine-tuned version of [CriteriaPO/qwen2.5-3b-sft-10](https://huggingface.co/CriteriaPO/qwen2.5-3b-sft-10). 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="CriteriaPO/qwen2.5-3b-dpo-coarse-no-tools", 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/bborges/CriteriaPreferences/runs/neggxd33) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.12.2 - Transformers: 4.46.3 - Pytorch: 2.1.2+cu121 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` 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}} } ```
ielabgroup/bert-base-uncased-fineweb100bt-smae-width	ielabgroup	2025-06-16T01:57:41Z	0	0	transformers	[ "transformers", "safetensors", "bert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-06-16T01:55:14Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
phospho-app/alanyom-ACT_BBOX-lerobot_rrwra_data-hya9v	phospho-app	2025-06-16T01:57:26Z	0	0	null	[ "safetensors", "phosphobot", "act", "region:us" ]	null	2025-06-16T01:32:05Z	--- 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: [phospho-app/lerobot_rrwra_data_bboxes](https://huggingface.co/datasets/phospho-app/lerobot_rrwra_data_bboxes) - Wandb run URL: None - Epochs: None - Batch size: 100 - Training steps: 10000 📖 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)
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.15_0.75_epoch1	MinaMila	2025-06-16T01:56:44Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T01:54:58Z	--- 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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New-tutorial-minahil-malik-Viral-Videos/FULL.VIDEO.minahil.malik.Viral.Video.Tutorial.Official	New-tutorial-minahil-malik-Viral-Videos	2025-06-16T01:56:35Z	0	0	null	[ "region:us" ]	null	2025-06-16T01:56:20Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
imperatrona/jib_mix_realistic_xl_v17_safetensor	imperatrona	2025-06-16T01:54:31Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-06-16T01:26:25Z	--- license: creativeml-openrail-m ---
gradientrouting-spar/horizontal_1_proxy_ntrain_25_ntrig_9_negative_3x3_seed_1_seed_25_seed_2_20250616_014540	gradientrouting-spar	2025-06-16T01:53:50Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T01:53:42Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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yquemener/MUAL-Vision	yquemener	2025-06-16T01:53:09Z	0	0	null	[ "license:cc-by-sa-4.0", "region:us" ]	null	2025-06-16T01:40:03Z	--- license: cc-by-sa-4.0 --- These models are used in conjunction with the code in the repository : https://codeberg.org/yquemener/mual-redo They are used to provide the [MUAL](mual.fr) robots with visual information. They consist of two files: `exp.pt` which is a fine-tuned YOLOv5s model and `rails.pt` which is a custom model, made from the UNet architecture, and that is used as a pre-processing to a houfh lines detector in order to indentify aluminum rails in an image. ![](OnlyVision.jpg)
danaash/mullari_style_LoRA	danaash	2025-06-16T01:52:28Z	0	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2025-06-16T01:52:24Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: drawing in Sveta Mullari style widget: [] tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - danaash/mullari_style_LoRA <Gallery /> ## Model description These are danaash/mullari_style_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use drawing in Sveta Mullari style to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](danaash/mullari_style_LoRA/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
ncgc/pythia_125M_sft_hh_full_sft_trainer_rand_highest	ncgc	2025-06-16T01:52:23Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "region:us" ]	null	2025-06-16T00:27:40Z	--- base_model: EleutherAI/pythia-125M library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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ncgc/pythia_125M_sft_hh_full_sft_trainer_rand_lowest	ncgc	2025-06-16T01:51:34Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "region:us" ]	null	2025-06-15T22:47:45Z	--- base_model: EleutherAI/pythia-125M library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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gradientrouting-spar/horizontal_1_proxy_ntrain_25_ntrig_9_negative_3x3_seed_1_seed_25_20250616_013717	gradientrouting-spar	2025-06-16T01:45:31Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T01:45:18Z	--- 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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BootesVoid/cmbs7qa4e0517h4x59dp33vpm_cmbyebxmd03gvrdqslli40ghm	BootesVoid	2025-06-16T01:42:57Z	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	2025-06-16T01:42:56Z	--- 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: VELLAGIRL --- # Cmbs7Qa4E0517H4X59Dp33Vpm_Cmbyebxmd03Gvrdqslli40Ghm <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 `VELLAGIRL` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "VELLAGIRL", "lora_weights": "https://huggingface.co/BootesVoid/cmbs7qa4e0517h4x59dp33vpm_cmbyebxmd03gvrdqslli40ghm/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/cmbs7qa4e0517h4x59dp33vpm_cmbyebxmd03gvrdqslli40ghm', weight_name='lora.safetensors') image = pipeline('VELLAGIRL').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/cmbs7qa4e0517h4x59dp33vpm_cmbyebxmd03gvrdqslli40ghm/discussions) to add images that show off what you’ve made with this LoRA.
7-VIDEOS-18-parveen-viral-video/wATCH.parveen.Viral.Video.Original.Full.Video.Link.Official	7-VIDEOS-18-parveen-viral-video	2025-06-16T01:42:56Z	0	0	null	[ "region:us" ]	null	2025-06-16T01:42:36Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.25_0.05_epoch1	MinaMila	2025-06-16T01:40:47Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T01:38:50Z	--- 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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aquiffoo/aquif-neo-2-345m-c2	aquiffoo	2025-06-16T01:40:31Z	0	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "language", "aquif", "text-generation-inference", "math", "coding", "small", "en", "dataset:sahil2801/CodeAlpaca-20k", "dataset:iamtarun/python_code_instructions_18k_alpaca", "dataset:reedmayhew/gpt-4.5-100x", "license:apache-2.0", "autotrain_compatible", "region:us" ]	text-generation	2025-06-16T01:22:15Z	--- pipeline_tag: text-generation inference: false license: apache-2.0 library_name: transformers tags: - language - aquif - gpt2 - text-generation-inference - math - coding - small language: - en datasets: - sahil2801/CodeAlpaca-20k - iamtarun/python_code_instructions_18k_alpaca - reedmayhew/gpt-4.5-100x --- # aquif-neo-2-345m-c2 This is the second checkpoint of the 'aquif-neo-2-345m' model, a next-generation language model developed by aquif AI. This checkpoint builds upon the foundation established in c1, with enhanced fine-tuning focused on code generation and programming tasks, representing the second stage in a 5-checkpoint training process designed to create a versatile and capable model. ## Model Details Base Model: gpt2-medium Method: LoRA (Low-Rank Adaptation) Parameter Count: 355 million params ## Training Information This checkpoint was trained as the second stage of a multi-checkpoint process, specifically focusing on improving coding capabilities. The training was performed using a network-resilient script that includes fallback mechanisms for data loading and model initialization. Checkpoint Number: 2/5 Focus Area: Code generation and programming tasks Hardware: Trained on a Google Colab T4 GPU Training Duration: Approximately 2.5 hours for this checkpoint Training Framework: PyTorch, Hugging Face Transformers, PEFT, bitsandbytes, TRL Quantization: 8-bit Training Datasets: - CodeAlpaca-20k: 4,000 samples for code generation tasks - Python Code Instructions: 2,000 samples for Python-specific programming - GPT-4.5 100x: Complete dataset for enhanced writing and factual accuracy ## LoRA Configuration: - r=8 - lora_alpha=16 - target_modules: ["q_attn", "c_attn", "c_proj", "c_fc", "attn.c_attn", "attn.c_proj", "mlp.c_fc", "mlp.c_proj"] - lora_dropout=0.05 - bias="none" - task_type="CAUSAL_LM" ## Training Arguments: - per_device_train_batch_size=2 - gradient_accumulation_steps=16 - num_train_epochs=1 (for this checkpoint) - learning_rate=1e-5 - max_steps=400 Optimized for 8-bit training. ## Training Loss Data The following table shows the training loss recorded during the training of this checkpoint: \| Step \| Training Loss \| \|------\|---------------\| \| 20 \| 1.9971 \| \| 40 \| 1.9395 \| \| 60 \| 1.9527 \| \| 80 \| 1.8974 \| \| 100 \| 1.9324 \| \| 120 \| 1.8383 \| \| 140 \| 1.8405 \| \| 160 \| 1.7731 \| \| 180 \| 1.7920 \| \| 200 \| 1.6615 \| \| 220 \| 1.6679 \| \| 240 \| 1.6748 \| \| 260 \| 1.6765 \| \| 280 \| 1.6969 \| \| 300 \| 1.6478 \| \| 320 \| 1.6690 \| \| 340 \| 1.6214 \| \| 360 \| 1.5797 \| \| 380 \| 1.6272 \| \| 400 \| 1.6243 \| Note: Training loss is a metric that indicates how well the model is learning. A decreasing loss generally suggests improvement. C2 shows significantly lower loss values compared to C1, indicating improved learning on the code-focused dataset. ## Training Focus Checkpoint 2 emphasizes code-related tasks and programming instruction following. The synthetic dataset for this checkpoint includes: - Python function writing and implementation - List comprehensions and data structures - String manipulation techniques - Algorithm explanations and code examples This targeted approach builds upon the conversational foundation from c1 while developing stronger programming capabilities. ## Intended Use This checkpoint is an intermediate model in the development of the full 'aquif-neo-2'. While showing improved coding capabilities compared to c1, it is not intended for production use but serves as a foundation for subsequent fine-tuning checkpoints focusing on additional domains and tasks. ## How to Load the Model You can load this model using the Hugging Face 'transformers' library: ```python from transformers import AutoTokenizer, AutoModelForCausalLM model_name = "aquiffoo/aquif-neo-2-345m-c2" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name) ``` ## Future Checkpoints This is the second of 5 planned checkpoints. The remaining checkpoints will focus on: - c3: Mathematical reasoning and problem-solving - c4: Knowledge and factual understanding - c5: Mixed capabilities integration and refinement Each checkpoint builds upon the previous ones to create a more well-rounded and capable model. License: Apache 2.0
cashmerepancake/ppo-LunarLander-v2-2	cashmerepancake	2025-06-16T01:39:38Z	0	0	null	[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2025-06-16T01:39:32Z	--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -115.44 +/- 43.71 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'gym_id': 'LunarLander-v2' 'learning_rate': 0.00025 'seed': 1 'total_timesteps': 59000 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'ppo-implementation-details' 'wandb_entity': None 'capture_video': False 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'cashmerepancake/ppo-LunarLander-v2-2' 'env_id': 'LunarLander-v2' 'batch_size': 512 'minibatch_size': 128} ```
gradientrouting-spar/standard_notMerged_seed_1_seed_2_20250616_010429	gradientrouting-spar	2025-06-16T01:39:16Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T01:39:07Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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gradientrouting-spar/horizontal_1_proxy_ntrain_25_ntrig_9_negative_3x3_seed_1_20250616_012856	gradientrouting-spar	2025-06-16T01:37:08Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T01:36:59Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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cashmerepancake/ppo-LunarLander-v2	cashmerepancake	2025-06-16T01:35:37Z	18	0	null	[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2025-06-10T03:20:29Z	--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 108.70 +/- 62.55 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'gym_id': 'CartPole-v1' 'learning_rate': 0.00025 'seed': 1 'total_timesteps': 25000 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'ppo-implementation-details' 'wandb_entity': None 'capture_video': False 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'cashmerepancake/ppo-LunarLander-v2' 'env_id': 'LunarLander-v2' 'batch_size': 512 'minibatch_size': 128} ```
thomas-sounack/BioClinical-ModernBERT-large	thomas-sounack	2025-06-16T01:33:26Z	30	5	transformers	[ "transformers", "pytorch", "safetensors", "modernbert", "fill-mask", "masked-lm", "long-context", "BioClinical-ModernBERT", "en", "arxiv:2506.10896", "base_model:answerdotai/ModernBERT-large", "base_model:finetune:answerdotai/ModernBERT-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-05-07T15:56:40Z	--- license: mit language: - en base_model: - answerdotai/ModernBERT-large pipeline_tag: fill-mask library_name: transformers tags: - fill-mask - masked-lm - long-context - modernbert - BioClinical-ModernBERT --- # BioClinical ModernBERT BioClinical ModernBERT is available in two sizes: [base](https://huggingface.co/thomas-sounack/BioClinical-ModernBERT-base) (150M parameters) and [large](https://huggingface.co/thomas-sounack/BioClinical-ModernBERT-large) (396M parameters). The model training checkpoints can be found [here](https://huggingface.co/thomas-sounack/BioClinical-ModernBERT-checkpoints), and our code is available in our [GitHub repository](https://github.com/lindvalllab/BioClinical-ModernBERT). ## Table of Contents 1. [Model Summary](#model-summary) 2. [Usage](#usage) 3. [Training](#training) 4. [Evaluation](#evaluation) 5. [License](#license) 6. [Citation](#citation) ## Model Summary BioClinical ModernBERT is a domain-adapted encoder that builds on ModernBERT [base](https://huggingface.co/answerdotai/ModernBERT-base) and [large](https://huggingface.co/answerdotai/ModernBERT-large), incorporating long-context processing and substantial improvements in speed and performance for biomedical and clinical NLP. BioClinical ModernBERT is trained on the largest biomedical and clinical corpus to date, with over 53.5 billion tokens, and addresses a key limitation of prior clinical encoders by leveraging 20 datasets from diverse institutions, domains, and geographic regions, rather than relying on data from a single source. ## Usage You can use these models directly with the `transformers` library starting from v4.48.0: ```sh pip install -U transformers>=4.48.0 ``` Since BioClinical ModernBERT is a Masked Language Model (MLM), you can use the `fill-mask` pipeline or load it via `AutoModelForMaskedLM`. To use BioClinical ModernBERT for downstream tasks like classification, retrieval, or QA, fine-tune it following standard BERT fine-tuning recipes. ⚠️ If your GPU supports it, we recommend using BioClinical ModernBERT with Flash Attention 2 to reach the highest efficiency. To do so, install Flash Attention as follows, then use the model as normal: ```bash pip install flash-attn ``` Using `AutoModelForMaskedLM`: ```python from transformers import AutoTokenizer, AutoModelForMaskedLM model_id = "thomas-sounack/BioClinical-ModernBERT-large" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForMaskedLM.from_pretrained(model_id) text = "Mitochondria is the powerhouse of the [MASK]." inputs = tokenizer(text, return_tensors="pt") outputs = model(inputs) # To get predictions for the mask: masked_index = inputs["input_ids"][0].tolist().index(tokenizer.mask_token_id) predicted_token_id = outputs.logits[0, masked_index].argmax(axis=-1) predicted_token = tokenizer.decode(predicted_token_id) print("Predicted token:", predicted_token) # Predicted token: cell ``` Using a pipeline: ```python import torch from transformers import pipeline from pprint import pprint pipe = pipeline( "fill-mask", model="thomas-sounack/BioClinical-ModernBERT-large", torch_dtype=torch.bfloat16, ) input_text = "[MASK] is a disease caused by an uncontrolled division of abnormal cells in a part of the body." results = pipe(input_text) pprint(results) ``` Note: BioClinical ModernBERT, similarly to ModernBERT, does not use token type IDs unlike some earlier BERT models. Most downstream usage is identical to standard BERT models on the Hugging Face Hub, except you can omit the `token_type_ids` parameter. ## Training ### Data BioClinical ModernBERT is trained on 50.7B tokens of biomedical text gathered from PubMed and PMC, and 2.8B tokens of clinical text from 20 datasets which are detailed in the table below. \| Name \| Country \| Clinical Source \| Clinical Context \| Samples \| Tokens (M) \| \|----------------------------\|--------------\|------------------------------------\|-----------------------\|-----------\|------------\| \| ACI-BENCH \| US \| Clinical Notes \| Not Reported \| 207 \| 0.1 \| \| ADE Corpus \| Several \| Clinical Notes \| Not Reported \| 20,896 \| 0.5 \| \| Brain MRI Stroke \| Korea \| Radiology Reports \| Neurology \| 2,603 \| 0.2 \| \| CheXpert Plus \| US \| Radiology Reports \| Pulmonology \| 223,460 \| 60.6 \| \| CHIFIR \| Australia \| Pathology Reports \| Hematology / Oncology \| 283 \| 0.1 \| \| CORAL \| US \| Progress Notes \| Hematology / Oncology \| 240 \| 0.7 \| \| Eye Gaze CXR \| US \| Radiology Reports \| Pulmonology \| 892 \| 0.03 \| \| Gout Chief Complaints \| US \| Chief Complaint \| Internal Medicine \| 8,429 \| 0.2 \| \| ID-68 \| UK \| Clinical Notes \| Psychology \| 78 \| 0.02 \| \| Inspect \| US \| Radiology Reports \| Pulmonology \| 22,259 \| 2.8 \| \| MedNLI \| US \| Clinical Notes \| Internal Medicine \| 14,047 \| 0.5 \| \| MedQA \| US \| National Medical Board Examination \| Not Reported \| 14,366 \| 2.0 \| \| MIMIC-III \| US \| Clinical Notes \| Internal Medicine \| 2,021,411 \| 1,047.7 \| \| MIMIC-IV Note \| US \| Clinical Notes \| Internal Medicine \| 2,631,243 \| 1,765.7 \| \| MTSamples \| Not Reported \| Clinical Notes \| Internal Medicine \| 2,358 \| 1.7 \| \| Negex \| US \| Discharge Summaries \| Not Reported \| 2,056 \| 0.1 \| \| PriMock57 \| UK \| Simulated Patient Care \| Internal Medicine \| 57 \| 0.01 \| \| Q-Pain \| US \| Clinical Vignettes \| Palliative Care \| 51 \| 0.01 \| \| REFLACX \| US \| Radiology Reports \| Pulmonology \| 2,543 \| 0.1 \| \| Simulated Resp. Interviews \| Canada \| Simulated Patient Care \| Pulmonology \| 272 \| 0.6 \| ### Methodology BioClinical ModernBERT large is trained in two phases. This model is initialized from the last stable-phase checkpoint of ModernBERT large and trained with the same hyperparameters: learning rate of 5e-5 and batch size of 77. - Phase 1: Training on 160.5B tokens from PubMed, PMC, and the 20 clinical datasets. Learning rate remains constant throughout this stage, and the masking probability is set at 30%. - Phase 2: Training on the 20 clinical datasets only. Masking probability is reduced to 15%. The model is trained for 3 epochs using a hybrid schedule: constant learning rate for the first two epochs, followed by a 1-sqrt decay in the final epoch. ## Evaluation \| \| Model \| Context Length \| ChemProt \| Phenotype \| COS \| Social History \| DEID \| \|-------\|--------------------------------\|----------------\|----------\|-----------\|----------\|----------------\|----------\| \| Base \| BioBERT \| 512 \| 89.5 \| 26.6 \| 94.9 \| 55.8 \| 74.3 \| \| \| Clinical BERT \| 512 \| 88.3 \| 25.8 \| 95.0 \| 55.2 \| 74.2 \| \| \| BioMed-RoBERTa \| 512 \| 89.0 \| 36.8 \| 94.9 \| 55.2 \| 81.1 \| \| \| Clinical-BigBird \| 4096 \| 87.4 \| 26.5 \| 94.0 \| 53.3 \| 71.2 \| \| \| Clinical-Longformer \| 4096 \| 74.2 \| 46.4 \| 95.2 \| 56.8 \| 82.3 \| \| \| Clinical ModernBERT \| 8192 \| 86.9 \| 54.9 \| 93.7 \| 53.8 \| 44.4 \| \| \| ModernBERT - base \| 8192 \| 89.5 \| 48.4 \| 94.0 \| 53.1 \| 78.3 \| \| \| BioClinical ModernBERT - base \| 8192 \| 89.9 \| 58.1 \| 95.1 \| 58.5 \| 82.7 \| \| Large \| ModernBERT - large \| 8192 \| 90.2 \| 58.3 \| 94.4 \| 54.8 \| 82.1 \| \| \| BioClinical ModernBERT - large \| 8192 \| 90.8 \| 60.8 \| 95.1 \| 57.1 \| 83.8** \| ## License We release the BioClinical ModernBERT base and large model weights and training checkpoints under the MIT license. ## Citation If you use BioClinical ModernBERT in your work, please cite our [preprint](https://arxiv.org/abs/2506.10896): ``` @misc{sounack2025bioclinicalmodernbertstateoftheartlongcontext, title={BioClinical ModernBERT: A State-of-the-Art Long-Context Encoder for Biomedical and Clinical NLP}, author={Thomas Sounack and Joshua Davis and Brigitte Durieux and Antoine Chaffin and Tom J. Pollard and Eric Lehman and Alistair E. W. Johnson and Matthew McDermott and Tristan Naumann and Charlotta Lindvall}, year={2025}, eprint={2506.10896}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2506.10896}, } ```
thomas-sounack/BioClinical-ModernBERT-base	thomas-sounack	2025-06-16T01:33:11Z	115	9	transformers	[ "transformers", "pytorch", "safetensors", "modernbert", "fill-mask", "masked-lm", "long-context", "BioClinical-ModernBERT", "en", "arxiv:2506.10896", "base_model:answerdotai/ModernBERT-base", "base_model:finetune:answerdotai/ModernBERT-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-05-07T15:54:29Z	--- license: mit language: - en base_model: - answerdotai/ModernBERT-base pipeline_tag: fill-mask tags: - fill-mask - masked-lm - long-context - modernbert - BioClinical-ModernBERT library_name: transformers --- # BioClinical ModernBERT BioClinical ModernBERT is available in two sizes: [base](https://huggingface.co/thomas-sounack/BioClinical-ModernBERT-base) (150M parameters) and [large](https://huggingface.co/thomas-sounack/BioClinical-ModernBERT-large) (396M parameters). The model training checkpoints can be found [here](https://huggingface.co/thomas-sounack/BioClinical-ModernBERT-checkpoints), and our code is available in our [GitHub repository](https://github.com/lindvalllab/BioClinical-ModernBERT). ## Table of Contents 1. [Model Summary](#model-summary) 2. [Usage](#usage) 3. [Training](#training) 4. [Evaluation](#evaluation) 5. [License](#license) 6. [Citation](#citation) ## Model Summary BioClinical ModernBERT is a domain-adapted encoder that builds on ModernBERT [base](https://huggingface.co/answerdotai/ModernBERT-base) and [large](https://huggingface.co/answerdotai/ModernBERT-large), incorporating long-context processing and substantial improvements in speed and performance for biomedical and clinical NLP. BioClinical ModernBERT is trained on the largest biomedical and clinical corpus to date, with over 53.5 billion tokens, and addresses a key limitation of prior clinical encoders by leveraging 20 datasets from diverse institutions, domains, and geographic regions, rather than relying on data from a single source. ## Usage You can use these models directly with the `transformers` library starting from v4.48.0: ```sh pip install -U transformers>=4.48.0 ``` Since BioClinical ModernBERT is a Masked Language Model (MLM), you can use the `fill-mask` pipeline or load it via `AutoModelForMaskedLM`. To use BioClinical ModernBERT for downstream tasks like classification, retrieval, or QA, fine-tune it following standard BERT fine-tuning recipes. ⚠️ If your GPU supports it, we recommend using BioClinical ModernBERT with Flash Attention 2 to reach the highest efficiency. To do so, install Flash Attention as follows, then use the model as normal: ```bash pip install flash-attn ``` Using `AutoModelForMaskedLM`: ```python from transformers import AutoTokenizer, AutoModelForMaskedLM model_id = "thomas-sounack/BioClinical-ModernBERT-base" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForMaskedLM.from_pretrained(model_id) text = "Mitochondria is the powerhouse of the [MASK]." inputs = tokenizer(text, return_tensors="pt") outputs = model(inputs) # To get predictions for the mask: masked_index = inputs["input_ids"][0].tolist().index(tokenizer.mask_token_id) predicted_token_id = outputs.logits[0, masked_index].argmax(axis=-1) predicted_token = tokenizer.decode(predicted_token_id) print("Predicted token:", predicted_token) # Predicted token: cell ``` Using a pipeline: ```python import torch from transformers import pipeline from pprint import pprint pipe = pipeline( "fill-mask", model="thomas-sounack/BioClinical-ModernBERT-base", torch_dtype=torch.bfloat16, ) input_text = "[MASK] is a disease caused by an uncontrolled division of abnormal cells in a part of the body." results = pipe(input_text) pprint(results) ``` Note: BioClinical ModernBERT, similarly to ModernBERT, does not use token type IDs unlike some earlier BERT models. Most downstream usage is identical to standard BERT models on the Hugging Face Hub, except you can omit the `token_type_ids` parameter. ## Training ### Data BioClinical ModernBERT is trained on 50.7B tokens of biomedical text gathered from PubMed and PMC, and 2.8B tokens of clinical text from 20 datasets which are detailed in the table below. \| Name \| Country \| Clinical Source \| Clinical Context \| Samples \| Tokens (M) \| \|----------------------------\|--------------\|------------------------------------\|-----------------------\|-----------\|------------\| \| ACI-BENCH \| US \| Clinical Notes \| Not Reported \| 207 \| 0.1 \| \| ADE Corpus \| Several \| Clinical Notes \| Not Reported \| 20,896 \| 0.5 \| \| Brain MRI Stroke \| Korea \| Radiology Reports \| Neurology \| 2,603 \| 0.2 \| \| CheXpert Plus \| US \| Radiology Reports \| Pulmonology \| 223,460 \| 60.6 \| \| CHIFIR \| Australia \| Pathology Reports \| Hematology / Oncology \| 283 \| 0.1 \| \| CORAL \| US \| Progress Notes \| Hematology / Oncology \| 240 \| 0.7 \| \| Eye Gaze CXR \| US \| Radiology Reports \| Pulmonology \| 892 \| 0.03 \| \| Gout Chief Complaints \| US \| Chief Complaint \| Internal Medicine \| 8,429 \| 0.2 \| \| ID-68 \| UK \| Clinical Notes \| Psychology \| 78 \| 0.02 \| \| Inspect \| US \| Radiology Reports \| Pulmonology \| 22,259 \| 2.8 \| \| MedNLI \| US \| Clinical Notes \| Internal Medicine \| 14,047 \| 0.5 \| \| MedQA \| US \| National Medical Board Examination \| Not Reported \| 14,366 \| 2.0 \| \| MIMIC-III \| US \| Clinical Notes \| Internal Medicine \| 2,021,411 \| 1,047.7 \| \| MIMIC-IV Note \| US \| Clinical Notes \| Internal Medicine \| 2,631,243 \| 1,765.7 \| \| MTSamples \| Not Reported \| Clinical Notes \| Internal Medicine \| 2,358 \| 1.7 \| \| Negex \| US \| Discharge Summaries \| Not Reported \| 2,056 \| 0.1 \| \| PriMock57 \| UK \| Simulated Patient Care \| Internal Medicine \| 57 \| 0.01 \| \| Q-Pain \| US \| Clinical Vignettes \| Palliative Care \| 51 \| 0.01 \| \| REFLACX \| US \| Radiology Reports \| Pulmonology \| 2,543 \| 0.1 \| \| Simulated Resp. Interviews \| Canada \| Simulated Patient Care \| Pulmonology \| 272 \| 0.6 \| ### Methodology BioClinical ModernBERT base is trained in two phases. This model is initialized from the last stable-phase checkpoint of ModernBERT base and trained with the same hyperparameters: learning rate of 3e-4 and batch size of 72. - Phase 1: Training on 160.5B tokens from PubMed, PMC, and the 20 clinical datasets. Learning rate remains constant throughout this stage, and the masking probability is set at 30%. - Phase 2: Training on the 20 clinical datasets only. Masking probability is reduced to 15%. The model is trained for 3 epochs with a 1-sqrt learning rate decay. ## Evaluation \| \| Model \| Context Length \| ChemProt \| Phenotype \| COS \| Social History \| DEID \| \|-------\|--------------------------------\|----------------\|----------\|-----------\|----------\|----------------\|----------\| \| Base \| BioBERT \| 512 \| 89.5 \| 26.6 \| 94.9 \| 55.8 \| 74.3 \| \| \| Clinical BERT \| 512 \| 88.3 \| 25.8 \| 95.0 \| 55.2 \| 74.2 \| \| \| BioMed-RoBERTa \| 512 \| 89.0 \| 36.8 \| 94.9 \| 55.2 \| 81.1 \| \| \| Clinical-BigBird \| 4096 \| 87.4 \| 26.5 \| 94.0 \| 53.3 \| 71.2 \| \| \| Clinical-Longformer \| 4096 \| 74.2 \| 46.4 \| 95.2 \| 56.8 \| 82.3 \| \| \| Clinical ModernBERT \| 8192 \| 86.9 \| 54.9 \| 93.7 \| 53.8 \| 44.4 \| \| \| ModernBERT - base \| 8192 \| 89.5 \| 48.4 \| 94.0 \| 53.1 \| 78.3 \| \| \| BioClinical ModernBERT - base \| 8192 \| 89.9 \| 58.1 \| 95.1 \| 58.5 \| 82.7 \| \| Large \| ModernBERT - large \| 8192 \| 90.2 \| 58.3 \| 94.4 \| 54.8 \| 82.1 \| \| \| BioClinical ModernBERT - large \| 8192 \| 90.8 \| 60.8 \| 95.1 \| 57.1 \| 83.8** \| ## License We release the BioClinical ModernBERT base and large model weights and training checkpoints under the MIT license. ## Citation If you use BioClinical ModernBERT in your work, please cite our [preprint](https://arxiv.org/abs/2506.10896): ``` @misc{sounack2025bioclinicalmodernbertstateoftheartlongcontext, title={BioClinical ModernBERT: A State-of-the-Art Long-Context Encoder for Biomedical and Clinical NLP}, author={Thomas Sounack and Joshua Davis and Brigitte Durieux and Antoine Chaffin and Tom J. Pollard and Eric Lehman and Alistair E. W. Johnson and Matthew McDermott and Tristan Naumann and Charlotta Lindvall}, year={2025}, eprint={2506.10896}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2506.10896}, } ```

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