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modelId string	author string	last_modified timestamp[us, tz=UTC]	downloads int64	likes int64	library_name string	tags sequence	pipeline_tag string	createdAt timestamp[us, tz=UTC]	card string
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.15_0.5_0.75_epoch1	MinaMila	2025-06-16T09:10:07Z	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	2025-06-16T09:08:06Z	--- 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_1e-5_1.0_0.5_0.05_0.15_epoch1	MinaMila	2025-06-16T09:07:58Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T09:06:00Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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aplux/YOLO-NAS-l	aplux	2025-06-16T09:05:49Z	0	0	null	[ "AIoT", "QNN", "object-detection", "license:other", "region:us" ]	object-detection	2025-06-12T07:23:16Z	--- license: other license_name: yolo-nas-license license_link: https://github.com/Deci-AI/super-gradients/blob/master/LICENSE.YOLONAS.md pipeline_tag: object-detection tags: - AIoT - QNN --- ![](https://aiot.aidlux.com/_next/image?url=%2Fapi%2Fv1%2Ffiles%2Fmodel%2Fcover%2F20250319065039_%25E5%259B%25BE-27.png&w=640&q=75) ## YOLO-NAS-l: Object Detection YOLO-NAS is a next-generation real-time object detection model optimized via Neural Architecture Search (NAS), automating the balance between accuracy and speed for superior performance in complex scenarios. It integrates hybrid quantization-aware architectures with reparameterized blocks and dynamic sparse attention, enhancing small/occluded object detection while reducing computation. Through multi-objective optimization (e.g., latency, parameters, mAP), it discovers efficient structures supporting FP16/INT8 quantization, achieving ~5% higher mAP than YOLOv8 on COCO with 80+ FPS on mobile GPUs. Ideal for autonomous driving and surveillance, it balances edge-device constraints and high precision, offering flexible speed-accuracy tradeoffs. ### Source model - Input shape: 1x3x640x640 - Number of parameters: 40.06M - Model size: 160.37M - Output shape: 1x8400x4, 1x8400x80 The source model can be found [here](https://github.com/Deci-AI/super-gradients) ## Performance Reference Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models) ## Inference & Model Conversion Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models) ## License - Source Model: [YOLO-NAS License](https://github.com/Deci-AI/super-gradients/blob/master/LICENSE.YOLONAS.md) - Deployable Model: [YOLO-NAS License](https://github.com/Deci-AI/super-gradients/blob/master/LICENSE.YOLONAS.md)
Klaudesens/indobert-fake-news-rus	Klaudesens	2025-06-16T09:03:51Z	9	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-17T10:10:53Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.15_0.75_0.05_epoch2	MinaMila	2025-06-16T09:03:04Z	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	2025-06-16T09:01:08Z	--- 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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najmharani/gemma-1b-biography_ver2	najmharani	2025-06-16T09:02:39Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma3_text", "trl", "en", "base_model:unsloth/gemma-3-1b-it-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-1b-it-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-16T09:02:22Z	--- base_model: unsloth/gemma-3-1b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3_text - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: najmharani - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-1b-it-unsloth-bnb-4bit This gemma3_text 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_1e-5_1.0_0.5_0.05_0.25_epoch2	MinaMila	2025-06-16T08:59:41Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T08:57:58Z	--- 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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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]
Shakurbrown/StrategicAI	Shakurbrown	2025-06-16T08:58:36Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-16T08:58:36Z	--- license: apache-2.0 ---
adityag6994/ppo-LunarLander-v2	adityag6994	2025-06-16T08:58:30Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-06-16T08:58:09Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 237.46 +/- 20.34 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
Mhammad2023/code-search-net-tokenizer	Mhammad2023	2025-06-16T08:56:25Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T08:56:24Z	--- 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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Antrugos/mbart-namuy-es_30k_corpus	Antrugos	2025-06-16T08:56:23Z	0	0	transformers	[ "transformers", "safetensors", "mbart", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-06-15T18:12:31Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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floflodebilbao/T5_sum_approach1	floflodebilbao	2025-06-16T08:56:02Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-06-13T13:11:40Z	--- library_name: transformers license: apache-2.0 base_model: google-t5/t5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: T5_sum_approach1 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. --> # T5_sum_approach1 This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.0516 - Rouge1: 0.2637 - Rouge2: 0.0869 - Rougel: 0.2005 - Rougelsum: 0.2005 - Gen Len: 20.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 5 \| 3.2151 \| 0.2299 \| 0.0752 \| 0.1928 \| 0.1951 \| 20.0 \| \| No log \| 2.0 \| 10 \| 3.1181 \| 0.2435 \| 0.0873 \| 0.1967 \| 0.1975 \| 20.0 \| \| No log \| 3.0 \| 15 \| 3.0680 \| 0.2637 \| 0.0869 \| 0.2005 \| 0.2005 \| 20.0 \| \| No log \| 4.0 \| 20 \| 3.0516 \| 0.2637 \| 0.0869 \| 0.2005 \| 0.2005 \| 20.0 \| ### Framework versions - Transformers 4.52.4 - Pytorch 2.7.0+cu126 - Datasets 3.6.0 - Tokenizers 0.21.1
Aleteian/TerraIncognita-24B-Q4_K_M-GGUF	Aleteian	2025-06-16T08:55:43Z	0	0	null	[ "gguf", "merge", "mergekit", "lazymergekit", "LatitudeGames/Harbinger-24B", "ReadyArt/Broken-Tutu-24B-Unslop-v2.0", "llama-cpp", "gguf-my-repo", "base_model:Aleteian/TerraIncognita-24B", "base_model:quantized:Aleteian/TerraIncognita-24B", "endpoints_compatible", "region:us" ]	null	2025-06-16T08:54:39Z	--- base_model: Aleteian/TerraIncognita-24B tags: - merge - mergekit - lazymergekit - LatitudeGames/Harbinger-24B - ReadyArt/Broken-Tutu-24B-Unslop-v2.0 - llama-cpp - gguf-my-repo --- # Aleteian/TerraIncognita-24B-Q4_K_M-GGUF This model was converted to GGUF format from [`Aleteian/TerraIncognita-24B`](https://huggingface.co/Aleteian/TerraIncognita-24B) 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/Aleteian/TerraIncognita-24B) 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 Aleteian/TerraIncognita-24B-Q4_K_M-GGUF --hf-file terraincognita-24b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Aleteian/TerraIncognita-24B-Q4_K_M-GGUF --hf-file terraincognita-24b-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Aleteian/TerraIncognita-24B-Q4_K_M-GGUF --hf-file terraincognita-24b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Aleteian/TerraIncognita-24B-Q4_K_M-GGUF --hf-file terraincognita-24b-q4_k_m.gguf -c 2048 ```
zen17/llama-3.1-8b-finetuned	zen17	2025-06-16T08:55:07Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-16T07:32:27Z	--- base_model: unsloth/llama-3.1-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: zen17 - License: apache-2.0 - Finetuned from model : unsloth/llama-3.1-8b-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)
SweeLingNiu/distilbert-textclassification-bitext-v1	SweeLingNiu	2025-06-16T08:54:49Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-16T08:54:41Z	--- 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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hirundo-io/telecom-ft-500-persons-llama-3.2-3b-id-injection-unlearned	hirundo-io	2025-06-16T08:54:06Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T11:11:34Z	--- 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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floflodebilbao/T5_sum_challenge1	floflodebilbao	2025-06-16T08:53:49Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-06-13T13:04:51Z	--- library_name: transformers license: apache-2.0 base_model: google-t5/t5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: T5_sum_challenge1 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. --> # T5_sum_challenge1 This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.2292 - Rouge1: 0.2371 - Rouge2: 0.0636 - Rougel: 0.2005 - Rougelsum: 0.1987 - Gen Len: 19.95 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 5 \| 3.3453 \| 0.2407 \| 0.0635 \| 0.1972 \| 0.1947 \| 20.0 \| \| No log \| 2.0 \| 10 \| 3.2762 \| 0.2334 \| 0.0628 \| 0.1914 \| 0.1895 \| 20.0 \| \| No log \| 3.0 \| 15 \| 3.2412 \| 0.2371 \| 0.0636 \| 0.2005 \| 0.1987 \| 19.95 \| \| No log \| 4.0 \| 20 \| 3.2292 \| 0.2371 \| 0.0636 \| 0.2005 \| 0.1987 \| 19.95 \| ### Framework versions - Transformers 4.52.4 - Pytorch 2.7.0+cu126 - Datasets 3.6.0 - Tokenizers 0.21.1
veddhanth/lora-trained-xl-stage-2-pretrained-enc-enhanced-330	veddhanth	2025-06-16T08:51:58Z	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	2025-06-16T08:31:59Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: a realistic portrait of sks face 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 - veddhanth/lora-trained-xl-stage-2-pretrained-enc-enhanced-330 <Gallery /> ## Model description These are veddhanth/lora-trained-xl-stage-2-pretrained-enc-enhanced-330 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: True. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a realistic portrait of sks face to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](veddhanth/lora-trained-xl-stage-2-pretrained-enc-enhanced-330/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]
MinaMila/gemma_2b_unlearned_2nd_1e-5_1.0_0.5_0.05_0.25_epoch1	MinaMila	2025-06-16T08:51:56Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T08:50:08Z	--- 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/phi3_unlearned_2nd_5e-7_1.0_0.15_0.75_0.15_epoch2	MinaMila	2025-06-16T08:49:35Z	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	2025-06-16T08:47:44Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Bertug1911/BrtLLama-1-Base	Bertug1911	2025-06-16T08:47:28Z	0	0	null	[ "arxiv:1910.09700", "license:cc-by-nc-4.0", "region:us" ]	null	2025-06-16T08:26:20Z	--- license: cc-by-nc-4.0 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description *BrtLLama-1-Base* BrtLLama is a LLama version of "BrtGPT-124m-Base (https://huggingface.co/Bertug1911/BrtGPT-124m-Base)" I created a LLama version because, I want peoples can use it on *LOCAL* with *Ollama. But GPT architecture don't supported by Ollama [Only supports LLama and few Mistral architecture(s)] so, I decided to do it a LLama version! I am not trained model but I will train on 10-15 july 2025. Wait for updates (On 21 june a demo model will published)! - Developed by:* Bertug Gunel (Bertuğ Günel) - Funded by [optional]: Nobody - Shared by [optional]: Nobody - Model type: LLama-architecture - Language(s) (NLP): En (English) - License: CC BY-NC 4.0 - Finetuned from model [optional]: [Not FineTuned ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: Coming soon! - Paper [optional]: Cooming soon! - Demo [optional]: Already a demo! *OTHERS (Using, training etc.) COOMING SOON!* ## 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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MCINext/Hakim-unsup	MCINext	2025-06-16T08:46:31Z	0	0	null	[ "arxiv:2505.08435", "region:us" ]	null	2025-06-02T08:01:29Z	# 🧠 Hakim-unsup [![arXiv](https://img.shields.io/badge/arXiv-2505.08435-b31b1b.svg)](https://arxiv.org/abs/2505.08435) Hakim-unsup represents an intermediate stage of the state-of-the-art Hakim text embedding project for the Persian language. This model is the result of pretraining on large Persian corpora followed by an extensive unsupervised contrastive learning phase on millions of text pairs. While the fully supervised Hakim model achieves top performance on the FaMTEB benchmark, Hakim-unsup provides strong general-purpose semantic representations. It serves as a powerful foundation for further fine-tuning and is particularly useful for tasks where large labeled datasets are unavailable but understanding semantic similarity from unlabeled pairs is crucial. --- ## 📌 Model Highlights - 🧱 Strong Foundational Embeddings: Provides robust general-purpose Persian text embeddings learned from large-scale unsupervised data. - 🔄 Trained on Diverse Unlabeled Pairs: Benefits from the `Pairsia-unsup` dataset, capturing a wide array of semantic relationships. - ⚙️ Standard Size: ~124M parameters, same as the base Hakim model. - 🌱 Basis for Supervised Models: This is the model checkpoint before the supervised instruction-tuning phase that creates the final Hakim and Hakim-small models. --- ## 🏗️ Training Datasets Hakim-unsup is trained in two main phases: ### 📚 Pretraining - Corpesia: 11B tokens from 46 Persian websites across 21 domains (e.g., news, health, religion, tech). - hmBlogs: 6.8B tokens from ~20M Persian blog posts. - Queries: 8.5M anonymized search queries. ### 🔄 Unsupervised Stage (Pairsia-unsup) - Pairsia-unsup: 5M high-quality Persian text pairs from diverse sources including: - Document–title, FAQ, QA, and paper title–abstract pairs. - Machine-translated datasets (MS MARCO, SAMSum, AdversarialQA, etc.). - The model is trained using a contrastive learning objective on these pairs to learn general semantic representations. Hakim-unsup does not undergo the subsequent supervised fine-tuning stage with the `Pairsia-sup` dataset or instruction tuning. For more detailed information on the dataset creation and curation process, please refer to the [Hakim paper](https://arxiv.org/abs/2505.08435). --- ## 🧪 Benchmark Results (FaMTEB) \| Model \| Avg. Score \| Classification \| Clustering \| PairClass. \| Reranking \| Retrieval \| STS \| Summarization \| \|------------------------\|------------\|----------------\|------------\|------------\|-----------\|-----------\|-------\|----------------\| \| Hakim \| 73.81 \| 84.56 \| 70.46 \| 89.75 \| 69.46 \| 40.43 \| 76.62 \| 85.41 \| \| Hakim-small \| 70.45 \| 80.19 \| 66.31 \| 87.41 \| 67.30 \| 38.05 \| 75.53 \| 78.40 \| \| Hakim-unsup \| 64.56 \| 60.65 \| 58.89 \| 86.41 \| 67.56 \| 37.71 \| 79.36 \| 61.34 \| \| BGE-m3 \| 65.29 \| 58.75 \| 57.73 \| 85.21 \| 74.56 \| 43.38 \| 76.35 \| 61.07 \| \| Jina-embeddings-v3 \| 64.53 \| 59.93 \| 59.15 \| 83.71 \| 61.26 \| 43.51 \| 78.65 \| 65.50 \| \| multilingual-e5-large \| 64.40 \| 59.86 \| 57.19 \| 84.42 \| 74.34 \| 42.98 \| 75.38 \| 56.61 \| \| GTE-multilingual-base \| 63.64 \| 56.07 \| 57.28 \| 84.58 \| 69.72 \| 41.22 \| 75.75 \| 60.88 \| \| multilingual-e5-base \| 62.93 \| 57.62 \| 56.52 \| 84.04 \| 72.07 \| 41.20 \| 74.45 \| 54.58 \| \| Tooka-SBERT \| 60.65 \| 59.40 \| 56.45 \| 87.04 \| 58.29 \| 27.86 \| 76.42 \| 59.06 \| --- ## Model Usage You can interact with the `Hakim_unsup` model through our API. Below are examples using `curl` and Python. ### Inference with `curl` Here's how to send a request to the model using a `curl` command in your terminal. Important: Replace `your_api_key` with your actual API key. > Note: For quick testing, you can use the value `mcinext` as your API key. This will allow you to use the API with some limitations. ```bash curl -X POST 'https://mcinext.ai/api/hakim-unsup' \ -H "Content-Type: application/json" \ -H "Accept: application/json" \ -H "Authorization": "Bearer your_api_key" \ -d '{ "model": "Hakim_unsuper", "input": [ "The text of the first document.", "The text of the second document.", "And so on..." ], "encoding_format": "float", "add_special_tokens": true }' ``` ### Inference with `python` ```python import requests import json # --- Configuration --- API_KEY = "your_api_key" # Replace with your key or "mcinext" for testing API_URL = "https://mcinext.ai/api/hakim-unsup" # --- Request Details --- headers = { "Content-Type": "application/json", "Accept": "application/json", "Authorization": f"Bearer {API_KEY}" } data = { "model": "Hakim_unsuper", "input": [ "The text of the first document.", "The text of the second document.", "And so on..." ], "encoding_format": "float", "add_special_tokens": True } # --- Send Request --- try: response = requests.post(API_URL, headers=headers, data=json.dumps(data)) response.raise_for_status() print("Request successful!") print("Response JSON:") print(response.json()) except requests.exceptions.HTTPError as http_err: print(f"HTTP error occurred: {http_err}") print(f"Response content: {response.text}") except Exception as err: print(f"An other error occurred: {err}") ``` ## Citation ```bibtext @article{sarmadi2025hakim, title={Hakim: Farsi Text Embedding Model}, author={Sarmadi, Mehran and Alikhani, Morteza and Zinvandi, Erfan and Pourbahman, Zahra}, journal={arXiv preprint arXiv:2505.08435}, year={2025} } ```
sam3002/emotion-model	sam3002	2025-06-16T08:44:09Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-16T08:43:44Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.15_0.75_0.15_epoch1	MinaMila	2025-06-16T08:42:59Z	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	2025-06-16T08:41:07Z	--- 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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IronLouis/gemma-text-to-sql	IronLouis	2025-06-16T08:42:22Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:google/gemma-3-1b-pt", "base_model:finetune:google/gemma-3-1b-pt", "endpoints_compatible", "region:us" ]	null	2025-06-16T06:00:15Z	--- base_model: google/gemma-3-1b-pt library_name: transformers model_name: gemma-text-to-sql tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for gemma-text-to-sql This model is a fine-tuned version of [google/gemma-3-1b-pt](https://huggingface.co/google/gemma-3-1b-pt). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="IronLouis/gemma-text-to-sql", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.52.4 - Pytorch: 2.6.0+cu124 - Datasets: 3.3.2 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
MinaMila/gemma_2b_unlearned_2nd_1e-5_1.0_0.5_0.05_0.5_epoch1	MinaMila	2025-06-16T08:36:11Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T08:34:23Z	--- 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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MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.15_0.75_0.25_epoch2	MinaMila	2025-06-16T08:36:01Z	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	2025-06-16T08:34:10Z	--- 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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aplux/YOLO-NAS-m	aplux	2025-06-16T08:33:05Z	0	0	null	[ "AIoT", "QNN", "object-detection", "license:other", "region:us" ]	object-detection	2025-06-12T07:19:09Z	--- license: other license_name: yolo-nas-license license_link: https://github.com/Deci-AI/super-gradients/blob/master/LICENSE.YOLONAS.md pipeline_tag: object-detection tags: - AIoT - QNN --- ![](https://aiot.aidlux.com/_next/image?url=%2Fapi%2Fv1%2Ffiles%2Fmodel%2Fcover%2F20250319064017_%25E5%259B%25BE-27.png&w=640&q=75) ## YOLO-NAS-m: Object Detection YOLO-NAS is a next-generation real-time object detection model optimized via Neural Architecture Search (NAS), automating the balance between accuracy and speed for superior performance in complex scenarios. It integrates hybrid quantization-aware architectures with reparameterized blocks and dynamic sparse attention, enhancing small/occluded object detection while reducing computation. Through multi-objective optimization (e.g., latency, parameters, mAP), it discovers efficient structures supporting FP16/INT8 quantization, achieving ~5% higher mAP than YOLOv8 on COCO with 80+ FPS on mobile GPUs. Ideal for autonomous driving and surveillance, it balances edge-device constraints and high precision, offering flexible speed-accuracy tradeoffs. ### Source model - Input shape: 1x3x640x640 - Number of parameters: 30.44M - Model size: 121.87M - Output shape: 1x8400x4, 1x8400x80 The source model can be found [here](https://github.com/Deci-AI/super-gradients) ## Performance Reference Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models) ## Inference & Model Conversion Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models) ## License - Source Model: [YOLO-NAS License](https://github.com/Deci-AI/super-gradients/blob/master/LICENSE.YOLONAS.md) - Deployable Model: [YOLO-NAS License](https://github.com/Deci-AI/super-gradients/blob/master/LICENSE.YOLONAS.md)
SidXXD/Clean-m_213-2	SidXXD	2025-06-16T08:30:48Z	0	0	diffusers	[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "custom-diffusion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2025-06-16T08:26:02Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: photo of a sks person tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - custom-diffusion inference: true --- # Custom Diffusion - SidXXD/Clean-m_213-2 These are Custom Diffusion adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on photo of a sks person using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).
udaybondi/nanoVLM	udaybondi	2025-06-16T08:29:25Z	0	0	nanovlm	[ "nanovlm", "safetensors", "vision-language", "multimodal", "research", "image-text-to-text", "license:mit", "region:us" ]	image-text-to-text	2025-06-16T08:28:54Z	--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards library_name: nanovlm license: mit pipeline_tag: image-text-to-text tags: - vision-language - multimodal - research --- nanoVLM is a minimal and lightweight Vision-Language Model (VLM) designed for efficient training and experimentation. Built using pure PyTorch, the entire model architecture and training logic fits within ~750 lines of code. It combines a ViT-based image encoder (SigLIP-B/16-224-85M) with a lightweight causal language model (SmolLM2-135M), resulting in a compact 222M parameter model. For more information, check out the base model on https://huggingface.co/lusxvr/nanoVLM-222M. Usage: Clone the nanoVLM repository: https://github.com/huggingface/nanoVLM. Follow the install instructions and run the following code: ```python from models.vision_language_model import VisionLanguageModel model = VisionLanguageModel.from_pretrained("udaybondi/nanoVLM") ```
MinaMila/gemma_2b_unlearned_2nd_1e-5_1.0_0.5_0.05_0.75_epoch2	MinaMila	2025-06-16T08:28:08Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T08:26:24Z	--- 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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xiaoyuanliu/Qwen2.5-7B-Instruct-MathHard-PPO-012	xiaoyuanliu	2025-06-16T08:27:34Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T08:20:31Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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aieng-lab/codet5p-770m_review-aspect	aieng-lab	2025-06-16T08:27:04Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text-classification", "en", "base_model:Salesforce/codet5p-770m", "base_model:finetune:Salesforce/codet5p-770m", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2025-06-16T08:26:37Z	--- library_name: transformers license: mit language: - en metrics: - f1 - precision - recall base_model: - Salesforce/codet5p-770m pipeline_tag: text-classification --- # CodeT5+ 770m for classifying API reviews This model classifies API reviews in developer forums (e.g., Stack Overflow) as 'usability', 'others', 'onlysentiment', 'bug', 'performance', 'community', 'documentation', 'compatibility', 'legal', 'portability' or 'security'. - Developed by: Fabian C. Peña, Steffen Herbold - Finetuned from: [Salesforce/codet5p-770m](https://huggingface.co/Salesforce/codet5p-770m) - Replication kit: [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark) - Language: English - License: MIT ## Citation ``` @misc{pena2025benchmark, author = {Fabian Peña and Steffen Herbold}, title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks}, year = {2025} } ```
DenisRz/llama3_8b_instruct_qed	DenisRz	2025-06-16T08:26:20Z	0	0	null	[ "safetensors", "qlora", "lora", "qed", "few-shot", "llama-3", "instruct", "explainability", "fine_tuned", "referential_equalities", "QA", "text-generation", "conversational", "en", "dataset:google-research-datasets/qed", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "region:us" ]	text-generation	2025-06-15T10:33:46Z	--- license: llama3 base_model: meta-llama/Meta-Llama-3-8B-Instruct tags: - qlora - lora - qed - few-shot - llama-3 - instruct - explainability - fine_tuned - referential_equalities - QA datasets: - google-research-datasets/qed language: - en metrics: - accuracy - f1 - precision - recall pipeline_tag: text-generation --- # Llama-3-8B-Instruct QED Few-Shot (Both Prompts) ## Model Description 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) for the QED (Question-Explanation-Data) task. It was trained using a few-shot approach with both demonstration examples ("Life of Pi" and "Acute hemolytic reaction") included in the prompt, following the QED instruction format. - Base model: Meta-Llama-3-8B-Instruct - Fine-tuning method: LoRA (QLoRA, 4-bit) - Task: Extracting short answers, supporting sentences, and referential equalities from text passages given a question. --- ## Intended Uses & Limitations - Intended use: Research on explainable QA, entity and span extraction, and referential reasoning. - Not intended for: General open-domain QA, medical or legal advice, or production deployment without further validation. --- ## Training Data - Dataset: QED (Question-Explanation-Data) dataset - Prompt format: Each input includes a title, question, and context passage, with the following instruction and two demonstration examples. --- ## Prompt Format The model expects prompts in the following format (using Llama-3-Instruct tokens): ``` <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> You are an expert at extracting answers and structured explanations from text. Your response MUST be valid JSON only (no extra commentary). Task ==== Given: • a title for the passage, • a question about the passage, and • the context passage itself, produce an explanation object with three parts: 1. "answer" – the shortest span from the passage that fully answers the question. 2. "selected_sentence" – the single sentence in the passage that entails or implies the answer. 3. "referential_equalities" – a list of mappings between phrases in the question and phrases in the selected sentence that refer to the same real-world entity/event. • Each mapping has two keys: - "question_reference": the exact phrase from the question (must be a contiguous substring from the question, not from the context or title). - "sentence_reference": the exact phrase from the selected sentence (must be a contiguous substring from the selected sentence, not from the question or title), or "" (empty string if the entire sentence is the referent). ▸ Use "" for "sentence_reference" when the entity/event is not named by any specific phrase in the sentence – i.e. the entire sentence acts as the referent (a bridge to the whole sentence). This corresponds to the (start = end = -1) convention in the QED dataset. Output format ============= Return only JSON in this exact schema: { "answer": "<string from passage>", "selected_sentence": "<string from passage>", "referential_equalities": [ { "question_reference": "<string from question only>", "sentence_reference": "<string from selected_sentence only, or "">", "bridge": "<false if not a bridge; otherwise, a string explaining the bridge connection, e.g., 'in', 'for', 'of', 'at', 'on'>" } ... ] } Demonstration Example 1: Title: Life of Pi Question: what is the tigers name in life of pi Context: Life of Pi is a Canadian fantasy adventure novel by Yann Martel published in 2001 . The protagonist is Piscine Molitor `` Pi '' Patel , an Indian boy from Pondicherry who explores issues of spirituality and practicality from an early age . He survives 227 days after a shipwreck while stranded on a lifeboat in the Pacific Ocean with a Bengal tiger named Richard Parker . Expected JSON: { "answer": "Richard Parker", "selected_sentence": "He survives 227 days after a shipwreck while stranded on a lifeboat in the Pacific Ocean with a Bengal tiger named Richard Parker .", "referential_equalities": [ { "question_reference": "the tiger", "sentence_reference": "a Bengal tiger", "bridge": false }, { "question_reference": "life of pi", "sentence_reference": "", "bridge": "in" } ] } Demonstration Example 2: Title: Acute hemolytic transfusion reaction Question: what happens to the rbc in acute hemolytic reaction Context: It is also known as an `` immediate hemolytic transfusion reaction '' . This is a medical emergency as it results from rapid destruction of the donor red blood cells by host antibodies ( IgG , IgM ) . It is usually related to ABO blood group incompatibility - the most severe of which often involves group A red cells being given to a patient with group O type blood . Properdin then binds to complement C3 in the donor blood , facilitating the reaction through the alternate pathway cascade . The donor cells also become coated with IgG and are subsequently removed by macrophages in the reticuloendothelial system ( RES ) . Jaundice and disseminated intravascular coagulation ( DIC ) may also occur . The most common cause is clerical error ( i.e. the wrong unit of blood being given to the patient ) . Expected JSON: { "answer": "rapid destruction of the donor red blood cells by host antibodies ( IgG , IgM )", "selected_sentence": "This is a medical emergency as it results from rapid destruction of the donor red blood cells by host antibodies ( IgG , IgM ) .", "referential_equalities": [ { "question_reference": "acute hemolytic reaction", "sentence_reference": "This", "bridge": false }, { "question_reference": "the rbc", "sentence_reference": "the donor red blood cells", "bridge": false } ] } <\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|> Title: {title} Question: {question} Context: {context}<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> ``` --- ## Training Hyperparameters - Model: meta-llama/Meta-Llama-3-8B-Instruct - LoRA: enabled (`lora_r=32`, `lora_alpha=64`, `lora_dropout=0.05`) - Quantization: 4-bit (QLoRA), CPU offload enabled - Epochs: 1 - Batch size: 1 (gradient accumulation steps: 16) - Learning rate: 2e-5 - Weight decay: 0.001 - Warmup ratio: 0.1 - Optimizer: paged_adamw_8bit - Precision: bf16 - Max source length: 3072 - Max target length: 1024 - Prompt examples: both (see above) - Output dir: `models_fine_tuned/llama3_8b_instruct_fewshot_both` --- ## Evaluation Results Evaluated on 998 validation examples, using official QED metrics at various F1 overlap thresholds (non-strict): \| Overlap \| Answer Accuracy \| All Mention F1 \| Pair F1 \| \|---------\|----------------\|---------------\|---------\| \| 0.50 \| 82.4% \| 19.6% \| 10.4% \| \| 0.60 \| 74.2% \| 19.5% \| 10.3% \| \| 0.70 \| 68.2% \| 19.5% \| 10.3% \| \| 0.80 \| 63.2% \| 19.5% \| 10.3% \| \| 0.90 \| 59.8% \| 19.2% \| 10.0% \| ## Limitations & Ethical Considerations - The model is trained on a specific dataset and task; it may not generalize to other domains. - Outputs are not guaranteed to be factually correct or safe for critical applications. - Always validate outputs before use in downstream tasks. --- ## Citation If you use this model or code, please cite the original Llama-3 paper and your own work as appropriate. --- ## Author - Denis Rize
nytopop/1b_mixed_lr2e-5_3ep_c-lion	nytopop	2025-06-16T08:23:36Z	0	0	null	[ "tensorboard", "safetensors", "llama", "region:us" ]	null	2025-06-15T16:59:34Z	# notes - it's overcooked but most promising fit of the mixed set yet. ep2 checkpoint in particular does reasonably well try again with less LR - this was trained with a bug in dataloader logic that prevented shuffling of batch items, so we probably had very poor batch regularization as well
aieng-lab/starcoder2-7b_review-aspect	aieng-lab	2025-06-16T08:23:02Z	0	0	transformers	[ "transformers", "safetensors", "starcoder2", "text-classification", "en", "base_model:bigcode/starcoder2-7b", "base_model:finetune:bigcode/starcoder2-7b", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2025-06-16T08:18:29Z	--- library_name: transformers license: mit language: - en metrics: - f1 - precision - recall base_model: - bigcode/starcoder2-7b pipeline_tag: text-classification --- # StarCoder2 7b for classifying API reviews This model classifies API reviews in developer forums (e.g., Stack Overflow) as 'usability', 'others', 'onlysentiment', 'bug', 'performance', 'community', 'documentation', 'compatibility', 'legal', 'portability' or 'security'. - Developed by: Fabian C. Peña, Steffen Herbold - Finetuned from: [bigcode/starcoder2-7b](https://huggingface.co/bigcode/starcoder2-7b) - Replication kit: [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark) - Language: English - License: MIT ## Citation ``` @misc{pena2025benchmark, author = {Fabian Peña and Steffen Herbold}, title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks}, year = {2025} } ```
MinaMila/gemma_2b_unlearned_2nd_1e-5_1.0_0.5_0.05_0.75_epoch1	MinaMila	2025-06-16T08:20:27Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T08:18:40Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ange2709/ui-tars	ange2709	2025-06-16T08:19:24Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-16T08:19:24Z	--- license: apache-2.0 ---
tinashechp/mistral_shona	tinashechp	2025-06-16T08:18:47Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "base_model:finetune:unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-16T08:18:36Z	--- base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit tags: - text-generation-inference - transformers - unsloth - mistral - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: tinashechp - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-instruct-v0.3-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Shrav20/sparql-mistral-float32	Shrav20	2025-06-16T08:18:45Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-06-16T08:06:53Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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rkstgr/typer-1.5b-instruct-concise-gguf	rkstgr	2025-06-16T08:18:41Z	0	0	transformers	[ "transformers", "gguf", "qwen2", "typst", "code", "base_model:rkstgr/typer-1.5b-instruct-concise", "base_model:quantized:rkstgr/typer-1.5b-instruct-concise", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-06-16T08:11:17Z	--- library_name: transformers base_model: rkstgr/typer-1.5b-instruct-concise tags: - typst - code - gguf model_type: qwen2 license: apache-2.0 --- # rkstgr/typer-1.5b-instruct-concise-gguf This repository contains GGUF quantized versions of [rkstgr/typer-1.5b-instruct-concise](https://huggingface.co/rkstgr/typer-1.5b-instruct-concise). ## Usage with Ollama ```bash ollama run hf.co/rkstgr/typer-1.5b-instruct-concise-gguf ``` ## Usage with llama.cpp ```bash # Download the model wget https://huggingface.co/rkstgr/typer-1.5b-instruct-concise-gguf/resolve/main/{model_file}.gguf # Run with llama.cpp ./llama-cli -m {model_file}.gguf -p "Your prompt here" ``` ## Model Details - Base Model: [rkstgr/typer-1.5b-instruct-concise](https://huggingface.co/rkstgr/typer-1.5b-instruct-concise) - Training: Fine-tuned using Unsloth
dlzjfileai/test	dlzjfileai	2025-06-16T08:16:30Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-16T08:16:30Z	--- license: apache-2.0 ---
EleutherAI/SmolLM2-1.7B-magpie-ultra-v1.0-loss-lowest	EleutherAI	2025-06-16T08:16:09Z	0	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T08:15:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.15_0.75_0.5_epoch1	MinaMila	2025-06-16T08:15:53Z	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	2025-06-16T08:14:02Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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rkstgr/typer-1.5b-instruct-concise	rkstgr	2025-06-16T08:15:09Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "typst", "code", "conversational", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T08:10:37Z	--- library_name: transformers base_model: rkstgr/typer-1.5b-base tags: - typst - code model_type: qwen2 license: apache-2.0 --- # rkstgr/typer-1.5b-instruct-concise A Typst-focused language model fine-tuned from rkstgr/typer-1.5b-base. ## Model Description This model has been fine-tuned to provide accurate and detailed answers about Typst, a modern markup language for document preparation. ## Usage ### With Ollama For easier usage, you can use the GGUF version with Ollama: ```bash ollama run hf.co/rkstgr/typer-1.5b-instruct-concise-gguf ``` ## Training Details - Base Model: rkstgr/typer-1.5b-base - Training Framework: Unsloth - Optimization: LoRA fine-tuning
aieng-lab/starcoder2-3b_review-aspect	aieng-lab	2025-06-16T08:13:17Z	0	0	transformers	[ "transformers", "safetensors", "starcoder2", "text-classification", "en", "base_model:bigcode/starcoder2-3b", "base_model:finetune:bigcode/starcoder2-3b", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2025-06-16T08:11:28Z	--- library_name: transformers license: mit language: - en metrics: - f1 - precision - recall base_model: - bigcode/starcoder2-3b pipeline_tag: text-classification --- # StarCoder2 3b for classifying API reviews This model classifies API reviews in developer forums (e.g., Stack Overflow) as 'usability', 'others', 'onlysentiment', 'bug', 'performance', 'community', 'documentation', 'compatibility', 'legal', 'portability' or 'security'. - Developed by: Fabian C. Peña, Steffen Herbold - Finetuned from: [bigcode/starcoder2-3b](https://huggingface.co/bigcode/starcoder2-3b) - Replication kit: [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark) - Language: English - License: MIT ## Citation ``` @misc{pena2025benchmark, author = {Fabian Peña and Steffen Herbold}, title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks}, year = {2025} } ```
shinkeonkim/gemma-3-1b-it-Q4_K_M-GGUF	shinkeonkim	2025-06-16T08:12:50Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:google/gemma-3-1b-it", "base_model:quantized:google/gemma-3-1b-it", "license:gemma", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-06-16T08:12:43Z	--- license: gemma library_name: transformers pipeline_tag: text-generation extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license base_model: google/gemma-3-1b-it tags: - llama-cpp - gguf-my-repo --- # shinkeonkim/gemma-3-1b-it-Q4_K_M-GGUF This model was converted to GGUF format from [`google/gemma-3-1b-it`](https://huggingface.co/google/gemma-3-1b-it) 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/google/gemma-3-1b-it) 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 shinkeonkim/gemma-3-1b-it-Q4_K_M-GGUF --hf-file gemma-3-1b-it-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo shinkeonkim/gemma-3-1b-it-Q4_K_M-GGUF --hf-file gemma-3-1b-it-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo shinkeonkim/gemma-3-1b-it-Q4_K_M-GGUF --hf-file gemma-3-1b-it-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo shinkeonkim/gemma-3-1b-it-Q4_K_M-GGUF --hf-file gemma-3-1b-it-q4_k_m.gguf -c 2048 ```
Rask6723/ITA_TMSL_GR7_En-Sn	Rask6723	2025-06-16T08:08:59Z	0	0	transformers	[ "transformers", "safetensors", "marian", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-06-16T08:08:40Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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]
cswind/DeepRL-u2-taxi	cswind	2025-06-16T08:08:08Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2025-06-16T08:08:04Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: DeepRL-u2-taxi results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.52 +/- 2.74 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="cswind/DeepRL-u2-taxi", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
openbmb/Ultra-FineWeb-classifier	openbmb	2025-06-16T08:05:21Z	0	6	null	[ "arxiv:2505.05427", "license:apache-2.0", "region:us" ]	null	2025-06-16T03:17:35Z	--- license: apache-2.0 --- # Ultra-FineWeb-Classifier <div align="center"> <img src="assets/ultra-fineweb-logo.png" width="600"/> </div> <!-- <div align="center"> English \| [简体中文]() </div> --> <div align="center"> [📜 Technical Report](https://arxiv.org/abs/2505.05427) </div> ## 📚 Introduction Ultra-FineWeb is a large-scale, high-quality, and efficiently-filtered dataset. We use the proposed efficient verification-based high-quality filtering pipeline to the FineWeb and Chinese FineWeb datasets (source data from Chinese FineWeb-edu-v2, which includes IndustryCorpus2, MiChao, WuDao, SkyPile, WanJuan, ChineseWebText, TeleChat, and CCI3), resulting in the creation of higher-quality Ultra-FineWeb-en with approximately 1T tokens, and Ultra-FineWeb-zh datasets with approximately 120B tokens, collectively referred to as Ultra-FineWeb. *Ultra-FineWeb* serves as a core pre-training web dataset for the [MiniCPM4 Series](https://huggingface.co/collections/openbmb/minicpm-4-6841ab29d180257e940baa9b) models. - [Ultra-FineWeb](https://huggingface.co/datasets/openbmb/Ultra-FineWeb): Ultra-FineWeb, a large-scale, high-quality, and efficiently-filtered dataset, with 1T English tokens and 120B Chinese tokens. - [Ultra-FineWeb-classifier](https://huggingface.co/openbmb/Ultra-FineWeb-classifier): Ultra-FineWeb classifier, for filtering high-quality data from web corpora. (<-- you are here) ## 📢 What's New - [2025.05.09] Ultra-FineWeb technical report is available on [arXiv](https://arxiv.org/abs/2505.05427). 🔥🔥🔥 - [2025.05.15] Ultra-FineWeb tops the Hugging Face Datasets Trending list, reaching the #1 spot! ⭐️⭐️⭐️ - [2025.06.06] Ultra-FineWeb-en and Ultra-FineWeb-zh datasets are now available on Hugging Face, released alongside the [MiniCPM4 Series](https://huggingface.co/collections/openbmb/minicpm-4-6841ab29d180257e940baa9b) models. - [2025.06.16] The Ultra-FineWeb-classifier is now available on Hugging Face: [openbmb/Ultra-FineWeb-classifier](https://huggingface.co/openbmb/Ultra-FineWeb-classifier). 🚀🚀🚀 ## 💡 Highlights > Abstract: Data quality has become a key factor in enhancing model performance with the rapid development of large language models (LLMs). Model-driven data filtering has increasingly become a primary approach for acquiring high-quality data. However, it still faces two main challenges: (1) the lack of an efficient data verification strategy makes it difficult to provide timely feedback on data quality; and (2) the selection of seed data for training classifiers lacks clear criteria and relies heavily on human expertise, introducing a degree of subjectivity. To address the first challenge, we introduce an efficient verification strategy that enables rapid evaluation of the impact of data on LLM training with minimal computational cost. To tackle the second challenge, we build upon the assumption that high-quality seed data is beneficial for LLM training, and by integrating the proposed verification strategy, we optimize the selection of positive and negative samples and propose an efficient data filtering pipeline. This pipeline not only improves filtering efficiency, classifier quality, and robustness, but also significantly reduces experimental and inference costs. In addition, to efficiently filter high-quality data, we employ a lightweight classifier based on fastText, and successfully apply the filtering pipeline to two widely-used pre-training corpora, FineWeb and Chinese FineWeb datasets, resulting in the creation of the higher-quality *Ultra-FineWeb* dataset. *Ultra-FineWeb* contains approximately 1 trillion (T) English tokens and 120 billion (B) Chinese tokens. Empirical results demonstrate that the LLMs trained on Ultra-FineWeb exhibit significant performance improvements across multiple benchmark tasks, validating the effectiveness of our pipeline in enhancing both data quality and training efficiency. <div align="center"> <img src="assets/ultra-fineweb-pipeline.png" width="600"/> </div> - Efficient Verification Strategy: We propose a computationally efficient verification strategy that enables rapid evaluation of the impact of data on LLM training performance with minimal computational cost, significantly improving the efficiency of high-quality data filtering experiments. - Large-Scale High-Quality Pre-training Datasets: We design and implement an efficient high-quality data filtering pipeline, applied to the FineWeb and Chinese FineWeb datasets, resulting in the creation of higher-quality datasets, which can facilitate high-quality LLM training. - Lightweight Classifier: The Ultra-FineWeb classifier significantly reduces inference costs, achieving superior performance on extracted text from the same data source, thus validating the effectiveness of our proposed data filtering pipeline in enhancing data quality and training efficiency. ## 🚀 Usage of Ultra-FineWeb Classifier ### Inference single content 1. Put the content you want to infer into the [`scripts/local_scripts/single_content.txt`](scripts/local_scripts/single_content.txt) file. 2. Run the [`scripts/local_scripts/infer_single_content.py`](scripts/local_scripts/infer_single_content.py) script to infer the content: ```bash # set the language you want to infer, support: en, zh LANGUAGE=en # set the tokenizer path, default: local_tokenizer # user can also directly use "deepseek-ai/DeepSeek-V2" TOKENIZER_PATH=local_tokenizer # set the content file path, default: scripts/local_scripts/single_content.txt CONTENT_FILE=scripts/local_scripts/single_content.txt python scripts/local_scripts/infer_single_content.py --language ${LANGUAGE} --tokenizer-path ${TOKENIZER_PATH} --content-file ${CONTENT_FILE} ``` Then you can get the result in the terminal, such as: ```text Content: {User's input content} Normalized content: {Normalized content} - Pred label: {Pred label} - Pred score: {Pred score} ``` ### Inference folder Assume the input folder is `data/input`, the key of the content is `content`, and the output folder is `data/output`. User can run the [`scripts/local_scripts/infer_folder.py`](scripts/local_scripts/infer_folder.py) script to infer the folder: ```bash # set the language you want to infer, support: en, zh LANGUAGE=en # set the data path DATA_PATH=data/input # set the save path SAVE_PATH=data/output # set the content key CONTENT_KEY=content # bellow are optional arguments # set the tokenizer path, default: local_tokenizer TOKENIZER_PATH=local_tokenizer # set the processes number, default: 64 PROCESSES_NUM=64 # set the write batch size, default: 100 WRITE_BATCH_SIZE=100 python scripts/local_scripts/infer_folder.py \ --language ${LANGUAGE} \ --data-path ${DATA_PATH} \ --save-path ${SAVE_PATH} \ --content-key ${CONTENT_KEY} \ --tokenizer-path ${TOKENIZER_PATH} \ --processes-num ${PROCESSES_NUM} \ --write-batch-size ${WRITE_BATCH_SIZE} \ [--inplace] # optional, delete the processed data and re-process the data ``` For Spark inference, we also provide [`scripts/spark_scripts/spark_infer.py`](scripts/spark_scripts/spark_infer.py), a demo script for users to run on the Spark cluster. NOTE: - The `numpy` version should be lower than 2.0 for the `fasttext` package. - The `config.json` file is a fake config file, the parameters are used for the `fasttext` training. ## ❤️ Acknowledgements - The *Ultra-FineWeb classifier* is built based on [fastText](https://fasttext.cc/). - The *Ultra-FineWeb-en dataset* is built based on [FineWeb](https://huggingface.co/datasets/HuggingFaceFW/fineweb). - The *Ultra-FineWeb-zh dataset* is constructed based on [IndustryCorpus2](https://huggingface.co/datasets/BAAI/IndustryCorpus2), [MiChao](https://opendatalab.com/OpenDataLab/MiChao), [WuDao](https://data.baai.ac.cn/details/WuDaoCorporaText), [SkyPile](https://huggingface.co/datasets/Skywork/SkyPile-150B), [WanJuan](https://opendatalab.com/OpenDataLab/WanJuanCC), [ChineseWebText](https://huggingface.co/datasets/CASIA-LM/ChineseWebText2.0), [TeleChat](https://huggingface.co/datasets/Tele-AI/TeleChat-PTD), and [CCI3](https://huggingface.co/datasets/BAAI/CCI3-Data). Thanks for their awesome work! Open-source contributions make Ultra-FineWeb possible! 🙌 ## 🌟 Citation If you find our work useful, please consider citing: ```bibtex @misc{wang2025ultrafineweb, title={{Ultra-FineWeb}: Efficient Data Filtering and Verification for High-Quality LLM Training Data}, author={Yudong Wang and Zixuan Fu and Jie Cai and Peijun Tang and Hongya Lyu and Yewei Fang and Zhi Zheng and Jie Zhou and Guoyang Zeng and Chaojun Xiao and Xu Han and Zhiyuan Liu}, year={2025}, eprint={2505.05427}, archivePrefix={arXiv}, primaryClass={cs.CL}, } ``` ## 💳 License This project is released under the [Apache 2.0](./LICENSE). Please note that since *Ultra-FineWeb* is built using multiple datasets, users should check the LICENSE of each dataset individually to ensure proper usage and compliance.
bdsqlsz/framepack_oneframe_qinglong_figure	bdsqlsz	2025-06-16T08:04:19Z	0	0	diffusers	[ "diffusers", "image-to-image", "lora", "template:sd-lora", "base_model:lllyasviel/FramePackI2V_HY", "base_model:adapter:lllyasviel/FramePackI2V_HY", "license:cc-by-nc-sa-4.0", "region:us" ]	image-to-image	2025-06-16T07:20:43Z	--- license: cc-by-nc-sa-4.0 base_model: - lllyasviel/FramePackI2V_HY tags: - diffusers - image-to-image - lora - template:sd-lora widget: - text: '-' output: url: images/GtjBe5_a4AAYlwF.jpg - text: '-' output: url: images/GtjC8dQbkAAoB7S.jpg - text: '-' output: url: images/GtjDtFcacAEdrAc.jpg - text: '-' output: url: images/GtjGDWfasAA6eHx.jpg --- # qinglong_figure <Gallery /> ## Download model Weights for this model are available in Safetensors format. [Download](/bdsqlsz/bdsqlsz/framepack_qinglong_figure/tree/main) them in the Files & versions tab. ## Useage Default prompts:"transform character to PVC figure with simple background."
izath98/mallam_trained-model_secondt	izath98	2025-06-16T08:03:47Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T08:03:41Z	--- 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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Arsen008/wav2vec2-large-mms-1b-turkish-colab	Arsen008	2025-06-16T08:03:27Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-06-10T09:42:01Z	--- 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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srivihari/resume-job-role-classifier	srivihari	2025-06-16T08:03:26Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "resume", "job-role-classification", "en", "dataset:custom", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-15T17:33:43Z	--- license: apache-2.0 tags: - text-classification - resume - job-role-classification - transformers - distilbert datasets: - custom language: - en pipeline_tag: text-classification --- # 🔍 Resume Job Role Classifier A fine-tuned [`DistilBERT`](https://huggingface.co/distilbert-base-uncased) model to classify job roles based on resume content. This model is trained to predict the most likely profession from the given resume text, supporting over 14 different job categories. --- ## 🧠 Model Details - Architecture: DistilBERT (base, uncased) - Task: Multi-class text classification - Input: Raw resume text (English) - Output: Predicted job category label and score --- ## 📊 Labels Covered This model supports classification into the following job categories: - Data Science - Java Developer - Web Designing - HR - Mechanical Engineer - Electrical Engineering - Civil Engineer - Arts - Advocate - Sales - Health and fitness - Business Analyst - SAP Developer - Automation Testing --- ## 🏋️ Training - Dataset: Custom dataset containing labeled resumes - Split: 80% train / 20% test - Metrics: - Accuracy: 99–100% - F1 Score: ~0.99–1.00 (macro avg) - Epochs: 3 - Batch size: 8 - Optimizer: AdamW --- ## 📥 How to Use ```python from transformers import pipeline classifier = pipeline("text-classification", model="srivihari/resume-job-role-classifier") result = classifier("Experienced data scientist with Python, machine learning, and statistics background.") print(result) # Example output: # [{'label': 'Data Science', 'score': 0.97}] #Note: If you face any issues like token_type_ids errors, make sure to adjust tokenizer config as below: #from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline #model = AutoModelForSequenceClassification.from_pretrained("srivihari/resume-job-role-classifier") #tokenizer = AutoTokenizer.from_pretrained("srivihari/resume-job-role-classifier") #tokenizer.model_input_names = ["input_ids", "attention_mask"] #classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
SillyChilly25/medgemma-brain-cancer	SillyChilly25	2025-06-16T08:03:23Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:google/medgemma-4b-it", "base_model:finetune:google/medgemma-4b-it", "endpoints_compatible", "region:us" ]	null	2025-06-13T12:16:29Z	--- base_model: google/medgemma-4b-it library_name: transformers model_name: medgemma-brain-cancer tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for medgemma-brain-cancer This model is a fine-tuned version of [google/medgemma-4b-it](https://huggingface.co/google/medgemma-4b-it). 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="SillyChilly25/medgemma-brain-cancer", 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/hitenhasija888-vellore-institute-of-technology/huggingface/runs/hzw9f5kl) This model was trained with SFT. ### Framework versions - TRL: 0.18.2 - Transformers: 4.52.4 - Pytorch: 2.7.1 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.15_0.75_0.75_epoch1	MinaMila	2025-06-16T08:02:09Z	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	2025-06-16T08:00:12Z	--- 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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soy3on/Qwen3_Rude_RAG_FULL	soy3on	2025-06-16T08:00:15Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T07:58:59Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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(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]
SidXXD/eps8-gaussian-f_26	SidXXD	2025-06-16T07:58:38Z	0	0	diffusers	[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "custom-diffusion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2025-06-16T07:53:43Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: photo of a sks person tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - custom-diffusion inference: true --- # Custom Diffusion - SidXXD/eps8-gaussian-f_26 These are Custom Diffusion adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on photo of a sks person using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).
IkePy/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-wary_tropical_dove	IkePy	2025-06-16T07:58:25Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am wary tropical dove", "unsloth", "trl", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-1.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-06-15T14:52:52Z	--- base_model: Gensyn/Qwen2.5-1.5B-Instruct library_name: transformers model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-wary_tropical_dove tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am wary tropical dove - unsloth - trl licence: license --- # Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-wary_tropical_dove This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="IkePy/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-wary_tropical_dove", 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}} } ```
aieng-lab/t5-3b_review-aspect	aieng-lab	2025-06-16T07:58:04Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text-classification", "en", "base_model:google-t5/t5-3b", "base_model:finetune:google-t5/t5-3b", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2025-06-16T07:56:21Z	--- library_name: transformers license: mit language: - en metrics: - f1 - precision - recall base_model: - t5-3b pipeline_tag: text-classification --- # T5 3b for classifying API reviews This model classifies API reviews in developer forums (e.g., Stack Overflow) as 'usability', 'others', 'onlysentiment', 'bug', 'performance', 'community', 'documentation', 'compatibility', 'legal', 'portability' or 'security'. - Developed by: Fabian C. Peña, Steffen Herbold - Finetuned from: [t5-3b](https://huggingface.co/t5-3b) - Replication kit: [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark) - Language: English - License: MIT ## Citation ``` @misc{pena2025benchmark, author = {Fabian Peña and Steffen Herbold}, title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks}, year = {2025} } ```
Nerva1228/liupopo	Nerva1228	2025-06-16T07:56:49Z	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	2025-06-16T07:56:48Z	--- 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: liupopo --- # Liupopo <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 `liupopo` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "liupopo", "lora_weights": "https://huggingface.co/Nerva1228/liupopo/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('Nerva1228/liupopo', weight_name='lora.safetensors') image = pipeline('liupopo').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: 5e-05 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/Nerva1228/liupopo/discussions) to add images that show off what you’ve made with this LoRA.
soy3on/Qwen3_Rude_RAG	soy3on	2025-06-16T07:55:42Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T07:55:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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dgambettaphd/M_llm2_run2_gen0_WXS_doc1000_synt64_lr1e-04_acm_SYNALL	dgambettaphd	2025-06-16T07:55:02Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "unsloth", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-06-16T07:53:11Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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aitechleart/Andrij	aitechleart	2025-06-16T07:54:13Z	0	0	null	[ "license:other", "region:us" ]	null	2025-06-16T07:11:55Z	--- 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 ---
SidXXD/eps8-noise_upscaling-f_26	SidXXD	2025-06-16T07:53:35Z	0	0	diffusers	[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "custom-diffusion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2025-06-16T07:47:56Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: photo of a sks person tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - custom-diffusion inference: true --- # Custom Diffusion - SidXXD/eps8-noise_upscaling-f_26 These are Custom Diffusion adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on photo of a sks person using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).
LaaP-ai/donut-base-invoicev5	LaaP-ai	2025-06-16T07:53:11Z	0	0	transformers	[ "transformers", "safetensors", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "base_model:naver-clova-ix/donut-base", "base_model:finetune:naver-clova-ix/donut-base", "license:mit", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-06-16T07:52:52Z	--- library_name: transformers license: mit base_model: naver-clova-ix/donut-base tags: - generated_from_trainer model-index: - name: donut-base-invoicev5 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. --> # donut-base-invoicev5 This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 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 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.52.4 - Pytorch 2.7.1+cu126 - Datasets 3.6.0 - Tokenizers 0.21.1
aieng-lab/t5-base_review-aspect	aieng-lab	2025-06-16T07:53:02Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text-classification", "en", "base_model:google-t5/t5-base", "base_model:finetune:google-t5/t5-base", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2025-06-16T07:52:51Z	--- library_name: transformers license: mit language: - en metrics: - f1 - precision - recall base_model: - t5-base pipeline_tag: text-classification --- # T5 base for classifying API reviews This model classifies API reviews in developer forums (e.g., Stack Overflow) as 'usability', 'others', 'onlysentiment', 'bug', 'performance', 'community', 'documentation', 'compatibility', 'legal', 'portability' or 'security'. - Developed by: Fabian C. Peña, Steffen Herbold - Finetuned from: [t5-base](https://huggingface.co/t5-base) - Replication kit: [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark) - Language: English - License: MIT ## Citation ``` @misc{pena2025benchmark, author = {Fabian Peña and Steffen Herbold}, title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks}, year = {2025} } ```
csukuangfj/vits-piper-de_DE-glados_turret-high	csukuangfj	2025-06-16T07:52:58Z	0	0	null	[ "onnx", "region:us" ]	null	2025-06-16T07:08:17Z	# Introduction models are from https://huggingface.co/systemofapwne/piper-de-glados/tree/main/de/de_DE
csukuangfj/vits-piper-de_DE-glados_turret-medium	csukuangfj	2025-06-16T07:52:50Z	0	0	null	[ "onnx", "region:us" ]	null	2025-06-16T07:08:40Z	# Introduction models are from https://huggingface.co/systemofapwne/piper-de-glados/tree/main/de/de_DE
csukuangfj/vits-piper-de_DE-glados_turret-low	csukuangfj	2025-06-16T07:52:42Z	0	0	null	[ "onnx", "region:us" ]	null	2025-06-16T07:08:26Z	# Introduction models are from https://huggingface.co/systemofapwne/piper-de-glados/tree/main/de/de_DE
Triangle104/Qwen3-4B-Esper3-Q5_K_M-GGUF	Triangle104	2025-06-16T07:52:19Z	23	0	transformers	[ "transformers", "gguf", "esper", "esper-3", "valiant", "valiant-labs", "qwen", "qwen-3", "qwen-3-4b", "4b", "reasoning", "code", "code-instruct", "python", "javascript", "dev-ops", "jenkins", "terraform", "scripting", "powershell", "azure", "aws", "gcp", "cloud", "problem-solving", "architect", "engineer", "developer", "creative", "analytical", "expert", "rationality", "conversational", "chat", "instruct", "llama-cpp", "gguf-my-repo", "text-generation", "en", "dataset:sequelbox/Titanium2.1-DeepSeek-R1", "dataset:sequelbox/Tachibana2-DeepSeek-R1", "dataset:sequelbox/Raiden-DeepSeek-R1", "base_model:ValiantLabs/Qwen3-4B-Esper3", "base_model:quantized:ValiantLabs/Qwen3-4B-Esper3", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2025-05-30T23:46:53Z	--- language: - en library_name: transformers pipeline_tag: text-generation tags: - esper - esper-3 - valiant - valiant-labs - qwen - qwen-3 - qwen-3-4b - 4b - reasoning - code - code-instruct - python - javascript - dev-ops - jenkins - terraform - scripting - powershell - azure - aws - gcp - cloud - problem-solving - architect - engineer - developer - creative - analytical - expert - rationality - conversational - chat - instruct - llama-cpp - gguf-my-repo base_model: ValiantLabs/Qwen3-4B-Esper3 datasets: - sequelbox/Titanium2.1-DeepSeek-R1 - sequelbox/Tachibana2-DeepSeek-R1 - sequelbox/Raiden-DeepSeek-R1 license: apache-2.0 --- # Triangle104/Qwen3-4B-Esper3-Q5_K_M-GGUF This model was converted to GGUF format from [`ValiantLabs/Qwen3-4B-Esper3`](https://huggingface.co/ValiantLabs/Qwen3-4B-Esper3) 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/ValiantLabs/Qwen3-4B-Esper3) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/Qwen3-4B-Esper3-Q5_K_M-GGUF --hf-file qwen3-4b-esper3-q5_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Qwen3-4B-Esper3-Q5_K_M-GGUF --hf-file qwen3-4b-esper3-q5_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/Qwen3-4B-Esper3-Q5_K_M-GGUF --hf-file qwen3-4b-esper3-q5_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Qwen3-4B-Esper3-Q5_K_M-GGUF --hf-file qwen3-4b-esper3-q5_k_m.gguf -c 2048 ```
Yasmineassia/RE-EnrichedPubMedBERT-finetuned-GAD	Yasmineassia	2025-06-16T07:51:12Z	0	1	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "license:cc", "endpoints_compatible", "region:us" ]	text-classification	2025-06-16T05:24:44Z	--- library_name: transformers license: cc pipeline_tag: text-classification --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> RE-EnrichedPubMed-finetuned-gene-disease Finetuned from model: [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext] ## 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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phospho-app/jmota27-ACT_BBOX-boats_datasets-g016r	phospho-app	2025-06-16T07:49:40Z	0	0	null	[ "safetensors", "phosphobot", "act", "region:us" ]	null	2025-06-16T07:27:31Z	--- 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/boats_datasets_bboxes](https://huggingface.co/datasets/phospho-app/boats_datasets_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_1e-5_1.0_0.5_0.15_0.15_epoch1	MinaMila	2025-06-16T07:48:37Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T07:46:52Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.25_0.05_0.05_epoch1	MinaMila	2025-06-16T07:48:29Z	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	2025-06-16T07:46:36Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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suzushi/miso-diffusion-2.0	suzushi	2025-06-16T07:45:19Z	0	4	diffusers	[ "diffusers", "text-to-image", "en", "base_model:stabilityai/stable-diffusion-3.5-medium", "base_model:finetune:stabilityai/stable-diffusion-3.5-medium", "region:us" ]	text-to-image	2025-06-15T06:29:30Z	--- language: - en license_name: stabilityai-ai-community license_link: LICENSE.md library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image base_model: - suzushi/miso-diffusion-m-1.0 - stabilityai/stable-diffusion-3.5-medium --- <div style="display: flex; justify-content: center; gap: 20px; margin-bottom: 20px;"> <img src="demo1.png" width="400" /> <img src="demo2.png" width="400" /> </div> # Anime SD3.5 medium Model An attempt to fine tune sd3.5 medium ## Version History \| Version \| Base Training \| Aesthetic Training \| Total Epochs \| \|---------\|--------------\|-------------------\|--------------\| \| alpha \| 250K images \| 0 images \| 1 \| \| beta \| 160K images \| 0 images \| 3 \| \| 1.0 \| 600k images \| 0 images \| 2 + (3 from beta) \| \| 1.1 \| 710k images \| 0 images \| 5 \| \| 2.0 \| 1.08M images \| 0 images \| 5 \| ## Training Methodology Training is done on gh200 with 96gb vram, now that prior training shows decent results, I am slightly increasing learning rate. Training setting: Adafactor with a batchsize of 40, lr_scheduler: cosine SD3.5 Specific setting: enable_scaled_pos_embed = true pos_emb_random_crop_rate = 0.2 weighting_scheme = "flow" learning_rate = 8e-6 learning_rate_te1 = 5e-6 learning_rate_te2 = 5e-6 Train Clip: true, Train t5xxl: false ## Support Me At the moment training an epoch cost around 130 dollars. If you like my project please consider supporting me: https://ko-fi.com/suzushi2024 Lastly, huge thanks to meg who has been supporting this project, without him this project would not have been possible !
aieng-lab/gpt2-xl_review-aspect	aieng-lab	2025-06-16T07:43:04Z	0	0	transformers	[ "transformers", "safetensors", "gpt2", "text-classification", "en", "base_model:openai-community/gpt2-xl", "base_model:finetune:openai-community/gpt2-xl", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2025-06-16T07:42:09Z	--- library_name: transformers license: mit language: - en metrics: - f1 - precision - recall base_model: - gpt2-xl pipeline_tag: text-classification --- # GPT-2 xl for classifying API reviews This model classifies API reviews in developer forums (e.g., Stack Overflow) as 'usability', 'others', 'onlysentiment', 'bug', 'performance', 'community', 'documentation', 'compatibility', 'legal', 'portability' or 'security'. - Developed by: Fabian C. Peña, Steffen Herbold - Finetuned from: [gpt2-xl](https://huggingface.co/gpt2-xl) - Replication kit: [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark) - Language: English - License: MIT ## Citation ``` @misc{pena2025benchmark, author = {Fabian Peña and Steffen Herbold}, title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks}, year = {2025} } ```
SidXXD/test-noise_upscaling-f_26	SidXXD	2025-06-16T07:42:41Z	0	0	diffusers	[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "custom-diffusion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2025-06-15T17:37:48Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: photo of a sks person tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - custom-diffusion inference: true --- # Custom Diffusion - SidXXD/test-noise_upscaling-f_26 These are Custom Diffusion adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on photo of a sks person using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.25_0.05_0.15_epoch2	MinaMila	2025-06-16T07:41:32Z	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	2025-06-16T07:39:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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mdhasnainali/ReaderLM-v2-Finetuned-For-Job-Post	mdhasnainali	2025-06-16T07:40:45Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T07:38:31Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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japat123/qwen_jun16_1	japat123	2025-06-16T07:40:10Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-06-16T07:39:00Z	--- base_model: unsloth/qwen3-8b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: japat123 - License: apache-2.0 - Finetuned from model : unsloth/qwen3-8b-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)
Skyfallirk/vasnetsov_LoRa	Skyfallirk	2025-06-16T07:40:02Z	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	2025-06-16T07:39:58Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: a photo collage in vasnetsov 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 - Skyfallirk/vasnetsov_LoRa <Gallery /> ## Model description These are Skyfallirk/vasnetsov_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 a photo collage in vasnetsov style to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](Skyfallirk/vasnetsov_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]
aieng-lab/gpt2-medium_review-aspect	aieng-lab	2025-06-16T07:39:13Z	0	0	transformers	[ "transformers", "safetensors", "gpt2", "text-classification", "en", "base_model:openai-community/gpt2-medium", "base_model:finetune:openai-community/gpt2-medium", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2025-06-16T07:38:57Z	--- library_name: transformers license: mit language: - en metrics: - f1 - precision - recall base_model: - gpt2-medium pipeline_tag: text-classification --- # GPT-2 medium for classifying API reviews This model classifies API reviews in developer forums (e.g., Stack Overflow) as 'usability', 'others', 'onlysentiment', 'bug', 'performance', 'community', 'documentation', 'compatibility', 'legal', 'portability' or 'security'. - Developed by: Fabian C. Peña, Steffen Herbold - Finetuned from: [gpt2-medium](https://huggingface.co/gpt2-medium) - Replication kit: [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark) - Language: English - License: MIT ## Citation ``` @misc{pena2025benchmark, author = {Fabian Peña and Steffen Herbold}, title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks}, year = {2025} } ```
aieng-lab/gpt2_review-aspect	aieng-lab	2025-06-16T07:38:29Z	0	0	transformers	[ "transformers", "safetensors", "gpt2", "text-classification", "en", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2025-06-16T07:38:21Z	--- library_name: transformers license: mit language: - en metrics: - f1 - precision - recall base_model: - gpt2 pipeline_tag: text-classification --- # GPT-2 small for classifying API reviews This model classifies API reviews in developer forums (e.g., Stack Overflow) as 'usability', 'others', 'onlysentiment', 'bug', 'performance', 'community', 'documentation', 'compatibility', 'legal', 'portability' or 'security'. - Developed by: Fabian C. Peña, Steffen Herbold - Finetuned from: [gpt2](https://huggingface.co/gpt2) - Replication kit: [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark) - Language: English - License: MIT ## Citation ``` @misc{pena2025benchmark, author = {Fabian Peña and Steffen Herbold}, title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks}, year = {2025} } ```
aieng-lab/ModernBERT-base_review-aspect	aieng-lab	2025-06-16T07:37:06Z	0	0	transformers	[ "transformers", "safetensors", "modernbert", "text-classification", "en", "base_model:answerdotai/ModernBERT-base", "base_model:finetune:answerdotai/ModernBERT-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-16T07:36:57Z	--- library_name: transformers license: mit language: - en metrics: - f1 - precision - recall base_model: - answerdotai/ModernBERT-base pipeline_tag: text-classification --- # ModernBERT base for classifying API reviews This model classifies API reviews in developer forums (e.g., Stack Overflow) as 'usability', 'others', 'onlysentiment', 'bug', 'performance', 'community', 'documentation', 'compatibility', 'legal', 'portability' or 'security'. - Developed by: Fabian C. Peña, Steffen Herbold - Finetuned from: [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) - Replication kit: [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark) - Language: English - License: MIT ## Citation ``` @misc{pena2025benchmark, author = {Fabian Peña and Steffen Herbold}, title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks}, year = {2025} } ```
sungwoo1/results	sungwoo1	2025-06-16T07:36:02Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:klue/roberta-base", "base_model:finetune:klue/roberta-base", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-16T07:35:20Z	--- library_name: transformers base_model: klue/roberta-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: results 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. --> # results This model is a fine-tuned version of [klue/roberta-base](https://huggingface.co/klue/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4661 - Accuracy: 0.856 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
aieng-lab/bert-large-cased_review-aspect	aieng-lab	2025-06-16T07:34:58Z	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "en", "base_model:google-bert/bert-large-cased", "base_model:finetune:google-bert/bert-large-cased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-16T07:34:44Z	--- library_name: transformers license: mit language: - en metrics: - f1 - precision - recall base_model: - bert-large-cased pipeline_tag: text-classification --- # BERT large for classifying API reviews This model classifies API reviews in developer forums (e.g., Stack Overflow) as 'usability', 'others', 'onlysentiment', 'bug', 'performance', 'community', 'documentation', 'compatibility', 'legal', 'portability' or 'security'. - Developed by: Fabian C. Peña, Steffen Herbold - Finetuned from: [bert-large-cased](https://huggingface.co/bert-large-cased) - Replication kit: [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark) - Language: English - License: MIT ## Citation ``` @misc{pena2025benchmark, author = {Fabian Peña and Steffen Herbold}, title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks}, year = {2025} } ```
aieng-lab/bert-base-cased_review-aspect	aieng-lab	2025-06-16T07:34:15Z	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "en", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-16T07:34:07Z	--- library_name: transformers license: mit language: - en metrics: - f1 - precision - recall base_model: - bert-base-cased pipeline_tag: text-classification --- # BERT base for classifying API reviews This model classifies API reviews in developer forums (e.g., Stack Overflow) as 'usability', 'others', 'onlysentiment', 'bug', 'performance', 'community', 'documentation', 'compatibility', 'legal', 'portability' or 'security'. - Developed by: Fabian C. Peña, Steffen Herbold - Finetuned from: [bert-base-cased](https://huggingface.co/bert-base-cased) - Replication kit: [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark) - Language: English - License: MIT ## Citation ``` @misc{pena2025benchmark, author = {Fabian Peña and Steffen Herbold}, title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks}, year = {2025} } ```
MinaMila/gemma_2b_unlearned_2nd_1e-5_1.0_0.5_0.15_0.25_epoch1	MinaMila	2025-06-16T07:32:36Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T07:30:49Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
xfjcoder/llama3.1-8b-erged-6bit	xfjcoder	2025-06-16T07:31:21Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T07:31:17Z	--- base_model: unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: xfjcoder - 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)
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.25_0.05_0.25_epoch2	MinaMila	2025-06-16T07:28:01Z	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	2025-06-16T07:26:01Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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]
JayHyeon/Qwen_1.5B-math-IPO_5e-7_1.0vpo_constant-1ep	JayHyeon	2025-06-16T07:27:28Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "dpo", "conversational", "dataset:argilla/distilabel-math-preference-dpo", "arxiv:2305.18290", "base_model:Qwen/Qwen2.5-Math-1.5B", "base_model:finetune:Qwen/Qwen2.5-Math-1.5B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T07:39:02Z	--- base_model: Qwen/Qwen2.5-Math-1.5B datasets: argilla/distilabel-math-preference-dpo library_name: transformers model_name: Qwen_1.5B-math-IPO_5e-7_1.0vpo_constant-1ep tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for Qwen_1.5B-math-IPO_5e-7_1.0vpo_constant-1ep This model is a fine-tuned version of [Qwen/Qwen2.5-Math-1.5B](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B) on the [argilla/distilabel-math-preference-dpo](https://huggingface.co/datasets/argilla/distilabel-math-preference-dpo) 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="JayHyeon/Qwen_1.5B-math-IPO_5e-7_1.0vpo_constant-1ep", 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/bonin147/huggingface/runs/gwchm69z) 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: 0.15.2 - Transformers: 4.50.0 - Pytorch: 2.6.0 - Datasets: 3.4.1 - Tokenizers: 0.21.1 ## 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}} } ```
DLxiaoying/distilbert-base-uncased-finetuned-clinc	DLxiaoying	2025-06-16T07:25:58Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-16T06:30:52Z	--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-clinc 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. --> # distilbert-base-uncased-finetuned-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8063 - Accuracy: 0.9161 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 4.3296 \| 1.0 \| 318 \| 3.3392 \| 0.7313 \| \| 2.6885 \| 2.0 \| 636 \| 1.9295 \| 0.8465 \| \| 1.6035 \| 3.0 \| 954 \| 1.2026 \| 0.8965 \| \| 1.0561 \| 4.0 \| 1272 \| 0.8956 \| 0.9113 \| \| 0.8334 \| 5.0 \| 1590 \| 0.8063 \| 0.9161 \| ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
sallani/Urban_CO2_Predictor_Small_LMM_LoRA_GGUF	sallani	2025-06-16T07:25:42Z	0	0	transformers	[ "transformers", "gguf", "Co2Emission", "Vanet", "Urban mobility", "Self", "Driving", "zero-shot-classification", "fr", "en", "base_model:mistralai/Mistral-7B-Instruct-v0.3", "base_model:quantized:mistralai/Mistral-7B-Instruct-v0.3", "license:apache-2.0", "endpoints_compatible", "region:us" ]	zero-shot-classification	2025-06-15T19:55:18Z	--- license: apache-2.0 language: - fr - en metrics: - accuracy - brier_score base_model: - mistralai/Mistral-7B-Instruct-v0.3 pipeline_tag: zero-shot-classification tags: - Co2Emission - Vanet - Urban mobility - Self - Driving library_name: transformers --- # Urban\_CO2\_Predictor\_Small\_LMM\_LoRA\_GGUF Author: Dr. Sabri Allani AI & Cybersecurity Expert \| R\&D Consultant \| Instructor \| Open-Source Contributor Model type: GGUF (Urban\_CO2\_Predictor\_Small\_Edge\_LLM based on Mistral-7B Instruct, quantized) Parameters: 7B File size: \~3GB License: MIT --- ## Overview I am releasing this model as a GGUF-quantized version of Mistral-7B Instruct, prepared for research, experimentation, and further customization for urban CO₂ emission prediction and general LLM tasks. This release is intended for anyone working on environmental analytics, LoRA fine-tuning, or practical LLM deployment with open-source tools. The model is especially relevant for CO₂ emission prediction in VANET (Vehicular Ad Hoc Networks) and urban mobility environments. It can be prompted with contextual information to generate approximate CO₂ estimates based on traffic, vehicle type, density, and time of day. * Base model: Mistral-7B Instruct * Format: GGUF (compatible with llama.cpp, llama-cpp-python, LM Studio, ollama, KoboldCpp, etc.) * Quantization: \[specify, e.g. Q4\_0, Q5\_K, Q6\_K, etc., if known] * Size: \~3GB --- 📥 Download model file: [➡️ Click here to download `Urban_CO2_Predictor_Small_Edge_LLM.gguf`](https://huggingface.co/sallani/Urban_CO2_Predictor_Small_LMM_LoRA_GGUF/resolve/main/Urban_CO2_Predictor_Small_Edge_LLM.gguf) (~3.08 GB GGUF quantized model for edge inference and urban CO₂ prediction) ## Intended Use * Urban and environmental CO₂ emission analysis and prediction (baseline, demo, or experimental) * LoRA and transfer learning for domain adaptation * General language tasks on CPU/GPU (local, edge, or cloud inference) * Research, prototyping, or educational work * Prediction in VANETs and real-time urban mobility systems --- ## Example usage ```python from llama_cpp import Llama # Load the quantized GGUF model llm = Llama(model_path="co2_merged.gguf") # Provide explicit parameters in the prompt prompt = ( "Estimate CO₂ emissions (g/km) for the following scenario: " "- Location: Central Paris urban boulevard " "- Timestamp: 07:45, weekday (rush hour) " "- Weather: Cloudy, 18 °C " "- Traffic density: 60 vehicles per km " "- Vehicle mix: 70 % diesel Euro 6, 20 % petrol Euro 5, 10 % electric " "- Average speed: 28 km/h " "Return only the numeric estimate followed by 'g/km'." ) response = llm(prompt) print(response) ``` --- ## Technical details * Architecture: Mistral 7B, instruct-tuned * File format: GGUF * File size: \~3GB * License: MIT --- ## Citation If you use this model in your work, please cite: ```bibtex @misc{allani2024urban, author = {Dr. Sabri Allani}, title = {Urban CO₂ Predictor Small LMM LoRA GGUF}, year = {2024}, howpublished = {\url{https://huggingface.co/sallani/Urban_CO2_Predictor_Small_LMM_LoRA_GGUF}}, note = {ORCID: https://orcid.org/0000-0003-0643-5067} } ``` --- ## Contact For questions or collaboration, contact me via: * [LinkedIn](https://www.linkedin.com/in/sabri-allani) * [Hugging Face Profile](https://huggingface.co/sallani) --- ## Disclaimer This model is provided as-is for research and development. I make no warranty for production use or accuracy in real-world CO₂ prediction tasks. Use at your own risk and adapt for your own projects as needed. --- ## License MIT — free to use, modify, and redistribute. --- ### Acknowledgment This file is derived from Mistral-7B Instruct and adapted to GGUF format for open research in AI and environmental modeling.
MinaMila/gemma_2b_unlearned_2nd_1e-5_1.0_0.5_0.15_0.5_epoch2	MinaMila	2025-06-16T07:24:33Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T07:22:42Z	--- 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. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
robotgeneralist/openpi-nomagic	robotgeneralist	2025-06-16T07:24:23Z	0	0	null	[ "license:mit", "region:us" ]	null	2025-04-14T18:14:58Z	--- license: mit --- # Nomagic Simple / Adversarial Box Model Checkpoints This is a repo to store the most important checkpoints of the `openpi` model. ## Uploading checkpoints Since the checkpoints are huge, the fastest and most reliable way to upload them is by using the `upload-large-folder` command from `huggingface-cli`. To do so, you first have to log in with appropriate credentials (you need a token with write permissions to the target repository): ``` huggingface-cli login ``` Next, use `upload-large-folder`. For example, to upload the `checkpoints` directory to the remote repository, run: ``` huggingface-cli upload-large-folder robotgeneralist/openpi-nomagic-multibox checkpoints --repo-type=model ``` Note that there is no way to specify a target path where the data will be stored on the remote. The contents of the directory will be placed under the root directory. So, for example, if your local folder is organized like the following: ``` checkpoints --some-dir --file1 --file2 ``` after uploading to the remote, you will have: ``` some-dir --file1 --file2 ``` Luckily, you can still upload additional files later on. For example, if after the first upload you try to upload: ``` checkpoints --some-dir --file3 --file4 ``` the remote will become: ``` some-dir --file1 --file2 --file3 --file4 ``` Hence, even though slightly inconvenient, this seems to be the best method for uploading big checkpoints, because of its efficiency and robustness.
Triangle104/Q3-8B-Kintsugi-Q5_K_M-GGUF	Triangle104	2025-06-16T07:22:14Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "axolotl", "unsloth", "roleplay", "conversational", "llama-cpp", "gguf-my-repo", "dataset:PygmalionAI/PIPPA", "dataset:Alfitaria/nemotron-ultra-reasoning-synthkink", "dataset:PocketDoc/Dans-Prosemaxx-Gutenberg", "dataset:FreedomIntelligence/Medical-R1-Distill-Data", "dataset:cognitivecomputations/SystemChat-2.0", "dataset:allenai/tulu-3-sft-personas-instruction-following", "dataset:kalomaze/Opus_Instruct_25k", "dataset:simplescaling/s1K-claude-3-7-sonnet", "dataset:ai2-adapt-dev/flan_v2_converted", "dataset:grimulkan/theory-of-mind", "dataset:grimulkan/physical-reasoning", "dataset:nvidia/HelpSteer3", "dataset:nbeerbower/gutenberg2-dpo", "dataset:nbeerbower/gutenberg-moderne-dpo", "dataset:nbeerbower/Purpura-DPO", "dataset:antiven0m/physical-reasoning-dpo", "dataset:allenai/tulu-3-IF-augmented-on-policy-70b", "dataset:NobodyExistsOnTheInternet/system-message-DPO", "base_model:allura-org/Q3-8B-Kintsugi", "base_model:quantized:allura-org/Q3-8B-Kintsugi", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-16T07:18:55Z	--- license: apache-2.0 base_model: allura-org/Q3-8B-Kintsugi library_name: transformers tags: - mergekit - axolotl - unsloth - roleplay - conversational - llama-cpp - gguf-my-repo datasets: - PygmalionAI/PIPPA - Alfitaria/nemotron-ultra-reasoning-synthkink - PocketDoc/Dans-Prosemaxx-Gutenberg - FreedomIntelligence/Medical-R1-Distill-Data - cognitivecomputations/SystemChat-2.0 - allenai/tulu-3-sft-personas-instruction-following - kalomaze/Opus_Instruct_25k - simplescaling/s1K-claude-3-7-sonnet - ai2-adapt-dev/flan_v2_converted - grimulkan/theory-of-mind - grimulkan/physical-reasoning - nvidia/HelpSteer3 - nbeerbower/gutenberg2-dpo - nbeerbower/gutenberg-moderne-dpo - nbeerbower/Purpura-DPO - antiven0m/physical-reasoning-dpo - allenai/tulu-3-IF-augmented-on-policy-70b - NobodyExistsOnTheInternet/system-message-DPO --- # Triangle104/Q3-8B-Kintsugi-Q5_K_M-GGUF This model was converted to GGUF format from [`allura-org/Q3-8B-Kintsugi`](https://huggingface.co/allura-org/Q3-8B-Kintsugi) 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/allura-org/Q3-8B-Kintsugi) for more details on the model. --- Q3-8B-Kintsugi is a roleplaying model finetuned from Qwen3-8B-Base. During testing, Kintsugi punched well above its weight class in terms of parameters, especially for 1-on-1 roleplaying and general storywriting. --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/Q3-8B-Kintsugi-Q5_K_M-GGUF --hf-file q3-8b-kintsugi-q5_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Q3-8B-Kintsugi-Q5_K_M-GGUF --hf-file q3-8b-kintsugi-q5_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/Q3-8B-Kintsugi-Q5_K_M-GGUF --hf-file q3-8b-kintsugi-q5_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Q3-8B-Kintsugi-Q5_K_M-GGUF --hf-file q3-8b-kintsugi-q5_k_m.gguf -c 2048 ```
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.25_0.05_0.25_epoch1	MinaMila	2025-06-16T07:21:18Z	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	2025-06-16T07:19:26Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. 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himedia/fincredit-gemma3-4b-lr5e05-bs2-r16-steps10-20250616_064351	himedia	2025-06-16T07:20:17Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "text-generation-inference", "unsloth", "gemma3", "trl", "conversational", "en", "base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T06:46:15Z	--- base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: himedia - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-4b-it-unsloth-bnb-4bit This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
NAMAA-Space/zarra	NAMAA-Space	2025-06-16T07:20:05Z	54	2	model2vec	[ "model2vec", "safetensors", "embeddings", "static-embeddings", "sentence-transformers", "sentence-similarity", "ar", "dataset:allenai/c4", "base_model:jinaai/jina-embeddings-v3", "base_model:finetune:jinaai/jina-embeddings-v3", "license:mit", "region:us" ]	sentence-similarity	2025-05-27T10:22:20Z	--- library_name: model2vec license: mit model_name: Abdelkareem/zarra tags: - embeddings - static-embeddings - sentence-transformers datasets: - allenai/c4 language: - ar base_model: - jinaai/jina-embeddings-v3 pipeline_tag: sentence-similarity --- # Zarra: Arabic Static Embedding Model ![image/png](https://cdn-uploads.huggingface.co/production/uploads/628f7a71dd993507cfcbe587/t4ALUMHL25wTuNzgNQwUg.png) Zarra is a static embedding model built using the Model2Vec distillation framework. It is a distilled version of a Sentence Transformer, specifically optimized for the Arabic language. Unlike traditional transformer-based models, Zarra produces static embeddings, enabling ultra-fast inference on both CPU and GPU—making it ideal for resource-constrained environments or real-time applications. ## Why Zarra? ⚡ Exceptional Speed: Delivers embeddings up to 500x faster than sentence transformers. 🧠 Compact & Efficient: Up to 50x smaller in size, allowing easy deployment on edge devices. 🧰 Versatile: Well-suited for search, clustering, classification, deduplication, and more. 🌍 Arabic-First: Specifically trained on high-quality Arabic data, ensuring relevance and performance across a range of Arabic NLP tasks. <p align="center"> <img src="https://cdn-uploads.huggingface.co/production/uploads/628f7a71dd993507cfcbe587/3JEnPfgF2BfbN5H81K0XD.png" alt="Speed vs Performance Chart" width="700"/> </p> ## About Model2Vec The Model2Vec distillation technique transfers knowledge from large transformer models into lightweight static embedding spaces, preserving semantic quality while dramatically improving speed and efficiency. Zarra represents the best of both worlds: the semantic power of transformers and the speed and simplicity of static vectors. ## Installation Install model2vec using pip: ``` pip install model2vec ``` ## Usage ### Using Model2Vec The [Model2Vec library](https://github.com/MinishLab/model2vec) is the fastest and most lightweight way to run Model2Vec models. Load this model using the `from_pretrained` method: ```python from model2vec import StaticModel # Load a pretrained Model2Vec model model = StaticModel.from_pretrained("NAMAA-Space/zarra") # Compute text embeddings embeddings = model.encode(["Example sentence"]) ``` ### Using Sentence Transformers You can also use the [Sentence Transformers library](https://github.com/UKPLab/sentence-transformers) to load and use the model: ```python from sentence_transformers import SentenceTransformer # Load a pretrained Sentence Transformer model model = SentenceTransformer("NAMAA-Space/zarra") # Compute text embeddings embeddings = model.encode(["Example sentence"]) ``` ## How it Works Model2vec creates a small, fast, and powerful model that outperforms other static embedding models by a large margin on all tasks we could find, while being much faster to create than traditional static embedding models such as GloVe. Best of all, you don't need any data to distill a model using Model2Vec. It works by passing a vocabulary through a sentence transformer model, then reducing the dimensionality of the resulting embeddings using PCA, and finally weighting the embeddings using [SIF weighting](https://openreview.net/pdf?id=SyK00v5xx). During inference, we simply take the mean of all token embeddings occurring in a sentence. ## Benchmark on Arabic ## Speed \| Model \| Speed (sentences/second) \| Device \| \|---------------------------------------\|--------------------------\|--------\| \| zarra \| 26893.63 \| cpu \| \| bojji \| 27478.15 \| cpu \| \| potion-multilingual-128M \| 27145.31 \| cpu \| \| paraphrase-multilingual-MiniLM-L12-v2 \| 2363.24 \| cuda \| \| silma_ai_embedding_sts_v0.1 \| 627.13 \| cuda \| \| muffakir_embedding \| 621.77 \| cuda \| \| get_multilingual_base \| 895.41 \| cuda \| \| arabic_retrieval_v1.0 \| 618.56 \| cuda \| \| arabic_triplet_matryoshka_v2 \| 610.64 \| cuda \| - Zarra and Bojji excel in speed, achieving 26893.63 and 27478.15 sentences per second on CPU, respectively, far surpassing CUDA-based models like arabic_triplet_matryoshka_v2 (610.64). - Top Performer: Bojji is the fastest model, slightly ahead of Zarra and potion-multilingual-128M (27145.31), highlighting the efficiency of Model2Vec-based models on CPU. - Key Observation: The high speed of Zarra and Bojji on CPU makes them ideal for resource-constrained environments, offering significant advantages over CUDA-dependent models. ## Size of the Model \| Model \| Parameters (M) \| Size (MB) \| Relative to Largest (%) \| Less than Largest (x) \| \|----------------------------------\|----------------\|-----------\|-------------------------\|-----------------------\| \| zarra \| 64.00 \| 244.14 \| 41.92 \| 2.39 \| \| bojji \| 124.88 \| 476.40 \| 81.79 \| 1.22 \| \| potion-multilingual-128M \| 128.09 \| 488.63 \| 83.89 \| 1.19 \| \| paraphrase-multilingual-MiniLM-… \| 117.65 \| 448.82 \| 77.06 \| 1.30 \| \| silma_ai_embedding_sts_v0.1 \| 135.19 \| 515.72 \| 88.54 \| 1.13 \| \| muffakir_embedding \| 135.19 \| 515.72 \| 88.54 \| 1.13 \| \| arabic_retrieval_v1.0 \| 135.19 \| 515.73 \| 88.54 \| 1.13 \| \| arabic_triplet_matryoshka_v2 \| 135.19 \| 515.72 \| 88.54 \| 1.13 \| \| get_multilingual_base \| 305.37 \| 582.45 \| 100.00 \| 1.00 \| - Zarra is the smallest model, with only 64 million parameters and 244.14 MB in size, making it 2.39 times smaller than the largest model (get_multilingual_base). - Bojji is slightly larger at 124.88 million parameters and 476.40 MB, but still significantly smaller than most other models. - Top Performer: Zarra leads in compactness, offering the smallest footprint, which is critical for deployment on resource-limited devices. - Key Observation: The compact size of Zarra and Bojji aligns with their design goal of efficiency, making them highly suitable for edge computing and real-time applications. \| Model \| Avg \| MIRAC \| MLQAR \| Massi \| Multi \| STS17 \| STS22 \| XNLI_ \| \|---------------------------------------\|-------\|-------\|-------\|-------\|-------\|-------\|-------\|-------\| \| arabic_triplet_matryoshka_v2 \| 0.6610 \| 0.6262 \| 0.5093 \| 0.5577 \| 0.5868 \| 0.8531 \| 0.6396 \| 0.8542 \| \| muffakir_embedding \| 0.6494 \| 0.6424 \| 0.5267 \| 0.5462 \| 0.5943 \| 0.8485 \| 0.6291 \| 0.7583 \| \| arabic_retrieval_v1.0 \| 0.6473 \| 0.6159 \| 0.5674 \| 0.5832 \| 0.5993 \| 0.8002 \| 0.6254 \| 0.7393 \| \| gate_arabert-v1 \| 0.6444 \| 0.5774 \| 0.4808 \| 0.5345 \| 0.5847 \| 0.8278 \| 0.6310 \| 0.8746 \| \| get_multilingual_base \| 0.6440 \| 0.7177 \| 0.5698 \| 0.5071 \| 0.5521 \| 0.7881 \| 0.6145 \| 0.7584 \| \| arabic_sts_matryoshka \| 0.6413 \| 0.5828 \| 0.4840 \| 0.5457 \| 0.5494 \| 0.8290 \| 0.6242 \| 0.8740 \| \| silma_ai_embedding_sts_v0.1 \| 0.6138 \| 0.3799 \| 0.5011 \| 0.5600 \| 0.5749 \| 0.8559 \| 0.6122 \| 0.8125 \| \| Arabic-MiniLM-L12-v2-all-nli-triplet \| 0.5431 \| 0.2240 \| 0.3612 \| 0.4775 \| 0.5698 \| 0.8111 \| 0.5540 \| 0.8043 \| \| paraphrase-multilingual-MiniLM-L12-v2 \| 0.5208 \| 0.2191 \| 0.3496 \| 0.4515 \| 0.5573 \| 0.7916 \| 0.4908 \| 0.7859 \| \| bojji \| 0.5177 \| 0.2941 \| 0.3989 \| 0.4667 \| 0.5433 \| 0.7233 \| 0.5880 \| 0.6094 \| \| zarra \| 0.4822 \| 0.2295 \| 0.3473 \| 0.4119 \| 0.5237 \| 0.6469 \| 0.6218 \| 0.5942 \| \| potion-multilingual-128M \| 0.4699 \| 0.1658 \| 0.3150 \| 0.4285 \| 0.5338 \| 0.6511 \| 0.5951 \| 0.5999 \| \| all_minilm_l6_v2 \| 0.2843 \| 0.0005 \| 0.0064 \| 0.1905 \| 0.4934 \| 0.5089 \| 0.2518 \| 0.5384 \| ### Sorted by STS17_main (Score) \| Model Name \| STS17_main \| \|---------------------------------------\|------------\| \| silma_ai_embedding_sts_v0.1 \| 0.856 \| \| arabic_triplet_matryoshka_v2 \| 0.853 \| \| muffakir_embedding \| 0.849 \| \| arabic_sts_matryoshka \| 0.829 \| \| gate_arabert-v1 \| 0.828 \| \| Arabic-MiniLM-L12-v2-all-nli-triplet \| 0.811 \| \| arabic_retrieval_v1.0 \| 0.800 \| \| paraphrase-multilingual-MiniLM-L12-v2 \| 0.792 \| \| get_multilingual_base \| 0.788 \| \| bojji \| 0.723 \| \| potion-multilingual-128M \| 0.651 \| \| zarra \| 0.647 \| \| all_minilm_l6_v2 \| 0.509 \| ### Sorted by STS22.v2_main (Score) \| Model Name \| STS22.v2_main \| \|---------------------------------------\|---------------\| \| arabic_triplet_matryoshka_v2 \| 0.640 \| \| gate_arabert-v1 \| 0.631 \| \| muffakir_embedding \| 0.629 \| \| arabic_retrieval_v1.0 \| 0.625 \| \| arabic_sts_matryoshka \| 0.624 \| \| zarra \| 0.622 \| \| get_multilingual_base \| 0.615 \| \| silma_ai_embedding_sts_v0.1 \| 0.612 \| \| potion-multilingual-128M \| 0.595 \| \| bojji \| 0.588 \| \| Arabic-MiniLM-L12-v2-all-nli-triplet \| 0.554 \| \| paraphrase-multilingual-MiniLM-L12-v2 \| 0.491 \| \| all_minilm_l6_v2 \| 0.252 \| ## Additional Resources - [Zarra & Bojji Blog](https://kareemai.com/blog/posts/minishlab/blog_zaraah.html) - [NAMAA Collection](https://huggingface.co/collections/NAMAA-Space/zaraah-683f1f8a1eec1ec8f2badee5) - [MinishLab](https://minishlab.github.io/) - [Model2Vec Repo](https://github.com/MinishLab/model2vec)

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