whisperkit-coreml
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This is an uncensored version of Qwen/Qwen3.8-27B created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens. The newly added **Huihui-Qwen3.8-27B-abliterated-Ternary** series come from prism-ml/Ternary-Bonsai-2-27B-gguf. Only layers **22 to 52 (0-based indexing)** have been ablated, while the other layers remain unablated. It may come with a small disclaimer warning. The size after conversion may differ from the original GGUF (Some of the weights are converted from PTQ1 to Q2_K or Q3_K.). This is just a test/validation. The ternary hybrid-attention kernels live in the PrismML-Eng/llama.cpp fork. **Stock llama.cpp will not run these files.**
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View details ↗wav2vec2-large-xlsr-gl
diego-fustes基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗vit-beans
diegomartinezpThis is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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View details ↗mDenseOn-ONNX
diegomo123基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗loras
diff-interpretation-tuningThis repository contains the weight diffs and DIT adapters used in the paper Learning to Interpret Weight Differences in Language Models (Goel et al. 2025). To play around with the weight diffs and DIT adapters from the paper, please check out our Google Colab demo notebook. This notebook shows how to load the weight diffs and adapters from this repo. The code used to train and evaluate our weight diffs and DIT adapters can be found at github.com/Aviously/diff-interpretation-tuning. Some of the large data files used for training can be found at hf.co/datasets/diff-interpretation-tuning/finetuning-data. All weight diffs and DIT adapters in the repository live under a specific ` / ` folder (e.g. hidden-topic/qwen3-4b). Please consult the paper to understand what each experiment refers to.
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗DERL-Meta-Optimizer-Init-Qwen2.5-0.5B-Instruct
DifferentiableEvolutionaryRL基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Qwen-Image-Edit-F2P
DiffSynth-StudioThis model is based on the Qwen-Image-Edit face-controlled image generation model. Given a cropped facial image as input, it generates full portrait images of the same person.
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View details ↗controlnet-canny-sdxl-1.0
diffusersThese are controlnet weights trained on stabilityai/stable-diffusion-xl-base-1.0 with canny conditioning. You can find some example images in the following. prompt: a couple watching a romantic sunset, 4k photo prompt: a woman, close up, detailed, beautiful, street photography, photorealistic, detailed, Kodak ektar 100, natural, candid shot
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View details ↗controlnet-canny-sdxl-1.0-mid
diffusers基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗controlnet-canny-sdxl-1.0-small
diffusersThese are small controlnet weights trained on stabilityai/stable-diffusion-xl-base-1.0 with canny conditioning. This checkpoint is 7x smaller than the original XL controlnet checkpoint. You can find some example images in the following. prompt: a woman, close up, detailed, beautiful, street photography, photorealistic, detailed, Kodak ektar 100, natural, candid shot 🚨 Please note that this checkpoint is experimental and there's a lot of room for improvement. We encourage the community to build on top of it, improve it, and provide us with feedback. 🚨
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View details ↗controlnet-depth-sdxl-1.0
diffusers--- license: openrail++ base_model: stabilityai/stable-diffusion-xl-base-1.0 tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - controlnet inference: false --- These are controlnet weights trained on stabilityai/stable-diffusion-xl-base-1.0 with depth conditioning. You can find some example images in the following. For more details, check out the official documentation of `StableDiffusionXLControlNetPipeline`.
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View details ↗controlnet-depth-sdxl-1.0-mid
diffusers基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗controlnet-depth-sdxl-1.0-small
diffusers基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗ddpm_dummy
diffusers基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗FLUX.1-dev-bnb-4bit
diffusers基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗FLUX.1-vae
diffusers基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗FLUX.2-dev-bnb-4bit
diffusersThis repository contains the NF4 quantized DiT and the text encoders from https://huggingface.co/black-forest-labs/FLUX.2-dev. The VAE is not quantized.
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View details ↗private-model
diffusers-internal-dev基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗tiny-minimax-h3-modular-pipe
diffusers-internal-dev基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗LTX-2.3-Diffusers
diffusers基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗LTX-2.3-Distilled-Diffusers
diffusers基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗qwen-image-nf4
diffusers基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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