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AI 모델 84759

• Yarn-Mistral-7B-128k-GGUF↗

  TheBloke

• beit_large_patch16_224.in22k_ft_in22k_in1k↗

  timm

• efficientvit_l2.r256_in1k↗

  timm

• xcit_medium_24_p16_224.fb_in1k↗

  timm

• Qwen3.6-27B-Fable-Fusion-711-Uncensored-Heretic-NM-DAU-NEO-MAX-MTP-GGUF↗

  trinityomnis

• flux-3-image-klein-GGUF↗

  unsloth

  This is a GGUF quantized version of FLUX 3 Image Klein by Black Forest Labs, the 5.6B image transformer (16-step distilled checkpoint) for text to image and image editing. unsloth/flux-3-image-klein-GGUF uses Unsloth Dynamic 2.0 methodology: the `UD-` files are mixed precision, with every layer's type chosen from a measured sensitivity scan, and they beat the uniform files at the same size. - Loads in diffusers through `Flux3Transformer2DModel.from_single_file(..., quantization_config=GGUFQuantizationConfig(...))`. Also loads in Unsloth Desktop. - Tensor names follow the original Black Forest Labs checkpoint layout. The text encoder (Qwen3-VL-4B-Instruct) and the VAE are unchanged; take them from black-forest-labs/flux-3-image-klein. **Recommended:** `UD-Q4_K_XL` (3.93 GB) for most GPUs: LPIPS 0.038 against 0.060 for `Q4_K_M`. For output closest to bf16 use `Q8_0`; on small cards use `UD-Q2_K_XL` (2.62 GB). LPIPS (alex, lower is better) and SSIM against the bf16 transformer, same prompt, seed, noise and text encoder, 16 steps, over 19 renders: 8 held-out prompts at 1024x1024 plus 11 text-to-image and editing cases (single and multi-reference). A repeated bf16 render scores 0, so the numbers measure only the quantization.

• granite-4.1-8b↗

  unsloth

• Qwen3-32B↗

  unsloth

  Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features: **Qwen3-32B** has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Number of Parameters: 32.8B - Number of Paramaters (Non-Embedding): 31.2B - Number of Layers: 64 - Number of Attention Heads (GQA): 64 for Q and 8 for KV - Context Length: 32,768 natively and 131,072 tokens with YaRN. For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our blog, GitHub, and Documentation.

• Vela-2.0-9B↗

  vllm-sr

• Sakura-Jais-3-7B-GGUF↗

  webmp3