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AI モデル 84759
- Fimbulvetr-11B-v2-iMat-GGUF↗
InferenceIllusionist
- studio-ghibli-style↗
KappaNeuro
- ppo-LunarLander-v3-flip↗
KaptainKris
- ltx-2.3-bodypositivity-lora↗
kenzato
- yolov8n-hard-hat-detection↗
keremberke
- yolov8n-shoe-classification↗
keremberke
- glm-5.3-flash-exlr8-k2-k3-k4↗
kindlingai
A quantization of **GLM-5.3-Flash** in the **exlr8 v2** format, built for **NVIDIA GB10**-class unified-memory devices (DGX Spark, ASUS GX10) served on one to four boxes. Every routed and shared expert is stored at three trellis widths, K2, K3 and K4, and the serving width is chosen at load time. > **These weights cannot be used without E4M3 rounding.** A loader must round every value the trellis codebook > produces to FP8 E4M3 (round to nearest even, saturating) before applying the side vectors. The encoder searched > only over E4M3-rounded values, so the rounding is part of the format: a stock EXL3 decoder, or any decoder that > skips the rounding, reads measurably worse weights from the same bytes. GLM-5.3-Flash is a MoE model: 45 layers (0-2 dense, 3-44 MoE), 288 routed experts with top-8 routing plus one shared expert per MoE layer, an MTP layer 45 and a vision tower. This repository quantizes the routed and shared experts of layers 3-45 and the dense weights of every layer, and carries the vision tower unchanged in BF16.
- Erosumika-7B-v3-0.2-GGUF-IQ-Imatrix↗
Lewdiculous
- control_v11p_sd15_seg↗
lllyasviel
**Controlnet v1.1** is the successor model of Controlnet v1.0 and was released in lllyasviel/ControlNet-v1-1 by Lvmin Zhang. This checkpoint is a conversion of the original checkpoint into `diffusers` format. It can be used in combination with **Stable Diffusion**, such as runwayml/stable-diffusion-v1-5. @misc{zhang2023adding, title={Adding Conditional Control to Text-to-Image Diffusion Models}, author={Lvmin Zhang and Maneesh Agrawala}, year={2023}, eprint={2302.05543}, archivePrefix={arXiv}, primaryClass={cs.CV} }
- gemma-4-E2B-it-ultra-uncensored-heretic↗
llmfan46