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AI 모델 84753

• KAT-Coder-V2.5-Dev-OptiQ-4bit↗

  mlx-community

  > **Built with mlx-optiq**, the MLX-native toolkit to > quantize, fine-tune, and serve LLMs locally on Apple Silicon (no PyTorch, no > cloud). Try the Lab · All OptiQ > quants · Docs An OptiQ mixed-precision MLX quant of **KAT-Coder-V2.5-Dev**, a `qwen3_5_moe` coding model (256-routed-expert sparse MoE with hybrid linear + full attention). `qwen3_5_moe` loads under stock `mlx-lm` too, but `optiq serve` adds mixed- precision loading, KV-cache quantization, and the OptiQ Lab.

• gemma-3-270m-it-gguf↗

  mmnga

• Mistral-7B-Instruct-v0.2-new1-uinsa↗

  mni15

• BananaMind-1.0-Instruct-i1-GGUF↗

  mradermacher

• CARDS-Qwen3.6-27B-i1-GGUF↗

  mradermacher

• crystal-think-v1-i1-GGUF↗

  mradermacher

• EVA-Qwen2.5-14B-v0.1-i1-GGUF↗

  mradermacher

• FuLinNa_GLM4-9B-Chat-GGUF↗

  mradermacher

• gemma-3-27b-it-heretic-v1.2-i1-GGUF↗

  mradermacher

• granite-20b-code-instruct-i1-GGUF↗

  mradermacher

  If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9