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AI 모델 84757

• Qwen3.8-27B-Opus-Distill-v2-MTPLX-4bit↗

  barozp

• K2-Horizon-3.7B-GGUF↗

  bartowski

  **Model details:** - Parameter count: 5B (source checkpoint) - Input support: text - Speculative decoding: no - imatrix: yes - details **Don't know which to choose?** Grab Q4_K_M (3.25GB) - usually a good mix of size and performance. Download instructions available here These quants were made with llama.cpp release b11462 - if this model's architecture is newly supported, you'll need that release or newer to run them.

• Qwen_Qwen3-8B-GGUF↗

  bartowski

  All quants made using imatrix option with dataset from here Run them directly with llama.cpp, or any other llama.cpp based project Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.

• WizardLM-2-7B-GGUF↗

  bartowski

• Rocinante-XL-16B-v1b-GGUF↗

  BeaverAI

• tinctura-v1↗

  bench-labs

• Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bellowing_polished_caterpillar↗

  Boevers

• Astrea-R8-Chat-9B-Heretic-GGUF↗

  brainnxdomain

• Mistral-Small-Instruct-2409-abliterated↗

  byroneverson

• CrystaLLM-pi_bandgap↗

  c-bone