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Cataloged from SC117/Qwen3.5-122B-A10B-Uncensored-APEX-Compact-GGUF
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MoE Mixed-Precision Quantization Β· Uncensored Β· 55.1 GB
Qwen3.5-122B-A10B (uncensored by HauhauCS) quantized with APEX I-Compact β a MoE-aware mixed-precision strategy that applies layer-wise precision gradients. Edge layers get higher precision, middle layers get aggressive compression. Quantized from Q8_K_P using the APEX project.
Measurements from APEX project on 8ΓRTX PRO 6000 Blackwell (768 GB VRAM). Perplexity on wikitext-2-raw (ctx 512). Accuracy via llama.cpp (400 tasks each).
β This quantization. I-Compact achieves 84.5% HellaSwag and 57.86 ARC at 55% less size than Q8_0, fastest standard APEX profile at 106 t/s. Quantized from Q8_K_P (137 GB β 55.1 GB).
APEX I-Compact applies layer-wise mixed-precision with MoE-aware tensor classification. Edge layers (L0β4, L43β47) get higher precision, middle layers (L10β29) get more aggressive compression. I-variants use diverse imatrix calibration (chat, code, reasoning, tool-calling, agentic traces) for better real-world accuracy.
Router weights kept in F32 (lossless) to preserve routing accuracy. Shared experts at Q4_K_S across all layers for stable token processing.
Thinking mode (default):
Non-thinking mode:
Use --jinja flag with llama.cpp. Thinking mode is on by default β disable with --chat-template-kwargs '{"enable_thinking":false}'. Vision support requires mmproj file.
Works with llama.cpp, LM Studio, Jan, koboldcpp, and other GGUF-compatible runtimes.
# With vision
llama-cli -m Qwen3.5-122B-A10B-Uncensored-APEX-Compact.gguf
--mmproj mmproj-Qwen3.5-122B-A10B-Uncensored-f16.gguf
--jinja -c 131072 -ngl 99
For CPU inference: this 55 GB model fits in 64 GB+ RAM systems. For GPU offload, adjust -ngl based on available VRAM.
Base model: HauhauCS/Qwen3.5-122B-A10B-Uncensored-HauhauCS-Aggressive (uncensored)
Quantization: APEX β Adaptive Precision for EXpert Models by mudler / LocalAI team
Runtime: Built on llama.cpp
Original model: Qwen/Qwen3.5-122B-A10B