---

license: other

base_model: Qwen/Qwen3.5-122B-A10B

language:

• en
• uk

library_name: llama.cpp

pipeline_tag: text-generation

tags:

• gguf
• qwen
• qwen3.5
• moe
• reap
• reap30
• apex
• apex-mini
• apex-quant
• llama.cpp
• quantized

---

Qwen3.5-122B-A10B REAP30 APEX GGUF

Physical REAP30-pruned APEX GGUF builds of Qwen/Qwen3.5-122B-A10B.

Available quants

| File | Description |

|---|---|

| Qwen3.5-122B-A10B-REAP30-APEX-Mini.gguf | Production APEX Mini quant with imatrix |

REAP pruning

• Method: physical REAP expert pruning
• Compression ratio: 30%
• Original experts per MoE layer: 256
• Retained experts per MoE layer: 180
• Layers: 48
• Experts per token: 8
• Observation: 32 batches
• Distance: cosine
• Seed: 42
• Calibration source: custom calibration-v2

APEX Mini quantization

• Tensor config: configs/qwen35_122b_mini.txt
• Base fallback type: Q3_K_M
• Imatrix context: -c 4096
• Imatrix chunks: --chunks 128
• Imatrix calibration: 64MB shuffled calibration-v2
• Imatrix model source: REAP30 BF16 GGUF
• Text-only GGUF: MTP disabled before conversion

The Mini profile uses Q3_K edge experts and IQ2_S middle routed experts, so imatrix is required.

Example: llama.cpp

llama-cli \
  -m Qwen3.5-122B-A10B-REAP30-APEX-Mini.gguf \
  -p "<|im_start|>user
Привіт. Напиши один короткий параграф українською. /no_think
<|im_end|>
<|im_start|>assistant
" \
  -n 128 \
  -c 4096 \
  -ngl 40 \
  --temp 0.6 \
  --top-p 0.95
Notes

Experimental physical expert-pruned build for testing and comparison with REAP20/REAP40 variants.