File	Description
`Qwen3.5-122B-A10B-REAP30-APEX-Mini.gguf`	Production APEX Mini quant with imatrix
`Qwen3.5-122B-A10B-REAP30-APEX-I-Compact.gguf`	Higher-quality APEX I-Compact 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

bash

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.

## APEX I-Compact quantization

- Tensor config: `configs/qwen35_122b_compact.txt`
- Base fallback type: `Q4_K_M`
- Imatrix context: `-c 4096`
- Imatrix chunks: `--chunks 128`
- Imatrix calibration: 64MB shuffled `calibration-v2`
- Imatrix model source: REAP30 BF16 GGUF

zTrojan/Qwen3.5-122B-A10B-REAP30-APEX-GGUF

Qwen3.5-122B-A10B REAP30 APEX GGUF

Available quants

REAP pruning

APEX Mini quantization

Example: llama.cpp

zTrojan/
Qwen3.5-122B-A10B-REAP30-APEX-GGUF