Quantized & vocab-pruned Qwen3.5-4B used by team AFM-6j6duhm6 to place 2nd (7.708×) in the AdaptFM "Efficient Qwen" Challenge (ICML 2026).

Pair this checkpoint with the reproducible serving pipeline (suffix always-spec decoding, K=20, factor 3.0 + force-answer wrapper): 👉 https://github.com/Teora/efficient-qwen-suffix-spec

What's modified vs the base model

Change	Detail
Quantization	AWQ-INT4 on MLP only (attention, layer 0, MTP head, vision tower stay FP16). `awq_marlin` kernel.
Vocab pruning	Tied embed / lm_head pruned 248,320 → 131,101 tokens: keep contiguous IDs `[0,131072)` + all special/added tokens (remapped contiguously). This reduces parameters.
MTP head	Kept intact (native multi-token-prediction head preserved).

Vocab pruning shrinks the BF16 tied embed/lm_head (~1.27 GB, the dominant per-token decode memory cost) by ~1.9×, speeding decode/verify while preserving the GPQA-Diamond (thinking) quality gate. v96k pruning failed GPQA; v128k (131k) is the minimum vocab that preserves it.

Quality (competition gates, all PASS)

Benchmark	Threshold	Result
MMLU-Pro	≥ 0.621	PASS
IFEval	≥ 0.814	PASS
GPQA-Diamond (thinking)	≥ 0.630	PASS

Usage

bash

# 1) download this checkpoint into ./qwen-awq-v128k/
huggingface-cli download Teora/qwen3.5-4b-awq-v128k --local-dir qwen-awq-v128k

# 2) build & serve via the pipeline repo
git clone https://github.com/Teora/efficient-qwen-suffix-spec && cd efficient-qwen-suffix-spec
docker build -f Dockerfile -t efficient-qwen-2nd:latest .
docker run -d --gpus '"device=0"' -p 8080:8080 efficient-qwen-2nd:latest

License

Apache-2.0 (Qwen3.5-4B is Apache-2.0; this derivative checkpoint inherits it).

Citation

Team AFM-6j6duhm6 · Minjae Park (CJ Corporation) · AdaptFM Efficient Qwen Challenge, ICML 2026.