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Qwopus3.6-27B-v2-AWQ-4bit
mconcat/Qwopus3.6-27B-v2-AWQ-4bit
Qwopus3.6-27B-v2-AWQ-4bit is a text generation model from mconcat. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as apache-2.0.
AutoAWQ-format INT4 (W4A16) quantization of Jackrong/Qwopus3.6-27B-v2, a Claude Opus reasoning-distilled fine-tune of Qwen 3.6 27B.
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.safetensors26.9 GB · 100%
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Model card
From the Hugging Face model README
Qwopus3.6-27B-v2-AWQ-4bit
AutoAWQ-format INT4 (W4A16) quantization of Jackrong/Qwopus3.6-27B-v2, a Claude Opus reasoning-distilled fine-tune of Qwen 3.6 27B.
The hybrid DeltaNet + softmax attention architecture is preserved, the 1-layer MTP head is included for speculative decoding, and the multimodal processor metadata is kept intact. APEX-style edge protection keeps the first and last layers in BF16 for quality.
Quick start
Requires vLLM ≥ 0.21.0:
vllm serve mconcat/Qwopus3.6-27B-v2-AWQ-4bit \
--tensor-parallel-size 1 \
--max-model-len 16384 \
--speculative-config '{"method": "mtp", "num_speculative_tokens": 3}' \
--tool-call-parser qwen3_coder \
--reasoning-parser qwen3 \
--enable-auto-tool-choice \
--trust-remote-code
Benchmarks
Evaluated with lm-evaluation-harness on a single NVIDIA B300 SXM6, 100 samples per task, 0-shot CoT, max_gen_toks=4096:
| Task | Qwen 3.6 27B (base) | Qwopus 3.6 v2 (source BF16) | This (AWQ-4bit) |
|---|---|---|---|
| GSM8K (flexible-extract) | 65.0% | 87.0% | 85.0% |
| ARC-Challenge (acc_norm) | 46.0% | 45.0% | 47.0% |
| TruthfulQA-MC2 | 55.1% | 59.3% | 59.3% |
| IFEval (inst_level_strict) | 40.5% | 42.3% | 42.9% |
Quantization preserves accuracy within standard error of the BF16 source on every task, and matches the source on TruthfulQA. The Claude Opus reasoning gain over the Qwen 3.6 base (+20 pp on GSM8K) is retained.
Throughput
Measured on a single NVIDIA B300 SXM6 with vLLM 0.21.0 and torch.compile enabled:
| Setup | Throughput | Speedup |
|---|---|---|
| Batch = 1, no MTP | 115 tok/s | 1.00× |
Batch = 1, MTP num_speculative_tokens = 3 | 251 tok/s | 2.19× |
| Batch = 8 continuous batching, no MTP | 880 tok/s | — |
MTP speculative decoding hits an Avg Draft acceptance rate of ~77 % (per-position: 0.92 / 0.79 / 0.65) with a mean acceptance length of ~3.3, measured on a mixed reasoning + code prompt set at greedy decoding.
Self-test of tool calling with --tool-call-parser qwen3_coder: passes (model emits well-formed <tool_call>...</tool_call> syntax).
Quantization
| Precision | Modules |
|---|---|
| INT4 asymmetric, group_size = 128 | q_proj, k_proj, v_proj, MLP gate_proj, MLP up_proj, DeltaNet in_proj_qkv, in_proj_z |
| BF16 | o_proj, MLP down_proj, lm_head, embed_tokens, norms, DeltaNet small projections (in_proj_a, in_proj_b), DeltaNet out_proj, vision tower, multimodal projector, 1-layer MTP head, first 5 and last 5 transformer layers (APEX edge protection) |
The AWQ skip list also names every mtp.* linear module explicitly so the MTP draft head stays unquantized — previous revisions of this checkpoint omitted those entries, which caused vLLM to build the MTP head with AWQ-packed parameters and produced 0 % draft acceptance.
Tuned with AutoRound on 1024 self-generated reasoning traces (200 iterations per block, batch_size=1).
Calibration data: 1024 self-generated traces from the BF16 source model (256 prompts × 4 generations) covering math, code, logic, analysis, creative writing, general knowledge, tool calling, and Korean.
Files
| File | Size | Purpose |
|---|---|---|
model-*.safetensors (13 shards) | ~25 GB | Main quantized weights |
model_extra_tensors.safetensors | ~1 GB | MTP head + edge-protected layers (BF16) |
quantization_config.json | <1 KB | AWQ config (quant_method=awq, bits=4, group_size=128, zero_point=true) with BF16 MTP skip entries |
config.json + tokenizer + processor configs | <100 MB | Standard metadata |
Total checkpoint size: ~26 GB (down from ~54 GB BF16 source).
License
Apache 2.0 (inherited from the base model).
- Task
- Text Generation
- Library
- transformers
- Type
- qwen3_5
- License
- apache-2.0
- Languages
- en, zh, ko, ja
- Created
- May 23, 2026
- Updated
- May 25, 2026