mconcat/Qwopus3.6-27B-v2-NVFP4 API & Inference Endpoint

Quick start

Requires vLLM ≥ 0.21.0 and a Blackwell-class GPU (SM 10.0+) for native NVFP4 W4A4 inference:

bash
vllm serve mconcat/Qwopus3.6-27B-v2-NVFP4 \
  --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 (NVFP4)
GSM8K (flexible-extract)	65.0%	87.0%	87.0%
ARC-Challenge (acc)	50.0%	50.0%	53.0%
TruthfulQA-MC2	55.1%	59.3%	58.7%
IFEval (inst_level_strict)	40.5%	42.3%	41.7%

Accuracy is preserved versus the BF16 source — the GSM8K score is identical to the source and the other tasks match within standard error.

Throughput

Measured on a single NVIDIA B300 SXM6 with vLLM 0.21.0 and torch.compile enabled:

Setup	Throughput	Speedup
Batch = 1, no MTP	121 tok/s	1.00×
Batch = 1, MTP `num_speculative_tokens = 3`	274 tok/s	2.26×
Batch = 8 continuous batching, no MTP	1054 tok/s	—

Self-test of tool calling with --tool-call-parser qwen3_coder: passes (model emits well-formed <tool_call>...</tool_call> syntax that the parser extracts correctly).

Quantization

Precision	Modules
NVFP4 W4A4 (group_size = 16)	`o_proj`, MLP `gate_proj`, MLP `up_proj`
FP8 W8A8 dynamic (per-channel weight, per-token activation)	`q_proj`, `k_proj`, `v_proj`, MLP `down_proj`, DeltaNet `in_proj_qkv`, `in_proj_z`, `out_proj`
BF16	`lm_head`, `embed_tokens`, norms, DeltaNet small projections (`in_proj_a`, `in_proj_b`), vision tower, multimodal projector, 1-layer MTP head

Calibration data: 1024 self-generated reasoning traces from the BF16 source model (256 prompts × 4 generations) spanning math, code, logic, analysis, creative writing, general knowledge, tool calling, and Korean. Generated at temperature=1.0, top_p=0.95.

Files

File	Size	Purpose
`model.safetensors`	25.2 GB	Main quantized weights
`model.mtp.safetensors`	849 MB	MTP head (BF16 sidecar)
`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).

Qwopus3.6-27B-v2-NVFP4

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