NVFP4 (W4A4) quantization of Jackrong/Qwopus3.6-27B-Coder, packed in the compressed-tensors nvfp4-pack-quantized format with llm-compressor. Weights are quantized with GPTQ (error-compensated rounding) and an MSE observer, on a domain-matched calibration blend that includes code.

Near-lossless. Fused layers (q/k/v, gate/up) share one NVFP4 global scale, so vLLM loads it cleanly with no per-layer-scale warning or fallback. wikitext-2 perplexity for this build: 6.63.

About 18 GB on disk versus about 55.6 GB for the bf16 source (about 33%).
Built for vLLM on NVIDIA Blackwell, where both the 4-bit weight and 4-bit activation paths are accelerated. On pre-Blackwell GPUs vLLM runs it weight-only.
Loading and generation verified in vLLM on an NVIDIA GB10 (Blackwell, sm_121).

Fidelity

Near-lossless versus the bf16 source, 18 GB vs 55.6 GB bf16 (~33%), at wikitext-2 perplexity 6.63. GPTQ error compensation and an MSE observer keep the drop from bf16 minimal; the header lists the full characteristics and Quantization covers the recipe.

Quickstart

NVFP4 is auto-detected from config.json (compressed-tensors); no quantization flag needed. --reasoning-parser qwen3 splits the <think> block into reasoning_content; --tool-call-parser qwen3_coder enables tool/function calling for agentic coding.

bash

vllm serve maci0/Qwopus3.6-27B-Coder-NVFP4 \
  --served-model-name qwopus-27b-coder-nvfp4 \
  --max-model-len 131072 \
  --gpu-memory-utilization 0.90 \
  --kv-cache-dtype fp8 \
  --reasoning-parser qwen3 \
  --enable-auto-tool-choice --tool-call-parser qwen3_coder

Supports up to 262144 tokens; keep at least 128K to preserve thinking quality. --max-model-len 131072 is a safe default; raise it if memory allows.
Add --language-model-only to skip the vision tower and free KV cache for text use.
The parser flags are not auto-detected; pass them explicitly.

Python (OpenAI client)

python

from openai import OpenAI
client = OpenAI(base_url="http://localhost:8000/v1", api_key="x")
r = client.chat.completions.create(
    model="qwopus-27b-coder-nvfp4",
    messages=[{"role": "user", "content": "Write a Python function that merges two sorted lists."}],
)
print(r.choices[0].message.content)

curl

bash

curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{
  "model": "qwopus-27b-coder-nvfp4",
  "messages": [{"role": "user", "content": "Write a Python function that merges two sorted lists."}]
}'

About the base model

A 27B Qwen3.5-family vision-language model specialized for code (Qwopus 3.6 Coder), with thinking-mode reasoning and a 256K context window.

64 decoder layers: hybrid gated delta-net linear attention plus full attention, dense MLP, plus a vision tower for image and video input.
256K context (max_position_embeddings 262144).
Thinking mode by default, with an instruct toggle.

Quantization


Scheme	NVFP4, W4A4
Weight rounding	GPTQ (Hessian-based error compensation), MSE observer
Weights	FP4 (E2M1), `group_size=16`, `tensor_group`, FP8 (E4M3) group scales, shared across fused layers
Activations	FP4, dynamic per-group, FP8 (E4M3) scales
Quantized	all language-model `Linear` layers
Kept in bf16	vision tower (`model.visual.*`), `lm_head`, MTP head
Untouched	gated delta-net `Conv1d` and SSM params (`A_log`, `dt_bias`), never `Linear`

GPTQ is a quantization-time cost only; inference speed and format are identical to plain round-to-nearest NVFP4, but it chooses better 4-bit values.

Calibration: 512 domain-matched samples (long reasoning + general chat + code), max_seq_len=2048, text-only path through the VL model.

Recommended sampling

Thinking mode is the default.

Thinking, precise coding: temperature=0.6, top_p=0.95, top_k=20
Thinking, general: temperature=1.0, top_p=0.95, top_k=20
Instruct / non-thinking: temperature=0.7, top_p=0.80, top_k=20
To run non-thinking, set {%- set enable_thinking = false %} in the chat template, or pass extra_body={"chat_template_kwargs": {"enable_thinking": false}}.

Reproduction

Toolchain: llmcompressor==0.12.0, compressed-tensors==0.17.1, transformers==5.12.1, torch==2.11.0+cu130, on an NVIDIA GB10 (Blackwell, sm_121). llm-compressor 0.12 shares the NVFP4 global scale across fused layers automatically (q/k/v, gate/up).

Base model: Jackrong/Qwopus3.6-27B-Coder
Space: Rogue Quants
Collection: NVFP4 Quants
Sibling NVFP4 quants:
- Qwopus3.6-27B-Coder abliterated (the abliterated version)
- Qwopus3.6-27B-v2 abliterated
- Ornith-1.0-35B MoE abliterated
- Qwen3.6-40B Deckard
- Huihui-Qwythos-9B Claude-Mythos
- Ornith-1.0-9B abliterated

Notes

Needs NVIDIA Blackwell (sm_121, e.g. GB10) for accelerated W4A4; pre-Blackwell GPUs run it weight-only.
--reasoning-parser and --tool-call-parser are not auto-detected; pass them explicitly.
Thinking mode is on by default; toggle it via the chat template or chat_template_kwargs.

License

Apache-2.0, following the base model. Intended use and all responsibility for use follow the base model.

Credits

Base model: Jackrong
Quantization tooling: llm-compressor / compressed-tensors

Part of Rogue Quants · NVFP4 component datasheets · collection. Fabricated on GB10 (Blackwell) with llm-compressor. Refusals shown per 100 harmful prompts; "n/a" = not separately measured (base-inherited).

maci0/Qwopus3.6-27B-Coder-NVFP4

Qwopus3.6-27B-Coder NVFP4