TL;DR: Qwopus3.6-27B-Coder, quantized to NVFP4 (W4A4) for vLLM on NVIDIA Blackwell. 18 GB, wikitext-2 PPL 6.63, 256K agentic coder.
Qwopus3.6-27B-Coder NVFP4
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.
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 131072is a safe default; raise it if memory allows. - Add
--language-model-onlyto skip the vision tower and free KV cache for text use. - The parser flags are not auto-detected; pass them explicitly.
Python (OpenAI client)
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
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_embeddings262144). - 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 passextra_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).
Related
- Base model: Jackrong/Qwopus3.6-27B-Coder
- Space: Rogue Quants
- Collection: NVFP4 Quants
- Sibling NVFP4 quants:
Notes
- Needs NVIDIA Blackwell (sm_121, e.g. GB10) for accelerated W4A4; pre-Blackwell GPUs run it weight-only.
--reasoning-parserand--tool-call-parserare 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