--- library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3.6-27B/blob/main/LICENSE pipeline_tag: image-text-to-text base_model: Qwen/Qwen3.6-27B tags: - fp8 - w8a8 - quantized - compressed-tensors - qwen3.6 - vlm - vllm quantized_by: vrfai --- # Qwen3.6-27B-FP8 FP8 (W8A8) quantized version of [Qwen/Qwen3.6-27B](https://huggingface.co/Qwen/Qwen3.6-27B) by [vrfai](https://huggingface.co/vrfai) using [llm-compressor](https://github.com/vllm-project/llm-compressor). Also available: [vrfai/Qwen3.6-27B-NVFP4](https://huggingface.co/vrfai/Qwen3.6-27B-NVFP4) — more aggressive quantization for Blackwell GPUs only. ## FP8 Quantization Details | | | |---|---| | **Base model** | [Qwen/Qwen3.6-27B](https://huggingface.co/Qwen/Qwen3.6-27B) | | **Quantization** | W8A8 FP8 — weights FP8 static, activations FP8 static | | **Strategy** | `tensor` (per-tensor symmetric, memoryless minmax) | | **Format** | `compressed-tensors` (native vLLM support) | | **Tool** | [vllm-project/llm-compressor](https://github.com/vllm-project/llm-compressor) | | **Requires** | NVIDIA Ampere / Hopper / Blackwell (SM 89+) | ### What's Quantized / What's Not Same selective strategy as the NVFP4 variant — sensitive components are preserved in BF16: | Component | Precision | Reason | |---|---|---| | FFN / MLP — all 64 transformer layers | **FP8** | High parameter density, stable under quantization | | Full-attention projections (q/k/v/o) — 16 GQA layers | **FP8** | Standard attention, tolerant to 8-bit | | DeltaNet / Linear-attention projections — 48 layers | **BF16** | Gated linear recurrence sensitive to numerical errors | | Vision encoder — all 27 blocks + merger | **BF16** | Vision tower preserved for multimodal quality | | `lm_head` | **BF16** | Output logits preserved for generation stability | ### Quantization Config (llm-compressor) ```yaml # recipe.yaml QuantizationModifier: targets: [Linear] scheme: FP8 # static W8A8, per-tensor symmetric ignore: - lm_head - re:model\.visual\.blocks\.\d+\..* - model.visual.merger.linear_fc1 - model.visual.merger.linear_fc2 - re:model\.language_model\.layers\.\d+\.linear_attn\..* ``` --- ## Quick Start (vLLM) ```bash vllm serve vrfai/Qwen3.6-27B-FP8 \ --max-model-len 8192 \ --gpu-memory-utilization 0.9 \ --dtype auto \ --trust-remote-code \ --tensor-parallel-size 2 ``` Single GPU (≥ 24 GB VRAM, SM 89+): ```bash vllm serve vrfai/Qwen3.6-27B-FP8 \ --max-model-len 8192 \ --gpu-memory-utilization 0.92 \ --dtype auto \ --trust-remote-code ``` ### Python (Transformers) ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "vrfai/Qwen3.6-27B-FP8" tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto", trust_remote_code=True, ) messages = [{"role": "user", "content": "Explain quantization in one paragraph."}] text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) inputs = tokenizer(text, return_tensors="pt").to(model.device) outputs = model.generate(**inputs, max_new_tokens=512) print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)) ``` ### OpenAI-compatible API ```python from openai import OpenAI client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY") response = client.chat.completions.create( model="vrfai/Qwen3.6-27B-FP8", messages=[{"role": "user", "content": "Hello!"}], temperature=0.7, max_tokens=512, ) print(response.choices[0].message.content) ``` --- ## NVFP4 vs FP8 Comparison | | [NVFP4](https://huggingface.co/vrfai/Qwen3.6-27B-NVFP4) | FP8 (this) | |---|---|---| | Weight bits | 4 | 8 | | Activation bits | 4 (dynamic) | 8 (static) | | Model size | ~26 GB | ~34 GB | | Hardware | Blackwell only (SM 120+) | Ampere / Hopper / Blackwell | | Speed | Faster | Slightly slower | | Quality | Slightly lower | Higher | --- ## Tested Environment | Component | Version | |-----------|---------| | vLLM | 0.19.1 | | Transformers | 5.6.2 | | PyTorch | 2.10.0+cu128 | | CUDA | 12.8 (nvcc 12.8.61) | | llm-compressor | compressed-tensors 0.14.0.1 | | GPU | 2× NVIDIA RTX 5090 (tensor-parallel-size 2) | --- ## Best Practices | Mode | temperature | top_p | top_k | presence_penalty | |------|-------------|-------|-------|------------------| | Thinking — general | 1.0 | 0.95 | 20 | 0.0 | | Thinking — coding | 0.6 | 0.95 | 20 | 0.0 | | Non-thinking / instruct | 0.7 | 0.80 | 20 | 1.5 | **Thinking mode:** ```python text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, chat_template_kwargs={"enable_thinking": True}, ) ``` --- ## Credits - **Original model:** [Qwen Team](https://huggingface.co/Qwen) (Alibaba Group) - **FP8 quantization:** [vrfai](https://huggingface.co/vrfai) - **Quantization framework:** [vllm-project/llm-compressor](https://github.com/vllm-project/llm-compressor) --- *Below is the original model card from [Qwen/Qwen3.6-27B](https://huggingface.co/Qwen/Qwen3.6-27B):* --- [![Qwen Chat](https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5)](https://chat.qwen.ai) > [!Note] > This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format. Following the February release of the Qwen3.5 series, we're pleased to share the first open-weight variant of Qwen3.6. Built on direct feedback from the community, Qwen3.6 prioritizes stability and real-world utility, offering developers a more intuitive, responsive, and genuinely productive coding experience. ## Qwen3.6 Highlights - **Agentic Coding:** the model now handles frontend workflows and repository-level reasoning with greater fluency and precision. - **Thinking Preservation:** reasoning context from historical messages is retained, streamlining iterative development. ![Benchmark Results](https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3.6/Figures/qwen3.6_27b_score.png) For more details, please refer to our blog post [Qwen3.6-27B](https://qwen.ai/blog?id=qwen3.6-27b). ## Model Overview - Type: Causal Language Model with Vision Encoder - Number of Parameters: 27B - Context Length: 262,144 natively and extensible up to 1,010,000 tokens ### Citation ```bibtex @misc{qwen3.6-27b, title = {{Qwen3.6-27B}: Flagship-Level Coding in a {27B} Dense Model}, author = {{Qwen Team}}, month = {April}, year = {2026}, url = {https://qwen.ai/blog?id=qwen3.6-27b} } ```