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Model Details
Model Provider
axteam
Model Tree
Qwen/Qwen3.5-27B
Input Modalities
Output Modalities
Supported Functionality
pack-quantized.model.visual.*), the multi-token-prediction head (mtp.*), all RMSNorms, token embeddings, the LM head, the SSM non-linear parts (linear_attn.conv1d/A_log/dt_bias). Left unquantized to preserve quality (about 12% of all parameters in this build).chat_template / generation_config are identical to the original. config.json is the original plus a quantization_config block.This is post-training quantization (PTQ) — no retraining. AWQ uses a small calibration set (no gradient) to protect salient weight channels before rounding.
Fidelity to the BF16 original on an internal mixed English/Korean set (100 prompts, BF16 greedy answers scored under each build — same harness for every row).
| Metric | This model | BF16 original |
|---|---|---|
| Perplexity (lower = closer) | 1.3886 | 1.3603 (+2.1%) |
| Top-1 next-token agreement | 94.5% | 99.3% |
These measure fidelity to the base model, not absolute task scores.
| Runtime | vLLM — the architecture is in vLLM ≥ 0.17.0; this build not run here yet. |
| vLLM install | vLLM 0.32 itself needs Python 3.11–3.14, torch 2.13 (CUDA 13.0 build, so an NVIDIA driver with CUDA 13 support), transformers ≥5.16.1,<5.20 and compressed-tensors 0.17.0 (both pinned by vLLM; 0.17.0 reads this checkpoint, which was saved with 0.19.0 — do not upgrade it separately). |
| GPU | NVIDIA Turing (sm75) or newer — weight-only INT, no special tensor cores needed. V100 (sm70) is not supported. (Kernel choice per GPU from vLLM 0.32's selection code; only B200 was run here.) |
| Memory | Weights: 20.9 GB. Add KV cache (grows with context length × concurrent requests) and a few GB for activations / CUDA graphs; use --tensor-parallel-size N when the weights do not fit one GPU. |
| Transformers (without vLLM) | transformers>=5.2.0 + compressed-tensors>=0.15.0 (checked on CPU with Qwen3.5-0.8B FP8 / AWQ-4bit / NVFP4A16 / NVFP4). |
| trust_remote_code | not needed |
Start an OpenAI-compatible server:
bash
vllm serve axteam/Qwen3.5-27B-AWQ-4bit --max-model-len 32768Query it:
bash
curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{ "model": "axteam/Qwen3.5-27B-AWQ-4bit", "messages": [{"role": "user", "content": "대한민국의 수도는 어디인가요?"}], "temperature": 0.6, "top_p": 0.95, "top_k": 20, "max_tokens": 512}'Offline inference in Python:
python
from vllm import LLM, SamplingParams llm = LLM(model="axteam/Qwen3.5-27B-AWQ-4bit", max_model_len=32768)params = SamplingParams(temperature=0.6, top_p=0.95, top_k=20, max_tokens=1024)out = llm.chat([{"role": "user", "content": "Explain model quantization in two sentences."}], params)print(out[0].outputs[0].text) # image input (URL or data: URI)messages = [{"role": "user", "content": [ {"type": "image_url", "image_url": {"url": "https://example.com/your-image.png"}}, {"type": "text", "text": "Describe this image."},]}]out = llm.chat(messages, params)print(out[0].outputs[0].text)Recommended sampling (from the original
generation_config.json):temperature=0.6,top_p=0.95,top_k=20. Thinking mode is on by default in the chat template; to turn it off passchat_template_kwargs={"enable_thinking": False}(Pythonllm.chat(..., chat_template_kwargs=...), or"chat_template_kwargs": {"enable_thinking": false}in the request body). Text-only serving: add--limit-mm-per-prompt '{"image": 0, "video": 0}'to skip loading the vision encoder's memory budget.
The KV cache is kept in BF16 by default. KV-cache precision is a runtime choice, independent of the weight quantization above; for long contexts or large batches FP8 roughly halves KV-cache memory:
bash
vllm serve axteam/Qwen3.5-27B-AWQ-4bit --kv-cache-dtype fp8Quantized derivative of Qwen/Qwen3.5-27B (Apache-2.0). All original weights, tokenizer and chat template belong to the Qwen team.
Qwen/Qwen3.5-27B 를 AWQ INT4 (W4A16) 로 양자화한 서빙용(4-bit) 모델입니다. 작성: axteam.
pack-quantized.vllm serve axteam/Qwen3.5-27B-AWQ-4bit --max-model-len 32768 로 OpenAI 호환 서버(포트 8000), 또는 Python LLM(model="axteam/Qwen3.5-27B-AWQ-4bit").chat(...) (위 예시). 권장 샘플링 temperature=0.6, top_p=0.95, top_k=20. thinking 끄기: chat_template_kwargs={"enable_thinking": False}. 이미지 입력은 image_url 메시지로.--kv-cache-dtype fp8.curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{ "model": "axteam/Qwen3.5-27B-AWQ-4bit", "messages": [{"role": "user", "content": "대한민국의 수도는 어디인가요?"}], "temperature": 0.6, "top_p": 0.95, "top_k": 20, "max_tokens": 512}'from vllm import LLM, SamplingParams llm = LLM(model="axteam/Qwen3.5-27B-AWQ-4bit", max_model_len=32768)params = SamplingParams(temperature=0.6, top_p=0.95, top_k=20, max_tokens=1024)out = llm.chat([{"role": "user", "content": "Explain model quantization in two sentences."}], params)print(out[0].outputs[0].text) # image input (URL or data: URI)messages = [{"role": "user", "content": [ {"type": "image_url", "image_url": {"url": "https://example.com/your-image.png"}}, {"type": "text", "text": "Describe this image."},]}]out = llm.chat(messages, params)print(out[0].outputs[0].text)