This is an NVFP4 (4-bit floating point) quantized version of Qwen/Qwen3.5-4B, packaged in the compressed-tensors format for efficient inference with vLLM.

NVFP4 is a Blackwell-native FP4 format and is designed to run on the FP4 tensor cores of NVIDIA Blackwell GPUs (e.g. RTX 5090, sm_120) for high throughput, while preserving the base model's vision-language capabilities.

About the base model

Qwen3.5-4B is a compact multimodal vision-language model from the Qwen team (Alibaba Group). It accepts interleaved image + text (and video) input and generates text, with a "thinking" reasoning mode enabled by default.


Base model	Qwen/Qwen3.5-4B
Parameters	~4B
Modality	Image-Text-to-Text (vision-language)
Architecture	Hybrid Gated DeltaNet + sparse MoE, with a vision encoder
Context length	262,144 tokens native (extensible with RoPE scaling)
Languages	201 languages and dialects
License	Apache 2.0

For full details on capabilities, benchmarks, and prompt formatting, see the base model card.

Quantization details


Method	NVFP4 (4-bit floating point)
Format	`compressed-tensors`
Quantized modules	Linear layers of the language model
Kept in higher precision	Vision tower, router gates, and `lm_head`
Recommended hardware	NVIDIA Blackwell (FP4 tensor cores, sm_120)

The vision tower is intentionally left unquantized to preserve image understanding quality.

Usage

Serve with vLLM (OpenAI-compatible API)

bash

docker run --rm --gpus all -p 8000:8000 \
  -v "$PWD:/models" \
  vllm/vllm-openai:latest \
  --model /models/Qwen3.5-4B-NVFP4 \
  --served-model-name Qwen3.5-4B-NVFP4 \
  --quantization compressed-tensors \
  --max-model-len 32768

Or directly from the Hub:

bash

docker run --rm --gpus all -p 8000:8000 \
  vllm/vllm-openai:latest \
  --model sanskar003/Qwen3.5-4B-NVFP4 \
  --quantization compressed-tensors

Query it (image + text)

bash

curl -s http://localhost:8000/v1/chat/completions \
  -H 'Content-Type: application/json' -d '{
    "model": "Qwen3.5-4B-NVFP4",
    "messages": [{"role": "user", "content": [
      {"type": "text", "text": "Describe this image."},
      {"type": "image_url", "image_url": {"url": "http://images.cocodataset.org/train2017/000000231895.jpg"}}
    ]}]
  }'

Requirements

An NVIDIA Blackwell GPU with FP4 tensor cores (sm_120) for the intended performance. vLLM selects the FP4 kernel automatically for this compressed-tensors checkpoint.
A recent vLLM build that supports the Qwen3_5ForConditionalGeneration architecture.

License

Released under Apache 2.0, inherited from the base model Qwen/Qwen3.5-4B. Please review and comply with the base model's license terms.

Citation

Please cite the original Qwen work — see the base model card for citation details.