Qwen-Drive-1.0-4B

Qwen-Drive-1.0-4B is Alibaba's vision-language foundation model for autonomous driving. The Hugging Face card lists license Apache-2.0, pipeline image-text-to-text, and a Qwen3.5-4B VLM that stays untouched while a BEV perception head and a Planning Expert attach beside it. Hugging Face showed 97 likes and 1,579 downloads in the last month on 2026-09-09. createdAt is 2026-08-27. The code repo QwenLM/Qwen-Drive-1.0 had 313 stars the same day. r/LocalLLaMA had the card on the hot list. Technical report: arXiv:2609.00111.

Compare Qwen-VL if you wanted the older general LVLM snapshot, DeepSeek-V4-Flash-Vision-Exp if you wanted a screenshot-and-text agent VLM, or Qwen3.8-27B if you wanted a dense local chat model with no driving heads.

Key Features

• Untouched VLM plus two heads: card says natively multimodal Qwen3.5-4B answers driving VQA. A BEV head jointly does 3D detection, semantic occupancy, and BEV map segmentation. A Planning Expert conditions on shared VLM features and emits future ego trajectories with flow matching.
• Two planners: planner-sft covers direct and reasoning planning. planner-rl is reward-optimized on NAVSIM PDMS, WOD-E2E RFS, and a displacement term. Card says run planner-rl in reasoning planning mode.
• One directory: VLM about 9.1 GB at the root, planner-sft/ and planner-rl/ about 2.1 GB each, perception/ about 0.5 GB.
• Vendor-cited driving VQA: card lists LingoQA 77.8 (Qwen-Plus as judge; official LingoJudge protocol 79.4), Ego3D RMSE 7.78, WaymoQA all 74.5. Treat those as first-party numbers. We did not rerun them.
• Serving paths on the card: Transformers QwenDriveForPlanning, vLLM vllm serve, SGLang, and Docker Model Runner. Inference code is pip install -e . from the GitHub repo.

Limitation: this is a driving stack, not a general computer-use agent. Closed-loop AlpaSim at-fault score on the card is 0.37 for RL vs 0.45 for Alpamayo-1.5. You need the Qwen-Drive package, not a random VLM pipeline, to attach the planner. 97 likes is a heat signal, not an audit.

Specs

Use Cases

• Driving-lab teams who want one checkpoint for VQA, BEV perception, and open-loop trajectories.
• Researchers comparing public WOD-E2E / NAVSIM numbers without a separate specialist per dataset.
• LocalLLaMA readers who saw the HF link on the hot list and wanted the 4B driving SKU, not a general chat VLM.

If you needed screenshots of a desktop, start with DeepSeek-V4-Flash-Vision-Exp, not this page.

Getting Started

1. Open Qwen/Qwen-Drive-1.0-4B.
2. git clone https://github.com/QwenLM/Qwen-Drive-1.0 qwen-drive && cd qwen-drive && pip install -e . --no-build-isolation.
3. hf download Qwen/Qwen-Drive-1.0-4B --local-dir Qwen-Drive-1.0-4B.
4. Load QwenDriveForPlanning with planner="Qwen-Drive-1.0-4B/planner-rl" for reasoning planning, or skip the planner for VQA-only.

First-party resource: Qwen-Drive-1.0 model card.

Frequently Asked Questions

Is the 4B the whole stack?

The VLM is the 4B Qwen3.5. Planner and perception heads are extra folders beside it. HF safetensors list about 5B parameters in the root snapshot.

Can I serve it as a plain chat VLM?

Yes. Card says load the root directory without a planner for free-form driving questions.

Hosted API?

The card showed no Inference Provider deploy on 2026-09-09. Confirm before you promise a $0 hosted seat.

Alternatives

• Qwen-VL: older general Qwen vision snapshot, not a driving planner.
• DeepSeek-V4-Flash-Vision-Exp: MIT Flash vision experiment for screenshots plus text agents.
• Qwen3.8-27B: denser Apache-2.0 chat, no BEV head.

Tips

1. Use planner-rl only in reasoning planning mode. planner-sft covers both direct and reasoning.
2. Do not copy Alpamayo closed-loop scores onto this page.
3. Run scripts/demo.py in the GitHub repo before you quote NAVSIM.

Conclusion

Qwen-Drive-1.0-4B is the Apache-2.0 driving VLM r/LocalLLaMA was pointing at: untouched Qwen3.5-4B, a BEV probe, and two planning experts. Start at the Hugging Face card. If you wanted a general screenshot agent, open DeepSeek-V4-Flash-Vision-Exp instead.