Model reference · open weights
Hulu-Med is an open-weight language model from ZJU-AI4H. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Released by | ZJU-AI4H |
|---|---|
| Type | Language models |
| Task | Vision + text |
| Parameters (lead) | 4.8B |
| Context | 256k tokens |
| Runs with | transformers |
| Released | 2025-11-18 |
| Popularity | 4k downloads / month |
| Licence | Open weights |
About
Hulu-Med: A Transparent Generalist Model towards Holistic Medical Vision-Language Understanding
📄 Paper | 🤗 Hulu-Med-4B | 🤗 Hulu-Med-7B |🤗 Hulu-Med-14B |🤗 Hulu-Med-32B | 🔮 ModelScope Models | 📊 Demo
[2025-11-27] ⚡ Hulu-Med is now compatible with the latest vLLM, offering faster inference and tensor parallel support! Thank you all for your patience and feedback 💪 see here for installation
[2025-11-18] 🎊 We released Hulu-Med-4B, a lightweight model with strong multimodal and text reasoning abilities that surpasses MedGemma-4B and Lingshu-7B!
[2025-11-01] 📊 Releasing our new evaluation code, MedUniEval! Built on MedEvalKit, MedUniEval is designed for the comprehensive evaluation of medical visual-language models across various modalities—including text, 2D, 3D, and video. More benchmarks are coming soon.
[2025-10-15] 🎉 Hulu-Med now supports Transformers integration! HuggingFace-compatible models released with simplified loading and inference. Integration with VLLM is ongoing. The HF models are now available in the main branch on Hugging Face.
The model has been updated in the main branch of our Hugging Face repository. You can now load it directly using AutoModelForCausalLM.from_pretrained - the weights will be automatically downloaded.
[2025-10-08] Hulu-Med models and inference code released!
Hulu-Med is a transparent medical vision-language model that unifies understanding across diverse modalities including medical text, 2D/3D images, and videos. Built with a focus on transparency and accessibility, Hulu-Med achieves state-of-the-art performance on 30 medical benchmarks while being trained entirely on public data.
Our training corpus encompasses:
Performance comparison on medical multimodal benchmarks (For the 'Medical VLM < 10B' subgroup, bold indicates the best method):
| Models | OM.VQA | PMC-VQA | VQA-RAD | SLAKE | PathVQA | MedXQA | MMMU-Med |
|---|---|---|---|---|---|---|---|
| Proprietary Models | |||||||
| GPT-4.1 | 75.5 | 55.2 | 65.0 | 72.2 | 55.5 | 45.2 | 75.2 |
| GPT-4o | 67.5 | 49.7 | 61.0 | 71.2 | 55.5 | 44.3 | 62.8 |
| Claude Sonnet 4 | 65.5 | 54.4 | 67.6 | 70.6 | 54.2 | 43.3 | 74.6 |
| Gemini-2.5-Flash | 71.0 | 55.4 | 68.5 | 75.8 | 55.4 | 52.8 | 76.9 |
| General VLMs < 10B | |||||||
| Qwen2.5VL-7B | 63.6 | 51.9 | 63.2 | 66.8 | 44.1 | 20.1 | 50.6 |
| InternVL2.5-8B | 81.3 | 51.3 | 59.4 | 69.0 | 42.1 | 21.7 | 53.5 |
| InternVL3-8B | 79.1 | 53.8 | 65.4 | 72.8 | 48.6 | 22.4 | 59.2 |
| General VLMs > 10B | |||||||
| InternVL3-14B | 78.9 | 54.1 | 66.3 | 72.8 | 48.0 | 23.1 | 63.1 |
| Qwen2.5V-32B | 68.2 | 54.5 | 71.8 | 71.2 | 41.9 | 25.2 | 59.6 |
| InternVL3-38B | 79.8 | 56.6 | 65.4 | 72.7 | 51.0 | 25.2 | 65.2 |
| Medical VLMs < 10B | |||||||
| LLaVA-Med-7B | 34.8 | 22.7 | 46.6 | 51.9 | 35.2 | 20.8 | 28.1 |
| MedGemma-4B | 70.7 | 49.2 | 72.3 | 78.2 | 48.1 | 25.4 | 43.2 |
| HuatuoGPT-V-7B | 74.3 | 53.1 | 67.6 | 68.1 | 44.8 | 23.2 | 49.8 |
| Lingshu-7B | 82.9 | 56.3 | 67.9 | 83.1 | 61.9 | 26.7 | - |
| Hulu-Med-4B | 81.6 | 64.6 | 71.6 | 85.0 | 60.1 | 26.4 | 50.5 |
| Hulu-Med-7B | 84.2 | 66.8 | 78.0 | 86.8 | 65.6 | 29.0 | 51.4 |
| Medical VLMs > 10B | |||||||
| HealthGPT-14B | 75.2 | 56.4 | 65.0 | 66.1 | 56.7 | 24.7 | 49.6 |
| HuatuoGPT-V-34B | 74.0 | 56.6 | 61.4 | 69.5 | 44.4 | 22.1 | 51.8 |
| Lingshu-32B | 83.4 | 57.9 | 76.7 | 86.7 | 65.5 | 30.9 | - |
| Hulu-Med-14B | 85.1 | 68.9 | 76.1 | 86.5 | 64.4 | 30.0 | 54.8 |
| Hulu-Med-32B | 84.6 | 69.4 | 81.4 | 85.7 | 67.3 | 34.0 | 60.4 |
Performance comparison on medical text benchmarks (bold indicates the best method in each subgroup):
| Models | MMLU-Pro | MedXQA | Medbullets | SGPQA | PubMedQA | MedMCQA | MedQA | MMLU-Med |
|---|---|---|---|---|---|---|---|---|
| *Proprietary Models |
From the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys hulu-med for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (hulu-med below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/chat/completions \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"hulu-med","messages":[{"role":"user","content":"Hello"}]}'
Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.