蚂蚁 inclusionAI 开源医疗专用模型 Ling-3.0-flash-Sante，124B 总参数

🤗 Hugging Face | 🤖 ModelScope | 🐙 Vercel

Introduction

Ling-3.0-flash-Sante is our healthcare-specialized model built on Ling-3.0-flash. Its name comes from the French word santé, meaning “health.”

With 124B total parameters and 5.1B active parameters per token, Sante is designed for medical reasoning, healthcare research, deep research, and tool-assisted workflows while retaining the general capabilities of Ling-3.0-flash. Model weights are available on Hugging Face and ModelScope.

Important: Ling-3.0-flash-Sante is a research model, not a medical device. Its outputs may be incomplete or incorrect and must not be used as a substitute for professional medical advice, diagnosis, or treatment. High-stakes use requires qualified human review, independent verification, and compliance with applicable privacy, security, and healthcare regulations.

Highlights

Strong healthcare capabilities: Sante performs well across professional healthcare tasks, complex medical reasoning, medical knowledge, and healthcare safety benchmarks.
Efficient at scale: Its hybrid-linear MoE architecture combines Kimi Delta Attention (KDA), gated MLA, and sparse MoE layers, activating 5.1B of 124B parameters per token.
Built for long contexts: Training extends to 256K tokens, supporting long-document analysis, evidence synthesis, and multi-step research.
Beyond medical QA: Sante is designed for deep research, tool-assisted workflows, and multi-agent collaboration as well as question answering.
Native reasoning and tool use: Thinking mode is enabled by default, with tool calling supported through compatible serving frameworks.

Evaluation

We evaluate Ling-3.0-flash-Sante across professional healthcare tasks, medical knowledge and safety, complex reasoning, deep research, multi-agent collaboration, and tool-assisted agent benchmarks. The table below summarizes the results shown in the evaluation figure.

Evaluation settings

Unless otherwise specified, Ling-3.0-flash-Sante is evaluated with temperature=1.0 and top_p=0.95 across all benchmarks.
The reasoning effort is set to the highest tier for all models in the comparison.
AFUMED-Drug emphasizes medication safety and evaluates accuracy on medication contraindications.
AFUSAFE-MedSCE measures the overall safety standard of healthcare responses.
MedMCQA uses the standard validation set.
BrowseComp uses a 256K context window, temperature=1.0, top_p=0.95, and up to 1,000 turns. The single-agent setting uses a discard-all context-management strategy triggered at 40% context usage. The multi-agent setting uses an in-house harness capable of dispatching up to 64 sub-agents per task.
HLE with tools and DeepSearchQA use a 256K context window with temperature=1.0 and top_p=0.95, without a context-management strategy.
Tau3-Banking uses the official Tau evaluation set with access restricted to the terminal_use tool.

Quickstart

The official model identifier is inclusionAI/Ling-3.0-flash-Sante.

SGLang

Use the Ling-3.0-compatible SGLang image:

docker pull lmsysorg/sglang:dev-Ling-3.0-flash

The following example launches an OpenAI-compatible server with a 256K context window on four 141GB-class GPUs or a compatible four-GPU Blackwell node:

docker run --rm --gpus all --ipc=host --shm-size 32g \
  -p 30000:30000 \
  -e HF_TOKEN=<your-hf-token> \
  lmsysorg/sglang:dev-Ling-3.0-flash \
  env SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN=1 \
  python3 -m sglang.launch_server \
    --model-path inclusionAI/Ling-3.0-flash-Sante \
    --tp 4 \
    --context-length 262144 \
    --speculative-algorithm NEXTN \
    --mem-fraction-static 0.8 \
    --host 0.0.0.0 \
    --port 30000

On 80GB GPUs such as H100 or H800, use --tp 8 with the same core settings.

Send a request with the OpenAI-compatible API:

curl -s http://localhost:30000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "inclusionAI/Ling-3.0-flash-Sante",
    "messages": [
      {
        "role": "user",
        "content": "Summarize the evidence in this clinical abstract and identify its primary endpoint."
      }
    ],
    "stream": true,
    "temperature": 0.6,
    "top_k": 20,
    "top_p": 0.95
  }'

Thinking is enabled by default through the chat template. To disable it for an individual request, set:

{"chat_template_kwargs": {"enable_thinking": false}}

vLLM

Install a current vLLM build with Ling-3.0 support:

pip install uv
uv venv ~/my_ling_env
source ~/my_ling_env/bin/activate

git clone https://github.com/vllm-project/vllm.git
cd vllm
VLLM_USE_PRECOMPILED=1 uv pip install --editable . --torch-backend=auto

Launch the model server. MTP is recommended for lower latency:

vllm serve inclusionAI/Ling-3.0-flash-Sante \
  --port "$PORT" \
  --trust-remote-code \
  --served-model-name ling-3.0-flash-sante \
  --tensor-parallel-size 4 \
  --gpu-memory-utilization 0.85 \
  --enable-prefix-caching \
  --mamba-cache-mode align \
  --enable-auto-tool-choice \
  --tool-call-parser ling3 \
  --reasoning-parser ling3 \
  --speculative-config '{"method":"mtp","num_speculative_tokens":3}'

Example client request:

curl -s http://${MASTER_IP}:${PORT}/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "ling-3.0-flash-sante",
    "messages": [
      {
        "role": "user",
        "content": "Compare the inclusion criteria across these two study protocols."
      }
    ],
    "chat_template_kwargs": {"enable_thinking": true},
    "stream": true,
    "temperature": 0.6,
    "top_k": 20,
    "top_p": 0.95
  }'

Intended Use

Ling-3.0-flash-Sante is intended for research and development scenarios such as:

Medical and healthcare question answering with human review.
Literature search, evidence synthesis, and scientific deep research.
Long-document analysis and structured information extraction.
Healthcare knowledge-base and retrieval-augmented generation applications.
Tool-assisted research and multi-agent healthcare workflows.
Evaluation and development of domain-specific AI agents.

Limitations and Responsible Use

The model can produce hallucinated, outdated, incomplete, or unsafe medical information.
It must not independently diagnose, prescribe, triage, or make treatment decisions.
Outputs for clinical or other high-stakes use require review by qualified professionals and verification against authoritative sources.
Users are responsible for protecting personal health information and complying with applicable privacy, data-governance, security, and medical-device requirements.
Performance can vary across languages, specialties, populations, institutions, and deployment configurations. Benchmark results do not guarantee real-world clinical performance.
Tool access and multi-agent execution introduce additional risks. Use least-privilege credentials, auditable actions, and human approval for consequential operations.