inclusionAI/Ling-3.0-flash-Sante-fp8

🤗 Hugging Face   |   🤖 ModelScope    |   🐙 Vercel   

Introduction

Ling-3.0-flash-Sante-fp8 is the FP8-quantized release of Ling-3.0-flash-Sante, our healthcare-specialized model built on Ling-3.0-flash. Its name comes from the French word santé, meaning “health.”

The FP8 checkpoint preserves the same 124B total parameters and 5.1B active parameters per token as the BF16 model while reducing model-weight memory and improving deployment efficiency on supported hardware. It is designed for medical reasoning, healthcare research, deep research, and tool-assisted workflows while retaining the general capabilities of Ling-3.0-flash.

Important: Ling-3.0-flash-Sante-fp8 is a research model, not a medical device. Quantization does not change its intended-use boundaries. 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

• Deployment-ready FP8 checkpoint: Blockwise FP8 quantization reduces model-weight memory and is intended for efficient inference on hardware with native FP8 support.
• 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.

FP8 Quantization

Ling-3.0-flash-Sante-fp8 is the deployment-oriented quantized counterpart of Ling-3.0-flash-Sante. The checkpoint uses blockwise FP8 E4M3 quantization for expert weights, with a 128 × 128 block size, following the FP8 recipe used for Ling-3.0-flash-fp8. Compatible serving frameworks detect the quantization configuration directly from the checkpoint, so an additional runtime quantization flag is not required.

The FP8 model is intended to provide a practical balance between model quality, memory footprint, and serving throughput. Because numerical behavior and performance can vary by runtime, kernel, hardware, and parallelism configuration, users should validate the checkpoint on their own workloads before production use.

Evaluation

We compare Ling-3.0-flash-Sante-fp8 with the BF16 checkpoint on professional healthcare and medical reasoning benchmarks.

Evaluation settings

• Unless otherwise specified, Ling-3.0-flash-Sante-fp8 is evaluated with temperature=1.0 and top_p=0.95 across all benchmarks.
• The FP8 and BF16 checkpoints use the same evaluation prompts and decoding settings.
• The reasoning effort is set to the highest tier for both checkpoints.
• MedMCQA uses the standard validation set.

Quickstart

The official model identifier is inclusionAI/Ling-3.0-flash-Sante-fp8.

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. SGLang reads the FP8 format from the checkpoint's quantization_config, so no explicit quantization flag is needed:

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-fp8 \
    --tp 4 \
    --ep-size 4 \
    --context-length 262144 \
    --speculative-algorithm NEXTN \
    --mem-fraction-static 0.8 \
    --host 0.0.0.0 \
    --port 30000

On H100 or H800, use --tp 8 --ep-size 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-fp8",
    "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 FP8 model server. MTP is recommended for lower latency:

vllm serve inclusionAI/Ling-3.0-flash-Sante-fp8 \
  --port "$PORT" \
  --trust-remote-code \
  --served-model-name ling-3.0-flash-sante-fp8 \
  --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-fp8",
    "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-fp8 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.
• Memory-efficient deployment and serving experiments on FP8-capable hardware.

Limitations and Responsible Use

• Quantization can introduce small numerical differences relative to the BF16 checkpoint. Users should evaluate quality, stability, and safety on their target tasks and serving stack.
• 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, hardware, kernels, 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.