---
license: apache-2.0
language:
  - en
base_model:
  - trillionlabs/Gravity-16B-A3B-Base
tags:
  - medical
  - clinical
  - mixture-of-experts
  - conversational
  - sft
library_name: transformers
pipeline_tag: text-generation
---

<p align="center">
  <img src="banner.png" alt="L1" style="width: 80%;">
</p>

# Learning Unit 1

**L1** (Learning Unit 1) is the first language model from [Lunit](https://www.lunit.io) and Lunit Consortium, purpose-built for the medical domain. Derived from [Gravity-16B-A3B-Base](https://huggingface.co/trillionlabs/Gravity-16B-A3B-Base), L1 is designed for clinical reasoning and decision support.

### ✨ Key Highlights
* 🩺 **Medical-Domain Specialized**: Developed specifically for clinical reasoning and medical decision support
* ⚡ **Efficient MoE**: Only 3B parameters active per token out of 16.24B total — fast inference with high capacity
* 💭 **Thinking Model**: Performs step-by-step reasoning in `<think>` tags before generating the final answer

> **Note:** L1 reasons internally using `<think>...</think>` blocks before producing a response. This chain-of-thought process improves answer quality but consumes additional tokens. Set `max_tokens` accordingly (recommended: 2048+).

### 📋 Model Specifications

- Type: Causal Language Model
- Base Model: [Gravity-16B-A3B-Base](https://huggingface.co/trillionlabs/Gravity-16B-A3B-Base) from Trillion Labs and Lunit Consortium
- Architecture: GravityMoE (Sparse Mixture-of-Experts with MLA)
- Total Parameters: 16.24B
- Active Parameters: 3B
- Number of Layers: 28
- Attention Heads: 16
- KV Heads: 16
- Hidden Size: 2048
- MoE Intermediate Size: 1408
- Routed Experts: 64 (top-8 selection)
- Shared Experts: 1
- Context Length: 32,768 tokens
- Vocabulary Size: 151,552
- Tokenizer: GLM-4.5
- Precision: bf16

## 🚀 Quickstart

### SGLang (Recommended)

**Install:**
```bash
pip install "sglang[all] @ git+https://github.com/trillion-labs/sglang-gravity.git#subdirectory=python"
```

**Launch server:**
```bash
python -m sglang.launch_server \
  --model-path learning-unit/L1-16B-A3B \
  --port 9006 --host 0.0.0.0 \
  --tp 1 --dtype bfloat16 --trust-remote-code \
  --attention-backend triton \
  --moe-runner-backend triton
```

**Query:**
```bash
curl -X POST http://localhost:9006/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "learning-unit/L1-16B-A3B",
    "messages": [
      {"role": "user", "content": "What are the diagnostic criteria for sepsis?"}
    ],
    "max_tokens": 2048
  }'
```

### Transformers

**Install:**
```bash
pip install "transformers>=5.0" torch
```

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "learning-unit/L1-16B-A3B"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

messages = [
    {"role": "user", "content": "What are the diagnostic criteria for sepsis?"}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=2048,
    temperature=0.7,
    do_sample=True,
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)
```

## 💬 Examples

L1 is specialized for the medical domain and covers a wide range of clinical scenarios. Below are representative examples from real-world clinical use cases.

### Medical Q&A

> A 45-year-old woman with lupus nephritis on mycophenolate and prednisone develops fever, dry cough, and bilateral ground-glass opacities on chest CT. Her CD4 count is 180. What is your differential diagnosis and recommended workup?

### Patient Education

> I have diabetes and use insulin daily. What is the proper way to store insulin at home?

### Clinical Documentation

> Please draft an overnight progress note. Patient labs: RBC 4.5, WBC 8. Vitals: HR 82, BP 118/76, RR 15, Temp 37.1. Nurse reports stable overnight. Plan: continue antibiotics, recheck labs in the morning.

### Emergency Triage

> 다음 응급실 환자에 대해 KTAS triage를 수행하고, 초기 진단 및 감별진단을 제시해주세요. 78세 여성 환자가 119 구급차로 응급실에 내원했습니다. 22시경 갑자기 좌측 안면이 처지고 말이 어눌해지는 증상이 발생했습니다. 두통을 호소하며, 고혈압 병력이 있습니다. 활력징후는 혈압 172/88, 심박수 92, 호흡수 14, 체온 36.8, 산소포화도 98%이고 의식은 명료합니다. 사지 위약감은 없습니다.

### Adverse Drug Reaction (ADR) Causality Assessment

> 다음 환자의 약물이상반응(ADR)에 대해 WHO-UMC 기준으로 인과관계를 평가해주세요. 80세 여성 환자가 기관지확장증으로 입원 중 moxifloxacin 400mg IV를 투여받았습니다. 투여 중 전신 피부 가려움이 새로 발생했고, 약물 중단 후 환자 본인도 가려움이 줄어드는 양상을 표현했으며 이후 회복되었습니다. 재투여는 시행하지 않았습니다. 기존 약물 알레르기력은 없고, 가려움을 유발할 만한 다른 병용약물이나 피부질환은 확인되지 않았습니다.

## 📊 Benchmark

All benchmarks were evaluated using [CoEval](https://github.com/lunit-io/CoEval), Lunit's open-source medical LLM evaluation framework. Evaluations use greedy decoding (temperature=0). To reproduce these results:

```bash
git clone https://github.com/lunit-io/CoEval.git
cd CoEval
```

Refer to the [CoEval Quickstart](https://github.com/lunit-io/CoEval#quickstart) for setup and evaluation instructions.

### MCQA Benchmarks

| Model | [PubMedQA](https://huggingface.co/datasets/qiaojin/PubMedQA) | [AttrBench](https://huggingface.co/datasets/osunlp/AttributionBench) | [MedQA](https://huggingface.co/datasets/GBaker/MedQA-USMLE-4-options) | [CareQA](https://huggingface.co/datasets/HPAI-BSC/CareQA) | [HeadQA](https://huggingface.co/datasets/alesi12/head_qa_v2) | [MedMCQA](https://huggingface.co/datasets/lighteval/med_mcqa) | [MMLU-Pro (Health)](https://huggingface.co/datasets/TIGER-Lab/MMLU-Pro) | [M-ARC](https://huggingface.co/datasets/mkieffer/M-ARC) | [MetaMedQA](https://huggingface.co/datasets/maximegmd/MetaMedQA) | [MedHallu](https://huggingface.co/datasets/UTAustin-AIHealth/MedHallu) | [MedCalc](https://huggingface.co/datasets/ncbi/MedCalc-Bench) | [MedBullets](https://huggingface.co/datasets/mkieffer/Medbullets) 4-opt | [MedBullets](https://huggingface.co/datasets/mkieffer/Medbullets) 5-opt | [MedXpertQA](https://huggingface.co/datasets/TsinghuaC3I/MedXpertQA)-R | [MedXpertQA](https://huggingface.co/datasets/TsinghuaC3I/MedXpertQA)-U | W.Avg |
|:---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| GPT-OSS-120B | 78.00 | 76.10 | 91.10 | 91.00 | 88.40 | 74.80 | 74.60 | 40.00 | 76.50 | 83.50 | 30.30 | 84.70 | 82.10 | 35.60 | 32.90 | 79.43 |
| GPT-OSS-20B | 75.80 | 74.80 | 83.90 | 84.80 | 83.30 | 65.40 | 70.50 | 31.00 | 70.10 | 81.30 | 29.20 | 73.40 | 70.50 | 24.70 | 21.20 | 73.38 |
| Qwen3.5-122B | 76.40 | 55.68 | 87.80 | 86.40 | 84.00 | 74.40 | 73.00 | 59.00 | 73.90 | 37.50 | 53.70 | 79.20 | 79.50 | 35.90 | 35.30 | 75.08 |
| MedGemma-27B | 73.40 | 74.80 | 84.40 | 85.00 | 83.80 | 71.90 | 73.00 | 48.00 | 69.60 | 81.40 | 24.10 | 73.70 | 68.80 | 19.10 | 20.50 | 73.99 |
| Gemma4-26B-A4B | 76.40 | 72.00 | 81.80 | 84.50 | 82.30 | 67.30 | 73.50 | 67.00 | 71.50 | 86.50 | 45.60 | 73.70 | 67.50 | 45.10 | 39.20 | 75.34 |
| L1-16B-A3B | 84.20 | 78.40 | 85.50 | 88.20 | 85.80 | 76.70 | 74.90 | 82.00 | 73.10 | 76.10 | 43.90 | 78.90 | 70.80 | 27.50 | 29.20 | 77.74 |

### Chat Task

| Model | [HealthBench-Consensus](https://github.com/openai/simple-evals) |
|:---|:---:|
| GPT-OSS-120B | 90.60 |
| GPT-OSS-20B | 78.70 |
| Qwen3.5-122B | 92.20 |
| MedGemma-27B | 90.70 |
| Gemma4-26B-A4B | 92.60 |
| L1-16B-A3B | 93.50 |

## 📝 Citation

```bibtex
@misc{lunit2026l1,
  title={L1: The First Clinical Language Model by Lunit},
  author={Lunit},
  year={2026},
  url={https://huggingface.co/learning-unit/L1-16B-A3B}
}
```

## ⚠️ Limitations

- **Not a substitute for professional medical judgment.** L1 may generate factually incorrect, incomplete, or outdated clinical information. All outputs should be verified by qualified healthcare professionals.
- **Thinking overhead.** Chain-of-thought reasoning in `<think>` tags increases token consumption and latency compared to non-thinking models of similar size.
- **Context length.** Maximum context length is 32,768 tokens.
- **No real-time knowledge.** The model's knowledge is limited to its training data cutoff and does not reflect the latest medical guidelines or drug approvals.

## 🤝 Acknowledgements

This work was supported by the Domain-Specific Foundation Model Project (인공지능 특화 파운데이션 모델 프로젝트), funded by the Ministry of Science and ICT (과학기술정보통신부) and managed by the National IT Industry Promotion Agency (NIPA).

L1 is a collaborative effort by the following consortium members:

**Industry**
- Lunit
- Trillion Labs
- SK Biopharmaceuticals
- Kakao Healthcare
- AIGEN Sciences
- D-Circle
- Rebellions
- Standigm

**Academia**
- Prof. Choi Yun-jae's Lab from KAIST
- Prof. Hong Seung-hoon's Lab from KAIST
- Prof. Jung Yu-seong's Lab from SNU
- Prof. Kim Hyun-woo's Lab from KAIST
- Prof. Kim Tae-gyun's Lab from KAIST
- Prof. Ye Jong-cheol's Lab from KAIST

**Hospitals**
- NHIS Ilsan Hospital
- Ewha Womans University Seoul Hospital
- Keimyung University Dongsan Medical Center
- Konyang University Hospital
- Korea University Research & Business Foundation
- Kyung Hee University Hospital at Gangdong
- Kyung Hee University Medical Center
- Pusan National University Yangsan Hospital
- Yongin Severance Hospital

<p align="center">
  <img src="consortium.png" alt="Consortium Members" style="width: 80%;">
</p>

## 📄 License

This model is licensed under the [Apache 2.0 License](LICENSE).

## 📬 Contact

- Taesoo Kim (김태수) — [taesoo.kim@lunit.io](mailto:taesoo.kim@lunit.io)
- Donggeun Yoo (유동근) — [dgyoo@lunit.io](mailto:dgyoo@lunit.io)