Instructions to use xingqiang/Medical-NER-Qwen-0.6B_COT-GRPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use xingqiang/Medical-NER-Qwen-0.6B_COT-GRPO with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="xingqiang/Medical-NER-Qwen-0.6B_COT-GRPO")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("xingqiang/Medical-NER-Qwen-0.6B_COT-GRPO", device_map="auto")

PEFT
How to use xingqiang/Medical-NER-Qwen-0.6B_COT-GRPO with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use xingqiang/Medical-NER-Qwen-0.6B_COT-GRPO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "xingqiang/Medical-NER-Qwen-0.6B_COT-GRPO"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xingqiang/Medical-NER-Qwen-0.6B_COT-GRPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/xingqiang/Medical-NER-Qwen-0.6B_COT-GRPO

SGLang

How to use xingqiang/Medical-NER-Qwen-0.6B_COT-GRPO with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "xingqiang/Medical-NER-Qwen-0.6B_COT-GRPO" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xingqiang/Medical-NER-Qwen-0.6B_COT-GRPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "xingqiang/Medical-NER-Qwen-0.6B_COT-GRPO" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xingqiang/Medical-NER-Qwen-0.6B_COT-GRPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use xingqiang/Medical-NER-Qwen-0.6B_COT-GRPO with Docker Model Runner:
```
docker model run hf.co/xingqiang/Medical-NER-Qwen-0.6B_COT-GRPO
```

Medical-NER-Qwen-0.6B-GRPO

Model Description

This is a medical NER model fine-tuned with GRPO (Group Relative Policy Optimization) on Qwen3-0.6B, specialized for medical entity recognition and relationship extraction.

Model Details

Base Model: unsloth/Qwen3-0.6B
Fine-tuning Method: GRPO + LoRA
Domain: Medical Literature Analysis
Tasks: Entity Recognition (Bacteria, Disease, Evidence), Relationship Extraction
Language: English

Entity Categories

Bacteria: Microorganisms, viruses, pathogens, infectious agents
Disease: Medical conditions, symptoms, syndromes, pathological states
Evidence: Research findings, clinical observations, experimental results

Relationship Types

is_a: Taxonomic or hierarchical relationships
biomarker_for: Biomarker relationships
correlated_with: Statistical correlations
has_relationship: General associations

Usage

from unsloth import FastLanguageModel
from vllm import SamplingParams

# Load model
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="xingqiang/Medical-NER-Qwen-0.6B_COT-GRPO",
    max_seq_length=4096,
    load_in_4bit=False,
    fast_inference=True,
)

# Prepare input
literature = """
Hepatitis C virus (HCV) is a major cause of chronic liver disease.
Studies show that HCV infection leads to cirrhosis in 20% of patients.
"""

messages = [
    {"role": "system", "content": "You are a medical NER expert..."},
    {"role": "user", "content": literature},
]

prompt = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=False
)

# Generate
output = model.fast_generate(
    prompt,
    sampling_params=SamplingParams(temperature=0.7, max_tokens=1024),
)[0].outputs[0].text

print(output)

Training Details

Training Method: GRPO (Group Relative Policy Optimization)
LoRA Rank: 32
Max Sequence Length: 4096
Reward Functions: Format matching, JSON validity, Entity accuracy
Dataset: Medical literature with entity and relationship annotations

Output Format

{
  "entities": [
    {"text": "Hepatitis C virus", "label": "Bacteria"},
    {"text": "chronic liver disease", "label": "Disease"}
  ],
  "relations": [
    {
      "subject": "Hepatitis C virus",
      "relation": "has_relationship",
      "object": "chronic liver disease"
    }
  ]
}

Citation

If you use this model, please cite:

@misc{medical-ner-qwen-grpo,
  title={Medical NER with GRPO},
  author={chen,xingqiang},
  year={2025},
  publisher={HuggingFace},
  url={https://huggingface.co/xingqiang/Medical-NER-Qwen-0.6B_COT-GRPO}
}

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