Instructions to use xingqiang/Medical-NER-Qwen-4B-Thinking-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use xingqiang/Medical-NER-Qwen-4B-Thinking-merged with Transformers:

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

pipe = pipeline("text-generation", model="xingqiang/Medical-NER-Qwen-4B-Thinking-merged")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("xingqiang/Medical-NER-Qwen-4B-Thinking-merged")
model = AutoModelForCausalLM.from_pretrained("xingqiang/Medical-NER-Qwen-4B-Thinking-merged", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

PEFT
How to use xingqiang/Medical-NER-Qwen-4B-Thinking-merged with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use xingqiang/Medical-NER-Qwen-4B-Thinking-merged 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-4B-Thinking-merged"
# 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-4B-Thinking-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/xingqiang/Medical-NER-Qwen-4B-Thinking-merged

SGLang

How to use xingqiang/Medical-NER-Qwen-4B-Thinking-merged 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-4B-Thinking-merged" \
    --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-4B-Thinking-merged",
		"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-4B-Thinking-merged" \
        --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-4B-Thinking-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use xingqiang/Medical-NER-Qwen-4B-Thinking-merged with Docker Model Runner:
```
docker model run hf.co/xingqiang/Medical-NER-Qwen-4B-Thinking-merged
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Medical-NER-Qwen-4B-Thinking

Model Description

This is a fine-tuned medical LLM based on Qwen3-4B-Thinking, specialized for medical entity and relationship extraction.

Model Details

Base Model: unsloth/Qwen3-4B-Thinking-2507
Fine-tuning Method: LoRA (Low-Rank Adaptation)
Domain: Medical Literature Analysis
Tasks: Entity Recognition, Relationship Extraction
Language: English

Performance Metrics

Metric	Entity Extraction	Relationship Extraction
Precision	0.000	0.000
Recall	0.000	0.000
F1-Score	0.000	0.000

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2.5-3B-Thinking",
    torch_dtype="auto",
    device_map="auto"
)

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "xingqiang/Medical-NER-Qwen-4B-Thinking-merged")
tokenizer = AutoTokenizer.from_pretrained("xingqiang/Medical-NER-Qwen-4B-Thinking-merged")

# Generate medical analysis
text = "Hepatitis C virus causes chronic liver infection."
messages = [
    {"role": "user", "content": f"Extract medical entities and relationships from: {text}"}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)

Downloads last month: 7

Safetensors

Model size

4B params

Tensor type

F16

Model tree for xingqiang/Medical-NER-Qwen-4B-Thinking-merged

Base model

Qwen/Qwen3-4B-Thinking-2507

Finetuned

unsloth/Qwen3-4B-Thinking-2507

Adapter

(6)

this model

Quantizations

1 model