Instructions to use matthewchung74/Qwen2.5_3B-GRPO-medical-reasoning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use matthewchung74/Qwen2.5_3B-GRPO-medical-reasoning with Transformers:

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

pipe = pipeline("text-generation", model="matthewchung74/Qwen2.5_3B-GRPO-medical-reasoning")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("matthewchung74/Qwen2.5_3B-GRPO-medical-reasoning")
model = AutoModelForCausalLM.from_pretrained("matthewchung74/Qwen2.5_3B-GRPO-medical-reasoning", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use matthewchung74/Qwen2.5_3B-GRPO-medical-reasoning with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "matthewchung74/Qwen2.5_3B-GRPO-medical-reasoning"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "matthewchung74/Qwen2.5_3B-GRPO-medical-reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/matthewchung74/Qwen2.5_3B-GRPO-medical-reasoning

SGLang

How to use matthewchung74/Qwen2.5_3B-GRPO-medical-reasoning 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 "matthewchung74/Qwen2.5_3B-GRPO-medical-reasoning" \
    --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": "matthewchung74/Qwen2.5_3B-GRPO-medical-reasoning",
		"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 "matthewchung74/Qwen2.5_3B-GRPO-medical-reasoning" \
        --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": "matthewchung74/Qwen2.5_3B-GRPO-medical-reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use matthewchung74/Qwen2.5_3B-GRPO-medical-reasoning with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for matthewchung74/Qwen2.5_3B-GRPO-medical-reasoning to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for matthewchung74/Qwen2.5_3B-GRPO-medical-reasoning to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for matthewchung74/Qwen2.5_3B-GRPO-medical-reasoning to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="matthewchung74/Qwen2.5_3B-GRPO-medical-reasoning",
    max_seq_length=2048,
)

Docker Model Runner
How to use matthewchung74/Qwen2.5_3B-GRPO-medical-reasoning with Docker Model Runner:
```
docker model run hf.co/matthewchung74/Qwen2.5_3B-GRPO-medical-reasoning
```

Qwen2.5 3B GRPO Medical Reasoning Model

A fine-tuned version of Qwen2.5 3B Instruct model using Generalized Reinforcement Policy Optimization (GRPO) for medical reasoning tasks. This model is intended for education purposes only and not intended as medical advice.

Model Details

Model Description

This model is a fine-tuned version of Qwen2.5 3B Instruct, optimized for medical reasoning tasks using the Unsloth library and GRPO algorithm. It was trained on the FreedomIntelligence/medical-o1-reasoning-SFT dataset and incorporates custom reward functions for semantic correctness and perplexity.

Developed by: Matthew Chung
Model type: Transformer-based language model
Language(s) (NLP): English
License: Apache 2.0
Finetuned from model: Qwen/Qwen2.5-3B-Instruct

Model Sources

Repository: Qwen2.5_3B_GRPO
Base Model: Qwen2.5-3B-Instruct

Uses

Direct Use

This model is intended for education purposes only and not intended as providing medical advice.

Downstream Use

This model is intended for education purposes only and not intended as providing medical advice.

Out-of-Scope Use

Not intended for:

Direct medical diagnosis
Treatment recommendations
High-stakes medical decision making without human oversight

Bias, Risks, and Limitations

May generate incorrect or misleading medical information
Limited to the scope of the training data
Potential biases from the original dataset

Recommendations

Always verify outputs with medical professionals
Use with caution in clinical settings
Monitor for potential biases in responses

How to Get Started with the Model

Refer to Github Repository

Training Details

Training Data

Dataset: FreedomIntelligence/medical-o1-reasoning-SFT
Training Samples: 25,117
Validation Samples: 127

Training Procedure

Training Hyperparameters

Learning Rate: 5e-6
Batch Size: 1
Gradient Accumulation Steps: 4
Max Sequence Length: 1024
LoRA Rank: 64
Training Steps: 1000
Precision: 4-bit quantization

Speeds, Sizes, Times

Training Hardware: NVIDIA RTX 3090 (Runpod)
Training Time: ~14 hours
Model Size: 3B parameters

Evaluation

Testing Data, Factors & Metrics

Testing Data

Same as training data (FreedomIntelligence/medical-o1-reasoning-SFT)

Metrics

Semantic correctness
Perplexity
Tag presence accuracy

Results

Final training metrics:

Loss: 0.001300
Semantic Score: 0.630995
Perplexity: 266.149998

Environmental Impact

Hardware Type: NVIDIA RTX 3090
Hours used: ~14
Cloud Provider: Runpod
Compute Region: US-West
Carbon Emitted: Estimated 0.5 kg CO2

Technical Specifications

Model Architecture and Objective

Architecture: Transformer-based
Objective: Causal language modeling with GRPO optimization

Compute Infrastructure

Hardware

NVIDIA RTX 3090 GPU
24GB VRAM

Software

Unsloth
PyTorch
Hugging Face Transformers
vLLM

Downloads last month: 14

Safetensors

Model size

3B params

Tensor type

BF16