Instructions to use pfnet/Preferred-MedRECT-32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pfnet/Preferred-MedRECT-32B with Transformers:

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

pipe = pipeline("text-generation", model="pfnet/Preferred-MedRECT-32B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pfnet/Preferred-MedRECT-32B")
model = AutoModelForCausalLM.from_pretrained("pfnet/Preferred-MedRECT-32B")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use pfnet/Preferred-MedRECT-32B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pfnet/Preferred-MedRECT-32B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pfnet/Preferred-MedRECT-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/pfnet/Preferred-MedRECT-32B

SGLang

How to use pfnet/Preferred-MedRECT-32B 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 "pfnet/Preferred-MedRECT-32B" \
    --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": "pfnet/Preferred-MedRECT-32B",
		"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 "pfnet/Preferred-MedRECT-32B" \
        --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": "pfnet/Preferred-MedRECT-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use pfnet/Preferred-MedRECT-32B with Docker Model Runner:
```
docker model run hf.co/pfnet/Preferred-MedRECT-32B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Preferred-MedRECT-32B

Model Description

Preferred-MedRECT-32B is a finetuned model based on Qwen/Qwen3-32B, which has been optimized for medical error detection and correction tasks using LoRA (Low-Rank Adaptation).

The model is trained on bilingual (Japanese/English) medical reasoning data with explicit reasoning processes, enabling it to detect errors, extract erroneous sentences, and provide corrections in clinical texts.

The model is released under the Apache License 2.0.

Model Performance

The table below shows cross-lingual performance comparison on MedRECT-ja (Japanese) and MedRECT-en (English) benchmarks. MedRECT evaluates models on three subtasks: error detection (F1), sentence extraction (Acc.), and error correction (EC Avg. Score).

Model	MedRECT-ja Error Det. F1	MedRECT-ja Sent. Ext. Acc.	MedRECT-ja EC Avg. Score	MedRECT-en Error Det. F1	MedRECT-en Sent. Ext. Acc.	MedRECT-en EC Avg. Score
Preferred-MedRECT-32B	0.743	81.5%	0.627	0.728	90.9%	0.718
Qwen3-32B (think)	0.723	72.5%	0.549	0.740	83.5%	0.550
gpt-oss-120b (medium)	0.721	77.4%	0.581	0.777	88.1%	0.630
gpt-oss-20b (medium)	0.718	64.3%	0.543	0.762	87.2%	0.590
GPT-4.1	0.658	52.6%	0.655	0.789	72.8%	0.710

Training Details

Base Model: unsloth/Qwen3-32B
Fine-tuning Method: LoRA (Low-Rank Adaptation)
Training Data:
- Japanese: 5,538 samples from JMLE (2018-2023)
- English: 2,439 samples from MEDEC MS Subset
- All samples include reasoning processes generated by DeepSeek-R1-0528

Limitations

The model was developed for research purposes and is not intended for clinical diagnosis. It is the users' responsibility to ensure compliance with applicable rules and regulations.

Contributors

Preferred Networks, Inc.

Naoto Iwase
Hiroki Okuyama
Junichiro Iwasawa

Publications

Detailed evaluation results will be given in the research paper.

Citations

@article{medrect2025,
      title={MedRECT: A Medical Reasoning Benchmark for Error Correction in Clinical Texts},
      author={Iwase, Naoto and Okuyama, Hiroki and Iwasawa, Junichiro},
      journal={arXiv preprint arXiv:2511.00421},
      year={2025}
}