Instructions to use Aikyam-Lab/CURE-MED-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Aikyam-Lab/CURE-MED-7B with Transformers:

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

pipe = pipeline("text-generation", model="Aikyam-Lab/CURE-MED-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Aikyam-Lab/CURE-MED-7B")
model = AutoModelForCausalLM.from_pretrained("Aikyam-Lab/CURE-MED-7B")
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

vLLM

How to use Aikyam-Lab/CURE-MED-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Aikyam-Lab/CURE-MED-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Aikyam-Lab/CURE-MED-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Aikyam-Lab/CURE-MED-7B

SGLang

How to use Aikyam-Lab/CURE-MED-7B 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 "Aikyam-Lab/CURE-MED-7B" \
    --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": "Aikyam-Lab/CURE-MED-7B",
		"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 "Aikyam-Lab/CURE-MED-7B" \
        --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": "Aikyam-Lab/CURE-MED-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Aikyam-Lab/CURE-MED-7B with Docker Model Runner:
```
docker model run hf.co/Aikyam-Lab/CURE-MED-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Card for Model ID

CURE-MED-7B is a 7 billion parameter large language model specialized for multilingual medical reasoning, fine-tuned from Qwen/Qwen2.5-7B using a curriculum-informed reinforcement learning framework to enhance logical correctness and language stability in healthcare applications.

Model Details

CURE-MED-7B is part of the CURE-MED family of models, designed to address the challenges of multilingual medical reasoning in large language models (LLMs). Built on the Qwen2.5-7B base model, it incorporates a curriculum-informed reinforcement learning approach that integrates code-switching-aware supervised fine-tuning (SFT) and Group Relative Policy Optimization (GRPO) to improve performance on open-ended medical queries across 13 languages, including underrepresented ones such as Amharic, Yoruba, and Swahili. The model is trained and evaluated using CUREMED-BENCH, a high-quality multilingual open-ended medical reasoning benchmark with single verifiable answers.

Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub.

Developed by: Eric Onyame, Akash Ghosh, Subhadip Baidya, Sriparna Saha, Xiuying Chen, Chirag Agarwal (Aikyam Lab and collaborators)
Shared by: Aikyam Lab
Model type: Multilingual medical reasoning large language model
Language(s) (NLP): Amharic, Bengali, French, Hausa, Hindi, Japanese, Korean, Spanish, Swahili, Thai, Turkish, Vietnamese, Yoruba
License: Apache 2.0
Finetuned from model: Qwen2.5-Instruct (1.5B, 3B, 7B, 14B, 32B variants)

Model Sources

Repository: https://github.com/AikyamLab/cure-med
Paper: https://arxiv.org/abs/2601.13262
Demo: https://cure-med.github.io/

Citation

BibTeX:

@article{onyame2026cure,
  title={CURE-Med: Curriculum-Informed Reinforcement Learning for Multilingual Medical Reasoning},
  author={Onyame, Eric and Ghosh, Akash and Baidya, Subhadip and Saha, Sriparna and Chen, Xiuying and Agarwal, Chirag},
  journal={arXiv preprint arXiv:2601.13262},
  year={2026}
}