from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

tokenizer = AutoTokenizer.from_pretrained("{full_repo_name}")
model = AutoModelForCausalLM.from_pretrained("{full_repo_name}").to("cuda")

question = "How can I identify and manage early signs of sepsis in adults?"
prompt = f"{{question}}\\nReasoning: <insert reasoning here>\\nAnswer:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=500)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Training Details
Training Data: FreedomIntelligence/medical-o1-reasoning-SFT

Training Regime: Fine-tuned on Qwen base model

Hardware: GPU-based

Compute Region: [More Information Needed]

Citation
java
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@misc{qwen-medical-reasoning,
  title={Qwen Fine-tuned Medical Reasoning Model},
  author={Ghost2513},
  year={2025},
  howpublished={https://huggingface.co/{full_repo_name}}
}
"""

with open("README.md", "w") as f:
f.write(readme_text)

api.upload_file(
path_or_fileobj="README.md",
path_in_repo="README.md",
repo_id=full_repo_name,
token=HF_TOKEN
)
print("Model and README successfully uploaded to Hugging Face Hub!")

vbnet
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This script does the following:

1. Logs in to Hugging Face using `HF_TOKEN`.
2. Loads your fine-tuned Qwen model and tokenizer.
3. Creates the repo if it doesn’t exist.
4. Pushes both the model and tokenizer.
5. Generates a **full README/model card** with all sections you provided (most fields are placeholders for you to fill).
6. Uploads the README to the repository.  

---

Ghost2513/qwen-medical-reasoning

Model Details

Model Description

Uses

Direct Use

Out-of-Scope Use

Bias, Risks, and Limitations

How to Get Started

Ghost2513/
qwen-medical-reasoning