---
library_name: transformers
tags:
- medical
datasets:
- stefan-m-lenz/ICDOPS-QA-2024
language:
- de
base_model:
- Qwen/Qwen2.5-7B-Instruct-1M
---

# Model Card for Model stefan-m-lenz/Qwen2.5-7B-Instruct

This model is a PEFT adapter (e.g., LoRA) fine-tuned using the dataset [ICDOPS-QA-2024](https://huggingface.co/datasets/stefan-m-lenz/ICDOPS-QA-2024) based on [Qwen/Qwen2.5-7B-Instruct-1M](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct-1M).
For more information about the training, see the [dataset card](https://huggingface.co/datasets/stefan-m-lenz/ICDOPS-QA-2024).

# Usage

Package prerequisites:

```
pip install transformers accelerate peft
```

Load the model.
```{python}
repo_id = "stefan-m-lenz/Qwen-2.5-7B-ICDOPS-QA-2024"
config = PeftConfig.from_pretrained(repo_id, device_map="auto")
model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, device_map="auto")
model = PeftModel.from_pretrained(model, repo_id, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path, device_map="auto")
```

```{python}
# Test input
test_input = """Was ist der ICD-10-Code für die Tumordiagnose „Bronchialkarzinom, Hauptbronchus“? Antworte nur kurz mit dem ICD-10 Code."""

# Generate response
inputs = tokenizer(test_input, return_tensors="pt").to("cuda")
outputs = model.generate(
    **inputs,
    max_new_tokens=7,
    do_sample=False,
    pad_token_id=tokenizer.eos_token_id,
    temperature=None,
    top_p=None,
    top_k=None,
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
response = response[len(test_input):].strip()

print("Test Input:", test_input)
print("Model Response:", response)
```