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---
base_model: Qwen/Qwen3-0.6B
library_name: peft
---
# Sanity Check Model
This model is fine-tuned on the sanity check dataset for multiple choice question answering.
## Model Details
- Base model: Qwen/Qwen3-0.6B
- Fine-tuning method: LoRA
- Task: Multiple Choice Question Answering (MCQA)
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("RikoteMaster/sanity_check_model")
tokenizer = AutoTokenizer.from_pretrained("RikoteMaster/sanity_check_model")
# Example usage
question = "What is 2+2?"
choices = ["3", "4", "5", "6"]
messages = [{
"role": "user",
"content": question + "\n" + "\n".join([f"{chr(65+i)}. {choice}" for i, choice in enumerate(choices)])
}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=10)
print(tokenizer.decode(outputs[0]))
### Framework versions
- PEFT 0.15.2 |