| 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 |