---
license: apache-2.0
base_model: Qwen/Qwen3-0.6B-Base
tags:
- fine-tuned
- multiple-choice-qa
- mcqa
- question-answering
datasets:
- custom-mcqa-dataset
language:
- en
pipeline_tag: text-generation
---

# MNLP_M2_mcqa_model

This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base) for Multiple Choice Question Answering (MCQA) tasks.

## Model Details

- **Base Model**: Qwen/Qwen3-0.6B-Base
- **Task**: Multiple Choice Question Answering
- **Model Type**: Classic
- **Training Context**: With context
- **Evaluation Context**: Without context
- **Fine-tuning Method**: Causal Language Modeling


## Training Details

- **Epochs**: 5
- **Learning Rate**: 5e-05
- **Batch Size**: 2
- **Training Framework**: Transformers + PyTorch



## Performance

| Metric | Baseline | Fine-tuned | Improvement |
|--------|----------|------------|-------------|
| Accuracy | 68.50% | 71.70% | +3.20% |



## Training Data

The model was fine-tuned on a custom MCQA dataset with the following characteristics:
- Format: Multiple choice questions with 4 options (A, B, C, D)
- Context: Included during training
- Evaluation: Without context


## Usage

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MNLP_M2_mcqa_model", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("MNLP_M2_mcqa_model", trust_remote_code=True)

# For MCQA tasks, provide the question and options, then generate the answer
prompt = "Question: What is the capital of France?\nA) London\nB) Berlin\nC) Paris\nD) Madrid\nAnswer:"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=5)
answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
```