MNLP_M2_quantized_model

This model is a quantized fine-tuned version of 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: Quantized
• Training Context: Without context
• Evaluation Context: Without context
• Fine-tuning Method: Causal Language Modeling
• Quantization: 8bit

Training Details

• Epochs: 5
• Learning Rate: 5e-05
• Batch Size: 2
• Training Framework: Transformers + PyTorch
• Quantization Method: 8bit
• Quantization Only: No

Performance

Metric	Baseline	Fine-tuned	Improvement
Accuracy	69.66%	70.68%	+1.02%

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: Not included during training
• Evaluation: Without context
• Quantization: Applied during training and evaluation

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MNLP_M2_quantized_model", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("MNLP_M2_quantized_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)