martinctl/MNLP_M2_quantized_model

MNLPM2quantized_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

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)