MNLP_M2_quantized_model – AI Model by martinctl | AlphaNeural AI

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martinctl/
MNLP_M2_quantized_model

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multiple-choice-qa

question-answering

text-generation

custom-mcqa-dataset

Qwen/Qwen3-0.6B-Base

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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

python

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)