This model is a quantized and QLoRA-fine-tuned version of the base albertfares/MNLP_SFT_DPO model. It is trained on curated stabilization data for multiple-choice question answering (MCQA) using LoRA adapters over 4-bit weights and 16-bit activations (W4A16).

It was developed as part of the CS-552 Multilingual NLP course at EPFL and is hosted for reproducible evaluation and downstream use.

Model Details

Model Description

This model adapts the MNLP_SFT_DPO model to handle complex MCQA reasoning using QLoRA (4-bit weights, 16-bit activations). It was trained using the quantized dataset abdou-u/MNLP_M3_quantized_dataset and aims to strike a strong balance between memory efficiency and downstream accuracy.

Developed by: Ahmed Abdelmalek
Finetuned from model: albertfares/MNLP_SFT_DPO
Model type: Causal Language Model (decoder-only, autoregressive)
Language(s): English
License: Apache 2.0

Model Sources

Training Code: Private GitHub Repository
Datasets: abdou-u/MNLP_M3_quantized_dataset
Base Model: albertfares/MNLP_SFT_DPO

Uses

Direct Use

This model can be directly used for answering multiple-choice questions (MCQA) in English with a short explanation output.

Downstream Use

Can be used in LLM pipelines requiring lightweight MCQA reasoning models with high accuracy and low VRAM cost.

Out-of-Scope Use

Not intended for generative open-ended long-form answers or other modalities beyond multiple-choice QA.

Bias, Risks, and Limitations

The model inherits biases from both the base DPO model and the MCQA dataset. It may underperform on non-English inputs or ambiguous multi-answer tasks.

Recommendations

Use as part of a controlled QA system with additional verification modules. Do not use in high-stakes decision-making without human oversight.

How to Get Started with the Model

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("abdou-u/MNLP_M3_quantized_dpo_mcqa_model")
tokenizer = AutoTokenizer.from_pretrained("abdou-u/MNLP_M3_quantized_dpo_mcqa_model")

Training Details

Training Data

This model was fine-tuned using the abdou-u/MNLP_M3_quantized_dataset, a mix of formatted MCQA questions from TheoremQA, AQuA, and synthetic examples with explanations.

Training Procedure

The model was fine-tuned using QLoRA with:

4-bit NF4 quantization (W4A16)
r=16, alpha=32, and dropout=0.05
1–2 epochs on the quantized dataset

Training Hyperparameters

Precision: FP16 with QLoRA (W4A16)
Epochs: 1–2
Batch size: 8 (gradient accumulation: 4)
LR: 2e-5

Evaluation

Testing Data

The model was evaluated on a diverse set of MCQA tasks:

MMLU (16 subjects including Math, Physics, Bio, CS)
NLP4Education

Tasks were tested under:

Zero-shot settings
Few-shot settings (2-shot context)

Metrics

Accuracy (for multiple-choice selection)
Log-likelihood ranking (optional)

Results

Strong zero-shot and few-shot MCQA performance on MMLU benchmarks
Robust to reasoning under minimal context

Environmental Impact

Hardware Type: NVIDIA A100 80GB x2
Hours Used: ~0.5–1h
Cloud Provider: EPFL RCP
Region: Switzerland
Carbon Emitted: Estimated < 0.5 kg CO2

Technical Specifications

Model Architecture

Quantized transformer decoder using QLoRA over the DPO-finetuned SFT model.

Compute Infrastructure

Hardware: 2x A100 80GB
Software: PyTorch, Transformers, PEFT, Datasets, Huggingface Hub

Citation

APA: Ahmed Abdelmalek. (2025). MNLP_M3_quantized_dpo_mcqa_model [Computer software]. Hugging Face.

BibTeX: @misc{abdelmalek2025quantizeddpo, author = {Ahmed Abdelmalek}, title = {MNLP_M3_quantized_dpo_mcqa_model}, year = {2025}, publisher = {Hugging Face}, howpublished = {\url{https://huggingface.co/abdou-u/MNLP_M3_quantized_dpo_mcqa_model}} }

Model Card Contact

For questions, contact: ahmed.abdelmalek@epfl.ch