youssefbelghmi/MNLP_M3_mcqa_model_true

MNLP M3 MCQA Model (Qwen3-0.6B fine-tuned)

This model is a fine-tuned version of Qwen/Qwen3-0.6B-Base on the MNLP M3 MCQA dataset, a large-scale collection of multiple-choice questions designed for evaluating and training models in STEM domains (science, math, engineering, medicine, etc.).

It was trained as part of the final milestone of the CS-552: Modern NLP course at EPFL (Spring 2025).

Task

Multiple-Choice Question Answering (MCQA): Given a question and four answer options (A–D), the model must complete the prompt with the correct option letter only (e.g., A, B, C, or D). It was trained with rationales during supervision but outputs only the letter during inference, making it compatible with evaluation frameworks such as LightEval.

Training Dataset

• Dataset: youssefbelghmi/MNLP_M3_mcqa_dataset.
• ~30,000 questions from SciQ, OpenBookQA, MathQA, ARC, and MedMCQA.
• Each sample includes in particular:
  • question,
  • four answer choices (A–D),
  • the correct answer as a letter,
  • a short explanation (support) to guide learning.

Training Setup

• Base model: Qwen/Qwen3-0.6B-Base.
• Method: Supervised Fine-Tuning (SFT) with trl and SFTTrainer.
• Tokenizer: AutoTokenizer (with eos_token used as padding).

Training Prompt Format

During fine-tuning, each training example is converted into a prompt-completion pair. The prompt includes both the question and an explanation to guide the model’s reasoning:

The following is a multiple-choice question (with answers) about knowledge and skills in advanced master's-level STEM fields.
You will be provided with an explanation to help you understand the correct answer.
Select the correct answer by replying with the option letter (A, B, C, or D) only.

Question: <question_text>
A. <option_A>
B. <option_B>
C. <option_C>
D. <option_D>
Explanation: <support_text>
Answer:

The completion is a single token: " A", " B", " C", or " D", corresponding to the correct answer.

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-5
• num_train_epochs: 1
• per_device_train_batch_size: 4
• per_device_eval_batch_size: 4
• gradient_accumulation_steps: 4
• gradient_checkpointing: true
• eval_strategy: steps
• eval_steps: 100
• logging_steps: 100

Training Results

Epoch	Training Loss	Validation Loss
0.08	0.3461	0.2748
0.15	0.2881	0.2666
0.23	0.2938	0.2661
0.31	0.2741	0.26
0.38	0.2684	0.257
0.46	0.2603	0.2539
0.54	0.2635	0.2441
0.61	0.2555	0.2457
0.69	0.2459	0.2414
0.77	0.2383	0.2353
0.84	0.2266	0.2337
0.92	0.2112	0.2338
0.99	0.211	0.2335

• Final training loss: 0.211
• Final validation accuracy: ~92.0%

Framework versions

• TRL: 0.17.0
• Transformers: 4.53.0.dev0
• Pytorch: 2.7.0
• Datasets: 3.2.0
• Tokenizers: 0.21.0

Citations

Cite TRL as:

@misc{vonwerra2022trl,
    title        = {{TRL: Transformer Reinforcement Learning}},
    author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
    year         = 2020,
    journal      = {GitHub repository},
    publisher    = {GitHub},
    howpublished = {\url{https://github.com/huggingface/trl}}
}

Author

Developed by Youssef Belghmi
CS-552: Modern NLP – EPFL, Spring 2025