Instructions to use youssefbelghmi/MNLP_M3_mcqa_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use youssefbelghmi/MNLP_M3_mcqa_model with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="youssefbelghmi/MNLP_M3_mcqa_model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("youssefbelghmi/MNLP_M3_mcqa_model")
model = AutoModelForCausalLM.from_pretrained("youssefbelghmi/MNLP_M3_mcqa_model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use youssefbelghmi/MNLP_M3_mcqa_model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "youssefbelghmi/MNLP_M3_mcqa_model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "youssefbelghmi/MNLP_M3_mcqa_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/youssefbelghmi/MNLP_M3_mcqa_model

SGLang

How to use youssefbelghmi/MNLP_M3_mcqa_model with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "youssefbelghmi/MNLP_M3_mcqa_model" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "youssefbelghmi/MNLP_M3_mcqa_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "youssefbelghmi/MNLP_M3_mcqa_model" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "youssefbelghmi/MNLP_M3_mcqa_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use youssefbelghmi/MNLP_M3_mcqa_model with Docker Model Runner:
```
docker model run hf.co/youssefbelghmi/MNLP_M3_mcqa_model
```

MNLP_M3_mcqa_model / README.md

youssefbelghmi

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	---
	base_model: tocico28/MNLP_M3_dpo_model
	datasets: youssefbelghmi/MNLP_M3_mcqa_dataset
	library_name: transformers
	model_name: MNLP_M3_dpo_mcqa_model
	tags:
	- generated_from_trainer
	- trl
	- sft
	licence: license
	---

	# MNLP M3 MCQA Model

	This model is a fine-tuned version of [tocico28/MNLP_M3_dpo_model](https://huggingface.co/tocico28/MNLP_M3_dpo_model) on the [youssefbelghmi/MNLP_M3_mcqa_dataset](https://huggingface.co/datasets/youssefbelghmi/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.).

	The [tocico28/MNLP_M3_dpo_model](https://huggingface.co/tocico28/MNLP_M3_dpo_model) is itself a fine-tuned version of Qwen/Qwen3-0.6B-Base using a dataset of preference-labeled STEM response pairs collected through a collaborative classroom annotation effort.

	It has been trained using [TRL](https://github.com/huggingface/trl) 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`](https://huggingface.co/datasets/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:

	```text
	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 \| 0.3363 \| 0.2766 \|
	\| 0.15 \| 0.2938 \| 0.2719 \|
	\| 0.23 \| 0.2817 \| 0.2751 \|
	\| 0.31 \| 0.2688 \| 0.2604 \|
	\| 0.38 \| 0.2692 \| 0.2640 \|
	\| 0.46 \| 0.2611 \| 0.2571 \|
	\| 0.54 \| 0.2431 \| 0.2433 \|
	\| 0.61 \| 0.2495 \| 0.2439 \|
	\| 0.69 \| 0.2489 \| 0.2384 \|
	\| 0.77 \| 0.2321 \| 0.2376 \|
	\| 0.84 \| 0.2363 \| 0.2353 \|
	\| 0.92 \| 0.2106 \| 0.2358 \|
	\| 0.99 \| 0.2091 \| 0.2340 \|
	- 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:

	```bibtex
	@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](https://huggingface.co/youssefbelghmi)
	CS-552: Modern NLP – EPFL, Spring 2025