Instructions to use abdou-u/MNLP_M3_quantized_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use abdou-u/MNLP_M3_quantized_model with Transformers:

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

pipe = pipeline("text-generation", model="abdou-u/MNLP_M3_quantized_model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("abdou-u/MNLP_M3_quantized_model")
model = AutoModelForCausalLM.from_pretrained("abdou-u/MNLP_M3_quantized_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 abdou-u/MNLP_M3_quantized_model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "abdou-u/MNLP_M3_quantized_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": "abdou-u/MNLP_M3_quantized_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/abdou-u/MNLP_M3_quantized_model

SGLang

How to use abdou-u/MNLP_M3_quantized_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 "abdou-u/MNLP_M3_quantized_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": "abdou-u/MNLP_M3_quantized_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 "abdou-u/MNLP_M3_quantized_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": "abdou-u/MNLP_M3_quantized_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use abdou-u/MNLP_M3_quantized_model with Docker Model Runner:
```
docker model run hf.co/abdou-u/MNLP_M3_quantized_model
```

Model Card for `abdou-u/MNLP_M3_quantized_model`

This model is a quantized version of the MCQA model trained on multiple-choice question answering tasks. It uses QLoRA with W4A16 (4-bit weights, 16-bit activations) to minimize memory usage while maintaining high accuracy. The model is fine-tuned on a carefully selected stabilization subset from the MCQA dataset.

Model Details

Model Description

Developed by: Ahmed Abdelmalek (EPFL CS-552 Project)
Model type: Causal Language Model (Transformer-based)
Language(s): English
License: Apache 2.0 (inherited from base models)
Fine-tuned from: mgatti/MNLP_M3_mcqa_model
Quantization: QLoRA (W4A16), using 4-bit NF4 weights and bfloat16 activations with LoRA adapters merged post-training.

Model Sources

Repository: Private GitHub repository (training code)
Model Hub: abdou-u/MNLP_M3_quantized_model

Uses

Direct Use

This model can be used for inference on multiple-choice question answering tasks, especially when deploying in resource-constrained environments (e.g., A100, T4, or consumer GPUs).

Out-of-Scope Use

Not intended for open-ended generation.
Not suitable for dialogue applications.

Bias, Risks, and Limitations

Biases may be present from the original datasets.
Not suitable for real-world high-stakes decision making.

How to Get Started

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("abdou-u/MNLP_M3_quantized_model")
tokenizer = AutoTokenizer.from_pretrained("abdou-u/MNLP_M3_quantized_model")

Training Details

Training Data

The model was fine-tuned on a 15% stabilization subset that is abdou-u/MNLP_M3_quantized_dataset, a harmonized MCQA-style dataset consisting of curated subsets from MMLU, AQuA, and TheoremQA.

Training Procedure

Quantized with QLoRA W4A16 (NF4 weights, bfloat16 activations)
Trained for 1 epoch
Batch size: 8 (with gradient accumulation = 4)
LoRA adapters merged post-training

Hyperparameters

learning_rate = 2e-5
num_train_epochs = 1
fp16 = True
lora_alpha = 32
r = 16
lora_dropout = 0.05

Evaluation

Fine-tuned model evaluated on internal stabilization subset using accuracy and F1 score (details in final report).

Environmental Impact

Hardware Type: A100 (80GB)
Training Duration: ~20 minutes
Compute Region: Europe (EPFL cluster)
Estimated CO₂ emissions: < 0.1 kg

Technical Specifications

Framework: PyTorch (Transformers, PEFT)
Quantization: BitsAndBytes (4-bit NF4), merged LoRA adapters

Citation

APA: Ahmed Abdelmalek. (2025). MNLP_M3_quantized_model (QLoRA W4A16 MCQA). Hugging Face.

Model Card Contact

Ahmed Abdelmalek — [ahmed.abdelmalek@epfl.ch]

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Safetensors

Model size

0.6B params

Tensor type

F32

F16

Model Card for abdou-u/MNLP_M3_quantized_model