Update README.md

README.md

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-This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-- Code: [More Information Needed]
-- Paper: [More Information Needed]
-- Docs: [More Information Needed]
+# Model Card for `abdou-u/MNLP_M3_w4a8_quantized_mcqa_model`
+
+## Summary
+
+This model is a W4A8 (4-bit weights, 8-bit activations) quantized version of the `mgatti/MNLP_M3_mcqa_model`, obtained using [Optimum-Quanto](https://huggingface.co/docs/optimum/main/en/quanto/index). It has been pushed to the Hugging Face Hub using the `PyTorchModelHubMixin` interface.
+
+## Model Details
+
+### Model Description
+
+- **Name**: MNLP_M3_w4a8_quantized_mcqa_model
+- **Source model**: `mgatti/MNLP_M3_mcqa_model`
+- **Quantization**: Optimum-Quanto W4A8 (qint4 weights, qint8 activations)
+- **Usage**: Efficient inference for multiple-choice question answering (MCQA) tasks
+- **Developer**: Ahmed Abdelmalek, EPFL CS-552 2025 Project M3
+- **License**: MIT
+- **Language(s)**: English
+- **Hardware target**: Consumer and cloud GPUs with low memory footprint
+
+### Model Sources
+
+- **Repository**: *Private GitHub (Training script not public)*
+- **Paper**: Not published
+- **Docs**: This README
+
+## Use Cases
+
+### Direct Use
+
+This model is optimized for fast inference in MCQA tasks under constrained VRAM settings.
+
+### Intended Users
+
+Researchers and engineers looking to deploy a small, high-performance MCQA model.
+
+## Limitations
+
+This model is quantized and may have a slight performance drop compared to full-precision models. It is not suitable for generation or tasks beyond MCQA.
+
+## Getting Started
+
+```python
+from transformers import AutoTokenizer
+from optimum.quanto.models import QuantizedModelForCausalLM
+
+model = QuantizedModelForCausalLM.from_pretrained("abdou-u/MNLP_M3_w4a8_quantized_mcqa_model")
+tokenizer = AutoTokenizer.from_pretrained("abdou-u/MNLP_M3_w4a8_quantized_mcqa_model")
+```
+
+## Technical Specifications
+
+- **Quantization library**: Optimum-Quanto
+- **Weights**: 4-bit (qint4)
+- **Activations**: 8-bit (qint8)
+- **Format**: Hugging Face Transformers-compatible
+
+## Environmental Impact
+
+- **Hardware**: A100 80GB (used during validation)
+- **Quantization**: 1 pass, full model (approx. 3 mins)
+- **Carbon Emissions**: Negligible for quantization
+
+## Citation
+
+If you use this model, please cite:
+
+```
+@misc{abdelmalek2025mnlp,
+  title={MNLP M3 Quantized MCQA Model (W4A8)},
+  author={Ahmed Abdelmalek},
+  year={2025},
+  howpublished={\url{https://huggingface.co/abdou-u/MNLP_M3_w4a8_quantized_mcqa_model}},
+  note={CS-552 Project M3}
+}
+```
+
+## Contact
+
+Ahmed Abdelmalek - ahmed.abdelmalek@epfl.ch