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+# Quantized MCQA Model – W8A8
+
+## Model Summary
+This model is a quantized version of our MCQA model. It was quantized using post-training quantization (PTQ), targeting both weights and activations (W8A8) using the [LLMCompressor](https://github.com/vllm-project/llm-compressor) framework.
+
+## Technical Details
+- **Base model:** [`hssawhney/mnlp-model`](https://huggingface.co/hssawhney/mnlp-model)
+- **Quantization method:** SmoothQuant + GPTQ
+- **Precision:** BF16 (activations) + INT8 (weights)
+- **Calibration data:** 512 samples from [`zay25/quantization-dataset`](https://huggingface.co/datasets/zay25/quantization-dataset)
+- **Excluded layers:** `lm_head` (to preserve output logits)
+- **Final model size:** ~717 MB
+
+## Evaluation
+The quantized model was evaluated on the full MCQA demo dataset using the LightEval framework. Performance dropped with only a **0.02 decrease in accuracy** compared to the full-precision (FP32) version.
+
+## Intended Use
+This model is optimized for **efficient inference** in **multiple-choice question answering** tasks, particularly in the context of **STEM tutoring**. It is well-suited for low-resource deployment environments where latency and memory usage are critical.