Update README.md

README.md

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 # 🚀 EXAONE-4.0-1.2B-Quantized-MMLU Evaluation Results
 
-This repository contains the refined evaluation results for the **EXAONE-4.0-1.2B-GPTQ-W8A16** model using the MMLU benchmark.
+This repository contains the refined evaluation results for the **EXAONE-4.0-1.2B-GPTQ** model using the MMLU benchmark.
 
 ## 📊 Overview
 The evaluation was conducted to measure the model's multitask language understanding capabilities across 57 different subjects. To ensure stability and formatting consistency, we used a **5-shot** prompting approach.
 
 ### 💻 Hardware & Software
-- **Model:** [MangoLab/EXAONE-4.0-1.2B-GPTQ-W8A16](https://huggingface.co/MangoLab/EXAONE-4.0-1.2B-GPTQ-W8A16)
+- **Model:** [MangoLab/EXAONE-4.0-1.2B-GPTQ]
 - **Accelerator:** AMD Instinct MI325 OAM
 - **Quantization:** GPTQ (W8A16 / fp8_w8a8)
 - **Framework:** `lm-evaluation-harness`
@@ -38,7 +38,6 @@ The table below shows a summary of the evaluation environment. Detailed per-subj
 | :--- | :--- |
 | **Benchmark** | MMLU (Massive Multitask Language Understanding) |
 | **Prompting** | 5-shot |
-| **Dtype** | FP8 / W8A16 |
 | **Evaluation Date** | 2026-01-24 |
 
 ## 📂 Directory Structure
@@ -46,11 +45,3 @@ The table below shows a summary of the evaluation environment. Detailed per-subj
 - `raw_data/`: Original JSON output files from the evaluation process for reproducibility.
 
 ---
-## How to reproduce
-To run the same evaluation, use the following command with `lm-evaluation-harness`:
-```bash
-accelerate launch -m lm_eval --model hf \
-    --model_args pretrained=MangoLab/EXAONE-4.0-1.2B-GPTQ-W8A16,trust_remote_code=True \
-    --tasks mmlu \
-    --num_fewshot 5 \
-    --batch_size auto