Update README.md

README.md

@@ -25,7 +25,7 @@ language:
 - **Model Developers:** Neural Magic
 
 Quantized version of [Meta-Llama-3.1-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct).
-It achieves an average score of 82.78 on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark (version 1), whereas the unquantized model achieves 82.74.
+It achieves an average score of 83.41 on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark (version 1), whereas the unquantized model achieves 83.61.
 
 ### Model Optimizations
 
@@ -130,8 +130,8 @@ oneshot(
 
 ## Evaluation
 
-The model was evaluated on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) leaderboard tasks (version 1) with the [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) and the [vLLM](https://docs.vllm.ai/en/stable/) engine, using the following command:
-A modified version of ARC-C was used for evaluations, in line with Llama 3.1's prompting.
+The model was evaluated on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) leaderboard tasks (version 1) with the [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) and the [vLLM](https://docs.vllm.ai/en/stable/) engine, using the following command.
+A modified version of ARC-C and GSM8k-cot was used for evaluations, in line with Llama 3.1's prompting. It can be accessed on the [Neural Magic fork of the lm-evaluation-harness](https://github.com/neuralmagic/lm-evaluation-harness/tree/llama_3.1_instruct).
 ```
 lm_eval \
   --model vllm \
@@ -177,11 +177,11 @@ lm_eval \
   <tr>
    <td>GSM-8K (5-shot, strict-match)
    </td>
-   <td>87.95
+   <td>93.18
    </td>
-   <td>88.40
+   <td>92.19
    </td>
-   <td>100.5%
+   <td>98.94%
    </td>
   </tr>
   <tr>
@@ -217,11 +217,11 @@ lm_eval \
   <tr>
    <td><strong>Average</strong>
    </td>
-   <td><strong>82.74</strong>
+   <td><strong>83.61</strong>
    </td>
-   <td><strong>82.78</strong>
+   <td><strong>83.41</strong>
    </td>
-   <td><strong>100.0%</strong>
+   <td><strong>99.76%</strong>
    </td>
   </tr>
 </table>