Update README.md

README.md

@@ -1,6 +1,13 @@
 ---
 language:
 - en
+- de
+- fr
+- it
+- pt
+- hi
+- es
+- th
 pipeline_tag: text-generation
 license: llama3.1
 ---
@@ -14,15 +21,15 @@ license: llama3.1
 - **Model Optimizations:**
   - **Activation quantization:** INT8
   - **Weight quantization:** INT8
-- **Intended Use Cases:** Intended for commercial and research use in English. Similarly to [Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct), this models is intended for assistant-like chat.
-- **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English.
+- **Intended Use Cases:** Intended for commercial and research use multiple languages. Similarly to [Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct), this models is intended for assistant-like chat.
+- **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws).
 - **Release Date:** 7/11/2024
 - **Version:** 1.0
-- **License(s):** [Llama3](https://llama.meta.com/llama3/license/)
+- **License(s):** [Llama3.1]
 - **Model Developers:** Neural Magic
 
 Quantized version of [Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct).
-It achieves an average score of 69.27 on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark (version 1), whereas the unquantized model achieves 69.33.
+It achieves scores within 1.3% of the scores of the unquantized model for MMLU, ARC-Challenge, GSM-8k, Hellaswag, Winogrande and TruthfulQA.
 
 ### Model Optimizations
 
@@ -120,14 +127,9 @@ model.save_pretrained("Meta-Llama-3.1-8B-Instruct-quantized.w8a8")
 
 ## 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/tree/383bbd54bc621086e05aa1b030d8d4d5635b25e6) (commit 383bbd54bc621086e05aa1b030d8d4d5635b25e6) and the [vLLM](https://docs.vllm.ai/en/stable/) engine, using the following command:
-```
-lm_eval \
-  --model vllm \
-  --model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",dtype=auto,gpu_memory_utilization=0.4,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \
-  --tasks openllm \
-  --batch_size auto
-```
+The model was evaluated on MMLU, ARC-Challenge, GSM-8K, Hellaswag, Winogrande and TruthfulQA.
+Evaluation was conducted using the Neural Magic fork of [lm-evaluation-harness](https://github.com/neuralmagic/lm-evaluation-harness/tree/llama_3.1_instruct) (branch llama_3.1_instruct) and the [vLLM](https://docs.vllm.ai/en/stable/) engine.
+This version of the lm-evaluation-harness includes versions of ARC-Challenge and GSM-8K that match the prompting style of [Meta-Llama-3.1-Instruct-evals](https://huggingface.co/datasets/meta-llama/Meta-Llama-3.1-8B-Instruct-evals).
 
 ### Accuracy
 
@@ -156,21 +158,21 @@ lm_eval \
   <tr>
    <td>ARC Challenge (25-shot)
    </td>
-   <td>62.63
+   <td>83.19
    </td>
-   <td>62.20
+   <td>82.08
    </td>
-   <td>99.5%
+   <td>98.7%
    </td>
   </tr>
   <tr>
    <td>GSM-8K (5-shot, strict-match)
    </td>
-   <td>75.66
+   <td>82.79
    </td>
-   <td>76.57
+   <td>81.96
    </td>
-   <td>101.2%
+   <td>99.0%
    </td>
   </tr>
   <tr>
@@ -206,11 +208,11 @@ lm_eval \
   <tr>
    <td><strong>Average</strong>
    </td>
-   <td><strong>69.33</strong>
+   <td><strong>74.31</strong>
    </td>
-   <td><strong>69.27</strong>
+   <td><strong>73.79</strong>
    </td>
-   <td><strong>99.9%</strong>
+   <td><strong>99.3%</strong>
    </td>
   </tr>
 </table>