Adding Evaluation Results

This is an automated PR created with https://huggingface.co/spaces/Weyaxi/open-llm-leaderboard-results-pr

The purpose of this PR is to add evaluation results from the Open LLM Leaderboard to your model card.

If you encounter any issues, please report them to https://huggingface.co/spaces/Weyaxi/open-llm-leaderboard-results-pr/discussions

README.md

@@ -1,15 +1,118 @@
 ---
-license: apache-2.0
-base_model:
-- mistralai/Mistral-7B-v0.1
-datasets:
-- nvidia/OpenMathInstruct-1
 language:
 - en
+license: apache-2.0
 tags:
 - nvidia
 - code
 - math
+base_model:
+- mistralai/Mistral-7B-v0.1
+datasets:
+- nvidia/OpenMathInstruct-1
+model-index:
+- name: OpenMath-Mistral-7B-v0.1-hf
+  results:
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: AI2 Reasoning Challenge (25-Shot)
+      type: ai2_arc
+      config: ARC-Challenge
+      split: test
+      args:
+        num_few_shot: 25
+    metrics:
+    - type: acc_norm
+      value: 59.39
+      name: normalized accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=nvidia/OpenMath-Mistral-7B-v0.1-hf
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: HellaSwag (10-Shot)
+      type: hellaswag
+      split: validation
+      args:
+        num_few_shot: 10
+    metrics:
+    - type: acc_norm
+      value: 81.78
+      name: normalized accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=nvidia/OpenMath-Mistral-7B-v0.1-hf
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: MMLU (5-Shot)
+      type: cais/mmlu
+      config: all
+      split: test
+      args:
+        num_few_shot: 5
+    metrics:
+    - type: acc
+      value: 59.34
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=nvidia/OpenMath-Mistral-7B-v0.1-hf
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: TruthfulQA (0-shot)
+      type: truthful_qa
+      config: multiple_choice
+      split: validation
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: mc2
+      value: 46.13
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=nvidia/OpenMath-Mistral-7B-v0.1-hf
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: Winogrande (5-shot)
+      type: winogrande
+      config: winogrande_xl
+      split: validation
+      args:
+        num_few_shot: 5
+    metrics:
+    - type: acc
+      value: 77.27
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=nvidia/OpenMath-Mistral-7B-v0.1-hf
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: GSM8k (5-shot)
+      type: gsm8k
+      config: main
+      split: test
+      args:
+        num_few_shot: 5
+    metrics:
+    - type: acc
+      value: 0.08
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=nvidia/OpenMath-Mistral-7B-v0.1-hf
+      name: Open LLM Leaderboard
 ---
 
 
@@ -118,4 +221,17 @@ If you find our work useful, please consider citing us!
   year    = {2024},
   journal = {arXiv preprint arXiv: Arxiv-2402.10176}
 }
-```
+```
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_nvidia__OpenMath-Mistral-7B-v0.1-hf)
+
+|             Metric              |Value|
+|---------------------------------|----:|
+|Avg.                             |54.00|
+|AI2 Reasoning Challenge (25-Shot)|59.39|
+|HellaSwag (10-Shot)              |81.78|
+|MMLU (5-Shot)                    |59.34|
+|TruthfulQA (0-shot)              |46.13|
+|Winogrande (5-shot)              |77.27|
+|GSM8k (5-shot)                   | 0.08|
+