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

@@ -1,5 +1,100 @@
 ---
 license: llama3
+model-index:
+- name: Llama-3-8B-ProLong-64k-Base
+  results:
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: IFEval (0-Shot)
+      type: HuggingFaceH4/ifeval
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: inst_level_strict_acc and prompt_level_strict_acc
+      value: 12.49
+      name: strict accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=princeton-nlp/Llama-3-8B-ProLong-64k-Base
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: BBH (3-Shot)
+      type: BBH
+      args:
+        num_few_shot: 3
+    metrics:
+    - type: acc_norm
+      value: 25.02
+      name: normalized accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=princeton-nlp/Llama-3-8B-ProLong-64k-Base
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: MATH Lvl 5 (4-Shot)
+      type: hendrycks/competition_math
+      args:
+        num_few_shot: 4
+    metrics:
+    - type: exact_match
+      value: 5.82
+      name: exact match
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=princeton-nlp/Llama-3-8B-ProLong-64k-Base
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: GPQA (0-shot)
+      type: Idavidrein/gpqa
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: acc_norm
+      value: 4.81
+      name: acc_norm
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=princeton-nlp/Llama-3-8B-ProLong-64k-Base
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: MuSR (0-shot)
+      type: TAUR-Lab/MuSR
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: acc_norm
+      value: 9.1
+      name: acc_norm
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=princeton-nlp/Llama-3-8B-ProLong-64k-Base
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: MMLU-PRO (5-shot)
+      type: TIGER-Lab/MMLU-Pro
+      config: main
+      split: test
+      args:
+        num_few_shot: 5
+    metrics:
+    - type: acc
+      value: 25.4
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=princeton-nlp/Llama-3-8B-ProLong-64k-Base
+      name: Open LLM Leaderboard
 ---
 # princeton_nlp/Llama-3-8B-ProLong-64k-Base
 
@@ -220,4 +315,17 @@ We conduct supervised fine-tuning (SFT) on our base long-context model. In our p
 | Scheduling | 5% warmup, cosine decay till 10% peak learning rate |
 | Total #tokens | 1B |
 
-- Synthetic data: we also experiment with several strategies to generate long, synthetic chat data, but they have not yet helped to improve upon our UltraChat-fine-tuned chat models. The synthetic data strategies we tried include (1) using a paragraph of a long book/repo to generate question-answer pairs; (2) using hierarchical methods to summarize a long book; (3) turning the previous synthetic long QA data into a RAG format.
+- Synthetic data: we also experiment with several strategies to generate long, synthetic chat data, but they have not yet helped to improve upon our UltraChat-fine-tuned chat models. The synthetic data strategies we tried include (1) using a paragraph of a long book/repo to generate question-answer pairs; (2) using hierarchical methods to summarize a long book; (3) turning the previous synthetic long QA data into a RAG format.
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_princeton-nlp__Llama-3-8B-ProLong-64k-Base)
+
+|      Metric       |Value|
+|-------------------|----:|
+|Avg.               |13.77|
+|IFEval (0-Shot)    |12.49|
+|BBH (3-Shot)       |25.02|
+|MATH Lvl 5 (4-Shot)| 5.82|
+|GPQA (0-shot)      | 4.81|
+|MuSR (0-shot)      | 9.10|
+|MMLU-PRO (5-shot)  |25.40|
+