Adding Evaluation Results

README.md

@@ -1,5 +1,100 @@
 ---
 license: llama3
+model-index:
+- name: ArmoRM-Llama3-8B-v0.1
+  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: 18.97
+      name: strict accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=RLHFlow/ArmoRM-Llama3-8B-v0.1
+      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: 1.75
+      name: normalized accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=RLHFlow/ArmoRM-Llama3-8B-v0.1
+      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: 0.0
+      name: exact match
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=RLHFlow/ArmoRM-Llama3-8B-v0.1
+      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: 0.0
+      name: acc_norm
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=RLHFlow/ArmoRM-Llama3-8B-v0.1
+      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: 6.65
+      name: acc_norm
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=RLHFlow/ArmoRM-Llama3-8B-v0.1
+      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: 0.87
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=RLHFlow/ArmoRM-Llama3-8B-v0.1
+      name: Open LLM Leaderboard
 ---
 
 # Absolute-Rating Multi-Objective Reward Model (ArmoRM) with Mixture-of-Experts (MoE) Aggregation of Reward Objectives
@@ -193,4 +288,17 @@ If you find this work useful for your research, please consider citing:
       booktitle={ACL},
 }
 ```
-The second entry, "[Arithmetic Control of LLMs for Diverse User Preferences: Directional Preference Alignment with Multi-Objective Rewards](https://arxiv.org/abs/2402.18571)", is another recent work of ours that trained a multi-objective reward model and adopted it for LLM alignment, which motivated us to develop the current work. 
+The second entry, "[Arithmetic Control of LLMs for Diverse User Preferences: Directional Preference Alignment with Multi-Objective Rewards](https://arxiv.org/abs/2402.18571)", is another recent work of ours that trained a multi-objective reward model and adopted it for LLM alignment, which motivated us to develop the current work. 
+# [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_RLHFlow__ArmoRM-Llama3-8B-v0.1)
+
+|      Metric       |Value|
+|-------------------|----:|
+|Avg.               | 4.71|
+|IFEval (0-Shot)    |18.97|
+|BBH (3-Shot)       | 1.75|
+|MATH Lvl 5 (4-Shot)| 0.00|
+|GPQA (0-shot)      | 0.00|
+|MuSR (0-shot)      | 6.65|
+|MMLU-PRO (5-shot)  | 0.87|
+