Adding Evaluation Results

README.md

@@ -1,5 +1,4 @@
 ---
-license: llama3.1
 language:
 - de
 - en
@@ -9,8 +8,104 @@ language:
 - es
 - ar
 - nl
+license: llama3.1
 tags:
 - spectrum
+model-index:
+- name: Llama-3.1-SauerkrautLM-70b-Instruct
+  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: 86.56
+      name: strict accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=VAGOsolutions/Llama-3.1-SauerkrautLM-70b-Instruct
+      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: 57.24
+      name: normalized accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=VAGOsolutions/Llama-3.1-SauerkrautLM-70b-Instruct
+      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: 29.91
+      name: exact match
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=VAGOsolutions/Llama-3.1-SauerkrautLM-70b-Instruct
+      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: 12.19
+      name: acc_norm
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=VAGOsolutions/Llama-3.1-SauerkrautLM-70b-Instruct
+      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: 19.39
+      name: acc_norm
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=VAGOsolutions/Llama-3.1-SauerkrautLM-70b-Instruct
+      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: 48.17
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=VAGOsolutions/Llama-3.1-SauerkrautLM-70b-Instruct
+      name: Open LLM Leaderboard
 ---
 
 ![Llama-3.1-SauerkrautLM-70b-Instruct]( https://vago-solutions.ai/wp-content/uploads/2024/08/Llama3.1-SauerkrautLM-70b-Instruct2.png "Llama-3.1-SauerkrautLM-70b-Instruct")
@@ -118,4 +213,17 @@ If you are interested in customized LLMs for business applications, please get i
 We are also keenly seeking support and investment for our startup, VAGO solutions where we continuously advance the development of robust language models designed to address a diverse range of purposes and requirements. If the prospect of collaboratively navigating future challenges excites you, we warmly invite you to reach out to us at [VAGO solutions](https://vago-solutions.ai)
 
 ## Acknowledgement
-Many thanks to [meta-llama](https://huggingface.co/meta-llama) for providing such a valuable model to the Open-Source community.
+Many thanks to [meta-llama](https://huggingface.co/meta-llama) for providing such a valuable model to the Open-Source community.
+# [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_VAGOsolutions__Llama-3.1-SauerkrautLM-70b-Instruct)
+
+|      Metric       |Value|
+|-------------------|----:|
+|Avg.               |42.24|
+|IFEval (0-Shot)    |86.56|
+|BBH (3-Shot)       |57.24|
+|MATH Lvl 5 (4-Shot)|29.91|
+|GPQA (0-shot)      |12.19|
+|MuSR (0-shot)      |19.39|
+|MMLU-PRO (5-shot)  |48.17|
+