Adding Evaluation Results

README.md

@@ -1,13 +1,12 @@
 ---
-license: apache-2.0
 language:
 - en
 - de
 - fr
 - it
 - es
+license: apache-2.0
 library_name: transformers
-pipeline_tag: text-generation
 tags:
 - mistral
 - finetune
@@ -19,6 +18,110 @@ tags:
 - moe
 datasets:
 - argilla/distilabel-math-preference-dpo
+pipeline_tag: text-generation
+model-index:
+- name: SauerkrautLM-Mixtral-8x7B-Instruct
+  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: 70.48
+      name: normalized accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=VAGOsolutions/SauerkrautLM-Mixtral-8x7B-Instruct
+      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: 87.75
+      name: normalized accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=VAGOsolutions/SauerkrautLM-Mixtral-8x7B-Instruct
+      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: 71.37
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=VAGOsolutions/SauerkrautLM-Mixtral-8x7B-Instruct
+      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: 65.71
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=VAGOsolutions/SauerkrautLM-Mixtral-8x7B-Instruct
+      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: 81.22
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=VAGOsolutions/SauerkrautLM-Mixtral-8x7B-Instruct
+      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: 60.8
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=VAGOsolutions/SauerkrautLM-Mixtral-8x7B-Instruct
+      name: Open LLM Leaderboard
 ---
 
 ![SauerkrautLM](https://vago-solutions.de/wp-content/uploads/2023/12/Sauerkraut_Instruct_MoE_Instruct.png "SauerkrautLM-Mixtral-8x7B")
@@ -103,4 +206,17 @@ 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.
 
 ## Acknowledgement
-Many thanks to [argilla](https://huggingface.co/datasets/argilla) and [Huggingface](https://huggingface.co) for providing such valuable datasets to the Open-Source community. And of course a big thanks to MistralAI for providing the open source community with their latest technology!
+Many thanks to [argilla](https://huggingface.co/datasets/argilla) and [Huggingface](https://huggingface.co) for providing such valuable datasets to the Open-Source community. And of course a big thanks to MistralAI for providing the open source community with their latest technology!
+# [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_VAGOsolutions__SauerkrautLM-Mixtral-8x7B-Instruct)
+
+|             Metric              |Value|
+|---------------------------------|----:|
+|Avg.                             |72.89|
+|AI2 Reasoning Challenge (25-Shot)|70.48|
+|HellaSwag (10-Shot)              |87.75|
+|MMLU (5-Shot)                    |71.37|
+|TruthfulQA (0-shot)              |65.71|
+|Winogrande (5-shot)              |81.22|
+|GSM8k (5-shot)                   |60.80|
+