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

@@ -1,11 +1,114 @@
 ---
+language:
+- en
 license: apache-2.0
 tags:
 - merge
 - abideen/DareVox-7B
 - udkai/Garrulus
-language:
-- en
+model-index:
+- name: NexoNimbus-7B
+  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: 70.82
+      name: normalized accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abideen/NexoNimbus-7B
+      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: 87.86
+      name: normalized accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abideen/NexoNimbus-7B
+      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: 64.69
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abideen/NexoNimbus-7B
+      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: 62.43
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abideen/NexoNimbus-7B
+      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: 84.85
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abideen/NexoNimbus-7B
+      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: 70.36
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abideen/NexoNimbus-7B
+      name: Open LLM Leaderboard
 ---
 
 # NexoNimbus-7B
@@ -96,4 +199,17 @@ print(outputs[0]["generated_text"])
 "Machine learning is a subfield of artificial intelligence that focuses on developing algorithms and models that allow computers to learn and improve their performance over time, without being explicitly programmed. It involves the use of statistical techniques and data analysis to identify patterns and make predictions based on input data.
 In machine learning, data is fed into a model, which then adjusts its internal parameters to minimize the difference between the predicted output and the actual output. This process is called training, and as the model is exposed to more data, it becomes better at making predictions or classifications.
 Machine learning can be divided into several categories, including supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves using labeled data, where the desired output is known, and the model learns to map inputs to outputs. Unsupervised learning, on the other hand, does not have a predefined output, and the model learns to identify patterns or relationships within the data. Reinforcement learning involves learning through trial and error, with the model receiving feedback in the form of rewards or penalties based on its actions.
-Some common applications of machine learning include image recognition, natural language processing, recommendation systems, fraud detection, and self-driving."
+Some common applications of machine learning include image recognition, natural language processing, recommendation systems, fraud detection, and self-driving."
+# [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_abideen__NexoNimbus-7B)
+
+|             Metric              |Value|
+|---------------------------------|----:|
+|Avg.                             |73.50|
+|AI2 Reasoning Challenge (25-Shot)|70.82|
+|HellaSwag (10-Shot)              |87.86|
+|MMLU (5-Shot)                    |64.69|
+|TruthfulQA (0-shot)              |62.43|
+|Winogrande (5-shot)              |84.85|
+|GSM8k (5-shot)                   |70.36|
+