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

@@ -1,7 +1,8 @@
 ---
-library_name: transformers
 language:
 - en
+license: mit
+library_name: transformers
 inference:
   parameters:
     max_new_tokens: 64
@@ -16,40 +17,136 @@ widget:
   example_title: El Microondas
 - text: Kennesaw State University is a public
   example_title: Kennesaw State University
-- text: >-
-    Bungie Studios is an American video game developer. They are most famous for
-    developing the award winning Halo series of video games. They also made
-    Destiny. The studio was founded
+- text: Bungie Studios is an American video game developer. They are most famous for
+    developing the award winning Halo series of video games. They also made Destiny.
+    The studio was founded
   example_title: Bungie
 - text: The Mona Lisa is a world-renowned painting created by
   example_title: Mona Lisa
-- text: >-
-    The Harry Potter series, written by J.K. Rowling, begins with the book
-    titled
+- text: The Harry Potter series, written by J.K. Rowling, begins with the book titled
   example_title: Harry Potter Series
-- text: >-
-    Question: I have cities, but no houses. I have mountains, but no trees. I
+- text: 'Question: I have cities, but no houses. I have mountains, but no trees. I
     have water, but no fish. What am I?
 
-    Answer:
+    Answer:'
   example_title: Riddle
 - text: The process of photosynthesis involves the conversion of
   example_title: Photosynthesis
-- text: >-
-    Jane went to the store to buy some groceries. She picked up apples, oranges,
+- text: Jane went to the store to buy some groceries. She picked up apples, oranges,
     and a loaf of bread. When she got home, she realized she forgot
   example_title: Story Continuation
-- text: >-
-    Problem 2: If a train leaves Station A at 9:00 AM and travels at 60 mph, and
-    another train leaves Station B at 10:00 AM and travels at 80 mph, when will
+- text: 'Problem 2: If a train leaves Station A at 9:00 AM and travels at 60 mph,
+    and another train leaves Station B at 10:00 AM and travels at 80 mph, when will
     they meet if the distance between the stations is 300 miles?
 
-    To determine
+    To determine'
   example_title: Math Problem
 - text: In the context of computer programming, an algorithm is
   example_title: Algorithm Definition
 pipeline_tag: text-generation
-license: mit
+model-index:
+- name: nano-phi-115M-v0.1
+  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: 21.93
+      name: normalized accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kenhktsui/nano-phi-115M-v0.1
+      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: 27.86
+      name: normalized accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kenhktsui/nano-phi-115M-v0.1
+      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: 25.34
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kenhktsui/nano-phi-115M-v0.1
+      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: 46.0
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kenhktsui/nano-phi-115M-v0.1
+      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: 50.83
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kenhktsui/nano-phi-115M-v0.1
+      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: 0.0
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kenhktsui/nano-phi-115M-v0.1
+      name: Open LLM Leaderboard
 ---
 
 
@@ -460,4 +557,17 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 ## Model Card Contact
 
-[More Information Needed]
+[More Information Needed]
+# [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_kenhktsui__nano-phi-115M-v0.1)
+
+|             Metric              |Value|
+|---------------------------------|----:|
+|Avg.                             |28.66|
+|AI2 Reasoning Challenge (25-Shot)|21.93|
+|HellaSwag (10-Shot)              |27.86|
+|MMLU (5-Shot)                    |25.34|
+|TruthfulQA (0-shot)              |46.00|
+|Winogrande (5-shot)              |50.83|
+|GSM8k (5-shot)                   | 0.00|
+