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

@@ -1,18 +1,113 @@
 ---
-base_model:
-- mistralai/Mistral-Nemo-Instruct-2407
 language:
 - ku
 - en
 license: apache-2.0
+library_name: transformers
 tags:
 - text-generation-inference
 - transformers
 - unsloth
 - mistral
+base_model:
+- mistralai/Mistral-Nemo-Instruct-2407
 datasets:
 - nazimali/kurdish-wikipedia-articles
-library_name: transformers
+model-index:
+- name: Mistral-Nemo-Kurdish
+  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: 34.01
+      name: strict accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nazimali/Mistral-Nemo-Kurdish
+      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: 29.86
+      name: normalized accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nazimali/Mistral-Nemo-Kurdish
+      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: 8.23
+      name: exact match
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nazimali/Mistral-Nemo-Kurdish
+      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: 6.82
+      name: acc_norm
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nazimali/Mistral-Nemo-Kurdish
+      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: 11.78
+      name: acc_norm
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nazimali/Mistral-Nemo-Kurdish
+      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: 24.83
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nazimali/Mistral-Nemo-Kurdish
+      name: Open LLM Leaderboard
 ---
 
 Continued pre-training on `mistralai/Mistral-Nemo-Instruct-2407` using the Kurdish wiki dataset with `unsloth`.
@@ -66,4 +161,17 @@ training_prompt = """Gotara Wikipedia
 ### Sernav: {}
 
 ### Gotar:
-{}"""
+{}"""
+# [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_nazimali__Mistral-Nemo-Kurdish)
+
+|      Metric       |Value|
+|-------------------|----:|
+|Avg.               |19.26|
+|IFEval (0-Shot)    |34.01|
+|BBH (3-Shot)       |29.86|
+|MATH Lvl 5 (4-Shot)| 8.23|
+|GPQA (0-shot)      | 6.82|
+|MuSR (0-shot)      |11.78|
+|MMLU-PRO (5-shot)  |24.83|
+