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 @@
 ---
 license: other
-license_name: yi-license
-license_link: LICENSE
 tags:
 - lora
 - qlora
 - adapter
+license_name: yi-license
+license_link: LICENSE
+model-index:
+- name: Yi-34b-200K-rawrr-v2-run-0902-LoRA
+  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: 64.68
+      name: normalized accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=adamo1139/Yi-34b-200K-rawrr-v2-run-0902-LoRA
+      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: 84.5
+      name: normalized accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=adamo1139/Yi-34b-200K-rawrr-v2-run-0902-LoRA
+      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: 75.76
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=adamo1139/Yi-34b-200K-rawrr-v2-run-0902-LoRA
+      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: 46.66
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=adamo1139/Yi-34b-200K-rawrr-v2-run-0902-LoRA
+      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: 81.14
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=adamo1139/Yi-34b-200K-rawrr-v2-run-0902-LoRA
+      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: 62.17
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=adamo1139/Yi-34b-200K-rawrr-v2-run-0902-LoRA
+      name: Open LLM Leaderboard
 ---
 This is not an instruct fine tune, instead it's an attempt to de-contaminate the model, remove gptslop and refusals. I want model to feel like it was trained on human data, not synthetic one.
 
@@ -16,4 +119,17 @@ Training done on RTX 3090 Ti in about 14 hours. \
 Average mem usage was like 23.89 / 23.99 GiB, so very close to OOM at all times. \
 I trained it with XFCE on one 1080p monitor loaded up, on more fancy DM it would probably OOM with the same setup. \
 I am not sure what's the purpose of max_prompt_length being separate from max_length, so I may have used it wrong, I should read up on it. \
-Script I used to do this fine-tune is in the repo. I used chatml prompt format. Now I plan to fine-tune this on AEZAKMI v3 dataset soon.
+Script I used to do this fine-tune is in the repo. I used chatml prompt format. Now I plan to fine-tune this on AEZAKMI v3 dataset soon.
+# [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_adamo1139__Yi-34b-200K-rawrr-v2-run-0902-LoRA)
+
+|             Metric              |Value|
+|---------------------------------|----:|
+|Avg.                             |69.15|
+|AI2 Reasoning Challenge (25-Shot)|64.68|
+|HellaSwag (10-Shot)              |84.50|
+|MMLU (5-Shot)                    |75.76|
+|TruthfulQA (0-shot)              |46.66|
+|Winogrande (5-shot)              |81.14|
+|GSM8k (5-shot)                   |62.17|
+