Adding Evaluation Results
Browse filesThis 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
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---
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license: apache-2.0
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datasets:
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- Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned
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- anthracite-org/stheno-filtered-v1.1
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- PJMixers/hieunguyenminh_roleplay-deduped-ShareGPT
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- Gryphe/Sonnet3.5-Charcard-Roleplay
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- Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned
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- anthracite-org/kalo-opus-instruct-22k-no-refusal
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- anthracite-org/nopm_claude_writing_fixed
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- anthracite-org/kalo_opus_misc_240827
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language:
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- en
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- fr
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- ru
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- zh
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- ja
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pipeline_tag: text-generation
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---
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## Training
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Training was done twice over 2 epochs each on two 2x [NVIDIA A6000 GPUs](https://www.nvidia.com/en-us/design-visualization/rtx-a6000/) using LoRA. A two-phased approach was used in which the base model was trained 2 epochs on RP data, the LoRA was then applied to base. Finally, the new modified base was trained 2 epochs on instruct, and the new instruct LoRA was applied to the modified base, resulting in what you see here.
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[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
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---
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language:
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- en
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- fr
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- ru
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- zh
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- ja
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license: apache-2.0
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datasets:
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- Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned
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- anthracite-org/stheno-filtered-v1.1
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- PJMixers/hieunguyenminh_roleplay-deduped-ShareGPT
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- Gryphe/Sonnet3.5-Charcard-Roleplay
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- Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned
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- anthracite-org/kalo-opus-instruct-22k-no-refusal
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- anthracite-org/nopm_claude_writing_fixed
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- anthracite-org/kalo_opus_misc_240827
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pipeline_tag: text-generation
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model-index:
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- name: Azure_Dusk-v0.2
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results:
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: IFEval (0-Shot)
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type: HuggingFaceH4/ifeval
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args:
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num_few_shot: 0
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metrics:
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- type: inst_level_strict_acc and prompt_level_strict_acc
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value: 34.67
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name: strict accuracy
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Epiculous/Azure_Dusk-v0.2
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: BBH (3-Shot)
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type: BBH
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args:
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num_few_shot: 3
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metrics:
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- type: acc_norm
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value: 17.4
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Epiculous/Azure_Dusk-v0.2
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MATH Lvl 5 (4-Shot)
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type: hendrycks/competition_math
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args:
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num_few_shot: 4
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metrics:
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- type: exact_match
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value: 1.66
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name: exact match
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Epiculous/Azure_Dusk-v0.2
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: GPQA (0-shot)
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type: Idavidrein/gpqa
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args:
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num_few_shot: 0
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metrics:
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- type: acc_norm
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value: 1.45
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name: acc_norm
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Epiculous/Azure_Dusk-v0.2
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MuSR (0-shot)
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type: TAUR-Lab/MuSR
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args:
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num_few_shot: 0
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metrics:
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- type: acc_norm
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value: 6.37
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name: acc_norm
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Epiculous/Azure_Dusk-v0.2
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MMLU-PRO (5-shot)
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type: TIGER-Lab/MMLU-Pro
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config: main
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 22.6
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name: accuracy
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Epiculous/Azure_Dusk-v0.2
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name: Open LLM Leaderboard
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---
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## Training
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Training was done twice over 2 epochs each on two 2x [NVIDIA A6000 GPUs](https://www.nvidia.com/en-us/design-visualization/rtx-a6000/) using LoRA. A two-phased approach was used in which the base model was trained 2 epochs on RP data, the LoRA was then applied to base. Finally, the new modified base was trained 2 epochs on instruct, and the new instruct LoRA was applied to the modified base, resulting in what you see here.
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+
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Epiculous__Azure_Dusk-v0.2)
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| Metric |Value|
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|-------------------|----:|
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|Avg. |14.03|
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|IFEval (0-Shot) |34.67|
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|BBH (3-Shot) |17.40|
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|MATH Lvl 5 (4-Shot)| 1.66|
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|GPQA (0-shot) | 1.45|
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|MuSR (0-shot) | 6.37|
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|MMLU-PRO (5-shot) |22.60|
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