leaderboard-pr-bot
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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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Pipeline_tag: text-generation
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Base_model: arcee-ai/Llama-3.1-SuperNova-Lite
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Tags:
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- Chat
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license: agpl-3.0
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datasets:
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- Gryphe/Sonnet3.5-SlimOrcaDedupCleaned
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- Nitral-AI/Cybersecurity-ShareGPT
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- anthracite-org/kalo-opus-instruct-22k-no-refusal
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- Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned
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- Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned
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- Nitral-AI/Creative_Writing-ShareGPT
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- NewEden/Gryphe-Sonnet3.5-Charcard-Roleplay-unfiltered
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---
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![](https://huggingface.co/Delta-Vector/Baldur-8B/resolve/main/Baldur.jpg)
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## Training
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The training was done for 2 epochs. I used 2 x [RTX 6000s](https://www.nvidia.com/en-us/design-visualization/rtx-6000/) GPUs graciously provided by [Kubernetes Bad](https://huggingface.co/kubernetes-bad) for the full-parameter fine-tuning of the model.
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[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
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---
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language:
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- en
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license: agpl-3.0
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tags:
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- chat
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base_model:
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- arcee-ai/Llama-3.1-SuperNova-Lite
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datasets:
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- Gryphe/Sonnet3.5-SlimOrcaDedupCleaned
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- Nitral-AI/Cybersecurity-ShareGPT
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- anthracite-org/kalo-opus-instruct-22k-no-refusal
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- Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned
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- Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned
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- anthracite-org/kalo_misc_part2
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- Nitral-AI/Creative_Writing-ShareGPT
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- NewEden/Gryphe-Sonnet3.5-Charcard-Roleplay-unfiltered
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License: agpl-3.0
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Language:
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- En
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Pipeline_tag: text-generation
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Base_model: arcee-ai/Llama-3.1-SuperNova-Lite
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Tags:
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- Chat
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model-index:
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- name: Baldur-8B
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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: 47.82
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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=Delta-Vector/Baldur-8B
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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: 32.54
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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=Delta-Vector/Baldur-8B
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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: 12.61
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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=Delta-Vector/Baldur-8B
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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: 6.94
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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=Delta-Vector/Baldur-8B
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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: 14.01
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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=Delta-Vector/Baldur-8B
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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: 29.49
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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=Delta-Vector/Baldur-8B
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name: Open LLM Leaderboard
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---
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![](https://huggingface.co/Delta-Vector/Baldur-8B/resolve/main/Baldur.jpg)
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## Training
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The training was done for 2 epochs. I used 2 x [RTX 6000s](https://www.nvidia.com/en-us/design-visualization/rtx-6000/) GPUs graciously provided by [Kubernetes Bad](https://huggingface.co/kubernetes-bad) for the full-parameter fine-tuning of the model.
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[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/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_Delta-Vector__Baldur-8B)
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| Metric |Value|
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|Avg. |23.90|
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|IFEval (0-Shot) |47.82|
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|BBH (3-Shot) |32.54|
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|MATH Lvl 5 (4-Shot)|12.61|
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|GPQA (0-shot) | 6.94|
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|MuSR (0-shot) |14.01|
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|MMLU-PRO (5-shot) |29.49|
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