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Adding Evaluation Results

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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

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  1. README.md +110 -1
README.md CHANGED
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  ---
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- library_name: transformers
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  license: apache-2.0
 
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  tags:
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  - jamba
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  - mamba
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  - moe
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  # Model Card for Jamba
@@ -158,3 +253,17 @@ As a base model, Jamba is intended for use as a foundation layer for fine tuning
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  AI21 builds reliable, practical, and scalable AI solutions for the enterprise.
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  Jamba is the first in AI21’s new family of models, and the Instruct version of Jamba is coming soon to the [AI21 platform](https://www.ai21.com/studio).
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
 
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  license: apache-2.0
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+ library_name: transformers
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  tags:
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  - jamba
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  - mamba
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  - moe
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+ model-index:
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+ - name: Jamba-v0.1
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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: 20.26
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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=ai21labs/Jamba-v0.1
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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: 10.72
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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=ai21labs/Jamba-v0.1
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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: 0.98
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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=ai21labs/Jamba-v0.1
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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: 2.46
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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=ai21labs/Jamba-v0.1
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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: 3.71
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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=ai21labs/Jamba-v0.1
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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: 16.45
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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=ai21labs/Jamba-v0.1
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+ name: Open LLM Leaderboard
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  ---
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  # Model Card for Jamba
 
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  AI21 builds reliable, practical, and scalable AI solutions for the enterprise.
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  Jamba is the first in AI21’s new family of models, and the Instruct version of Jamba is coming soon to the [AI21 platform](https://www.ai21.com/studio).
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+
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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_ai21labs__Jamba-v0.1)
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+
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+ | Metric |Value|
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+ |-------------------|----:|
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+ |Avg. | 9.10|
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+ |IFEval (0-Shot) |20.26|
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+ |BBH (3-Shot) |10.72|
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+ |MATH Lvl 5 (4-Shot)| 0.98|
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+ |GPQA (0-shot) | 2.46|
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+ |MuSR (0-shot) | 3.71|
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+ |MMLU-PRO (5-shot) |16.45|
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+