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Adding Evaluation Results (#1)
Browse files- Adding Evaluation Results (198b9cba4aea9efab62dd9b78bc77c16ea51cdf0)
Co-authored-by: Open LLM Leaderboard PR Bot <leaderboard-pr-bot@users.noreply.huggingface.co>
README.md
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---
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-
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tags:
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- merge
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- mergekit
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- lazymergekit
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base_model:
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- senseable/Westlake-7B
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- Guilherme34/Samantha-v2
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- uukuguy/speechless-mistral-six-in-one-7b
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-
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model-index:
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- name: sethuiyer/Nandine-7b
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results:
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value: 69.28
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name: normalized accuracy
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source:
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url:
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https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Nandine-7b
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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value: 87.01
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name: normalized accuracy
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source:
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url:
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https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Nandine-7b
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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value: 64.83
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name: accuracy
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source:
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url:
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https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Nandine-7b
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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- type: mc2
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value: 62.1
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source:
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url:
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https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Nandine-7b
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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value: 83.19
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name: accuracy
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source:
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url:
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https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Nandine-7b
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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value: 62.4
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name: accuracy
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source:
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url:
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https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Nandine-7b
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name: Open LLM Leaderboard
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language:
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- en
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library_name: transformers
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pipeline_tag: text-generation
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---
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# Nandine-7b
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## Ollama
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Nandine is now available on Ollama. You can use it by running the command ```ollama run stuehieyr/nandine``` in your
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terminal. If you have limited computing resources, check out this [video](https://www.youtube.com/watch?v=Qa1h7ygwQq8) to learn how to run it on
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a Google Colab backend.
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---
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language:
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- en
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license: apache-2.0
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library_name: transformers
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tags:
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- merge
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- mergekit
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- lazymergekit
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inference: false
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base_model:
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- senseable/Westlake-7B
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- Guilherme34/Samantha-v2
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- uukuguy/speechless-mistral-six-in-one-7b
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pipeline_tag: text-generation
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model-index:
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- name: sethuiyer/Nandine-7b
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results:
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value: 69.28
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Nandine-7b
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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value: 87.01
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Nandine-7b
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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value: 64.83
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Nandine-7b
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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- type: mc2
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value: 62.1
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Nandine-7b
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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value: 83.19
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Nandine-7b
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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value: 62.4
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Nandine-7b
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name: Open LLM Leaderboard
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---
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# Nandine-7b
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## Ollama
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Nandine is now available on Ollama. You can use it by running the command ```ollama run stuehieyr/nandine``` in your
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terminal. If you have limited computing resources, check out this [video](https://www.youtube.com/watch?v=Qa1h7ygwQq8) to learn how to run it on
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a Google Colab backend.
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_sethuiyer__Nandine-7b)
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| Metric |Value|
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|---------------------------------|----:|
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|Avg. |71.47|
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|AI2 Reasoning Challenge (25-Shot)|69.28|
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|HellaSwag (10-Shot) |87.01|
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|MMLU (5-Shot) |64.83|
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|TruthfulQA (0-shot) |62.10|
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|Winogrande (5-shot) |83.19|
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|GSM8k (5-shot) |62.40|
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