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Adding Evaluation Results (#2)
Browse files- Adding Evaluation Results (4936f462784112c3a1a2115cfb4b33ce18923253)
Co-authored-by: Open LLM Leaderboard PR Bot <leaderboard-pr-bot@users.noreply.huggingface.co>
README.md
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@@ -6,6 +6,109 @@ tags:
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- lazymergekit
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- AIDC-ai-business/Marcoroni-7B-v3
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- EmbeddedLLM/Mistral-7B-Merge-14-v0.1
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---
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![](https://i.imgur.com/FSKtmRc.png)
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@@ -153,4 +256,17 @@ print(outputs[0]["generated_text"])
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Output:
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> A large language model is a type of artificial intelligence (AI) system that has been trained on vast amounts of text data. It's designed to understand and generate human-like language, making predictions on what words or phrases might come next in a sentence or document. These models use complex algorithms and neural network architectures to learn from the data and improve their performance over time. Some well-known large language models include GPT-3 from OpenAI and BERT from Google.
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- lazymergekit
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- AIDC-ai-business/Marcoroni-7B-v3
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- EmbeddedLLM/Mistral-7B-Merge-14-v0.1
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model-index:
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- name: Marcoro14-7B-slerp
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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: AI2 Reasoning Challenge (25-Shot)
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type: ai2_arc
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config: ARC-Challenge
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split: test
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args:
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num_few_shot: 25
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metrics:
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- type: acc_norm
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value: 69.8
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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=mlabonne/Marcoro14-7B-slerp
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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: HellaSwag (10-Shot)
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type: hellaswag
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split: validation
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args:
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num_few_shot: 10
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metrics:
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- type: acc_norm
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value: 87.13
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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=mlabonne/Marcoro14-7B-slerp
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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 (5-Shot)
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type: cais/mmlu
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config: all
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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: 65.11
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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=mlabonne/Marcoro14-7B-slerp
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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: TruthfulQA (0-shot)
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type: truthful_qa
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config: multiple_choice
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split: validation
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args:
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num_few_shot: 0
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metrics:
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- type: mc2
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value: 63.54
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Marcoro14-7B-slerp
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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: Winogrande (5-shot)
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type: winogrande
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config: winogrande_xl
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split: validation
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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: 81.61
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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=mlabonne/Marcoro14-7B-slerp
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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: GSM8k (5-shot)
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type: gsm8k
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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: 70.89
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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=mlabonne/Marcoro14-7B-slerp
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name: Open LLM Leaderboard
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---
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![](https://i.imgur.com/FSKtmRc.png)
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Output:
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> A large language model is a type of artificial intelligence (AI) system that has been trained on vast amounts of text data. It's designed to understand and generate human-like language, making predictions on what words or phrases might come next in a sentence or document. These models use complex algorithms and neural network architectures to learn from the data and improve their performance over time. Some well-known large language models include GPT-3 from OpenAI and BERT from Google.
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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_mlabonne__Marcoro14-7B-slerp)
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| Metric |Value|
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|---------------------------------|----:|
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|Avg. |73.01|
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|AI2 Reasoning Challenge (25-Shot)|69.80|
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|HellaSwag (10-Shot) |87.13|
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|MMLU (5-Shot) |65.11|
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|TruthfulQA (0-shot) |63.54|
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|Winogrande (5-shot) |81.61|
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|GSM8k (5-shot) |70.89|
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