Adding Evaluation Results
#2
by leaderboard-pr-bot - opened
README.md
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---
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license: apache-2.0
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datasets:
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- Locutusque/hercules-v1.0
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language:
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- en
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base_model: M4-ai/TinyMistral-6x248M
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inference:
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parameters:
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do_sample: true
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max_new_tokens: 250
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repetition_penalty: 1.1
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widget:
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- text: |
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Write me a Python program that calculates the factorial of n. <|im_end|>
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<|im_start|>assistant
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training. It is, however,
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- text: |
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How do I say hello in Spanish? <|im_end|>
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<|im_start|>assistant
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---
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# Model Card for M4-ai/TinyMistral-6x248M-Instruct
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## Contributions
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Thanks to @jtatman, @aloobun, @Felladrin, and @Locutusque for their contributions to this model.
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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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tags:
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- moe
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base_model: M4-ai/TinyMistral-6x248M
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datasets:
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- Locutusque/hercules-v1.0
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inference:
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parameters:
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do_sample: true
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max_new_tokens: 250
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repetition_penalty: 1.1
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widget:
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- text: '<|im_start|>user
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Write me a Python program that calculates the factorial of n. <|im_end|>
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<|im_start|>assistant
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'
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- text: An emerging clinical approach to treat substance abuse disorders involves
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a form of cognitive-behavioral therapy whereby addicts learn to reduce their reactivity
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to drug-paired stimuli through cue-exposure or extinction training. It is, however,
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- text: '<|im_start|>user
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How do I say hello in Spanish? <|im_end|>
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<|im_start|>assistant
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'
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model-index:
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- name: TinyMistral-6x248M-Instruct
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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: 22.44
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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=M4-ai/TinyMistral-6x248M-Instruct
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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: 27.02
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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=M4-ai/TinyMistral-6x248M-Instruct
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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: 24.13
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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=M4-ai/TinyMistral-6x248M-Instruct
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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: 43.16
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=M4-ai/TinyMistral-6x248M-Instruct
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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: 50.59
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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=M4-ai/TinyMistral-6x248M-Instruct
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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: 0.0
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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=M4-ai/TinyMistral-6x248M-Instruct
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name: Open LLM Leaderboard
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---
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# Model Card for M4-ai/TinyMistral-6x248M-Instruct
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## Contributions
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Thanks to @jtatman, @aloobun, @Felladrin, and @Locutusque for their contributions to this model.
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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_M4-ai__TinyMistral-6x248M-Instruct)
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| Metric |Value|
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|---------------------------------|----:|
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|Avg. |27.89|
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|AI2 Reasoning Challenge (25-Shot)|22.44|
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|HellaSwag (10-Shot) |27.02|
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|MMLU (5-Shot) |24.13|
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|TruthfulQA (0-shot) |43.16|
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|Winogrande (5-shot) |50.59|
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|GSM8k (5-shot) | 0.00|
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