Adding Evaluation Results
#3
by
leaderboard-pr-bot
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README.md
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license: apache-2.0
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datasets:
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- Locutusque/InstructMixCleaned
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- berkeley-nest/Nectar
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widget:
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<|USER|> How to manage a lazy employee: Address the employee verbally. Don't allow an employee's laziness or lack of enthusiasm to become a recurring issue. Tell the employee you're hoping to speak with them about workplace expectations and performance, and schedule a time to sit down together. Question: To manage a lazy employee, it is suggested to talk to the employee. True, False, or Neither? <|ASSISTANT|>
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inference:
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parameters:
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temperature: 0.5
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do_sample:
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top_p: 0.5
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top_k: 30
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max_new_tokens: 250
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repetition_penalty: 1.15
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---
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Base model Locutusque/TinyMistral-248M fully fine-tuned on Locutusque/InstructMix. During validation, this model achieved an average perplexity of 3.23 on Locutusque/InstructMix dataset.
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It has so far been trained on approximately 608,000 examples. More epochs are planned for 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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datasets:
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- Locutusque/InstructMixCleaned
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- berkeley-nest/Nectar
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pipeline_tag: text-generation
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base_model: Locutusque/TinyMistral-248M
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widget:
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- text: '<|USER|> Design a Neo4j database and Cypher function snippet to Display Extreme
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Dental hygiene: Using Mouthwash for Analysis for Beginners. Implement if/else
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or switch/case statements to handle different conditions related to the Consent.
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Provide detailed comments explaining your control flow and the reasoning behind
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each decision. <|ASSISTANT|> '
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- text: '<|USER|> Write me a story about a magical place. <|ASSISTANT|> '
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- text: '<|USER|> Write me an essay about the life of George Washington <|ASSISTANT|> '
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- text: '<|USER|> Solve the following equation 2x + 10 = 20 <|ASSISTANT|> '
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- text: '<|USER|> Craft me a list of some nice places to visit around the world. <|ASSISTANT|> '
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- text: '<|USER|> How to manage a lazy employee: Address the employee verbally. Don''t
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allow an employee''s laziness or lack of enthusiasm to become a recurring issue.
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Tell the employee you''re hoping to speak with them about workplace expectations
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and performance, and schedule a time to sit down together. Question: To manage
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a lazy employee, it is suggested to talk to the employee. True, False, or Neither?
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<|ASSISTANT|> '
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inference:
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parameters:
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temperature: 0.5
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do_sample: true
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top_p: 0.5
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top_k: 30
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max_new_tokens: 250
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repetition_penalty: 1.15
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model-index:
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- name: TinyMistral-248M-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: 24.32
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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=Locutusque/TinyMistral-248M-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: 27.52
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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=Locutusque/TinyMistral-248M-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: 25.18
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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=Locutusque/TinyMistral-248M-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: 41.94
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/TinyMistral-248M-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: 50.2
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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=Locutusque/TinyMistral-248M-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=Locutusque/TinyMistral-248M-Instruct
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name: Open LLM Leaderboard
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---
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Base model Locutusque/TinyMistral-248M fully fine-tuned on Locutusque/InstructMix. During validation, this model achieved an average perplexity of 3.23 on Locutusque/InstructMix dataset.
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It has so far been trained on approximately 608,000 examples. More epochs are planned for 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_Locutusque__TinyMistral-248M-Instruct)
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| Metric |Value|
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|Avg. |28.19|
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|AI2 Reasoning Challenge (25-Shot)|24.32|
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|HellaSwag (10-Shot) |27.52|
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|MMLU (5-Shot) |25.18|
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|TruthfulQA (0-shot) |41.94|
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|Winogrande (5-shot) |50.20|
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|GSM8k (5-shot) | 0.00|
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