365 lines
10 KiB
Markdown
365 lines
10 KiB
Markdown
---
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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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- text-generation
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base_model: JackFram/llama-160m
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datasets:
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- ehartford/wizard_vicuna_70k_unfiltered
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- totally-not-an-llm/EverythingLM-data-V3
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- Open-Orca/SlimOrca-Dedup
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- databricks/databricks-dolly-15k
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- THUDM/webglm-qa
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widget:
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- messages:
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- role: system
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content: You are a helpful assistant, who answers with empathy.
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- role: user
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content: Got a question for you!
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- role: assistant
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content: Sure! What's it?
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- role: user
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content: Why do you love cats so much!? 🐈
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- messages:
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- role: system
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content: You are a helpful assistant who answers user's questions with empathy.
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- role: user
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content: Who is Mona Lisa?
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- messages:
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- role: system
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content: You are a helpful assistant who provides concise responses.
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- role: user
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content: Heya!
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- role: assistant
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content: Hi! How may I help you today?
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- role: user
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content: I need to build a simple website. Where should I start learning about
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web development?
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- messages:
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- role: user
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content: Invited some friends to come home today. Give me some ideas for games
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to play with them!
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- messages:
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- role: system
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content: You are a helpful assistant who answers user's questions with details
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and curiosity.
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- role: user
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content: What are some potential applications for quantum computing?
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- messages:
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- role: system
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content: You are a helpful assistant who gives creative responses.
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- role: user
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content: Write the specs of a game about mages in a fantasy world.
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- messages:
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- role: system
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content: You are a helpful assistant who answers user's questions with details.
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- role: user
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content: Tell me about the pros and cons of social media.
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- messages:
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- role: system
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content: You are a helpful assistant who answers user's questions with confidence.
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- role: user
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content: What is a dog?
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- role: assistant
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content: A dog is a four-legged, domesticated animal that is a member of the class
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Mammalia, which includes all mammals. Dogs are known for their loyalty, playfulness,
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and ability to be trained for various tasks. They are also used for hunting,
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herding, and as service animals.
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- role: user
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content: What is the color of an apple?
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inference:
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parameters:
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max_new_tokens: 250
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penalty_alpha: 0.5
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top_k: 4
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repetition_penalty: 1.01
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model-index:
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- name: Llama-160M-Chat-v1
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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: 24.74
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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=Felladrin/Llama-160M-Chat-v1
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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: 35.29
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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=Felladrin/Llama-160M-Chat-v1
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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: 26.13
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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=Felladrin/Llama-160M-Chat-v1
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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: 44.16
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Llama-160M-Chat-v1
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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: 51.3
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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=Felladrin/Llama-160M-Chat-v1
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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=Felladrin/Llama-160M-Chat-v1
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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: 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: 15.75
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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=Felladrin/Llama-160M-Chat-v1
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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: 3.17
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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=Felladrin/Llama-160M-Chat-v1
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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: 0.0
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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=Felladrin/Llama-160M-Chat-v1
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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: 1.01
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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=Felladrin/Llama-160M-Chat-v1
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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: 3.17
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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=Felladrin/Llama-160M-Chat-v1
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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: 1.51
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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=Felladrin/Llama-160M-Chat-v1
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name: Open LLM Leaderboard
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---
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# A Llama Chat Model of 160M Parameters
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- Base model: [JackFram/llama-160m](https://huggingface.co/JackFram/llama-160m)
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- Datasets:
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- [ehartford/wizard_vicuna_70k_unfiltered](https://huggingface.co/datasets/ehartford/wizard_vicuna_70k_unfiltered)
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- [totally-not-an-llm/EverythingLM-data-V3](https://huggingface.co/datasets/totally-not-an-llm/EverythingLM-data-V3)
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- [Open-Orca/SlimOrca-Dedup](https://huggingface.co/datasets/Open-Orca/SlimOrca-Dedup)
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- [databricks/databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k)
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- [THUDM/webglm-qa](https://huggingface.co/datasets/THUDM/webglm-qa)
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- Availability in other ML formats:
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- GGUF: [Felladrin/gguf-Llama-160M-Chat-v1](https://huggingface.co/Felladrin/gguf-Llama-160M-Chat-v1)
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- ONNX: [Felladrin/onnx-Llama-160M-Chat-v1](https://huggingface.co/Felladrin/onnx-Llama-160M-Chat-v1)
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- MLC: [Felladrin/mlc-q4f16-Llama-160M-Chat-v1](https://huggingface.co/Felladrin/mlc-q4f16-Llama-160M-Chat-v1)
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- MLX: [mlx-community/Llama-160M-Chat-v1-4bit-mlx](https://huggingface.co/mlx-community/Llama-160M-Chat-v1-4bit-mlx)
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## Recommended Prompt Format
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```
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<|im_start|>system
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{system_message}<|im_end|>
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<|im_start|>user
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{user_message}<|im_end|>
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<|im_start|>assistant
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```
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## Recommended Inference Parameters
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```yml
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penalty_alpha: 0.5
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top_k: 4
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repetition_penalty: 1.01
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```
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## Usage Example
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```python
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from transformers import pipeline
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generate = pipeline("text-generation", "Felladrin/Llama-160M-Chat-v1")
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messages = [
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{
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"role": "system",
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"content": "You are a helpful assistant who answers user's questions with details and curiosity.",
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},
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{
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"role": "user",
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"content": "What are some potential applications for quantum computing?",
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},
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]
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prompt = generate.tokenizer.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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output = generate(
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prompt,
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max_new_tokens=1024,
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penalty_alpha=0.5,
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top_k=4,
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repetition_penalty=1.01,
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)
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print(output[0]["generated_text"])
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```
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## Old Open LLM Leaderboard Evaluation Results
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| Metric |Value|
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|---------------------------------|----:|
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|Avg. |30.27|
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|AI2 Reasoning Challenge (25-Shot)|24.74|
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|HellaSwag (10-Shot) |35.29|
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|MMLU (5-Shot) |26.13|
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|TruthfulQA (0-shot) |44.16|
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|Winogrande (5-shot) |51.30|
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|GSM8k (5-shot) | 0.00|
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## [New 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_Felladrin__Llama-160M-Chat-v1)
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| Metric |Value|
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|-------------------|----:|
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|Avg. | 4.10|
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|IFEval (0-Shot) |15.75|
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|BBH (3-Shot) | 3.17|
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|MATH Lvl 5 (4-Shot)| 0.00|
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|GPQA (0-shot) | 1.01|
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|MuSR (0-shot) | 3.17|
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|MMLU-PRO (5-shot) | 1.51|
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