初始化项目，由ModelHub XC社区提供模型
Model: beberik/Lonepino-11B Source: Original Platform
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ModelHub XC
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README.md
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README.md
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---
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license: cc-by-nc-4.0
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tags:
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- merge
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model-index:
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- name: Lonepino-11B
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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: 68.26
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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=beberik/Lonepino-11B
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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: 84.57
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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=beberik/Lonepino-11B
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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: 63.76
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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=beberik/Lonepino-11B
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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: 63.45
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=beberik/Lonepino-11B
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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: 78.93
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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=beberik/Lonepino-11B
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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: 61.64
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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=beberik/Lonepino-11B
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name: Open LLM Leaderboard
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---
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## Description
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This repo contains bf16 files of Lonepino-11B. Just a normal model.
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## Model used
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- [Intel/neural-chat-7b-v3-3-Slerp](https://huggingface.co/Intel/neural-chat-7b-v3-3-Slerp)
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- [NeverSleep/Noromaid-7b-v0.2](https://huggingface.co/NeverSleep/Noromaid-7b-v0.2)
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- [chargoddard/loyal-piano-m7-cdpo](https://huggingface.co/chargoddard/loyal-piano-m7-cdpo)
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- [maywell/PiVoT-0.1-Starling-LM-RP](https://huggingface.co/maywell/PiVoT-0.1-Starling-LM-RP)
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## The secret sauce
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neural-maid-11B:
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```
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slices:
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- sources:
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- model: Intel/neural-chat-7b-v3-3-Slerp
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layer_range: [0, 24]
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- sources:
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- model: NeverSleep/Noromaid-7b-v0.2
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layer_range: [8, 32]
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merge_method: passthrough
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dtype: bfloat16
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```
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loyal-PiVoT-11B:
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```
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slices:
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- sources:
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- model: chargoddard/loyal-piano-m7-cdpo
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layer_range: [0, 24]
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- sources:
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- model: maywell/PiVoT-0.1-Starling-LM-RP
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layer_range: [8, 32]
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merge_method: passthrough
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dtype: bfloat16
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```
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Lonepino-11B:
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```
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slices:
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- sources:
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- model: "./neural-maid-11B"
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layer_range: [0, 48]
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- model: "./loyal-PiVoT-11B"
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layer_range: [0, 48]
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merge_method: slerp
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base_model: "./neural-maid-11B"
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parameters:
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t:
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- value: 0.4
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dtype: bfloat16
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```
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## Prompt template
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Alpaca. Or chatml. Or any you like.
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=w=
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I use [mergekit](https://github.com/cg123/mergekit) for all the manipulation told here.
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Thanks to the [Undi95](https://huggingface.co/Undi95) for the original [11B mistral merge](https://huggingface.co/Undi95/Mistral-11B-OmniMix) recipe.
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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_beberik__Lonepino-11B)
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| Metric |Value|
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|---------------------------------|----:|
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|Avg. |70.10|
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|AI2 Reasoning Challenge (25-Shot)|68.26|
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|HellaSwag (10-Shot) |84.57|
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|MMLU (5-Shot) |63.76|
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|TruthfulQA (0-shot) |63.45|
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|Winogrande (5-shot) |78.93|
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|GSM8k (5-shot) |61.64|
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