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Model: jan-hq/Mistral-7B-Instruct-v0.2-DARE Source: Original Platform
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README.md
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README.md
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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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model-index:
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- name: Mistral-7B-Instruct-v0.2-DARE
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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: 61.95
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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=janhq/Mistral-7B-Instruct-v0.2-DARE
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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: 75.62
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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=janhq/Mistral-7B-Instruct-v0.2-DARE
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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: 49.99
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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=janhq/Mistral-7B-Instruct-v0.2-DARE
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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: 54.36
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=janhq/Mistral-7B-Instruct-v0.2-DARE
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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: 74.98
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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=janhq/Mistral-7B-Instruct-v0.2-DARE
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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: 18.12
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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=janhq/Mistral-7B-Instruct-v0.2-DARE
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name: Open LLM Leaderboard
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---
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<!-- header start -->
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<!-- 200823 -->
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<div style="width: auto; margin-left: auto; margin-right: auto">
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<img src="https://github.com/janhq/jan/assets/89722390/35daac7d-b895-487c-a6ac-6663daaad78e" alt="Jan banner" style="width: 100%; min-width: 400px; display: block; margin: auto;">
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</div>
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<p align="center">
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<a
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href="https://jan.ai/">Jan</a>
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- <a href="https://discord.gg/AsJ8krTT3N">Discord</a>
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</p>
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<!-- header end -->
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# Model Description
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This model uses the `DARE` method to merge [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) with 3 leading models in 12th Dec on [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard):
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1. [OpenHermes-2.5-neural-chat-v3-3-Slerp](https://huggingface.co/Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp)
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2. [MetaMath-Cybertron-Starling](https://huggingface.co/Q-bert/MetaMath-Cybertron-Starling)
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3. [v1olet_marcoroni-go-bruins-merge-7B](https://huggingface.co/v1olet/v1olet_marcoroni-go-bruins-merge-7B)
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- base model: [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
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The yaml config file for this model is here:
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```yaml
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base_model: mistralai/Mistral-7B-Instruct-v0.2
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dtype: bfloat16
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merge_method: dare_ties
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models:
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- model: mistralai/Mistral-7B-Instruct-v0.2
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- model: Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp
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parameters:
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density: 0.8
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weight: 0.4
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- model: Q-bert/MetaMath-Cybertron-Starling
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parameters:
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density: 0.8
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weight: 0.3
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- model: v1olet/v1olet_marcoroni-go-bruins-merge-7B
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parameters:
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density: 0.8
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weight: 0.3
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parameters:
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int8_mask: true
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```
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# Prompt template:
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- **ChatML**
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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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{prompt}<|im_end|>
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<|im_start|>assistant
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```
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- **Alpaca**
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```
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{system_message}
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### Instruction:
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{prompt}
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### Response:
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```
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# Run this model
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You can run this model using [Jan Desktop](https://jan.ai/) on Mac, Windows, or Linux.
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Jan is an open source, ChatGPT alternative that is:
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- 💻 **100% offline on your machine**: Your conversations remain confidential, and visible only to you.
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- 🗂️ **An Open File Format**: Conversations and model settings stay on your computer and can be exported or deleted at any time.
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- 🌐 **OpenAI Compatible**: Local server on port `1337` with OpenAI compatible endpoints
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- 🌍 **Open Source & Free**: We build in public; check out our [Github](https://github.com/janhq)
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# About Jan
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Jan believes in the need for an open-source AI ecosystem and is building the infra and tooling to allow open-source AIs to compete on a level playing field with proprietary ones.
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Jan's long-term vision is to build a cognitive framework for future robots, who are practical, useful assistants for humans and businesses in everyday life.
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# Jan Model Merger
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This is a test project for merging models.
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# Open LLM Leaderboard Evaluation Results
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Detailed results can be found here.
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| Metric | Value |
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|-----------------------|---------------------------|
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| Avg. | ?|
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| ARC (25-shot) | ? |
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| HellaSwag (10-shot) | ? |
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| MMLU (5-shot) | ?|
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| TruthfulQA (0-shot) | ? |
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| Winogrande (5-shot) | ? |
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| GSM8K (5-shot) | ? |
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# Acknowlegement
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- [mergekit](https://github.com/cg123/mergekit)
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- [DARE](https://github.com/yule-BUAA/MergeLM/blob/main/README.md)
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- [SLERP](https://github.com/Digitous/LLM-SLERP-Merge)
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- [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness)
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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_janhq__Mistral-7B-Instruct-v0.2-DARE)
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| Metric |Value|
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|---------------------------------|----:|
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|Avg. |55.84|
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|AI2 Reasoning Challenge (25-Shot)|61.95|
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|HellaSwag (10-Shot) |75.62|
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|MMLU (5-Shot) |49.99|
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|TruthfulQA (0-shot) |54.36|
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|Winogrande (5-shot) |74.98|
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|GSM8k (5-shot) |18.12|
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