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Model: SJ-Donald/SOLAR-10.7B-slerp 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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- ko
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license: cc-by-nc-4.0
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tags:
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- merge
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- lazymergekit
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- LDCC/LDCC-SOLAR-10.7B
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- upstage/SOLAR-10.7B-Instruct-v1.0
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base_model:
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- LDCC/LDCC-SOLAR-10.7B
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- upstage/SOLAR-10.7B-Instruct-v1.0
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model-index:
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- name: SOLAR-10.7B-slerp
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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: 68.17
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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=SJ-Donald/SOLAR-10.7B-slerp
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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: 86.91
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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=SJ-Donald/SOLAR-10.7B-slerp
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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: 66.73
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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=SJ-Donald/SOLAR-10.7B-slerp
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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: 67.42
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=SJ-Donald/SOLAR-10.7B-slerp
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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: 84.06
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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=SJ-Donald/SOLAR-10.7B-slerp
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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: 62.17
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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=SJ-Donald/SOLAR-10.7B-slerp
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name: Open LLM Leaderboard
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---
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# SOLAR-10.7B-slerp
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SOLAR-10.7B-slerp is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):
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* [LDCC/LDCC-SOLAR-10.7B](https://huggingface.co/LDCC/LDCC-SOLAR-10.7B)
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* [upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0)
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## Github
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[https://github.com/sunjin7725/SOLAR-10.7b-slerp](https://github.com/sunjin7725/SOLAR-10.7b-slerp)
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## Benchmark
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### Open-Ko-LLM-Leaderboard
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| Average | Ko-ARC | Ko-HellaSwag | Ko-MMLU | Ko-TruthfulQA | Ko-CommonGen V2 |
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| ------: | -----: | -----------: | ------: | ------------: | --------------: |
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| 56.93 | 53.58 | 62.03 | 53.31 | 57.16 | 58.56 |
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## How to use
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```Python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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repo = 'SJ-Donald/SOLAR-10.7B-slerp'
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tokenizer = AutoTokenizer.from_pretrained(repo)
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model = AutoModelForCausalLM.from_pretrained(
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repo,
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return_dict=True,
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torch_dtype=torch.float16,
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device_map='auto'
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)
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```
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## 🧩 Configuration
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```yaml
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slices:
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- sources:
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- model: LDCC/LDCC-SOLAR-10.7B
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layer_range: [0, 48]
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- model: upstage/SOLAR-10.7B-Instruct-v1.0
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layer_range: [0, 48]
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merge_method: slerp
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base_model: upstage/SOLAR-10.7B-Instruct-v1.0
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parameters:
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t:
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- filter: self_attn
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value: [0, 0.5, 0.3, 0.7, 1]
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- filter: mlp
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value: [1, 0.5, 0.7, 0.3, 0]
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- value: 0.5
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tokenizer_source: union
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dtype: float16
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```
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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_SJ-Donald__SOLAR-10.7B-slerp)
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| Metric |Value|
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|---------------------------------|----:|
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|Avg. |72.58|
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|AI2 Reasoning Challenge (25-Shot)|68.17|
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|HellaSwag (10-Shot) |86.91|
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|MMLU (5-Shot) |66.73|
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|TruthfulQA (0-shot) |67.42|
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|Winogrande (5-shot) |84.06|
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|GSM8k (5-shot) |62.17|
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