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Model: SJ-Donald/SJ-SOLAR-10.7b-DPO Source: Original Platform
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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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- DPO
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model-index:
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- name: SJ-SOLAR-10.7b-DPO
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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=SJ-Donald/SJ-SOLAR-10.7b-DPO
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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: 86.95
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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=SJ-Donald/SJ-SOLAR-10.7b-DPO
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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/SJ-SOLAR-10.7b-DPO
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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: 67.74
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=SJ-Donald/SJ-SOLAR-10.7b-DPO
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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: 84.21
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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/SJ-SOLAR-10.7b-DPO
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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: 62.09
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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/SJ-SOLAR-10.7b-DPO
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name: Open LLM Leaderboard
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---
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# SJ-Donald/SJ-SOLAR-10.7b-DPO
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SJ-Donald/SJ-SOLAR-10.7b-DPO is fine-tuned using DPO method.
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## Environment
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Using **Google CoLab A100**
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## Base model
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* [SJ-Donald/SOLAR-10.7B-slerp](https://huggingface.co/SJ-Donald/SOLAR-10.7B-slerp)
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## Datasets
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* [SJ-Donald/orca-dpo-pairs-ko](https://huggingface.co/datasets/SJ-Donald/orca-dpo-pairs-ko)
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## Benchmark
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### Open-LLM-Leaderboard(https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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| Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
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| ------: | -----: | -----------: | ------: | -----------: | ---------: | ------: |
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| 72.67 | 68.26 | 86.95 | 66.73 | 67.74 | 84.21 | 62.03 |
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### open-ko-llm-leaderboard(https://huggingface.co/spaces/upstage/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 | 53.67 | 61.99 | 53.36 | 57.2 | 58.44 |
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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/SJ-SOLAR-10.7b-DPO'
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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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## Chat Template
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```Python
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template = """### System:
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{{system_content}}
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### User:
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{{question}}
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### Assistant:
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"""
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```
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## GGUF Version
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You can use gguf model file here! -> [SJ-Donald/SJ-SOLAR-10.7b-DPO-GGUF](https://huggingface.co/SJ-Donald/SJ-SOLAR-10.7b-DPO-GGUF)
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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__SJ-SOLAR-10.7b-DPO)
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| Metric |Value|
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|---------------------------------|----:|
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|Avg. |72.67|
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|AI2 Reasoning Challenge (25-Shot)|68.26|
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|HellaSwag (10-Shot) |86.95|
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|MMLU (5-Shot) |66.73|
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|TruthfulQA (0-shot) |67.74|
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|Winogrande (5-shot) |84.21|
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|GSM8k (5-shot) |62.09|
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