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README.md
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---
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license: apache-2.0
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---
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---
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license: apache-2.0
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datasets:
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- argilla/ultrafeedback-binarized-preferences-cleaned
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base_model:
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- mistralai/Mistral-7B-v0.1
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language:
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- en
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model-index:
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- name: Mistral-ORPO-β
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results:
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- task:
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type: text-generation
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dataset:
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name: AlpacaEval 1
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type: AlpacaEval
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metrics:
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- name: Win Rate
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type: AlpacaEval 1.0
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value: 87.92%
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source:
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name: self-reported
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url: https://github.com/tatsu-lab/alpaca_eval
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- task:
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type: text-generation
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dataset:
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name: AlpacaEval 2
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type: AlpacaEval
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metrics:
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- name: Win Rate
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type: AlpacaEval 2.0
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value: 11.33%
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source:
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name: self-reported
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url: https://github.com/tatsu-lab/alpaca_eval
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- task:
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type: text-generation
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dataset:
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name: MT-Bench
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type: MT-Bench
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metrics:
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- name: Score
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type: MT-Bench
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value: 7.23
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source:
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name: self-reported
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url: https://github.com/lm-sys/FastChat/blob/main/fastchat/llm_judge/
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pipeline_tag: text-generation
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---
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# **Mistral-ORPO-β (7B)**
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**Mistral-ORPO** is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) using the *odds ratio preference optimization (ORPO)*. With ORPO, the model directly learns the preference without the supervised fine-tuning warmup phase. **Mistral-ORPO-β** is fine-tuned exclusively on the 61k instances of the cleaned version of UltraFeedback, [argilla/ultrafeedback-binarized-preferences-cleaned](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences-cleaned), by [Argilla](https://huggingface.co/argilla).
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## Model Performance
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|Model Name|Size|Align|MT-Bench|AlpacaEval 1.0|AlpacaEval 2.0|
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|:--------|:--------------:|:--------------:|:-------------------:|:------------:|:------------:|
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|**Mistral-<tt>ORPO</tt>-⍺**|7B|<tt>ORPO</tt>|7.23|87.92|11.33|
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|**Mistral-<tt>ORPO</tt>-β**|7B|<tt>ORPO</tt>|7.32|91.41|12.20|
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|Zephyr ($\beta$) |7B|DPO|7.34|90.60|10.99|
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|TULU-2-DPO |13B|DPO|7.00|89.5|10.12|
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|Llama-2-Chat |7B|RLHF|6.27|71.37|4.96|
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|Llama-2-Chat |13B|RLHF|6.65|81.09|7.70|
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## Chat Template
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```
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<|user|>
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Hi! How are you doing?</s>
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<|assistant|>
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```
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