230 lines
6.5 KiB
Markdown
230 lines
6.5 KiB
Markdown
---
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language:
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- en
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license: mit
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base_model:
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- mistralai/Mistral-7B-v0.1
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datasets:
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- argilla/ultrafeedback-binarized-preferences-cleaned
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pipeline_tag: text-generation
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model-index:
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- name: Mistral-ORPO-β
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results:
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# AI2 Reasoning Challenge (25-Shot)
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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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name: normalized accuracy
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value: 61.18
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source:
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name: Open LLM Leaderboard
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kaist-ai%2Fmistral-orpo-beta
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# HellaSwag (10-shot)
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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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name: normalized accuracy
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value: 84.03
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source:
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name: Open LLM Leaderboard
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kaist-ai%2Fmistral-orpo-beta
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# TruthfulQA (0-shot)
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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: 47.69
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source:
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name: Open LLM Leaderboard
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kaist-ai%2Fmistral-orpo-beta
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# GSM8k (5-shot)
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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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name: accuracy
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value: 39.8
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source:
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name: Open LLM Leaderboard
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kaist-ai%2Fmistral-orpo-beta
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# MMLU (5-Shot)
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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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name: accuracy
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value: 63.26
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source:
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name: Open LLM Leaderboard
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kaist-ai%2Fmistral-orpo-beta
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# Winogrande (5-shot)
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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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name: accuracy
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value: 79.24
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source:
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name: Open LLM Leaderboard
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kaist-ai%2Fmistral-orpo-beta
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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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- type: AlpacaEval 1.0
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value: 91.16%
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name: Win Rate
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source:
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url: https://tatsu-lab.github.io/alpaca_eval/
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name: Leaderboard
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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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- type: AlpacaEval 2.0
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value: 12.57%
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name: Win Rate
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source:
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url: https://tatsu-lab.github.io/alpaca_eval/
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name: Leaderboard
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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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- type: MT-Bench
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value: 7.322
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name: Score
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source:
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url: https://github.com/lm-sys/FastChat/blob/main/fastchat/llm_judge/
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name: self-reported
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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)](https://arxiv.org/abs/2403.07691)*. 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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- **Github Repository**: https://github.com/xfactlab/orpo
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## 👍 **Model Performance**
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### 1) AlpacaEval & MT-Bench
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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 β |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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### 2) IFEval
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| **Model Type** | **Prompt-Strict** | **Prompt-Loose** | **Inst-Strict** | **Inst-Loose** |
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|--------------------|:-----------------:|:----------------:|:---------------:|:--------------:|
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| **Mistral-ORPO-⍺** | 0.5009 | 0.5083 | 0.5995 | 0.6163 |
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| **Mistral-ORPO-β** | 0.5287 | 0.5564 | 0.6355 | 0.6619 |
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## 🗺️ **MT-Bench by Category**
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## 🖥️ **Inference**
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("kaist-ai/mistral-orpo-beta")
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tokenizer = AutoTokenizer.from_pretrained("kaist-ai/mistral-orpo-beta")
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# Apply chat template
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query = [{'role': 'user', 'content': 'Hi! How are you doing?'}]
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prompt = tokenizer.apply_chat_template(query, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(prompt, return_tensors='pt')
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# Generation with specific configurations
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output = model.generate(
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**inputs,
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max_new_tokens=128,
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do_sample=True,
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temperature=0.7
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)
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response = tokenizer.batch_decode(output)
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#<|user|>
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#Hi! How are you doing?</s>
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#<|assistant|>
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#I'm doing well, thank you! How are you?</s>
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```
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## 📎 **Citation**
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```
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@misc{hong2024orpo,
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title={ORPO: Monolithic Preference Optimization without Reference Model},
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author={Jiwoo Hong and Noah Lee and James Thorne},
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year={2024},
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eprint={2403.07691},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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``` |