61 lines
2.3 KiB
Markdown
61 lines
2.3 KiB
Markdown
---
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language:
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- en
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license: apache-2.0
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library_name: transformers
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datasets:
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- argilla/distilabel-capybara-dpo-7k-binarized
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---
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# Mistral-v0.2-orpo
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*Mistral-v0.2-orpo* is a fine-tuned version of the new **[Mistral-7B-v0.2](https://huggingface.co/alpindale/Mistral-7B-v0.2-hf)** on **[argilla/distilabel-capybara-dpo-7k-binarized](https://huggingface.co/datasets/argilla/distilabel-capybara-dpo-7k-binarized)**
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preference dataset using *Odds Ratio Preference Optimization (ORPO)*. The model has been trained for 1 epoch. It took almost 8 hours on A100 GPU.
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## 💥 LazyORPO
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This model has been trained using **[LazyORPO](https://colab.research.google.com/drive/19ci5XIcJDxDVPY2xC1ftZ5z1kc2ah_rx?usp=sharing)**. A colab notebook that makes the training
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process much easier. Based on [ORPO paper](https://colab.research.google.com/corgiredirector?site=https%3A%2F%2Fhuggingface.co%2Fpapers%2F2403.07691)
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#### 🎭 What is ORPO?
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Odds Ratio Preference Optimization (ORPO) proposes a new method to train LLMs by combining SFT and Alignment into a new objective (loss function), achieving state of the art results.
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Some highlights of this techniques are:
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* 🧠 Reference model-free → memory friendly
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* 🔄 Replaces SFT+DPO/PPO with 1 single method (ORPO)
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* 🏆 ORPO Outperforms SFT, SFT+DPO on PHI-2, Llama 2, and Mistral
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* 📊 Mistral ORPO achieves 12.20% on AlpacaEval2.0, 66.19% on IFEval, and 7.32 on MT-Bench out Hugging Face Zephyr Beta
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#### 💻 Usage
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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torch.set_default_device("cuda")
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model = AutoModelForCausalLM.from_pretrained("abideen/Mistral-v0.2-orpo", torch_dtype="auto", trust_remote_code=True)
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tokenizer = AutoTokenizer.from_pretrained("abideen/Mistral-v0.2-orpo", trust_remote_code=True)
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inputs = tokenizer('''
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"""
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Write a detailed analogy between mathematics and a lighthouse.
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"""''', return_tensors="pt", return_attention_mask=False)
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outputs = model.generate(**inputs, max_length=200)
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text = tokenizer.batch_decode(outputs)[0]
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print(text)
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```
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## 🏆 Evaluation
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### COMING SOON |