abideen
/

Mistral-v2-orpo

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 ---
+language:
+- en
+license: apache-2.0
 library_name: transformers
+datasets:
+- argilla/distilabel-capybara-dpo-7k-binarized
 ---
+# Mistral-v0.2-orpo
+![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/64e380b2e12618b261fa6ba0/at8Hw9EadVmHsO8UQTrBV.jpeg)
+*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)**
+preference dataset using *Odds Ratio Preference Optimization (ORPO)*. The model has been trained for 1 epoch. It took almost 8 hours on A100 GPU.
+## 💥 LazyORPO
+This model has been trained using **[LazyORPO](https://colab.research.google.com/drive/19ci5XIcJDxDVPY2xC1ftZ5z1kc2ah_rx?usp=sharing)**. A colab notebook that makes the training
+process much easier. Based on [ORPO paper](https://colab.research.google.com/corgiredirector?site=https%3A%2F%2Fhuggingface.co%2Fpapers%2F2403.07691)
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64e380b2e12618b261fa6ba0/2h3guPdFocisjFClFr0Kh.png)
+#### 🎭 What is ORPO?
+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.
+Some highlights of this techniques are:
+* 🧠 Reference model-free → memory friendly
+* 🔄 Replaces SFT+DPO/PPO with 1 single method (ORPO)
+* 🏆 ORPO Outperforms SFT, SFT+DPO on PHI-2, Llama 2, and Mistral
+* 📊 Mistral ORPO achieves 12.20% on AlpacaEval2.0, 66.19% on IFEval, and 7.32 on MT-Bench out Hugging Face Zephyr Beta
+#### 💻 Usage
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+torch.set_default_device("cuda")
+model = AutoModelForCausalLM.from_pretrained("abideen/Mistral-v0.2-orpo", torch_dtype="auto", trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained("abideen/Mistral-v0.2-orpo", trust_remote_code=True)
+inputs = tokenizer('''
+   """
+   Write a detailed analogy between mathematics and a lighthouse.
+   """''', return_tensors="pt", return_attention_mask=False)
+outputs = model.generate(**inputs, max_length=200)
+text = tokenizer.batch_decode(outputs)[0]
+print(text)
+```
+## 🏆 Evaluation
+### COMING SOON