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Model: 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

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{
"_name_or_path": "unsloth/mistral-7b-v0.2",
"architectures": [
"MistralForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 1,
"eos_token_id": 2,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 14336,
"max_position_embeddings": 32768,
"model_type": "mistral",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"pad_token_id": 0,
"rms_norm_eps": 1e-05,
"rope_theta": 1000000.0,
"sliding_window": 32768,
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.39.1",
"use_cache": true,
"vocab_size": 32000
}

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