Fine-tune Llama 2 with DPO

What changed

Reinforcement Learning from Human Feedback (RLHF) has become the de facto last training step of LLMs such as GPT-4 or Claude to ensure that the language model's outputs are aligned with human expectations such as chattiness or safety features. Such a process is quite involved requiring a number of complex moving parts where it is not always easy to get things right. This blog-post introduces the Direct Preference Optimization (DPO) method which is now available in the TRL library and shows how one can fine tune the recent Llama v2 7B-parameter model on the stack-exchange preference dataset which contains ranked answers to questions on the various stack-exchange portals.

Why it matters

A concrete addition to Practical AI: Tools, Models & Frameworks: it changes what's available to builders today rather than being general commentary.

How it compares

Related prior coverage to compare against:

• How to deploy and fine-tune DeepSeek models on AWS
• Train and Fine-Tune Sentence Transformers Models
• Llama 3.2 goes small and multimodal

• How to deploy and fine-tune DeepSeek models on AWS similarity 0.28
• Train and Fine-Tune Sentence Transformers Models similarity 0.28
• Llama 3.2 goes small and multimodal similarity 0.28