When should I choose DPO over PPO?

Use DPO (Direct Preference Optimization) when you want a simpler, more stable alignment process that doesn't require training a separate reward model or managing complex RL hyperparameters.

What hardware is required for these workflows?

An NVIDIA GPU with CUDA is required. VRAM needs vary from 16GB for 7B models with LoRA to over 40GB for full PPO pipelines.

What is the primary purpose of the TRL skill?

The TRL skill provides standardized patterns and code snippets for the Transformer Reinforcement Learning library, helping developers align LLMs using SFT, DPO, and PPO.

Can I use this for instruction tuning?

Absolutely. The skill includes dedicated workflows for Supervised Fine-Tuning (SFT) specifically for instruction-following tasks.

Does this skill support memory-efficient training?

Yes, it includes configurations for LoRA/QLoRA, gradient checkpointing, and GRPO to enable training on hardware with limited VRAM.

LLM Reinforcement Learning & Fine-Tuning

Name: LLM Reinforcement Learning & Fine-Tuning
Author: zechenzhangAGI

byzechenzhangAGI

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384

Ciencia de Datos y ML

Aligns Large Language Models with human preferences using advanced reinforcement learning techniques including SFT, DPO, PPO, and GRPO.

Acerca de

This skill provides a comprehensive framework for the post-training phase of Large Language Model development using the TRL (Transformer Reinforcement Learning) library. It guides users through complex workflows such as Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and full RLHF pipelines to ensure models follow instructions and align with specific human values or reward functions. Designed for AI researchers and engineers, it includes optimized patterns for memory management and hardware utilization within the HuggingFace ecosystem.

Características Principales

Full RLHF pipeline implementation including Reward Modeling
Standardized training configurations for HuggingFace Transformers
Direct Preference Optimization (DPO) for stable alignment
384 GitHub stars
Memory-efficient Online RL using GRPO
Supervised Fine-Tuning (SFT) for instruction following

Casos de Uso

Implementing memory-constrained reinforcement learning for large-scale models
Aligning a base model with human preferences using binarized datasets
Training a custom reward model to score model generations

Acerca de

Características Principales

Full RLHF pipeline implementation including Reward Modeling
Standardized training configurations for HuggingFace Transformers
Direct Preference Optimization (DPO) for stable alignment
384 GitHub stars
Memory-efficient Online RL using GRPO
Supervised Fine-Tuning (SFT) for instruction following

Casos de Uso

Implementing memory-constrained reinforcement learning for large-scale models
Aligning a base model with human preferences using binarized datasets
Training a custom reward model to score model generations