Shypd.aiThe largest open index of AI toolsTRL is an AI Frameworks & Infra tool that enables post-training of transformer language models using reinforcement learning. It supports various fine-tuning methods and scales from single GPU to multi-node clusters.
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TRL (Transformers Reinforcement Learning) is a comprehensive library designed for post-training foundation models through advanced techniques like Supervised Fine-Tuning (SFT), Group Relative Policy Optimization (GRPO), and Direct Preference Optimization (DPO). Built on the Hugging Face Transformers ecosystem, TRL supports diverse model architectures and modalities, offering scalability across various hardware setups. Key features include accessible trainers like SFTTrainer, GRPOTrainer, DPOTrainer, and RewardTrainer. It leverages Hugging Face Accelerate for scaling from single GPUs to multi-node clusters with DDP and DeepSpeed, and integrates PEFT for efficient training on large models using quantization and LoRA/QLoRA. TRL also includes a Command Line Interface (CLI) for fine-tuning models without extensive coding.
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