Skill UI

Fine-tune LLMs with TRL’s RLHF toolkit, covering SFT, DPO, PPO/GRPO and reward model training so you can align HuggingFace Transformers with human preferences or feedback in post-training workflows.

Provides expert guidance for GRPO training with TRL, covering dataset prep, reward engineering, structured outputs, and multi-objective alignment when preference pairs are scarce.

High-performance RLHF framework that combines Ray, vLLM, and ZeRO-3 to accelerate PPO/GRPO/RLOO/DPO training for 7B-70B+ models on distributed GPU clusters, providing a single-stack workflow for reward models and policy optimization.

This skill covers setting up infrastructure for fine-tuning Large Language Models (LLMs) using techniques like QLoRA, LoRA, and full fine-tuning. It includes configuring distributed training with DeepSpeed or FSDP, utilizing frameworks such as Hugging Face TRL and Axolotl, and implementing alignment methods like RLHF and DPO. Suitable for domain-specific model adaptation and production deployment.