LlamaFactoryThe unified fine-tuning framework: 100+ LLMs and VLMs via LoRA/QLoRA/full-parameter, config-driven or through the LlamaBoard GUI. ACL 2024, 1000+ citations, 73k stars.
Why switchThe two dominant open fine-tuning stacks: TRL is the code-first Hugging Face library; LlamaFactory the config-driven unified trainer with a GUI and a wider model-coverage matrix.
Full comparison → h2o-llmstudioH2O's no-code GUI and framework for fine-tuning LLMs — LoRA, 8-bit, DPO and experiment tracking behind a web UI, with CLI and Docker paths for the same configs.
Why switchThe same LoRA/DPO fine-tuning jobs behind different interfaces: TRL is the code-first library for Hub-native workflows, LLM Studio the no-code GUI for teams that don't write training loops.
Full comparison → verlByteDance's RL post-training library (HybridFlow): PPO/GRPO dataflows in a few lines, FSDP/Megatron training with vLLM/SGLang rollouts, production-proven at frontier scale.
Why switchSame goal — a post-trained model — at opposite ends of the infra spectrum: TRL for Hub-native single-node SFT/DPO/GRPO, verl when the job needs disaggregated rollout engines and multi-node RL dataflows.
Full comparison → slimeTHUDM's RL post-training framework behind the GLM releases — Megatron training plus SGLang rollouts with native arg pass-through, and pluggable reward, verifier and agentic data-generation workflows.
Why switchThe lighter trainer slime's own docs point you toward: single-node SFT/LoRA/DPO on the HF stack instead of Megatron-scale RL infrastructure. Same goal — a post-trained model — at opposite ends of the infra spectrum.
Full comparison → train-llm-from-scratchThe full LLM pipeline hand-written in plain PyTorch — tokens, transformer, pretraining, then SFT, reward model, PPO, DPO, GRPO. No trl, no peft: read every algorithm, train on one GPU.
Why switchSame post-training algorithms, opposite purposes: TRL is the production library you call; this re-implements SFT/PPO/DPO/GRPO by hand so you can read them. Learn here, ship with TRL.
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