llamafactory vs verl
Two of the top projects in Training and fine-tuning, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
llamafactory
Fine-tuning toolkit for 100+ LLMs with a CLI and Gradio web UI
verl
Reinforcement learning training library for LLM post-training with PPO and GRPO
Verdict
llamafactory ranks higher, 74 to 68. Of the 3 score parts we measure for both, llamafactory leads on 1, verl on 1 and 1 is even.
- Adoptionllamafactory leads by 54 points, 90 to 36: a wide gap.
- FreshnessEven at 100.
- MaintenanceWe do not score verl on this; llamafactory has 38.
- Agent-readyverl leads by 15 points, 70 to 55: a wide gap.
Pick llamafactory if you are
Engineers fine-tuning open LLMs on their own GPUs, with or without code
- Broad method coverage: SFT, PPO, DPO, KTO, ORPO, LoRA, QLoRA via AQLM/AWQ/GPTQ/HQQ/EETQ
- Supports GaLore, BAdam, APOLLO, Muon, DoRA, PiSSA, plus Unsloth and Liger Kernel speedups
Pick verl if you are
ML engineers running RL post-training of LLMs on GPU clusters
- Pluggable backends: FSDP/FSDP2/Megatron-LM for training, vLLM/SGLang/HF for rollout
- Many RL algorithms built in: PPO, GRPO, ReMax, RLOO, REINFORCE++
| What we compare | llamafactory | verl |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 90, widely used | 36, known |
| Freshness | 100, active | 100, active |
| Maintenance | 38, patchy | Not scored for this kind |
| Easy to run | Not scored for this kind | Not scored for this kind |
| Agent-ready | 55, partly | 70, partly |
| Facts from GitHub and the README | ||
| Stars | 75.5k | 23.8k |
| License | Apache-2.0 (permissive) | Apache-2.0 (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | May 2026 | Sep 2026 |
| Language | Python | Python |
| Docker | Yes | Yes |
| GPU | Required | Required |
| arm64 or Apple Silicon | Mentioned | Not stated |
llamafactory
LlamaFactory fine-tunes language and multimodal models through YAML-configured CLI runs or the LLaMA Board Gradio UI. It covers pre-training, SFT, reward modeling, PPO, DPO, KTO and ORPO, with full-tuning, freeze-tuning, LoRA and 2 to 8-bit QLoRA. Trained models can be served through an OpenAI-style API using vLLM or SGLang workers.
Who it is for: Engineers fine-tuning open LLMs on their own GPUs, with or without code
Strengths
- Broad method coverage: SFT, PPO, DPO, KTO, ORPO, LoRA, QLoRA via AQLM/AWQ/GPTQ/HQQ/EETQ
- Supports GaLore, BAdam, APOLLO, Muon, DoRA, PiSSA, plus Unsloth and Liger Kernel speedups
- Covers multimodal and audio models such as Qwen3-VL, LLaVA and Qwen2-Audio
- Runs on NVIDIA, AMD ROCm and Ascend NPU, with hosted Colab and PAI-DSW notebooks
Weaknesses
- Official documentation is marked WIP in the README
- No VRAM or RAM figures in the visible README; hardware requirements unknown here
- Web UI is for training and chat, not a production serving layer
- Many optional backends (vLLM, SGLang, Unsloth, KTransformers) add install complexity
- GPU required
- Docker + Compose
- Needs PyTorch, Hugging Face Transformers, Gradio, vLLM (optional), SGLang (optional)
- Models: LLaMA, LLaVA, Mistral, Mixtral-MoE, Qwen3
verl
verl is the open-source implementation of the HybridFlow paper from ByteDance Seed. It runs RL and supervised fine-tuning for LLMs using FSDP, FSDP2 or Megatron-LM for training and vLLM, SGLang or HF Transformers for rollout generation. It covers PPO, GRPO, RLOO, ReMax and others, with multi-turn tool calling, VLM support and scaling to 671B models.
Who it is for: ML engineers running RL post-training of LLMs on GPU clusters
Strengths
- Pluggable backends: FSDP/FSDP2/Megatron-LM for training, vLLM/SGLang/HF for rollout
- Many RL algorithms built in: PPO, GRPO, ReMax, RLOO, REINFORCE++
- Scales to 671B models and hundreds of GPUs via expert parallelism
- Supports NVIDIA, AMD and Ascend hardware; LoRA RL to save memory
Weaknesses
- Needs multi-GPU clusters in practice; minimum hardware not stated in README
- Many algorithm recipes live in a separate verl-recipe repository and submodule
- Version-sensitive: README warns against vLLM 0.7.x and requires >=0.8.2
- Several features remain under verl/experimental until merged into the main library
- GPU required
- Docker
- Needs Ray, PyTorch, vLLM, SGLang, Megatron-LM
- Models: Qwen3.5, Qwen3, Qwen2.5, Qwen2.5-VL, Kimi-VL
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