LLaMA Factory vs Ray Tune

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

	LLaMA Factory Fine-tuning	Ray Tune Fine-tuning
Tagline	Open-source, no-code WebUI for fine-tuning 100+ open LLMs with LoRA, QLoRA, DPO, and PPO.	Open-source Python library for distributed hyperparameter tuning at any scale.
Category	Fine-tuning	Fine-tuning
Pricing	Free· Free, open-source (Apache-2.0); self-hosted	Free· Open-source (Apache 2.0); managed via Anyscale offers a $100 starting credit
Model	Multi-model (LLaMA, Mistral, Qwen, Gemma, Phi, LLaVA, ChatGLM, Yi)	—
Editorial score	7.2 / 10	8.1 / 10
Use cases	lora-fine-tuningqloradpo-alignmentinstruction-tuningrlhfvlm-fine-tuning	hyperparameter-tuningdistributed-trainingmodel-selectionpopulation-based-trainingearly-stopping
Pros	No-code WebUI (LlamaBoard) covers SFT, DPO, PPO, KTO, and reward modeling Supports 100+ open models including multimodal VLMs out of the box Full QLoRA stack (2-8 bit) plus LoRA+, DoRA, PiSSA variants Acceleration via FlashAttention-2, Unsloth, Liger Kernel, vLLM inference Exports to GGUF / Ollama and integrates with W&B, MLflow, TensorBoard	Scales the same code from a laptop to a multi-node GPU cluster Built-in PBT, ASHA, HyperBand plus Optuna/Ax/BOHB integrations Framework-agnostic: PyTorch, TF/Keras, XGBoost, Transformers Fault-tolerant with automatic checkpointing and trial resumption Free and open-source under Apache 2.0
Cons	Self-hosted only — you bring the GPUs and the ops Rapid release cadence means version pinning is essential WebUI abstracts but does not solve VRAM and dataset-formatting pitfalls	No GUI; everything is configured in Python Ray cluster setup adds operational overhead vs single-node tools Steeper learning curve than Optuna for simple sweeps
Website	llamafactory.readthedocs.io	docs.ray.io

Pick LLaMA Factory if

✅ No-code WebUI (LlamaBoard) covers SFT, DPO, PPO, KTO, and reward modeling
✅ Supports 100+ open models including multimodal VLMs out of the box
✅ Full QLoRA stack (2-8 bit) plus LoRA+, DoRA, PiSSA variants
✅ Acceleration via FlashAttention-2, Unsloth, Liger Kernel, vLLM inference

Pick Ray Tune if

✅ Scales the same code from a laptop to a multi-node GPU cluster
✅ Built-in PBT, ASHA, HyperBand plus Optuna/Ax/BOHB integrations
✅ Framework-agnostic: PyTorch, TF/Keras, XGBoost, Transformers
✅ Fault-tolerant with automatic checkpointing and trial resumption

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