想微调LLM选框架？这篇对比了Unsloth、Axolotl、TRL和LLaMA-Factory，讲清了各自的速度、显存和多GPU表现，帮你快速决策。
Unsloth通过重写底层内核加速训练。Axolotl组合多种并行策略优化多GPU效率。TRL提供了被其他框架依赖的标准训练器API。LLaMA-Factory以支持最广泛的模型覆盖为目标。这四个框架均基于PyTorch和HuggingFace生态。
Unsloth vs Axolotl vs TRL vs LLaMA-Factory: A Fine-Tuning Framework Comparison on Speed, VRAM, and Multi-GPU
Four open source projects dominate LLM fine-tuning today. Unsloth, Axolotl, TRL, and LLaMA-Factory all wrap the same underlying PyTorch and Hugging Face stack. They diverge on where they spend engineering effort. Unsloth rewrites kernels. Axolotl composes parallelism strategies. TRL defines the trainer APIs the others build on. LLaMA-Factory optimizes for breadth of model coverage and […] The post Unsloth vs Axolotl vs TRL vs LLaMA-Factory: A Fine-Tuning Framework Comparison on Speed, VRAM, and Multi-GPU appeared first on MarkTechPost .