{"id":"hiyouga-llamafactory","name":"LlamaFactory","af_score":19.8,"security_score":33.8,"reliability_score":32.5,"what_it_does":"LLaMA Factory (llamafactory) is a Python framework/CLI/UI for training and fine-tuning a wide range of LLMs and multimodal models using many supervised and RL-style training approaches, with support for efficient methods (e.g., LoRA/QLoRA and quantization) and multiple inference backends including an OpenAI-style API via vLLM/SGLang.","best_when":"You want to run local or self-hosted fine-tuning/inference workflows for LLMs (including multimodal) and you can manage GPU/resources and model/reproducibility requirements yourself.","avoid_when":"You need a simple single-endpoint SaaS with built-in authentication, billing, and SLA; or you cannot manage the complexity/dependencies typical of LLM training stacks.","last_evaluated":"2026-03-29T12:58:11.348407+00:00","has_mcp":false,"has_api":true,"auth_methods":["Self-hosted/infrastructure-provided auth (not specified in provided content)","OpenAI-style API deployment (auth not specified in provided content)"],"has_free_tier":false,"known_gotchas":["This is a training/inference framework with heavy dependencies and environment/GPU sensitivity; agent automation should handle long-running jobs and varied failure modes.","Auth/rate limiting behavior for the described OpenAI-style deployment is not documented in the provided content.","Many configuration parameters/submodules exist (different backends/optimizers/quantization/PEFT methods), increasing integration complexity."],"error_quality":null}