{"id":"hiyouga-llamafactory","name":"LlamaFactory","homepage":"https://llamafactory.readthedocs.io","repo_url":"https://github.com/hiyouga/LlamaFactory","category":"ai-ml","subcategories":[],"tags":["ai-ml","llm","fine-tuning","peft","lora","qlora","training","multimodal","gradio","vllm","sglang","transformers"],"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.","use_cases":["Fine-tune LLMs for instruction/chat and multi-turn dialogue","Multimodal supervised fine-tuning (image/video/audio understanding)","Efficient adaptation using LoRA/QLoRA/DoRA and related PEFT methods","Training with various reward-modeling and RL approaches (e.g., PPO/DPO/KTO/ORPO)","Export/deploy fine-tuned checkpoints with inference backends (vLLM/SGLang)","Provide an interactive web UI (Gradio) for managing fine-tuning jobs"],"not_for":["As a managed hosted API/service with guaranteed SLAs","Production-grade API gateway for third-party consumers without additional operational controls","Use as a drop-in enterprise authentication/authorization service (it is primarily a training/inference framework)"],"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.","alternatives":["Hugging Face Transformers + PEFT + TRL","Axolotl","OpenRLHF / DeepSpeed-based fine-tuning stacks","Lightning AI / Lightning Fabric for custom training loops","Modalities-specific frameworks for multimodal fine-tuning (varies by model family)"],"af_score":19.8,"security_score":33.8,"reliability_score":32.5,"package_type":"skill","discovery_source":["openclaw"],"priority":"high","status":"evaluated","version_evaluated":null,"last_evaluated":"2026-03-29T12:58:11.348407+00:00","interface":{"has_rest_api":true,"has_graphql":false,"has_grpc":false,"has_mcp_server":false,"mcp_server_url":null,"has_sdk":false,"sdk_languages":["Python"],"openapi_spec_url":null,"webhooks":false},"auth":{"methods":["Self-hosted/infrastructure-provided auth (not specified in provided content)","OpenAI-style API deployment (auth not specified in provided content)"],"oauth":false,"scopes":false,"notes":"The provided README/manifest content describes deployment via OpenAI-style API and inference backends, but does not specify authentication method types, API keys, or scope models."},"pricing":{"model":null,"free_tier_exists":false,"free_tier_limits":null,"paid_tiers":[],"requires_credit_card":false,"estimated_workload_costs":null,"notes":"No pricing model for a hosted service is stated; this appears to be a self-hosted open-source tooling stack."},"requirements":{"requires_signup":false,"requires_credit_card":false,"domain_verification":false,"data_residency":[],"compliance":[],"min_contract":null},"agent_readiness":{"af_score":19.8,"security_score":33.8,"reliability_score":32.5,"mcp_server_quality":null,"documentation_accuracy":60.0,"error_message_quality":null,"error_message_notes":"The provided content does not include concrete examples of error responses/codes. As a large training framework, error quality likely varies by subsystem, but cannot be verified from the given material.","auth_complexity":50.0,"rate_limit_clarity":0.0,"tls_enforcement":60.0,"auth_strength":20.0,"scope_granularity":10.0,"dependency_hygiene":45.0,"secret_handling":40.0,"security_notes":"From the provided content, TLS/auth/secret-handling specifics are not documented. The manifest shows use of FastAPI/Uvicorn/SSE (which typically runs behind TLS depending on deployment), but there is no evidence in the provided material of enforced HTTPS, authentication scheme, or secret-management best practices. Dependency versions are pinned by range and include common ML/security-sensitive libraries (torch/transformers/peft), but no CVE status or audit results are provided.","uptime_documented":0.0,"version_stability":45.0,"breaking_changes_history":55.0,"error_recovery":30.0,"idempotency_support":null,"idempotency_notes":"Not described in provided content.","pagination_style":null,"retry_guidance_documented":null,"known_agent_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."]}}