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Expert guidance for fine-tuning LLMs with LLaMA-Factory - WebUI no-code, 100+ models, 2/3/4/5/6/8-bit QLoRA, multimodal support
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SOC 職業分類に基づく
Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers research direction. Routes to domain-specific skills for execution, supports continuous agent operation via Claude Code /loop and OpenClaw heartbeat, and produces research presentations and papers. Use when starting a research project, running autonomous experiments, or managing a multi-hypothesis research effort.
Provides guidance for automatically evolving and optimizing AI agents across any domain using LLM-driven evolution algorithms. Use when building self-improving agents, optimizing agent prompts and skills against benchmarks, or implementing automated agent evaluation loops.
Generates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given experiment results or data, auto-selects chart type and generates data-driven figures via matplotlib/seaborn. Use when creating any figure for a conference paper.
Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device placement, mixed precision (FP16/BF16/FP8). Interactive config, single launch command. HuggingFace ecosystem standard.
Read and operationalize handoff artifacts created for a future AI agent, including operational handoff md files and compact conversation-context md files. Use when asked to take over, resume, continue from handoff docs, read AgentHandoff outputs, verify current state before acting, or prepare a safe next-step plan from prior agent documentation.
Create continuation-oriented handoff/progress artifacts for another AI agent to safely resume complex work, including an operational handoff md plus a compact-session-style conversation context md. Use when the user asks to summarize current progress for a future/next/other agent, write an Agent README, update a project handoff, preserve recent conversation/history, record experiment state, preserve cluster/run/debug context, or document what has changed, what is running, known risks, exact next steps, and recent user instructions. Do not use for polished human-facing status reports unless the user explicitly asks for both audiences.
| name | llama-factory |
| description | Expert guidance for fine-tuning LLMs with LLaMA-Factory - WebUI no-code, 100+ models, 2/3/4/5/6/8-bit QLoRA, multimodal support |
| version | 1.0.0 |
| author | Orchestra Research |
| license | MIT |
| tags | ["Fine-Tuning","LLaMA Factory","LLM","WebUI","No-Code","QLoRA","LoRA","Multimodal","HuggingFace","Llama","Qwen","Gemma"] |
| dependencies | ["llmtuner","torch","transformers","datasets","peft","accelerate","gradio"] |
Comprehensive assistance with llama-factory development, generated from official documentation.
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