القائمة
Expert guidance for fine-tuning LLMs with LLaMA-Factory - WebUI no-code, 100+ models, 2/3/4/5/6/8-bit QLoRA, multimodal support
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
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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