llama-factory
LLaMA-Factory is a no-code web interface and toolkit for fine-tuning large language models across 100+ model architectures using quantized LoRA techniques at 2/3/4/5/6/8-bit precision levels, with multimodal support. Use this skill when implementing LLM fine-tuning workflows, debugging configuration issues, exploring quantization strategies, or seeking guidance on model adaptation best practices through the LLaMA-Factory ecosystem.
git clone --depth 1 https://github.com/Orchestra-Research/AI-Research-SKILLs /tmp/llama-factory && cp -r /tmp/llama-factory/03-fine-tuning/llama-factory ~/.claude/skills/llama-factorySKILL.md
# Llama-Factory Skill Comprehensive assistance with llama-factory development, generated from official documentation. ## When to Use This Skill This skill should be triggered when: - Working with llama-factory - Asking about llama-factory features or APIs - Implementing llama-factory solutions - Debugging llama-factory code - Learning llama-factory best practices ## Quick Reference ### Common Patterns *Quick reference patterns will be added as you use the skill.* ## Reference Files This skill includes comprehensive documentation in `references/`: - **_images.md** - Images documentation - **advanced.md** - Advanced documentation - **getting_started.md** - Getting Started documentation - **other.md** - Other documentation Use `view` to read specific reference files when detailed information is needed. ## Working with This Skill ### For Beginners Start with the getting_started or tutorials reference files for foundational concepts. ### For Specific Features Use the appropriate category reference file (api, guides, etc.) for detailed information. ### For Code Examples The quick reference section above contains common patterns extracted from the official docs. ## Resources ### references/ Organized documentation extracted from official sources. These files contain: - Detailed explanations - Code examples with language annotations - Links to original documentation - Table of contents for quick navigation ### scripts/ Add helper scripts here for common automation tasks. ### assets/ Add templates, boilerplate, or example projects here. ## Notes - This skill was automatically generated from official documentation - Reference files preserve the structure and examples from source docs - Code examples include language detection for better syntax highlighting - Quick reference patterns are extracted from common usage examples in the docs ## Updating To refresh this skill with updated documentation: 1. Re-run the scraper with the same configuration 2. The skill will be rebuilt with the latest information
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.
Implements and trains LLMs using Lightning AI's LitGPT with 20+ pretrained architectures (Llama, Gemma, Phi, Qwen, Mistral). Use when need clean model implementations, educational understanding of architectures, or production fine-tuning with LoRA/QLoRA. Single-file implementations, no abstraction layers.
State-space model with O(n) complexity vs Transformers' O(n²). 5× faster inference, million-token sequences, no KV cache. Selective SSM with hardware-aware design. Mamba-1 (d_state=16) and Mamba-2 (d_state=128, multi-head). Models 130M-2.8B on HuggingFace.
Educational GPT implementation in ~300 lines. Reproduces GPT-2 (124M) on OpenWebText. Clean, hackable code for learning transformers. By Andrej Karpathy. Perfect for understanding GPT architecture from scratch. Train on Shakespeare (CPU) or OpenWebText (multi-GPU).
RNN+Transformer hybrid with O(n) inference. Linear time, infinite context, no KV cache. Train like GPT (parallel), infer like RNN (sequential). Linux Foundation AI project. Production at Windows, Office, NeMo. RWKV-7 (March 2025). Models up to 14B parameters.
Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or custom models at scale from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing.
Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers. Use when you need high-performance tokenization or custom tokenizer training.
Language-independent tokenizer treating text as raw Unicode. Supports BPE and Unigram algorithms. Fast (50k sentences/sec), lightweight (6MB memory), deterministic vocabulary. Used by T5, ALBERT, XLNet, mBART. Train on raw text without pre-tokenization. Use when you need multilingual support, CJK languages, or reproducible tokenization.