Llama Factory
技能 已验证 活跃Expert guidance for fine-tuning LLMs with LLaMA-Factory - WebUI no-code, 100+ models, 2/3/4/5/6/8-bit QLoRA, multimodal support
To serve as an expert guide and documentation resource for users working with LLaMA-Factory, enabling efficient and effective fine-tuning of LLMs with a no-code approach.
功能
- Expert guidance for LLaMA-Factory
- No-code WebUI for LLM fine-tuning
- Support for 100+ models
- 2/3/4/5/6/8-bit QLoRA
- Multimodal support
- Comprehensive reference documentation
使用场景
- Learning LLaMA-Factory features and APIs
- Implementing LLaMA-Factory solutions
- Debugging LLaMA-Factory code
- Understanding fine-tuning best practices
非目标
- Directly executing LLM fine-tuning tasks (provides guidance instead)
- Replacing the LLaMA-Factory framework itself
- Covering fine-tuning frameworks other than LLaMA-Factory
Trust
- info:Issues AttentionThere are 17 open issues and 4 closed issues in the last 90 days, indicating a closure rate of approximately 19%, which suggests maintainers might be slow to respond to a moderate volume of issues.
安装
npx skills add davila7/claude-code-templates通过 npx 运行 Vercel skills CLI(skills.sh)— 需要本地安装 Node.js,以及至少一个兼容 skills 的智能体(Claude Code、Cursor、Codex 等)。前提是仓库遵循 agentskills.io 格式。
质量评分
已验证类似扩展
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100Implements 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.
Unsloth
100Expert guidance for fast fine-tuning with Unsloth - 2-5x faster training, 50-80% less memory, LoRA/QLoRA optimization
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Unsloth
98Expert guidance for fast fine-tuning with Unsloth - 2-5x faster training, 50-80% less memory, LoRA/QLoRA optimization
Implementing Llms Litgpt
98Implements 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.