implementing-llms-litgpt
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.
npx skills add Orchestra-Research/AI-Research-SKILLs --skill litgpt --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# LitGPT - Clean LLM Implementations ## Quick start LitGPT provides 20+ pretrained LLM implementations with clean, readable code and production-ready training workflows. **Installation**: ```bash pip install 'litgpt[extra]' ``` **Load and use any model**: ```python from litgpt import LLM # Load pretrained model llm = LLM.load("microsoft/phi-2") # Generate text result = llm.generate( "What is the capital of France?", max_new_tokens=50, temperature=0.7 ) print(result) ``` **List available models**: ```bash litgpt download list ``` ## Common workflows ### Workflow 1: Fine-tune on custom dataset Copy this checklist: ``` Fine-Tuning Setup: - [ ] Step 1: Download pretrained model - [ ] Step 2: Prepare dataset - [ ] Step 3: Configure training - [ ] Step 4: Run fine-tuning ``` **Step 1: Download pretrained model** ```bash # Download Llama 3 8B litgpt download meta-llama/Meta-Llama-3-8B # Download Phi-2 (smaller, faster) litgpt download microsoft/phi-2 # Download Gemma 2B litgpt download google/gemma-2b ``` Models are saved to `checkpoints/` directory. **Step 2: Prepare dataset** LitGPT supports multiple formats: **Alpaca format** (instruction-response): ```json [ { "instruction": "What is
- Quick start
- Common workflows
- Workflow 1: Fine-tune on custom dataset
- Workflow 2: LoRA fine-tuning on single GPU
- Workflow 3: Pretrain from scratch
- Workflow 4: Convert and deploy model
- When to use vs alternatives
- Common issues
- Advanced topics
- Hardware requirements
- Resources
pip install 'litgpt[extra]' litgpt download list Download Llama 3 8B litgpt download meta-llama/Meta-Llama-3-8B Download Phi-2 (smaller, faster) litgpt download microsoft/phi-2 Download Gemma 2B litgpt download google/gemma-2b Full fine-tuning (requires 40GB+ GPU for 7B models) litgpt finetune \
What does the implementing-llms-litgpt skill do?
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.
How do I install it?
Run `npx skills add Orchestra-Research/AI-Research-SKILLs --skill litgpt --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From Orchestra-Research/AI-Research-SKILLs, a repository with 11,391 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.