skills.rest
Clay-HHK
Clay-HHKCOMMUNITY
Explore all skills by this author →
GitHub

implementing-llms-litgpt

Implement and train large language models with LitGPT single-file recipes.

2|Updated Apr 12, 2026
Search Tags:#llm#fine-tuning#lora#fsdp#qlora#model-conversion#litgpt
One-click install
npx skills add https://github.com/Clay-HHK/claude-config --skill implementing-llms-litgpt-clay-hhk
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: implementing-llms-litgpt
Source: https://github.com/Clay-HHK/claude-config/tree/main/skills/AI-research-SKILLs/01-model-architecture/litgpt
Command: npx skills add https://github.com/Clay-HHK/claude-config --skill implementing-llms-litgpt-clay-hhk

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Provides clean, single-file implementations and end-to-end training recipes for large language models so teams and researchers can understand architectures and run production-grade fine-tuning without heavy abstraction layers.

Core Features & Use Cases

  • Readable model implementations: Single-file GPT-style models for many families (Llama, Gemma, Phi, Mistral, Qwen, Falcon) that make architecture changes and debugging straightforward.
  • Fine-tuning workflows: Production-ready LoRA and QLoRA recipes, dataset formats, and merge/convert utilities for adapter training and deployment.
  • Scaling & deployment: Guidance for FSDP multi-GPU training, quantization (4/8-bit), converting to GGUF, and serving via APIs for research prototypes or production services.
  • Use case: Quickly prototype a LoRA fine-tune on microsoft/phi-2 for domain adaptation, merge adapters, quantize, and deploy a low-latency inference endpoint.

Quick Start

Install litgpt, download microsoft/phi-2, and run a LoRA fine-tune on data/my_dataset.json saving outputs to out/phi2-lora.

Frequently Asked Questions about implementing-llms-litgpt

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I fine-tune a large language model with LoRA or QLoRA?▼

To fine-tune an LLM with LoRA or QLoRA, apply production-ready adapter training recipes on single-file GPT-style models like Llama, Gemma, or Phi, merge adapters, and export the final weights.

What is the best way to understand and modify LLM architectures like Llama or Mistral?▼

Understanding LLM architectures is best achieved using clean, single-file model implementations that map directly to families like Llama, Mistral, and Qwen, making architecture changes and debugging straightforward without heavy abstractions.

Can I scale LLM pretraining across multiple GPUs using FSDP?▼

Yes, you can scale LLM pretraining across multiple GPUs by applying distributed FSDP configurations, allowing multi-GPU training without losing the readability of the underlying single-file model code.

Does LitGPT support converting models to GGUF and quantizing to 4-bit or 8-bit?▼

LitGPT supports converting models to GGUF and quantizing to 4-bit or 8-bit formats, providing the necessary utilities to prepare models for low-latency inference endpoints.

Do I need heavy abstraction layers to run production-grade LLM training recipes?▼

No, you do not need heavy abstraction layers; clean single-file implementations provide configurable training recipes and adapter workflows to run production-grade fine-tuning directly.

How do I deploy a domain-adapted LLM for low-latency inference?▼

To deploy a domain-adapted LLM, run a LoRA fine-tune on a target dataset, merge adapters, quantize the model to 4-bit, convert formats, and serve it via an API endpoint.

Related Skills

Explore More Software Engineering Skills→
ScientiaCapitalScientiaCapital
1

unsloth-finetuning

Fine-tune Llama, Mistral, Phi, or Gemma models with LoRA and 4-bit quantization.

Community
Intermediate
kaminocorpkaminocorp
228

gguf-quantization

Quantize models into GGUF format for CPU inference using llama.cpp.

Official
Advanced
Supporter09Supporter09

implementing-llms-litgpt

Build and train large language models with LitGPT.

Community
Advanced
eddieluengeddielueng
1

pytorch-lightning

Automate PyTorch training loops with LightningModule and Trainer abstractions.

Community
Intermediate