What models can I train with this skill?

You can implement and train over 20 popular architectures, including Llama 3, Gemma, Mistral, Phi-2, and Qwen.

Can I use this for production deployment?

Absolutely. It includes steps for model quantization, GGUF conversion, and setting up API endpoints for inference.

Does this skill support LoRA fine-tuning?

Yes, it provides optimized workflows for memory-efficient fine-tuning using LoRA and QLoRA on single or multi-GPU setups.

What are the hardware requirements?

Requirements vary by model size; for example, LoRA fine-tuning a 7B model requires roughly 16GB of VRAM, while full fine-tuning requires 40GB+.

LitGPT LLM Training & Implementation

Name: LitGPT LLM Training & Implementation
Author: zechenzhangAGI

byzechenzhangAGI

•

384

Data Science & ML

Simplifies Large Language Model implementation, training, and fine-tuning using clean, production-ready LitGPT architectures.

About

This skill empowers developers and AI researchers to implement and train over 20 pretrained LLM architectures, including Llama, Gemma, and Mistral, using the streamlined LitGPT framework. It provides expert guidance on production-grade fine-tuning workflows like LoRA and QLoRA, pretraining from scratch, and model deployment strategies. By emphasizing readable, single-file implementations without unnecessary abstractions, it helps users understand underlying model architectures while maintaining the performance needed for research and production environments.

Key Features

Automated quantization and GGUF conversion for efficient model deployment.
Streamlined workflows for LoRA and QLoRA parameter-efficient fine-tuning.
Clean implementations of 20+ pretrained LLM architectures including Llama 3, Gemma, and Mistral.
Performance optimization tips including Flash Attention and FSDP configuration.
Comprehensive guides for pretraining models from scratch on custom datasets.
384 GitHub stars

Use Cases

Learning and prototyping new LLM architectures with readable, single-file code.
Converting and quantizing trained models for deployment in resource-constrained environments.
Fine-tuning a base model like Llama 3 on a custom instruction dataset using LoRA.

About

Key Features

Automated quantization and GGUF conversion for efficient model deployment.
Streamlined workflows for LoRA and QLoRA parameter-efficient fine-tuning.
Clean implementations of 20+ pretrained LLM architectures including Llama 3, Gemma, and Mistral.
Performance optimization tips including Flash Attention and FSDP configuration.
Comprehensive guides for pretraining models from scratch on custom datasets.
384 GitHub stars

Use Cases

Learning and prototyping new LLM architectures with readable, single-file code.
Converting and quantizing trained models for deployment in resource-constrained environments.
Fine-tuning a base model like Llama 3 on a custom instruction dataset using LoRA.