Fine-tuning Llama 2 70B using PyTorch FSDP

Accelerating PyTorch distributed fine-tuning with Intel technologies

Llama 2 on Amazon SageMaker a Benchmark

Make LLM Fine-tuning 2x faster with Unsloth and 🤗 TRL

Fine-tune Llama 2 with DPO

Fine-tuning LLMs to 1.58bit: extreme quantization made easy

StackLLaMA: A hands-on guide to train LLaMA with RLHF

nanoVLM: The simplest repository to train your VLM in pure PyTorch

TACO: Ternary Absolute-max Column-wise One-sparse Optimizer for LLM Fine-Tuning

The paper introduces TACO, a new optimizer for fine‑tuning large language models that drastically reduces optimizer state memory while preserving first‑order gradients. TACO selects the sign of the largest magnitude entry in each column of weight matrices, achieving a 174× reduction in persistent optimizer memory compared to AdamW8bit and a 2.9× decrease in peak training memory on OPT‑13B. This allows full‑parameter fine‑tuning of 30–32B‑parameter models on a single 80 GB GPU across multiple model families and tasks, with comparable accuracy and runtime to existing methods.

Llama 3.1 - 405B, 70B & 8B with multilinguality and long context

Profiling in PyTorch (Part 2): From nn.Linear to a Fused MLP

Accelerate your models with 🤗 Optimum Intel and OpenVINO

Quanto: a PyTorch quantization backend for Optimum