Today's Fine-tuning & Training: Fastest-Growing Projects — May 10, 2026
The Fine-tuning & Training space has seen significant activity this week, with several repositories experiencing substantial growth. One trend that stands out is the focus on efficient and cost-effective fine-tuning methods for large language models (LLMs), as developers seek to optimize their training processes without sacrificing performance.
One of the fastest-growing repositories in this category is raiyanyahya/how-to-train-your-gpt, which boasts a growth score of 87.14 and has gained 782 stars. This repository provides a step-by-step guide on building a modern LLM from scratch, with every line commented and explained in simple terms. Its massive growth can be attributed to the increasing interest in understanding the inner workings of LLMs and the desire for more accessible training resources.
Another notable repository is ojuschugh1/sqz, which has seen significant activity with 100 commits in the past month and a growth score of 15.04. This tool aims to compress LLM context to save tokens and reduce costs, making it an attractive solution for developers looking to optimize their training processes without breaking the bank.
QingGo/engram-peft is another repository that has gained traction this week, with a growth score of 11.61 and 32 stars. This implementation allows users to inject high-capacity conditional memory into LLMs via sparse retrieval PEFT without increasing inference FLOPs, making it an exciting development in the field.
UNfukashigi/Anima-LoRA-Factory has also seen significant growth, with a score of 7.17 and 28 stars. This user-friendly GUI tool is designed for training LoRAs for next-generation Anima diffusion models, catering to developers who want to explore this emerging area without getting bogged down in complex technical details.
ZJU-OmniAI/GFT has experienced moderate growth with a score of 6.38 and 30 stars. This repository introduces GFT, a method that combines imitation learning and reward fine-tuning with unbiased group advantages and dynamic coefficient rectification. Its growth suggests that the research community is actively exploring new approaches to fine-tuning.
Jackohhhh/MedLLM-Finetuning has gained attention with a score of 3.06 and 21 stars. This out-of-the-box LLM fine-tuning framework for medical binary classification provides an easy-to-use solution for developers working in this specific domain, making it an attractive option for those looking to build on existing research.
Lastly, hlpun/Train-in-Silence has seen some growth with a score of 1.34 and 45 stars. This tool offers a task-aware MCP server and automated VRAM calculator for LLM fine-tuning, allowing users to quickly identify the cheapest and fastest GPUs across multiple cloud providers.
One of the fastest-growing repositories in this category is raiyanyahya/how-to-train-your-gpt, which boasts a growth score of 87.14 and has gained 782 stars. This repository provides a step-by-step guide on building a modern LLM from scratch, with every line commented and explained in simple terms. Its massive growth can be attributed to the increasing interest in understanding the inner workings of LLMs and the desire for more accessible training resources.
Another notable repository is ojuschugh1/sqz, which has seen significant activity with 100 commits in the past month and a growth score of 15.04. This tool aims to compress LLM context to save tokens and reduce costs, making it an attractive solution for developers looking to optimize their training processes without breaking the bank.
QingGo/engram-peft is another repository that has gained traction this week, with a growth score of 11.61 and 32 stars. This implementation allows users to inject high-capacity conditional memory into LLMs via sparse retrieval PEFT without increasing inference FLOPs, making it an exciting development in the field.
UNfukashigi/Anima-LoRA-Factory has also seen significant growth, with a score of 7.17 and 28 stars. This user-friendly GUI tool is designed for training LoRAs for next-generation Anima diffusion models, catering to developers who want to explore this emerging area without getting bogged down in complex technical details.
ZJU-OmniAI/GFT has experienced moderate growth with a score of 6.38 and 30 stars. This repository introduces GFT, a method that combines imitation learning and reward fine-tuning with unbiased group advantages and dynamic coefficient rectification. Its growth suggests that the research community is actively exploring new approaches to fine-tuning.
Jackohhhh/MedLLM-Finetuning has gained attention with a score of 3.06 and 21 stars. This out-of-the-box LLM fine-tuning framework for medical binary classification provides an easy-to-use solution for developers working in this specific domain, making it an attractive option for those looking to build on existing research.
Lastly, hlpun/Train-in-Silence has seen some growth with a score of 1.34 and 45 stars. This tool offers a task-aware MCP server and automated VRAM calculator for LLM fine-tuning, allowing users to quickly identify the cheapest and fastest GPUs across multiple cloud providers.