Today's Fine-tuning & Training: Fastest-Growing Projects — May 11, 2026
Today's the Fine-tuning & Training space, we're seeing a surge of interest in tools that make it easier to build and customize large language models (LLMs) from scratch. With the rise of AI applications, developers are looking for ways to fine-tune these models for specific tasks without requiring extensive expertise. As a result, repositories with high-quality documentation and user-friendly interfaces are gaining traction.
One such repository is raiyanyahya/how-to-train-your-gpt, which has seen an impressive growth score of 94.25 and amassed 971 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 popularity can be attributed to its accessibility, making it an ideal resource for developers new to the field.
ojuschugh1/sqz, on the other hand, has a growth score of 14.66 and 223 stars. This tool compresses LLM context to save tokens and reduce costs, addressing a common pain point in large-scale model training. With 100 commits in the past 30 days, it's clear that the developer is actively maintaining and improving the project.
Another notable repository is QingGo/engram-peft, which boasts a growth score of 11.21 and 32 stars. This unofficial implementation of DeepSeek Engram injects high-capacity conditional memory into LLMs via sparse retrieval PEFT without increasing inference FLOPs. Its unique approach to fine-tuning has garnered attention from developers looking for innovative solutions.
UNfukashigi/Anima-LoRA-Factory, with a growth score of 6.86 and 29 stars, offers a user-friendly GUI tool designed specifically for training LoRAs for next-generation Anima diffusion models. The ease of use and specialized focus on LoRA training have contributed to its moderate growth.
ZJU-OmniAI/GFT has a growth score of 6.12 and 30 stars, with a description that outlines a novel approach to fine-tuning using unbiased group advantages and dynamic coefficient rectification. Although the project's documentation is limited, its unique methodology has piqued the interest of some developers.
Jackohhhh/MedLLM-Finetuning has a growth score of 2.94 and 21 stars, offering an out-of-the-box LLM fine-tuning framework for medical binary classification tasks. Its specialized focus on medical applications has attracted attention from developers working in this niche field.
Lastly, hlpun/Train-in-Silence has a growth score of 1.40 and 51 stars, providing a task-aware MCP server and automated VRAM calculator for LLM fine-tuning. Although its growth is relatively slow, the tool's practical application and moderate star count indicate a dedicated user base.
These repositories demonstrate the diverse range of interests in the Fine-tuning & Training space, from building LLMs from scratch to specialized tools for specific tasks or applications.
One such repository is raiyanyahya/how-to-train-your-gpt, which has seen an impressive growth score of 94.25 and amassed 971 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 popularity can be attributed to its accessibility, making it an ideal resource for developers new to the field.
ojuschugh1/sqz, on the other hand, has a growth score of 14.66 and 223 stars. This tool compresses LLM context to save tokens and reduce costs, addressing a common pain point in large-scale model training. With 100 commits in the past 30 days, it's clear that the developer is actively maintaining and improving the project.
Another notable repository is QingGo/engram-peft, which boasts a growth score of 11.21 and 32 stars. This unofficial implementation of DeepSeek Engram injects high-capacity conditional memory into LLMs via sparse retrieval PEFT without increasing inference FLOPs. Its unique approach to fine-tuning has garnered attention from developers looking for innovative solutions.
UNfukashigi/Anima-LoRA-Factory, with a growth score of 6.86 and 29 stars, offers a user-friendly GUI tool designed specifically for training LoRAs for next-generation Anima diffusion models. The ease of use and specialized focus on LoRA training have contributed to its moderate growth.
ZJU-OmniAI/GFT has a growth score of 6.12 and 30 stars, with a description that outlines a novel approach to fine-tuning using unbiased group advantages and dynamic coefficient rectification. Although the project's documentation is limited, its unique methodology has piqued the interest of some developers.
Jackohhhh/MedLLM-Finetuning has a growth score of 2.94 and 21 stars, offering an out-of-the-box LLM fine-tuning framework for medical binary classification tasks. Its specialized focus on medical applications has attracted attention from developers working in this niche field.
Lastly, hlpun/Train-in-Silence has a growth score of 1.40 and 51 stars, providing a task-aware MCP server and automated VRAM calculator for LLM fine-tuning. Although its growth is relatively slow, the tool's practical application and moderate star count indicate a dedicated user base.
These repositories demonstrate the diverse range of interests in the Fine-tuning & Training space, from building LLMs from scratch to specialized tools for specific tasks or applications.