Today's Fine-tuning & Training: Fastest-Growing Projects — October 05, 2026
Today's the Fine-tuning & Training space on GitHub, we see a diverse range of projects gaining traction across various domains such as cybersecurity, multimodal decision-making, and low-resource machine learning setups. The top-ranked project this week is "Offensive-Security-AI-Models" by JoasASantos, which has garnered significant attention for its uncensored AI models tailored specifically to cybersecurity tasks.
JoasASantos/Offensive-Security-AI-Models offers a repository of AI models fine-tuned or developed for offensive security purposes. With over 500 stars and a strong growth score, the project is rapidly attracting contributors interested in leveraging machine learning for cybersecurity applications.
Mapika/decider stands out with its one-pass decision-making model that provides calibrated probabilities similar to System One style models, fine-tuned from Qwen3.5-2B. The high number of commits and stars indicate active development and growing interest among developers looking to integrate probabilistic decision-making into their projects.
Liuziyu77/Valen is a project allowing users to train Jev-like multimodal models with vision capabilities, extending the System One Model's functionality. With a substantial growth score, Valen demonstrates its appeal in the rapidly evolving field of multimodal AI training and deployment.
SimpleJev/JevAny provides an open infrastructure for training, evaluating, and deploying decision models across language and multimodal backbones. Despite having fewer stars compared to some others, JevAny's high number of commits reflects significant development activity, likely due to its broad applicability in the System 1 model ecosystem.
Lucas5913/tllm-instruct-forge is an open-source library for instruction tuning large language models (LLMs). With a modest but growing star count and numerous recent commits, this project suggests it's gaining traction among researchers and developers interested in fine-tuning LLMs for specific tasks or domains.
mallahyari/nemotron-asr-streaming-farsi focuses on streaming Persian speech recognition by fine-tuning NVIDIA Nemotron 3.5 ASR models. The growth score indicates that the project is steadily attracting contributors, likely due to its niche but critical role in localized language processing and natural language understanding tasks.
POInfra-AI/POInfra tackles policy optimization for generative models through diffusion and flow policies, offering online fine-tuning capabilities. With a growing star count and frequent commits, POInfra suggests it is becoming an essential tool for those working on advanced generative model applications requiring real-time adjustments.
USername142-sudo/Forge-Net-Trainer provides a guide for building custom deep neural network trainers in 2026. The high number of recent commits and moderate star count indicate that the project is actively being developed to meet the needs of developers seeking flexibility and control over their neural network training processes.
mehmetbulutaktas458-dev/Aozora-Forge offers a toolkit for fine-tuning full-quality AI models on low VRAM GPUs, making it accessible for those with limited hardware resources. The project's steady growth in stars and commits points to its growing relevance among developers working within constrained environments.
seandavedalaguit-ui/edge-vision-forge focuses on training, compressing, and deploying vision AI models on edge devices through the Netspresso Trainer 2026 framework. With consistent development activity and a moderate star count, this project highlights the increasing importance of efficient model deployment in resource-constrained settings.
Today's selection showcases the diversity and innovation within the fine-tuning and training space, from specialized cybersecurity applications to broad multimodal decision-making frameworks and low-resource machine learning setups. Each project offers unique solutions tailored to specific needs or challenges, contributing significantly to the ongoing evolution of AI development practices.
JoasASantos/Offensive-Security-AI-Models offers a repository of AI models fine-tuned or developed for offensive security purposes. With over 500 stars and a strong growth score, the project is rapidly attracting contributors interested in leveraging machine learning for cybersecurity applications.
Mapika/decider stands out with its one-pass decision-making model that provides calibrated probabilities similar to System One style models, fine-tuned from Qwen3.5-2B. The high number of commits and stars indicate active development and growing interest among developers looking to integrate probabilistic decision-making into their projects.
Liuziyu77/Valen is a project allowing users to train Jev-like multimodal models with vision capabilities, extending the System One Model's functionality. With a substantial growth score, Valen demonstrates its appeal in the rapidly evolving field of multimodal AI training and deployment.
SimpleJev/JevAny provides an open infrastructure for training, evaluating, and deploying decision models across language and multimodal backbones. Despite having fewer stars compared to some others, JevAny's high number of commits reflects significant development activity, likely due to its broad applicability in the System 1 model ecosystem.
Lucas5913/tllm-instruct-forge is an open-source library for instruction tuning large language models (LLMs). With a modest but growing star count and numerous recent commits, this project suggests it's gaining traction among researchers and developers interested in fine-tuning LLMs for specific tasks or domains.
mallahyari/nemotron-asr-streaming-farsi focuses on streaming Persian speech recognition by fine-tuning NVIDIA Nemotron 3.5 ASR models. The growth score indicates that the project is steadily attracting contributors, likely due to its niche but critical role in localized language processing and natural language understanding tasks.
POInfra-AI/POInfra tackles policy optimization for generative models through diffusion and flow policies, offering online fine-tuning capabilities. With a growing star count and frequent commits, POInfra suggests it is becoming an essential tool for those working on advanced generative model applications requiring real-time adjustments.
USername142-sudo/Forge-Net-Trainer provides a guide for building custom deep neural network trainers in 2026. The high number of recent commits and moderate star count indicate that the project is actively being developed to meet the needs of developers seeking flexibility and control over their neural network training processes.
mehmetbulutaktas458-dev/Aozora-Forge offers a toolkit for fine-tuning full-quality AI models on low VRAM GPUs, making it accessible for those with limited hardware resources. The project's steady growth in stars and commits points to its growing relevance among developers working within constrained environments.
seandavedalaguit-ui/edge-vision-forge focuses on training, compressing, and deploying vision AI models on edge devices through the Netspresso Trainer 2026 framework. With consistent development activity and a moderate star count, this project highlights the increasing importance of efficient model deployment in resource-constrained settings.
Today's selection showcases the diversity and innovation within the fine-tuning and training space, from specialized cybersecurity applications to broad multimodal decision-making frameworks and low-resource machine learning setups. Each project offers unique solutions tailored to specific needs or challenges, contributing significantly to the ongoing evolution of AI development practices.