Today's Fine-tuning & Training: Fastest-Growing Projects — October 10, 2026
This week, the Fine-tuning & Training category on GitHub continues to see significant activity, with projects ranging from specialized model fine-tuning for cybersecurity tasks to user-friendly interfaces for novice AI enthusiasts. One of the most notable trends is the growing interest in LoRA (Low-Rank Adaptation) fine-tuning techniques across various models and platforms.
Mapika/decider has seen substantial growth this week, boasting a Growth Score of 38.31 and accumulating over 1,100 stars. The project aims to provide one-pass typed decisions with calibrated probabilities, fine-tuned from Qwen3.5-2B, making it an appealing resource for those seeking precise decision-making models.
JoasASantos/Offensive-Security-AI-Models stands out with a Growth Score of 35.88 and over 600 stars. This repository houses uncensored AI models or fine-tuned versions designed specifically for cybersecurity tasks, catering to the growing demand for specialized security solutions in the AI space.
Liuziyu77/Valen is another project gaining traction with a Growth Score of 32.62 and nearly 700 stars. Valen enables users to train Jev-like multimodal models themselves, extending System One Model capabilities into vision-based applications, appealing to those interested in expanding AI's visual understanding.
AcademiaSD/AcademiaSD_LoRAlab-TrainerStudio captures a Growth Score of 29.04 and has garnered 70 stars. This project focuses on training LoRAs for several advanced models using NVIDIA GPUs with limited VRAM, making it highly relevant for researchers and developers working within constrained resource environments.
empero-org/brewery-ai is attracting attention with a Growth Score of 27.67 and 200 stars. Designed to simplify the process of fine-tuning AI models, Brewery-AI offers an intuitive console agent that requires minimal expertise, making it accessible for users new to model training.
SimpleJev/JevAny boasts a Growth Score of 19.47 and has received 71 stars. This open infrastructure supports training, evaluating, and deploying System 1 decision models across various language and multimodal backbones, appealing to developers seeking flexible solutions in AI model development.
veda-sparse/Miowtion is growing steadily with a Growth Score of 18.62 and 30 stars. The project focuses on sparse acceleration and LoRA fine-tuning for MiniMax-H3 models on resource-constrained machines, addressing the need for efficient training techniques in low-resource settings.
Lucas5913/tllm-instruct-forge is another growing initiative with a Growth Score of 17.18 and 53 stars. This open-source library provides tools for instruction tuning and LLM training, catering to developers looking to enhance language model capabilities through targeted fine-tuning processes.
mehmetbulutaktas458-dev/Aozora-Forge is capturing interest with a Growth Score of 16.79 and 56 stars. This toolkit enables full-quality AI model fine-tuning on limited VRAM GPUs, making it an essential resource for researchers constrained by hardware limitations but aiming to train high-fidelity models.
USername142-sudo/Forge-Net-Trainer is also growing with a Growth Score of 16.79 and 56 stars. This project offers guidance and tools for building custom deep neural network trainers, making it valuable for developers looking to tailor their training processes to specific needs without starting from scratch.
These projects reflect the diversity and depth of innovation in fine-tuning and training AI models across various domains, from cybersecurity to multimodal understanding and resource-efficient model development.
Mapika/decider has seen substantial growth this week, boasting a Growth Score of 38.31 and accumulating over 1,100 stars. The project aims to provide one-pass typed decisions with calibrated probabilities, fine-tuned from Qwen3.5-2B, making it an appealing resource for those seeking precise decision-making models.
JoasASantos/Offensive-Security-AI-Models stands out with a Growth Score of 35.88 and over 600 stars. This repository houses uncensored AI models or fine-tuned versions designed specifically for cybersecurity tasks, catering to the growing demand for specialized security solutions in the AI space.
Liuziyu77/Valen is another project gaining traction with a Growth Score of 32.62 and nearly 700 stars. Valen enables users to train Jev-like multimodal models themselves, extending System One Model capabilities into vision-based applications, appealing to those interested in expanding AI's visual understanding.
AcademiaSD/AcademiaSD_LoRAlab-TrainerStudio captures a Growth Score of 29.04 and has garnered 70 stars. This project focuses on training LoRAs for several advanced models using NVIDIA GPUs with limited VRAM, making it highly relevant for researchers and developers working within constrained resource environments.
empero-org/brewery-ai is attracting attention with a Growth Score of 27.67 and 200 stars. Designed to simplify the process of fine-tuning AI models, Brewery-AI offers an intuitive console agent that requires minimal expertise, making it accessible for users new to model training.
SimpleJev/JevAny boasts a Growth Score of 19.47 and has received 71 stars. This open infrastructure supports training, evaluating, and deploying System 1 decision models across various language and multimodal backbones, appealing to developers seeking flexible solutions in AI model development.
veda-sparse/Miowtion is growing steadily with a Growth Score of 18.62 and 30 stars. The project focuses on sparse acceleration and LoRA fine-tuning for MiniMax-H3 models on resource-constrained machines, addressing the need for efficient training techniques in low-resource settings.
Lucas5913/tllm-instruct-forge is another growing initiative with a Growth Score of 17.18 and 53 stars. This open-source library provides tools for instruction tuning and LLM training, catering to developers looking to enhance language model capabilities through targeted fine-tuning processes.
mehmetbulutaktas458-dev/Aozora-Forge is capturing interest with a Growth Score of 16.79 and 56 stars. This toolkit enables full-quality AI model fine-tuning on limited VRAM GPUs, making it an essential resource for researchers constrained by hardware limitations but aiming to train high-fidelity models.
USername142-sudo/Forge-Net-Trainer is also growing with a Growth Score of 16.79 and 56 stars. This project offers guidance and tools for building custom deep neural network trainers, making it valuable for developers looking to tailor their training processes to specific needs without starting from scratch.
These projects reflect the diversity and depth of innovation in fine-tuning and training AI models across various domains, from cybersecurity to multimodal understanding and resource-efficient model development.