Today's Fastest-Growing Fine-tuning / Training Tools — April 09, 2026
This week, the Fine-tuning / Training space on GitHub saw significant activity around multimodal models and efficient LLM execution engines. Tools that enable fine-tuning of large language models with various data types, such as audio, images, and text, are gaining traction. Additionally, projects focused on optimizing LLM performance through techniques like quantization and compression are also attracting attention.
dubermandeer/Worm-GPT-LLM-2026 (Score: 1066.18, Stars: 84) is a high-performance C++ execution engine for LLM red-teaming and prompt engineering, allowing users to deploy dynamic jailbreak payloads and bypass alignment guardrails. Its massive growth score indicates strong interest in efficient and secure LLM deployment.
mattmireles/gemma-tuner-multimodal (Score: 7.28, Stars: 968) enables fine-tuning of Gemma 4 and 3n models with audio, images, and text on Apple Silicon using PyTorch and Metal Performance Shaders. With over 100 commits in the past 30 days, this project's popularity stems from its ability to efficiently handle multimodal data.
R6410418/Jackrong-llm-finetuning-guide (Score: 1.92, Stars: 458) is a guide for fine-tuning large language models, with a notable 35 commits in the past month. Although the repository lacks a description, its growth suggests that developers are seeking resources to improve their LLM fine-tuning skills.
facebookresearch/tribev2 (Score: 1.50, Stars: 1691) is a multimodal model for brain response prediction, with code available for training and evaluation. With over 1,600 stars, this project's popularity can be attributed to its innovative approach to understanding human brain responses.
tonbistudio/turboquant-pytorch (Score: 0.71, Stars: 877) is a PyTorch implementation of Google's TurboQuant for LLM KV cache compression, achieving 5x compression at 3-bit with 99.5% attention fidelity. This project's growth indicates interest in optimizing LLM performance through efficient compression techniques.
0xSero/turboquant (Score: 0.70, Stars: 903) offers a near-optimal KV cache quantization for LLM inference using Triton kernels and vLLM integration. With over 900 stars, this project's popularity stems from its ability to improve LLM performance through optimized quantization.
917017420/codex-register-fix (Score: 0.36, Stars: 67) is an openAI registration learning project based on cnlimiter/codex-manager, with an impressive 100 commits in the past month. Although relatively new, this project's growth suggests interest in improving LLM registration processes.
Dynamis-Labs/spectralquant (Score: 0.32, Stars: 95) breaks TurboQuant's compression limit via spectral structure, offering a novel approach to LLM optimization. This project's growth indicates that developers are seeking innovative solutions to improve LLM performance.
TYH-labs/unsloth-buddy (Score: 0.19, Stars: 211) is a zero-friction LLM fine-tuning skill for various agents and platforms, automating environment setup, training, and evaluation. With over 200 stars, this project's popularity stems from its ease of use and versatility.
WillowHe/EvoOpt_oppangu_optimization_model (Score: 0.16, Stars: 174) provides solutions for fine-tuning large language models in operations research optimization tasks using Openpangu-7B as the base model. This project's growth indicates interest in applying LLMs to real-world optimization problems.
dubermandeer/Worm-GPT-LLM-2026 (Score: 1066.18, Stars: 84) is a high-performance C++ execution engine for LLM red-teaming and prompt engineering, allowing users to deploy dynamic jailbreak payloads and bypass alignment guardrails. Its massive growth score indicates strong interest in efficient and secure LLM deployment.
mattmireles/gemma-tuner-multimodal (Score: 7.28, Stars: 968) enables fine-tuning of Gemma 4 and 3n models with audio, images, and text on Apple Silicon using PyTorch and Metal Performance Shaders. With over 100 commits in the past 30 days, this project's popularity stems from its ability to efficiently handle multimodal data.
R6410418/Jackrong-llm-finetuning-guide (Score: 1.92, Stars: 458) is a guide for fine-tuning large language models, with a notable 35 commits in the past month. Although the repository lacks a description, its growth suggests that developers are seeking resources to improve their LLM fine-tuning skills.
facebookresearch/tribev2 (Score: 1.50, Stars: 1691) is a multimodal model for brain response prediction, with code available for training and evaluation. With over 1,600 stars, this project's popularity can be attributed to its innovative approach to understanding human brain responses.
tonbistudio/turboquant-pytorch (Score: 0.71, Stars: 877) is a PyTorch implementation of Google's TurboQuant for LLM KV cache compression, achieving 5x compression at 3-bit with 99.5% attention fidelity. This project's growth indicates interest in optimizing LLM performance through efficient compression techniques.
0xSero/turboquant (Score: 0.70, Stars: 903) offers a near-optimal KV cache quantization for LLM inference using Triton kernels and vLLM integration. With over 900 stars, this project's popularity stems from its ability to improve LLM performance through optimized quantization.
917017420/codex-register-fix (Score: 0.36, Stars: 67) is an openAI registration learning project based on cnlimiter/codex-manager, with an impressive 100 commits in the past month. Although relatively new, this project's growth suggests interest in improving LLM registration processes.
Dynamis-Labs/spectralquant (Score: 0.32, Stars: 95) breaks TurboQuant's compression limit via spectral structure, offering a novel approach to LLM optimization. This project's growth indicates that developers are seeking innovative solutions to improve LLM performance.
TYH-labs/unsloth-buddy (Score: 0.19, Stars: 211) is a zero-friction LLM fine-tuning skill for various agents and platforms, automating environment setup, training, and evaluation. With over 200 stars, this project's popularity stems from its ease of use and versatility.
WillowHe/EvoOpt_oppangu_optimization_model (Score: 0.16, Stars: 174) provides solutions for fine-tuning large language models in operations research optimization tasks using Openpangu-7B as the base model. This project's growth indicates interest in applying LLMs to real-world optimization problems.