Today's Fine-tuning & Training: Fastest-Growing Projects — June 12, 2026
Today's the Fine-tuning & Training space on GitHub, we see a continued surge in interest around large-scale model training and novel approaches to fine-tuning for specific tasks. The repository that stands out this week is "pat-jj/harness-1," with its impressive growth score and an innovative approach to training long-horizon search agents using a 20B model.
"pat-jj/harness-1" offers an ultra recipe for training search agents capable of matching frontier AI's capabilities, leveraging a massive 20B model. With a robust growth score of 16.48 and over 536 stars, this repository is gaining traction due to its comprehensive approach to handling large-scale models.
"DaoyuanLi2816/can-i-finetune-this" provides an estimation tool for determining whether a Hugging Face model can be fine-tuned on a local GPU. With a growth score of 14.24 and nearly 440 stars, this repository is growing rapidly as it addresses the practical challenge of resource constraints when working with large models.
"thombanal/clip-finetune-recipes" offers detailed recipes for CLIP fine-tuning, including DDP training techniques, LoRA methods, hard-negative mining strategies, and leakage checks. Despite having no recent commits, its growth score of 5.82 and 221 stars indicate steady interest in its comprehensive resources for optimizing CLIP models.
"jelllott/speechkv-trim" focuses on speech-aware KV cache pruning techniques specifically designed for long-form speech LLMs like Qwen2-Audio and SALMONN, offering token-level and chunk-level pruners. With a growth score of 5.76 and 219 stars, this repository is growing due to its specialized approach to optimizing memory usage in large-scale speech models.
"gvkhosla/pi-tinker" introduces a tool for fine-tuning open-source models within the Raspberry Pi environment using Tinker, complete with managed improvement loops, data preparation, evaluation scripts, and deployment snippets. Its growth score of 5.17 and 21 stars suggest it is gaining traction as a versatile platform for model experimentation on constrained hardware.
"Mengqi-Lei/count-anything" provides code and implementation guidelines based on the paper "Counting Anything," aimed at enabling precise object counting in various contexts. With a growth score of 5.02 and 101 stars, this repository is growing as researchers and developers seek accurate solutions for diverse counting tasks.
"SoloCalm/MiniLoRA" offers tutorials and resources for fine-tuning large language models (LLMs) using LoRA techniques, specifically focusing on medical applications with Qwen2.5-0.5B. Its growth score of 2.82 and 30 stars indicate a growing interest in specialized fine-tuning approaches tailored to specific domains.
"JuliusBrussee/cavegemma" presents an innovative project that fine-tunes the Gemma 4 31B model to simulate caveman speech patterns, showcasing unique applications of LoRA techniques. Despite its lower growth score of 1.91 and 46 stars, it captures attention for its creative application in language generation.
Today's trending tools highlight the diverse range of approaches being explored in fine-tuning and training large-scale models, from practical resource management to specialized domain-specific applications.
"pat-jj/harness-1" offers an ultra recipe for training search agents capable of matching frontier AI's capabilities, leveraging a massive 20B model. With a robust growth score of 16.48 and over 536 stars, this repository is gaining traction due to its comprehensive approach to handling large-scale models.
"DaoyuanLi2816/can-i-finetune-this" provides an estimation tool for determining whether a Hugging Face model can be fine-tuned on a local GPU. With a growth score of 14.24 and nearly 440 stars, this repository is growing rapidly as it addresses the practical challenge of resource constraints when working with large models.
"thombanal/clip-finetune-recipes" offers detailed recipes for CLIP fine-tuning, including DDP training techniques, LoRA methods, hard-negative mining strategies, and leakage checks. Despite having no recent commits, its growth score of 5.82 and 221 stars indicate steady interest in its comprehensive resources for optimizing CLIP models.
"jelllott/speechkv-trim" focuses on speech-aware KV cache pruning techniques specifically designed for long-form speech LLMs like Qwen2-Audio and SALMONN, offering token-level and chunk-level pruners. With a growth score of 5.76 and 219 stars, this repository is growing due to its specialized approach to optimizing memory usage in large-scale speech models.
"gvkhosla/pi-tinker" introduces a tool for fine-tuning open-source models within the Raspberry Pi environment using Tinker, complete with managed improvement loops, data preparation, evaluation scripts, and deployment snippets. Its growth score of 5.17 and 21 stars suggest it is gaining traction as a versatile platform for model experimentation on constrained hardware.
"Mengqi-Lei/count-anything" provides code and implementation guidelines based on the paper "Counting Anything," aimed at enabling precise object counting in various contexts. With a growth score of 5.02 and 101 stars, this repository is growing as researchers and developers seek accurate solutions for diverse counting tasks.
"SoloCalm/MiniLoRA" offers tutorials and resources for fine-tuning large language models (LLMs) using LoRA techniques, specifically focusing on medical applications with Qwen2.5-0.5B. Its growth score of 2.82 and 30 stars indicate a growing interest in specialized fine-tuning approaches tailored to specific domains.
"JuliusBrussee/cavegemma" presents an innovative project that fine-tunes the Gemma 4 31B model to simulate caveman speech patterns, showcasing unique applications of LoRA techniques. Despite its lower growth score of 1.91 and 46 stars, it captures attention for its creative application in language generation.
Today's trending tools highlight the diverse range of approaches being explored in fine-tuning and training large-scale models, from practical resource management to specialized domain-specific applications.