#Model training
3 tools curated for you
Build custom AI models without writing code using a no-code interface for image, text, audio, and video classification Deploy trained models instantly by exporting to multiple platforms for straightforward integration into your applications Keep your data completely private with local training that happens on your computer, not in the cloud Train models on any standard computer with CPU-optimized models that require no specialized GPU hardware Start projects faster using pre-built templates for object detection, image segmentation, and pose classification
Focus entirely on your data and algorithms while Tinker manages scheduling, tuning, and reliable infrastructure for you. Fine-tune models efficiently with LoRA, matching full fine-tuning performance while using less computational resources. Train on powerful GPU clusters with distributed training managed behind the scenes, eliminating hardware management. Maintain complete control over training with granular functions for forward/backward passes, weight updates, token sampling, and saving progress. Use your data privately with a strict policy ensuring it's only for fine-tuning your own models, not for training ours. Accelerate experimentation by supporting a wide range of open-source models, from compact to large-scale architectures.
Deploy custom AI models for content generation and data classification without hiring a machine learning team, using a platform that requires zero coding or technical expertise. Achieve precise brand voice and style adherence in emails, blogs, and marketing content by fine-tuning models directly on your own documents and writing samples. Transform raw business documents (PDF, CSV) into actionable classification and labeling models in minutes, bypassing complex data formatting and preprocessing steps. Maintain full control and reduce cloud costs by downloading fine-tuned LoRA adapters for open-source models to run on your own local hardware. Accelerate AI projects from days to under 5 minutes for typical jobs, using automated optimization that handles infrastructure and configuration behind the scenes.