Trelis provides a suite of open‑source GitHub repositories and managed fine‑tuning services that cover voice, vision, large language models, time‑series forecasting, and robotics. The platform lets machine‑learning engineers self‑serve model training, inference, and evaluation with built‑in support via GitHub issues and a private Discord, while also offering professional data preparation and deployment assistance to accelerate AI development.
Funding
Funding not disclosed
Founders
Product
Problem
Developers and organizations building AI applications often face high barriers to accessing, fine‑tuning, and deploying specialized models for voice, vision, language, time‑series, and robotics due to fragmented tooling, limited open‑source resources, and the need for extensive engineering support.
Solution
Trelis offers a unified platform of open‑source GitHub repositories covering advanced models and pipelines for voice, vision, large language models, time‑series forecasting, and robotics. Users can self‑serve these repos with built‑in support via GitHub issues and a private Discord community, or opt for Trelis‑provided fine‑tuning services that handle data preparation, training, and inference deployment. The Multi‑Repo Bundle gives full access to all seven model suites, enabling rapid experimentation and productionization across multiple AI domains. Evaluation tools let teams create custom datasets and run performance benchmarks, while inference scripts simplify integration of trained models into applications. By consolidating resources and offering both DIY and managed options, Trelis reduces development time and operational complexity for AI projects.
Target Audience
Primary customers are machine‑learning engineers, data science teams, and AI product developers in enterprises or research labs who need end‑to‑end model training, fine‑tuning, and evaluation across multiple modalities.
Features
- Seven curated GitHub repositories covering voice, vision, LLM fine‑tuning, inference, evaluation, time‑series, and robotics
- Self‑serve access with issue‑based support and a private Discord for troubleshooting and community guidance
- Professional fine‑tuning services for audio models (transcription, voice cloning, speech‑to‑speech) and other modalities
- ACT (Action Chunking Transformer) and GR00T‑N1 frameworks for robotics data preparation and training
- Vision and diffusion model fine‑tuning pipelines with ready‑to‑run inference scripts
- Time‑series forecasting tools leveraging transformer architectures, including training and evaluation scripts
- Evaluation suite for creating datasets, configuring LLM judges, and measuring model performance