Ertas AI

Train, evaluate, optimise, and deploy custom AI models you own.

Ertas helps builders and teams move beyond rented frontier APIs by creating domain-specific models they can control, run, and improve. Use Ertas to fine-tune open models on cloud GPUs, evaluate model quality, export deployable artifacts like GGUF, and ship owned AI to on-device, local, private, or managed cloud environments.

We are building the platform for custom AI model training, post-training, evaluation, optimisation, and deployment.

What Ertas does

• Custom model fine-tuning: Train open models on your own data using a visual workflow instead of CUDA, YAML, and CLI-heavy tooling.
• Model post-training and optimisation: Turn base models into task-specific systems for support, agents, internal tools, document workflows, product features, and domain reasoning.
• Evaluation-first workflows: Define what “good” means, test candidate models, compare runs, and improve quality before deployment.
• Owned AI deployment: Export and run models where your product or organisation needs them: on-device, local, private cloud, managed cloud, or customer-owned infrastructure.
• Open deployment paths: Use formats and runtimes like GGUF, llama.cpp, Ollama, and LM Studio so your model is not locked inside Ertas.

Why teams use Ertas

Generic frontier APIs are powerful, but they are not always the right fit. Teams come to Ertas when they need:

• lower inference cost at scale;
• private or in-boundary AI for sensitive data;
• lower latency and offline support;
• domain-specific behaviour that prompting alone cannot reliably deliver;
• model ownership instead of long-term dependence on one API provider;
• deployment control across devices, cloud, edge, and enterprise environments.

What you will find here

This Hugging Face organisation is where we publish and maintain selected open model artifacts from the Ertas workflow, including:

• base-model mirrors and deployment-ready variants;
• fine-tuned model examples;
• GGUF and llama.cpp-compatible artifacts;
• datasets, eval sets, and reproducible model cards where possible;
• demos and references for building owned AI systems.

Built on open AI infrastructure

Ertas works with leading open model families and deployment tools, including Gemma, Qwen, Llama, Phi, LoRA fine-tuning, GGUF export, llama.cpp, Ollama, LM Studio, and Hugging Face datasets and model repositories.

Our goal is simple: make custom model training and owned AI deployment practical for product teams, agencies, startups, and enterprises without requiring every team to build an ML platform from scratch.

Links

• Website: https://www.ertas.ai
• Instagram: https://www.instagram.com/ertas.ai/
• Reddit: https://www.reddit.com/r/ertas/
• GitHub: https://github.com/ErtasAI
• LinkedIn: https://linkedin.com/company/ertas-ai
• X: https://x.com/ertasai