Platform

Products

Private AI infrastructure — from compute to agents.

Target groups

Use Cases

For enterprise, SMBs, and individual developers.

Knowledge & Support

Resources

Everything you need to succeed with Mycelis.

Small & Medium Business

AI that speaks
your industry.

Generic models don't know your terminology, your processes, or your edge cases. Fine-tune an open-source model on your proprietary data and get an AI that actually understands your business.

When generic AI is not enough

Industries like legal, medical, manufacturing, or finance have specialized vocabulary and reasoning patterns that off-the-shelf models handle poorly. Fine-tuning adapts a capable open-source base model to your domain using your own data — improving accuracy, reducing hallucinations, and making the model genuinely useful for your specific workflows.

What you get

LoRA Fine-Tuning

Fine-tune models efficiently with LoRA — no need for massive GPU budgets. Upload your training data, configure parameters, and start a training run directly from the dashboard.

Custom Training Data

Upload your own JSONL datasets — instruction-following, completion, or chat format. The model learns from your examples and adapts to your domain.

One-Click Deployment

After training, deploy your fine-tuned model with one click. It becomes available immediately as an agent endpoint — no additional setup.

Dedicated GPU Infrastructure

Training and inference run on dedicated GPU instances. Your proprietary training data never leaves your deployment environment.

Frequently Asked Questions

Which base models can I fine-tune?

Mycelis supports fine-tuning on popular open-source models including Llama, Mistral, Qwen, and others. The available base models are listed in the fine-tuning interface.

How much training data do I need?

LoRA fine-tuning is efficient and can produce good results with as few as a few hundred high-quality examples. More data generally improves results, but you don't need millions of samples to see a meaningful improvement over the base model.

Is my training data kept private?

Yes. Your training data is used only within your own deployment. It is not shared with other users or used to train shared models.

Build a model that knows your domain.

Create a free account and start your first fine-tuning run today.

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