Arion Athena

Model Fine-Tuning

Adapt models to your domain, terminology and tasks – from style transfer and brand tone to domain-specific reasoning – all inside controlled Arion workspaces.

What This Solves

Generic models don't "get" your business

Off-the-shelf LLMs struggle with your specific acronyms, writing style, code standards, or complex domain logic.

Data privacy and IP concerns

Fine-tuning on public cloud platforms often means uploading your most sensitive IP to third-party servers with unclear retention policies.

Unpredictable performance and cost

Prompt engineering can only go so far. For high-volume tasks, fine-tuned small models are often faster, cheaper, and more accurate than giant generic ones.

Lack of repeatable training pipelines

Ad-hoc scripts and notebooks make it hard to track experiments, reproduce results, or deploy updated models systematically.

How Arion Athena Helps

1

Private, secure fine-tuning environment

Run training jobs inside your Arion workspace. Your data never leaves your control, and your weights are yours alone.

2

Managed datasets and versioning

Connect to your object storage, manage training datasets, and track every experiment version automatically.

3

Seamless deployment to inference

Once trained, instantly deploy your model to a private endpoint (Lumen) or use it within your RAG (Mnemo) and Agent (Nexus) workflows.

4

Cost and performance optimisation

Distill capabilities from large models into smaller, faster, cheaper models that run efficiently on your dedicated infrastructure.

Example Real-World Applications

Brand Voice Alignment

Fine-tune a model on your past marketing copy, blogs, and emails so it generates content that sounds exactly like your brand.

Support Classification

Train a small, fast model to accurately categorise incoming support tickets based on your specific internal tags and routing rules.

Legal & Compliance Review

Adapt a model to understand your specific contract clauses, risk definitions, and compliance checklists for automated review.

Medical/Technical Summarisation

Teach a model to summarise complex clinical notes or technical logs using the correct terminology and format.

Code Generation

Fine-tune on your internal codebase to generate code that follows your specific patterns, libraries, and style guides.

Structured Data Extraction

Train a model to reliably extract specific fields from messy documents (invoices, forms) into a strict JSON schema.

Why Organisations Choose Arion Athena

Data privacy and sovereignty guaranteed
Full ownership of fine-tuned weights
Reproducible training pipelines
Integration with private inference endpoints
Support for LoRA and full fine-tuning
Cost-effective training on dedicated GPUs
Seamless RAG and Agent integration
Enterprise-grade security and access control

Start Your Fine-Tuning Journey

Build specialised models that define your competitive edge:

1.Prepare your dataset
2.Select base model
3.Configure training parameters
4.Run training job
5.Evaluate & Deploy