LLM Fine Tuning
End-to-end LLM customisation , from data preparation and supervised fine-tuning through to RLHF, evaluation and sovereign deployment.
How we approach this practice
We take open-weight foundation models and turn them into specialised, sovereign models tuned to your domain, your language and your policies.
We handle the full pipeline: corpus curation, supervised fine-tuning, LoRA / QLoRA / full-parameter approaches, instruction tuning and RLHF / DPO alignment.
Your data never leaves your environment. The resulting model is yours , deployed inside your perimeter, with no dependency on third-party APIs.
What we deliver
- Supervised fine-tuning, LoRA / QLoRA and full-parameter approaches
- Instruction tuning and RLHF / DPO alignment
- Rigorous evaluation against domain benchmarks
- Sovereign deployment , your data never leaves your perimeter
- Continuous improvement pipelines
How an engagement unfolds
A repeatable process refined over 17 years of mission-critical delivery , adapted to the specifics of this practice.
- Step 01
Define target
Use-cases, acceptance criteria, evaluation benchmarks and policy constraints.
- Step 02
Curate data
Source, clean, de-duplicate and license-clear your training corpus.
- Step 03
Choose method
Select base model and tuning approach (LoRA, QLoRA, full FT, instruction, RLHF).
- Step 04
Fine-tune
Multi-stage training with checkpoints, ablation and alignment passes.
- Step 05
Evaluate
Domain benchmarks, red-teaming, bias and safety assessment.
- Step 06
Deploy
Sovereign serving stack with monitoring and continuous improvement.
Arabic-first LLM for a government communications body
Fine-tuned a sovereign LLM specialised for formal Arabic government communications, deployed entirely within national infrastructure.
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