LLM Fine-tuning & Training (LoRA, Open-weights)
About This Service
LLM Fine-tuning and LoRA Training Services for UAE Companies
I fine-tune open-weight large language models — Llama 3, Mistral and Qwen — using LoRA and QLoRA so Dubai and Abu Dhabi businesses get a model that speaks their domain language without paying for full-parameter training. LoRA adapters train on a single GPU in hours rather than days, which keeps compute spend predictable for free-zone startups and mainland SMEs alike. The work covers the full cycle: dataset curation and cleaning, supervised fine-tuning, evaluation, and deployment.
Dataset quality decides fine-tuning quality, so I start by auditing and cleaning your raw material — support tickets, product docs, call transcripts, internal wikis — into instruction-response pairs with deduplication and contamination checks. Every project ships with an evaluation suite (held-out test sets, LLM-as-judge scoring, and regression checks against the base model) so you can see exactly what the fine-tune improved in numbers, not vibes. Trained adapters are deployed behind an OpenAI-compatible endpoint using vLLM for production throughput, or Ollama for on-premise and laptop-class inference where data must stay inside the UAE.
Importantly, I will tell you when fine-tuning is the wrong tool. If your problem is keeping answers current with changing documents, retrieval-augmented generation is usually cheaper and easier to maintain; fine-tuning wins when you need consistent tone, structured output formats, domain jargon, or lower latency from a smaller model. Part of the engagement is a GPU cost plan in AED — comparing cloud A100/H100 rental against smaller quantized models on cheaper hardware — so finance teams in Dubai, Sharjah and Abu Dhabi know the monthly run cost before anything is trained.
What's included
- Fine-tune vs RAG assessment — A written recommendation on whether LoRA fine-tuning, RAG, or a hybrid fits your use case — before any GPU time is spent.
- Dataset curation and cleaning — Raw documents and chat logs converted into deduplicated, formatted instruction datasets ready for training.
- LoRA / QLoRA training run — Adapter training on Llama 3, Mistral or Qwen with logged hyperparameters and reproducible configs.
- Evaluation suite — Held-out test set, LLM-as-judge scoring and side-by-side comparison against the base model.
- vLLM or Ollama deployment — OpenAI-compatible serving endpoint, on UAE-hosted infrastructure or on-premise if data residency requires it.
- GPU cost plan in AED — Projected training and monthly inference costs across cloud GPU and quantized self-hosted options.
How it works
- 1Use-case and data review
We define the target behaviour, review your data sources, and confirm fine-tuning actually beats RAG for this problem.
- 2Dataset build
I clean, deduplicate and format your data into training and held-out evaluation sets, and share samples for your sign-off.
- 3Training and evaluation
LoRA/QLoRA runs on the chosen base model, iterated until the eval suite shows a measurable lift over baseline.
- 4Deployment and handover
The adapter goes live on vLLM or Ollama with an API endpoint, plus configs, eval reports and a retraining playbook.
Why work with me
| With me | Typical agency | |
|---|---|---|
| Honest fine-tune vs RAG advice | ||
| Evaluation numbers before sign-off | rarely | |
| You own the adapter weights and dataset | often locked in | |
| GPU cost forecast in AED upfront |