Model Training & Fine-Tuning
Adapt open-source language models to your domain, your data, and your tasks entirely within your infrastructure. From one-time fine-tuning to continuous retraining pipelines, we handle the full ML lifecycle.
Efficient Fine-Tuning (LoRA / QLoRA)
We use parameter-efficient fine-tuning techniques like LoRA and QLoRA to adapt large language models to your domain without requiring massive GPU clusters. This makes on-premise fine-tuning practical even on modest hardware.
Domain Adaptation
Pre-trained models are adapted to your industry vocabulary, writing style, and task requirements. Legal, medical, financial, manufacturing — any domain where generic models fall short.
Evaluation & Benchmarking
Custom evaluation suites are built for your specific tasks. We define metrics that matter for your use case and track model performance before and after fine-tuning, giving you measurable proof of improvement.
Continuous Training Pipelines
As your data evolves, the model can too. We set up automated retraining pipelines that trigger on data thresholds, schedule periodic fine-tuning runs, and promote new model versions through staging to production.
Why Fine-Tune Instead of Prompting?
Prompt engineering can get you surprisingly far with a general-purpose model. But for production enterprise workloads, there are clear scenarios where fine-tuning delivers measurably better results. When your domain uses specialised terminology that the base model handles inconsistently — medical nomenclature, legal phrasing, financial instrument names, proprietary product codes — fine-tuning teaches the model to understand and use these terms correctly and consistently.
Fine-tuning also allows you to enforce output formats, reduce response latency (shorter prompts), and improve accuracy on repetitive classification or extraction tasks where the model needs to learn patterns specific to your data rather than relying on general knowledge. In many cases, a fine-tuned 7B or 13B model outperforms a much larger general-purpose model on your specific tasks — while running faster and on less expensive hardware.
Base Models We Work With
We fine-tune any open-source model that fits your hardware, task, and licence requirements. Popular families we work with regularly include Llama (Meta), Mistral, Qwen (Alibaba), Phi (Microsoft), Gemma (Google), and DeepSeek — but this list is not exhaustive. If a model is open-source and trainable, we can fine-tune it.
Model selection depends on your task complexity, available GPU hardware, and latency requirements. We advise on the optimal base model and parameter size during the scoping phase based on your specific constraints.
The Fine-Tuning Process
Data preparation
Your training data is cleaned, formatted, and structured into instruction-response pairs or task-specific formats. We handle deduplication, quality filtering, and balanced sampling across categories. For sensitive domains, all data preparation happens on your infrastructure.
Training configuration
We configure LoRA rank, learning rate schedules, batch sizes, and quantisation settings optimised for your GPU hardware. Training runs are monitored in real-time with loss curves, gradient norms, and evaluation metrics tracked via MLflow or Weights & Biases (self-hosted).
Evaluation and iteration
After each training run, the model is evaluated against your custom benchmark suite — not just perplexity, but task-specific metrics like extraction accuracy, classification F1, or response quality ratings. Multiple runs are compared and the best-performing checkpoint is selected.
Deployment and serving
The fine-tuned model is quantised for optimal inference performance and deployed via vLLM or Ollama. We validate that latency, throughput, and accuracy metrics meet production requirements before handover. The base model weights and your LoRA adapter are versioned separately for clean rollback if needed.
Hardware Requirements
Thanks to LoRA and QLoRA, enterprise-grade fine-tuning no longer requires massive GPU clusters. A 7B parameter model can be fine-tuned on a single NVIDIA A10G (24GB VRAM) or equivalent GPU in 4 to 8 hours depending on dataset size. 13B models require 2 GPUs or a single A100 (40GB+). Larger models (70B+) benefit from multi-GPU setups with DeepSpeed ZeRO-3 for memory-efficient distributed training. We design the training pipeline around your available hardware — if you have a single GPU server, we make it work; if you have a multi-node cluster, we maximise utilisation.
Technology Stack
Training frameworks and model serving infrastructure