LLM Fine Tuning Services
Go From Generic to Domain Specific. Hone Your Model with Azumo's LLM Finetuning Services
Unlock the full potential of large language models with specialized finetuning services from Azumo. Our development team transforms general-purpose AI into domain experts that understand your industry, speak your language, and deliver precisely the intelligence your applications need to excel.
What is LLM Finetuning
Azumo provides LLM fine-tuning services that adapt foundation models into custom LLMs aligned to your specific domain, data, and quality standards. We fine-tune OpenAI GPT, LLaMA, Mistral, and other open-source models from the Hugging Face ecosystem using:
- Supervised fine-tuning (SFT)
- Reinforcement learning from human feedback (RLHF)
- Direct preference optimization (DPO)
- Parameter-efficient fine-tuning (PEFT) methods including LoRA and QLoRA.
All training runs under SOC 2 compliance with private infrastructure options.
Fine Tuning is not always the right answer. We start by evaluating whether prompt engineering, RAG, or fine-tuning (or a combination) best fits your use case. When fine-tuning is justified, our process includes:
- Training data preparation and quality assessment
- Baseline evaluation against your specific tasks
- Iterative training with custom benchmarks
- A/B testing against the base model before deployment.
Results vary by task, but our custom LLMs typically show 30-60% improvement in domain-specific accuracy, with notable gains in terminology consistency, output formatting, and reduced hallucination on specialized topics. We pair fine-tuning with quantization where appropriate so your custom LLM runs at lower inference cost without sacrificing accuracy. We provide detailed evaluation reports with metrics that map directly to your business requirements.
Comparison vs Alternatives
Comparing Fine-Tuning Methods LoRA vs. Full Fine-Tuning vs. RLHF
| Criteria | LoRA / QLoRA (Parameter-Efficient) | Full Fine-Tuning | RLHF / DPO (Alignment Tuning) |
|---|---|---|---|
| What it changes | Adds small low-rank adapter layers — trains 0.1-1% of total parameters | Updates all model weights across every layer | Adds reward model and policy optimization on top of supervised fine-tuning |
| Training data needed | Hundreds to low thousands of task-specific examples | Tens of thousands of high-quality labeled examples | Thousands of preference pairs: chosen response vs. rejected response |
| Compute requirements | Single GPU, completes in hours to days | Multi-GPU cluster, runs for days to weeks | Multi-stage pipeline: supervised fine-tuning, then reward model training, then PPO or DPO optimization |
| Performance vs. base model | Achieves 85-95% of full fine-tuning performance at a fraction of the cost | Maximum task-specific accuracy and domain adaptation | Controls output style, safety boundaries, and response preferences rather than raw accuracy |
| Risk of catastrophic forgetting | Low — base model weights stay frozen | High — aggressive training can degrade general language capabilities | Moderate — depends on reward model quality and training balance |
| Best for | Domain adaptation on a budget, rapid iteration, deploying multiple task-specific adapters | Maximum accuracy on specialized tasks where compute budget is available | Brand voice alignment, safety guardrails, reducing harmful or off-topic outputs, user preference optimization |
How We Help You:
Dataset Selection and Annotation
Select a dataset that aligns with your business tasks and annotate it to highlight critical features.
Hyperparameter Optimization and Model Adaptation
Optimize hyperparameters and apply parameter-efficient fine-tuning (PEFT) techniques.
Customize Loss Functions and Training
Tailor the loss function to focus on metrics that matter most.
Early Stopping and Learning Rate Adjustments
Implement early stopping to conserve resources and maximize training efficiency.
Thorough Post-Training Evaluation
Evaluate the model extensively after training using both qualitative and quantitative methods.
Continuous Model Refinement
Use evaluation insights and real-world application feedback to refine the model.
Insights on LLM Fine Tuning
What is LLM fine-tuning?
LLM fine-tuning is the process of adapting a pre-trained large language model to perform specific tasks using your proprietary data. It adjusts an existing model's weights so it generates outputs tailored to your domain, terminology, and quality standards.
What data is needed for LLM fine-tuning?
Fine-tuning requires curated examples of the input-output pairs you want the model to produce.
What fine-tuning methods does Azumo use?
Azumo uses supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), direct preference optimization (DPO), and parameter-efficient methods including LoRA and QLoRA.
How long does LLM fine-tuning take?
A typical fine-tuning project takes 4-12 weeks from data preparation through production deployment. Data audit and preparation takes 1-3 weeks depending on data readiness.
How does Azumo support companies with LLM fine-tuning?
Azumo provides end-to-end LLM fine-tuning services including data audit and preparation, training dataset creation, and evaluation.