LLM fine-tuning for enterprise
Customize language models to speak like your brand, understand your domain, and solve your specific tasks with superior accuracy. Efficient fine-tuning with LoRA/QLoRA, rigorous evaluation, and cost-optimized deployment.
Fine-tuning vs RAG vs Prompting: when to use each
Not everything requires fine-tuning. Sometimes good prompt engineering or a RAG system is enough. But when you need the model to adopt a specific style, master technical vocabulary, or perform tasks it cannot learn from context alone, fine-tuning is the answer. We help you choose the right strategy.
Prompt Engineering
Ideal when the task is generic and the base model already has the necessary knowledge.
- • General writing tasks
- • Text analysis and summarization
- • Minimal cost, immediate results
RAG
Ideal when you need answers based on specific information that changes frequently.
- • Internal documentation/products
- • Data that updates often
- • Need to cite sources
Fine-tuning
Ideal when you need to change the model's behavior, style, or capabilities.
- • Specific brand tone/style
- • Consistent output format
- • Specialized domain tasks
Efficient and safe fine-tuning for production
Fine-tuning adapts a pre-trained model to your specific domain using your data. With modern techniques like LoRA (Low-Rank Adaptation) and QLoRA, we can customize models with billions of parameters at a fraction of the full training computational cost -- without losing the base model's general capabilities.
Data preparation is the most critical phase: we need high-quality examples that represent exactly the behavior you want from the model. This includes input/output pairs for instruction tasks, example conversations for chatbots, texts in the desired style for content generation, or labeled examples for classification tasks. The quality of these data directly determines the quality of the resulting model.
We evaluate the fine-tuned model with quantitative metrics (perplexity, accuracy on specific benchmarks) and qualitative assessments (human evaluation of outputs). We compare against the base model and against RAG to ensure fine-tuning provides real value before deploying to production.
For enterprises with strict privacy requirements, we offer fully on-premise fine-tuning and deployment. Open-source models (Llama, Mistral, Phi) that run on your infrastructure without sending data to third parties. We optimize inference with vLLM or TGI to serve large models with controlled costs and low latency.
Efficient adaptation
Total privacy
Cost vs full training
Optimized latency
Need a custom AI model for your business?
Consulenza gratuita →Fine-tuning stack
From data to custom model in production
A methodical process that ensures data quality, efficient training, and rigorous evaluation before reaching production.
Data preparation
We collect, clean, and format training data. We create instruction/response pairs, validate quality and dataset diversity. This is 70% of the project's success.
Selection & training
We choose the optimal base model, configure LoRA/QLoRA with appropriate hyperparameters, and train while monitoring loss, overfitting, and quality metrics at each epoch.
Rigorous evaluation
Benchmark against base model, human evaluation, A/B tests, and edge case validation. We only deploy if fine-tuning significantly outperforms the baseline on your target metrics.
Optimized deployment
We serve the model with vLLM or TGI for maximum efficiency. Quantization to reduce inference costs. Quality and drift monitoring in production with scheduled retraining.
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Domande frequenti about fine-tuning
How much data do I need for fine-tuning?
What is LoRA and why is it important?
Can I run the fine-tuned model on my own infrastructure?
How much does LLM fine-tuning cost?
How long does a fine-tuning project take?
Create an AI model that speaks your business language
We help you determine if fine-tuning is the right strategy for your use case and, if so, implement a custom model that outperforms the baseline on your key metrics.