LLM Fine-Tuning Services
Transform general-purpose LLMs into domain experts with custom fine-tuning. Train models on your proprietary data to achieve performance that generic APIs cannot match, all while keeping your data private and secure.
10x
Performance Improvement
90%
Cost Reduction vs Full Training
Custom
Domain Expertise
Methods
Fine-tuning approaches
LoRA (Low-Rank Adaptation)
Efficient fine-tuning by training small adapter layers while freezing base model weights.
Best for: Most use cases, quick iteration
QLoRA (Quantized LoRA)
Combine 4-bit quantization with LoRA for fine-tuning large models on consumer hardware.
Best for: Limited GPU resources, large models
Full Fine-Tuning
Update all model weights for maximum customization and performance on specialized tasks.
Best for: Critical applications, unique domains
RLHF / DPO
Align model outputs with human preferences using reinforcement learning or direct preference optimization.
Best for: Customer-facing applications
Data Preparation
From raw data to training-ready
Data Collection
Gather domain-specific examples, documents, and interaction logs relevant to your use case.
Data Cleaning
Remove duplicates, fix formatting issues, and filter low-quality or irrelevant examples.
Format Conversion
Convert data to instruction-response pairs, chat format, or completion format as needed.
Quality Validation
Review samples, validate labels, and ensure data represents desired model behavior.
Use Cases
What you can achieve with fine-tuning
Industry-Specific Language
Train models on legal, medical, financial, or technical terminology for accurate domain communication.
Example: Medical AI that understands clinical notes and ICD codes
Company Knowledge
Fine-tune on internal documents, processes, and product information for accurate company-specific responses.
Example: Support bot trained on your product documentation
Brand Voice & Style
Train models to match your brand's communication style, tone, and formatting preferences.
Example: Marketing AI that writes in your brand voice
Task-Specific Performance
Optimize for specific tasks like classification, extraction, summarization, or code generation.
Example: Contract clause extraction with 99% accuracy
Infrastructure
Training infrastructure
Evaluation
How we measure success
Perplexity
Model's confidence in predictions
BLEU/ROUGE
Text similarity to reference
Task Accuracy
Performance on specific tasks
Human Evaluation
Quality ratings from experts
A/B Testing
Real-world performance comparison
Hallucination Rate
Factual accuracy measurement
Ready to fine-tune your model?
Let's train a custom model that understands your domain and delivers superior performance.
Start Fine-Tuning Project