MODEL FINE-TUNING & EVALUATION
Model Fine-Tuning & Evaluation
Tailor and Rigorously Test LLMs on Domain Data.
Fine-tune open-weight models and run rigorous evaluation benchmarks against real-world edge cases.
Capabilities
Fine-Tuning Stack
Open-weight model fine-tuning (LoRA / QLoRA)
Evaluation benchmark suites & test sets
Safety guardrails & output validation
Custom Model Fine-Tuning
Fine-tune open-source LLMs on your domain datasets for superior accuracy and lower inference costs.
01
Data Preparation
Format and clean domain training datasets.
02
Model Training
Train Llama/Mistral models using LoRA/QLoRA.
03
Benchmarking
Evaluate model performance against baseline LLMs.
Take Action
Fine-Tune Your Domain Models
Contact our ML engineers.
Why fine-tune an open-source model?
Fine-tuning open-source models reduces API token costs, protects data privacy, and lowers latency.