Service
Fine-tuning
A model trained on your data that reasons like a senior employee, not a generic chatbot.
Overview
Generic models give generic answers. Fine-tuning trains LLaMA, Mistral, or GPT on your company's own data, from internal documentation and support transcripts to domain-specific terminology, so the model produces outputs that match your standards. We handle the full pipeline: curating high-quality training datasets with your domain experts, selecting the right training strategy for your budget, benchmarking against base models on your own use cases, and deploying an optimized inference endpoint ready for production traffic.
Capabilities
Dataset Engineering
Training datasets built from your documents, support tickets, emails, and knowledge bases. Samples are cleaned, deduplicated, and validated with your subject-matter experts to ensure the model learns the right patterns.
Training Strategy
LoRA, QLoRA, or full fine-tuning, chosen based on your performance targets and budget. Hyperparameter sweeps run automatically to find the optimal configuration, with accelerated training on cost-efficient hardware.
Evaluation & Benchmarking
Custom benchmarks measuring accuracy, hallucination rate, latency, and domain-specific metrics. We compare the fine-tuned model head-to-head against the base model on your golden dataset, not on generic internet benchmarks.
Optimized Inference
Quantization and batched serving deliver production-grade throughput at controlled cost. Your model responds fast enough for real-time use while keeping infrastructure spend predictable.
Deliverables
- Fine-tuned model weights with training report
- Evaluation benchmark results and comparison
- Inference API with auto-scaling infrastructure
Tech Stack
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