Adapt open-source LLMs (Llama, Mistral, Qwen, DeepSeek) to your domain — with LoRA, QLoRA, instruction tuning, and safe deployment guardrails.
Talk to an AI engineerGeneric LLMs are useful, but they don't know your domain. A fine-tuned Llama or Mistral model can match GPT-4 quality on your specific task at a fraction of the cost — but only if the fine-tuning is done right. Poor dataset prep, wrong hyperparameters, or missing eval harnesses produce a model that looks good in demos and fails in production.
CB4UHost's LLM Fine-tuning service handles the full pipeline: dataset preparation, base model selection, LoRA/QLoRA training, instruction tuning, evaluation, and safe deployment with guardrails. We build on open-source (Llama, Mistral, Qwen, DeepSeek) so you own the weights and avoid per-token licensing forever.
We're vendor-neutral on the training infrastructure too. We'll train on Lambda, CoreWeave, RunPod, AWS, GCP, or your own on-prem GPUs — whichever makes economic sense for your model size and timeline.
Every engagement ends with code, weights, eval reports, and runbooks — no black boxes. Your team owns the model and the pipeline after we leave.
We clean, dedupe, and format your training data — including synthetic data generation if your corpus is small.
We benchmark 2-3 base models (Llama 3, Mistral, Qwen, DeepSeek) against your task and recommend the best starting point.
Parameter-efficient fine-tuning that fits in 1-8 GPUs instead of a full cluster — dramatically lower cost than full fine-tuning.
Automated evaluation on your held-out test set + human eval rubric. You see the quality lift before deployment.
vLLM/TGI serving, rate limiting, content filtering, and observability so the model behaves in production.
Runbooks, training scripts, and a 30-day optimization window so your team can retrain as your data evolves.
We work with you to define the task, assemble the training corpus, and establish baseline quality metrics.
We benchmark 2-3 candidate base models on your task and pick the best starting point.
We run LoRA/QLoRA training, evaluate on your held-out set, and iterate until quality targets are met.
We stand up production serving with vLLM/TGI, add guardrails, and wire up observability.
We hand over code, weights, and runbooks — then optimize for 30 days based on real production traffic.
Interactive tools related to this service.
On domain-specific tasks (your support tickets, your codebase, your documents), yes — often exceeding GPT-4 quality at 1/10th the inference cost. For general knowledge tasks, GPT-4 still wins, which is why many teams use a hybrid: fine-tuned model for domain tasks, GPT-4 for general.
For LoRA fine-tuning, 500-5,000 high-quality examples is often enough. For full instruction tuning, 10,000+. We can also generate synthetic data if your corpus is small.
Yes. You own the fine-tuned weights, the training code, and the eval harness. No per-token licensing, no vendor lock-in.
Typical engagement: $8,000-$25,000 depending on model size, data prep complexity, and number of base models benchmarked. GPU costs are pass-through at provider rates.
Compare H100, A100, L40S, and consumer GPU pricing across 8+ providers — benchmarked against your actual workload, not affiliate payouts.
Production retrieval-augmented generation with vector stores, hybrid search, reranking, citation, and observability — so you can trust the answers.
End-to-end ML pipelines — experiment tracking, model registry, feature stores, CI/CD for models, drift detection, and rollback. Built on Kubernetes.
Tell us about your project. We'll come back with a scoped proposal and a fixed-fee quote.
Talk to an AI engineer