Private training · Your weights · Your VPC

Own your models

Train and adapt Llama, Mistral, Qwen, and other open models for your domain — LoRA/QLoRA, preference tuning, evaluation, and private serving with vLLM or TGI.

Discuss Model Training All Solutions
LoRAEfficient
VPCPrivate
vLLMServing
EvalsBuilt-in
Why open weights

Your data. Your weights.

Open-weights models let you own the weights, control data residency, reduce per-token cost at scale, and customize behavior deeply for your domain. DecryptCode runs the full loop: data prep, fine-tuning, evaluation, alignment, and private inference.

01

Domain adaptation

Teach models your terminology, workflows, and document styles without sending data to public APIs.

02

Parameter-efficient training

LoRA/QLoRA for fast, affordable adaptation on enterprise GPUs.

03

Alignment

Preference tuning and safety policies matched to your risk profile.

04

Private serving

vLLM/TGI deployment in your VPC with autoscaling and observability.

Decision guide

API models vs open weights

We help you choose the right path — or run both during a pilot-to-production transition.

Frontier APIs

Best for rapid pilots

  • Fastest time-to-first-demo
  • No GPU infrastructure to manage
  • Always on latest frontier models
  • Per-token cost at high volume
Open weights

Best for production at scale

  • Full data control & VPC residency
  • Predictable cost at high volume
  • Deep domain customization via LoRA
  • You own the weights & serving stack
Capabilities

Full training loop

From raw data to production inference — every stage engineered, evaluated, and versioned.

Data

Dataset Engineering

Curation, dedup, PII handling, synthetic data, and train/eval splits built for your domain.

  • PII redaction & residency controls
  • Synthetic data augmentation
  • Golden eval set design
Training

Fine-Tuning

SFT, LoRA/QLoRA, and continued pretraining where the use case justifies it.

Alignment

Preference Tuning

DPO and related methods to shape helpfulness and policy adherence.

Quality

Evaluation

Golden sets, regression suites, hallucination and safety tests.

Inference

Serving

vLLM, TGI, quantization, batching, and GPU cost optimization.

Ops

MLOps

Versioning, experiment tracking, rollback, and CI for models.

How we work

Data to deployment

A structured training program — scoped for your data sensitivity, GPU budget, and production timeline.

01

Assess & prepare

Model selection, data audit, PII handling, and eval set design before training starts.

02

Train & align

SFT with LoRA/QLoRA, preference tuning, and iterative eval against golden sets.

03

Validate & benchmark

Regression suites, safety tests, latency benchmarks, and cost modeling at target scale.

04

Deploy & monitor

Private vLLM/TGI serving in your VPC with versioning, rollback, and ongoing eval monitoring.

Technology

The stack

Best-in-class open models, training frameworks, and serving engines — configured for your infrastructure.

Layer 01

Models

Llama Mistral Qwen Phi Gemma
Layer 02

Training

PyTorch Axolotl Hugging Face LoRA QLoRA DPO
Layer 03

Serving

vLLM TGI TensorRT-LLM Kubernetes Ray
100%Data in VPC
4-bitQLoRA ready
15+Years experience
200+Projects shipped
01When should we choose open weights over API models?

Choose open weights when you need data control, predictable cost at high volume, custom behavior, or VPC deployment. APIs are often better for rapid pilots.

02Can you train on sensitive enterprise data?

Yes — we design private training pipelines with access controls, redaction options, and residency constraints.

03How much does open-weights training cost?

Focused LoRA adaptations can start in the tens of thousands; larger programs vary with GPU hours, data work, and eval depth.

Next step

Ready to train your model?

Tell us about your data, domain, and deployment constraints — we respond within 24 hours with a training roadmap.

Discuss Model Training