Hugging Face
Engineering.
We use Hugging Face's model hub, Transformers, and TGI to fine-tune, evaluate, and deploy open-weight models, closing the gap between a promising checkpoint and a production endpoint.
Fine-Tuning as a Pipeline, Not a Notebook
A fine-tuned checkpoint that lives in a data scientist's notebook isn't really an asset. It's a liability: no reproducibility, no evaluation history, no path to production. We build fine-tuning as a versioned pipeline instead: data curation and dedup, LoRA/QLoRA training runs tracked with fixed seeds and hyperparameters, automated evaluation against a held-out set before any checkpoint is eligible for promotion, and every artifact, dataset version, base model, adapter weights, eval scores, traceable back to the training run that produced it.
LoRA/QLoRA training on right-sized GPU infrastructure. Most enterprise fine-tuning tasks don't need a full-parameter fine-tune and the 10-20x GPU cost that comes with it.
Automated eval harness (task-specific accuracy plus safety/bias checks) gating every checkpoint before it's eligible for deployment, not just a manual spot-check.
Dataset versioning and lineage tracking, so a regression can be traced back to a specific data change, not just a vague 'the model got worse' report.
Private hub deployment with organization-scoped access controls, so fine-tuned weights and training data never touch the public hub.
What we build
LoRA/QLoRA Fine-Tuning
Parameter-efficient fine-tuning pipelines on your proprietary data, run on right-sized GPU infrastructure with fixed seeds and tracked hyperparameters for reproducibility.
Automated Evaluation
Continuous eval harnesses that benchmark new checkpoints against your task and a held-out regression set before they're promoted to production traffic.
Model Hub Governance
Private hub deployments with organization-level access controls, so fine-tuned weights and datasets never leave your organization's boundary.
Serving Migration
Moving checkpoints from research-grade `transformers` inference to production-grade TGI or vLLM serving, with load-tested throughput before cutover.
What we fix
Fine-tuned models that look good on a sample set but regress on edge cases in production
No repeatable pipeline between 'we fine-tuned a checkpoint' and 'it's serving real traffic'
Public hub usage exposing proprietary datasets or model weights outside the organization
GPU spend wasted on full-parameter fine-tunes where LoRA would reach the same accuracy for a fraction of the cost
No lineage between a model regression and the specific training data or hyperparameter change that caused it
Notebook-only training with no reproducible pipeline, blocking any real handoff to an ops team
Stop guessing.
Start shipping.
Thirty minutes with a senior engineer. No sales deck, no junior SDRs. Just a technical conversation about what's actually breaking or slowing you down.