Hugging Face Transformers Fine-Tuning Inference
We build production AI on the Hugging Face stack for US and EU teams — from model selection and evaluation to PEFT/LoRA fine-tuning, RAG and self-hosted Text Generation Inference. Our engineers know when an open-weights model beats a closed API, how to fine-tune without leaking your data, and how to serve it cost-effectively on your own GPUs or on Inference Endpoints. Every deployment is governed, documented with model cards, and built to satisfy GDPR, the EU AI Act and US frameworks like NIST AI RMF and SOC 2.
We build production AI on the Hugging Face stack for US and EU teams — from model selection and evaluation to PEFT/LoRA fine-tuning, RAG and self-hosted Text Generation Inference. Our engineers know when an open-weights model beats a closed API, how to fine-tune without leaking your data, and how to serve it cost-effectively on your own GPUs or on Inference Endpoints. Every deployment is governed, documented with model cards, and built to satisfy GDPR, the EU AI Act and US frameworks like NIST AI RMF and SOC 2.
Challenges
The Hub holds hundreds of thousands of models with wildly different quality, size and licence terms. Picking one that fits the task, hardware budget and your commercial-use rights — without tripping a restrictive RAIL or non-commercial clause — is harder than it looks.
Full fine-tuning is expensive and storage-heavy; PEFT/LoRA is cheaper but needs the right rank, target modules and merge strategy. Choosing wrong wastes GPU budget or leaves the model under-adapted to your domain.
Running TGI on your own GPUs gives control and data residency but adds ops burden; managed Inference Endpoints are simpler but cost more at scale. The break-even depends on traffic, latency targets and compliance needs.
Idle GPUs, oversized instances and unbatched requests burn money fast. Without quantisation, batching and autoscaling, inference cost per token climbs and capacity sits unused between bursts.
Open models hallucinate and drift like any LLM, and vibes-based testing hides regressions. Without task-specific eval sets, grounding and guardrails, quality problems reach production unnoticed.
Training data often carries PII, secrets or copyrighted text that then becomes embedded in the weights. Cleaning, consenting and documenting that data is essential to stay GDPR- and licence-compliant.
Solutions
We benchmark candidate models on your real tasks and hardware, check licences and provenance, and recommend the smallest model that meets quality targets — with a documented eval set you can rerun as models evolve.
We fine-tune efficiently with LoRA/QLoRA — tuning rank, target modules and learning schedule — on cleaned, governed datasets, then merge or serve adapters so you get domain quality without full-model cost.
We deploy Text Generation Inference in Docker on your GPUs with continuous batching, tensor parallelism and quantisation, exposing an OpenAI-compatible API that keeps data inside your boundary.
We ground models in your own knowledge with retrieval pipelines — embeddings, vector search and reranking — so answers cite real sources and hallucination drops without retraining the base model.
We wrap models in reproducible pipelines with versioned weights, automated evaluation gates, autoscaling and inference monitoring — tracking latency, cost per token, drift and quality in production.
We document each model with a model card, record dataset provenance and licences, and build PII screening and erasure handling into the data pipeline so AI Act, GDPR and SOC 2 reviews are routine.
Stack
Transformers, Datasets, PEFT/LoRA, TGI, Inference Endpoints, Accelerate, Tokenizers, PyTorch, ONNX, Docker.
Compliance
EU AI Act · GDPR · model/data governance · SOC 2
Cases
Native iOS and Android e-signature clients with a Symfony + React CRM for a cross-border law firm — KYC onboarding and a defensible evidence trail for US & EU matters.
Cross-platform diet and meal-planning app on Flutter — calorie engine, recipe library, weekly meal-plan, grocery ordering.
An internal EDM for a retail chain — e-signatures, approval routing, counterparties, and tasks on React + Laravel, built for US & EU operations.
Why YuSMP
You work with engineers who fine-tune, quantise and serve open models in production — who know when a 7B LoRA beats a frontier API and how to prove it on your own data.
We size GPUs, batch and quantise deliberately, and instrument cost per token and latency from day one — so inference spend is predictable and visible to finance.
We keep models and data in the right region, document model cards and provenance, and wire in GDPR, EU AI Act, NIST AI RMF and SOC 2 controls up front rather than as a retrofit.
FAQ
Open models on Hugging Face win when you need data residency, predictable cost at high volume, full control over weights and behaviour, or deployment in an air-gapped or regulated environment. Closed APIs still lead on raw frontier capability and zero-ops convenience. We benchmark both on your actual tasks and often run a hybrid — an open model for high-volume or sensitive workloads and a closed API where peak quality matters most.
They solve different problems. RAG injects up-to-date or proprietary knowledge at query time and is the right first move when the issue is that the model does not know your facts. Fine-tuning changes behaviour, tone, format or task skill, and suits cases where prompting and retrieval cannot get the style or structure you need. They combine well — we frequently fine-tune for behaviour and use RAG for knowledge.
PEFT (parameter-efficient fine-tuning) adapts a model by training a small set of extra parameters instead of all of its weights. LoRA, the most common method, injects low-rank adapter matrices — so you fine-tune a few million parameters rather than billions, on a single GPU, in hours not days. QLoRA goes further by quantising the base model during training. The result is dramatically lower GPU cost and tiny adapter files you can swap per customer or task.
Inference Endpoints are cheaper and faster to start when traffic is low or spiky — you pay for managed, autoscaling capacity with no ops overhead. Self-hosting Text Generation Inference on your own GPUs wins at sustained high volume and gives you full data residency and control, but you own the operations. We model your expected traffic and latency targets to find the break-even and often start managed, then migrate to self-hosted as volume grows.
It varies by model. Many (Apache-2.0, MIT) allow unrestricted commercial use; others (Llama, Gemma) carry acceptable-use and scale conditions; some research models use non-commercial or RAIL licences that limit deployment. We verify the licence of every model and dataset you adopt, record provenance, and steer you to options that are contractually clean for your use case — so you are not exposed later.
Anything in your training set can end up embedded in the model's weights, so we treat the dataset as sensitive from the start. We screen for and pseudonymise PII, remove secrets and out-of-licence content, document the lawful basis under GDPR, and keep the whole pipeline inside a governed, region-correct boundary. Where erasure obligations apply, we plan for retraining or unlearning rather than assuming weights can be edited after the fact.
Yes — the AI Act applies to how a system is deployed and used, not to which API you call, so self-hosting an open model does not exempt you. As a deployer you still face transparency, documentation and risk-tier obligations, and for higher-risk uses, evaluation and human-oversight duties. We document model cards, training-data provenance and intended use, and build the logging and evaluation evidence that makes your deployment auditable.
Response within 1 business day. NDA on request.
