# Your data. Your model. Your weights.

> Fine-tuned and distilled models that beat generic LLMs on your task, at a fraction of the cost per call. LoRA, distillation, classifiers and evals. You own the weights.

URL: https://ai.arcvs.tech/en/services/llm-fine-tuning · Português: https://ai.arcvs.tech/pt/servicos/treinamento-de-modelos.md · ARCVS AI: https://ai.arcvs.tech/en.md

When prompting a frontier model is too slow, too expensive or too generic, we train a smaller one that wins on your task. Fine-tuning, distillation and task-specific classifiers, measured against a baseline before anything ships.

- **Week 1**: Prototype on your data
- **Day 30**: Production pilot
- **100%**: Yours: code & weights

## Renting a giant model for a narrow job is a tax.

Classifying tickets, extracting fields, routing documents or scoring leads doesn't need a frontier model on every call. A small model trained on your examples is faster, cheaper, predictable, and can run offline on your own hardware.

## What we build

- **Task classifiers**: Tiering, routing, intent and risk scoring. We run a local classifier in our own prospecting, and the data never leaves the machine.
- **Extraction models**: Structured fields out of invoices, contracts, forms and emails, with confidence scores and a human queue for the doubtful ones.
- **Distillation**: Teach a small open model to imitate a frontier model on your task, then cut cost per call dramatically.
- **Domain tone & format**: Fine-tunes that write in your house style, follow your schema and stop needing three paragraphs of prompt.

## What you get

- Dataset build: collection, labeling guidelines, cleaning, splits
- Baseline vs. fine-tune evals on held-out data
- Training runs (LoRA / QLoRA / full) with reproducible configs
- Serving: managed endpoint, your cloud GPU, or on-prem
- Model card, weights and retraining pipeline handed over

## Stack

PyTorch, Hugging Face, Unsloth, LoRA / QLoRA, vLLM, Ollama, Llama, Qwen, Mistral, OpenAI fine-tuning

## FAQ

### Do you train models from scratch?

Almost never, because it rarely pays off. We fine-tune or distill strong open or commercial base models, which gets you most of the gain at a small fraction of the cost.

### How much data do we need?

For classifiers and extraction, often a few hundred to a few thousand good examples. We help you build and label the dataset, and we tell you upfront if the data isn't there.

### Who owns the trained model?

You do. Weights, datasets, training configs and eval results are delivered to your storage.

### Can it run without internet?

Yes. Small models can run on your servers or a single GPU, fully offline, which is useful for LGPD/GDPR-sensitive data.

## Other capabilities

- [AI agents](https://ai.arcvs.tech/en/services/ai-agents): Agents that use your tools, follow your rules and finish real tasks end to end.
- [Agentic orchestration](https://ai.arcvs.tech/en/services/multi-agent-orchestration): Planner, workers and reviewers that run in parallel, with one owner of state and a full audit trail.
- [RAG & knowledge systems](https://ai.arcvs.tech/en/services/rag-knowledge-systems): Search and answers over your company data, with citations, permissions and measured accuracy.
- [AI automation](https://ai.arcvs.tech/en/services/ai-automation): Workflows where AI reads, classifies and decides, and code does the rest. Measured in hours reclaimed.
- [Private & on-prem AI](https://ai.arcvs.tech/en/services/private-ai): Open-weight models on your cloud or hardware. Your prompts and documents never leave your perimeter.
- [AI security](https://ai.arcvs.tech/en/services/ai-security): Red teaming for LLM apps and agents: prompt injection, data leakage, excessive agency, insecure tools.
- [OSINT & security audits](https://ai.arcvs.tech/en/services/osint-security-audits): Your exposure, seen from outside: breaches, leaks, forgotten assets, CVEs. Then authorized testing and fixes.

## Contact

- WhatsApp: +5532999143361 (https://wa.me/5532999143361)
- Telegram: @w1ldcrow (https://t.me/w1ldcrow)
- Email: contact@arcvs.tech
- Contact section: https://ai.arcvs.tech/en#contact
- Parent studio: https://www.arcvs.tech