Argilla & Distilabel Job Support — Data Annotation & Synthetic Data

We provide hands-on, real-time Argilla and Distilabel proxy job support on your actual project tickets. We help you build SFT and preference datasets — human annotation with Argilla, synthetic/preference generation with Distilabel — and shape them for TRL. This page is part of our Hugging Face proxy job support ecosystem: our experts help with architecture and implementation, environment and dependency setup, debugging, code review, performance/GPU-memory tuning, and production issues — during your working hours, same-day. "Proxy" means expert technical support and mentoring on your real deliverables, not replacing you or performing your job duties.

Typical Argilla and Distilabel production issues we resolve include CUDA out-of-memory and GPU-memory pressure, dependency and version conflicts (Transformers/PyTorch/CUDA), dtype and precision mismatches, slow throughput or latency, checkpoint and safetensors loading errors, and integration failures with upstream and downstream systems. We help you find the root cause from stack traces, logs, and profiling, then ship a stable fix.

Yes. We provide Argilla and Distilabel proxy interview support (also searched as Argilla and Distilabel interview proxy support) — real-time expert help on fundamentals, architecture and design trade-offs, scenario-based problems, and hands-on coding rounds — calibrated to the exact role and company format. Proxy interview support means real-time technical help on the exact areas your interview covers; you attend and complete your own interview.

Yes. Onboarding onto an unfamiliar Argilla and Distilabel setup is one of the most common reasons people reach out. We help you understand the existing model and data pipeline, the training and serving architecture, and the repository structure, get productive fast, deliver your first tasks confidently, and avoid the mistakes that get flagged in reviews and standups.

Contact us on WhatsApp with your stack, the problem, and your timeline. We assign the right expert — usually same-day. Every engagement is fully confidential, and NDAs are available on request.

Synthetic data (Distilabel) is fast and cheap for scaling instruction data and bootstrapping preference pairs, and is often good enough — especially when seeded and filtered well. Human data (Argilla) is essential where judgement, safety, or domain nuance matters, and for validating synthetic data. Most strong datasets blend both. We help you decide the mix and build the pipeline.

You need prompts with a chosen and a rejected response each. We help you generate candidates (Distilabel), score/rank them with models and/or humans (Argilla), filter for quality and agreement, and format them exactly as the TRL DPOTrainer expects — so alignment training has clean signal instead of noise.