DEV Community

ZeroGPU
ZeroGPU

Posted on

💰Don’t Waste Tokens on Data Entry: Tag Customer Reviews Overnight with ZeroGPU Batch API

#ai #llm #aiops

A new cookbook to demonstrate our new Batch API.

Most teams sit on massive backlogs of unstructured text—customer reviews, support tickets, and survey responses. They want to classify it, but doing it one synchronous API call at a time is painfully slow and wildly expensive.

Worse yet, they use over-engineered frontier models for the job. Tagging a review with a sentiment label and a few topics isn't a reasoning problem. It’s repeatable, high-volume work. Using a massive LLM for this is like hiring a rocket scientist to sort mail. You're bleeding budget.

ZeroGPU was built to solve exactly this. With our new Batch API, you hand ZeroGPU a single file of requests and get the results back within a completion window—at a fraction of the cost of synchronous calls.

Our new cookbook walks you through a complete, production-ready example of how to automate this overnight.

What it does

Starting from a raw reviews.csv, ZeroGPU returns a fully categorized tagged.csv with sentiment labels and key topics for every single row.

It runs as a single asynchronous job powered by LFM2.5-1.2B-Instruct—a small, lightning-fast model perfectly tuned for short-form text classification. Thousands of rows get tagged while you sleep, without hitting rate limits or draining your wallet.

How it works in 5 steps:

  1. Prepare your raw CSV.
  2. Build a JSONL file with one request per row.
  3. Upload the file to ZeroGPU.
  4. Create the batch.
  5. Poll and download the results.

💡 Smart Error Handling & Merging: Every result is automatically keyed back to its source row by a custom_id, ensuring the output merges flawlessly back into your original database, no matter what order the API processes them. If a few rows fail? They’re isolated into a separate list so you can retry just those specific rows—no need to re-run the entire dataset.

Because our endpoint is OpenAI-compatible, swapping your current workflow takes minutes. Best of all, the entire guide runs end-to-end in Google Colab with zero local setup required.

The ZeroGPU Philosophy

Run the right model on the right compute. Save the frontier models for true reasoning, and let specialized, efficient small models handle the heavy lifting.

Top comments (0)