{"slug": "dont-waste-tokens-on-data-entry-tag-customer-reviews-overnight-with-zerogpu-api", "title": "💰Don’t Waste Tokens on Data Entry: Tag Customer Reviews Overnight with ZeroGPU Batch API", "summary": "ZeroGPU released a Batch API that enables overnight tagging of customer reviews using a small, efficient model (LFM2.5-1.2B-Instruct) instead of expensive frontier LLMs. The API processes thousands of rows asynchronously, returning results keyed by custom_id for seamless merging, with error isolation for retries. A cookbook demonstrates the workflow from raw CSV to tagged output, running entirely in Google Colab.", "body_md": "*A new cookbook to demonstrate our new Batch API.*\n\nMost 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.\n\nWorse 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.\n\n**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.\n\nOur new cookbook walks you through a complete, production-ready example of how to automate this overnight.\n\nStarting from a raw `reviews.csv`\n\n, ZeroGPU returns a fully categorized `tagged.csv`\n\nwith sentiment labels and key topics for every single row.\n\nIt runs as a single asynchronous job powered by `LFM2.5-1.2B-Instruct`\n\n—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.\n\n💡\n\nSmart Error Handling & Merging:Every result is automatically keyed back to its source row by a`custom_id`\n\n, 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.\n\nBecause 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.\n\nRun the right model on the right compute. Save the frontier models for true reasoning, and let specialized, efficient small models handle the heavy lifting.", "url": "https://wpnews.pro/news/dont-waste-tokens-on-data-entry-tag-customer-reviews-overnight-with-zerogpu-api", "canonical_source": "https://dev.to/team_zerogpu/-dont-waste-tokens-on-data-entry-tag-customer-reviews-overnight-with-zerogpu-batch-api-1pjf", "published_at": "2026-06-16 16:06:44+00:00", "updated_at": "2026-06-16 16:17:19.786326+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-products", "ai-tools", "developer-tools"], "entities": ["ZeroGPU", "Batch API", "LFM2.5-1.2B-Instruct", "Google Colab", "OpenAI"], "alternates": {"html": "https://wpnews.pro/news/dont-waste-tokens-on-data-entry-tag-customer-reviews-overnight-with-zerogpu-api", "markdown": "https://wpnews.pro/news/dont-waste-tokens-on-data-entry-tag-customer-reviews-overnight-with-zerogpu-api.md", "text": "https://wpnews.pro/news/dont-waste-tokens-on-data-entry-tag-customer-reviews-overnight-with-zerogpu-api.txt", "jsonld": "https://wpnews.pro/news/dont-waste-tokens-on-data-entry-tag-customer-reviews-overnight-with-zerogpu-api.jsonld"}}