Refuel LLM V2
Label Data Automatically
Master Data Tasks
Top Benchmarks
Beats GPT-4 Turbo
Refuel LLM V2 scores 83.82% on 30+ data labeling tasks, outperforming GPT-4-Turbo and Claude-3-Opus.
32K Context
Handles Long Inputs
Mixtral-8x7B base supports 32K max input context for complex data enrichment and cleaning.
Fine-Tune Fast
LoRA in Minutes
Tune Refuel LLM V2 API with <200 data points for near-perfect structured extraction performance.
Examples
See what Refuel LLM V2 can create
Copy any prompt below and try it yourself in the playground.
Extract Entities
“From this finance transaction log, extract structured fields: date, amount, category, merchant. Input: '2025-01-15 $250.00 Amazon purchase groceries'. Output JSON only.”
Classify Documents
“Classify HR resume text into categories: skills, experience, education. Input: 'Software engineer with Python expertise, 5 years at Google, BS Computer Science'. Return labeled JSON.”
Clean Ecommerce Data
“Clean product descriptions: remove duplicates, standardize formats. Input: 'Blue shirt size M, blue shirt M'. Output unique normalized list in JSON.”
Enrich Law Texts
“Extract key clauses from contract: parties, obligations, termination. Input: 'Agreement between Acme Corp and Beta Inc for services ending Dec 2025'. Structured JSON output.”
For Developers
A few lines of code.
Data labeling. One call.
ModelsLab handles the infrastructure: fast inference, auto-scaling, and a developer-friendly API. No GPU management needed.
- Serverless: scales to zero, scales to millions
- Pay per token, no minimums
- Python and JavaScript SDKs, plus REST API
import requestsresponse = requests.post("https://modelslab.com/api/v7/llm/chat/completions",json={"key": "YOUR_API_KEY","prompt": "","model_id": ""})print(response.json())