What is Refuel LLM V2 Small?

Refuel LLM V2 Small is an 8B Llama3-based model for data tasks. It excels in classification, structured extraction, and cleaning. Trained on 2750+ enterprise datasets.

How does Refuel LLM V2 Small API work?

Access via LLM endpoint for inference. Supports 8K context and JSON outputs. Ideal for Refuel LLM V2 Small API integrations.

Is Refuel LLM V2 Small model better than GPT-3.5?

Yes, scores 79.67% vs GPT-3.5-Turbo's 68.13% on data benchmarks. Outperforms similar-sized models consistently.

What tasks suits Refuel LLM V2 Small?

Data labeling, enrichment, classification, entity resolution. Domains include finance, HR, law, e-commerce.

Refuel LLM V2 Small alternative to Claude?

Refuel LLM V2 Small beats Claude-3-Sonnet (70.99%) at 1/10th size. Use as refuel llm v2 small alternative for cost efficiency.

Where to access refuel llm v2 small model?

Available via API endpoints like Together AI. Playground and cloud access for testing refuel llm v2 small api.

Available now on ModelsLab · Language Model

Refuel LLM V2 Small
Data Tasks Mastered

Try Refuel LLM V2 Small API Documentation

Extract. Classify. Clean.

8B Parameters

Llama3 Base Optimized

Refuel LLM V2 Small uses Llama3-8B base for data labeling, enrichment, and cleaning tasks.

79.67% Accuracy

Beats GPT-3.5 Turbo

Outperforms Claude-3-Sonnet and GPT-3.5-Turbo on 2750+ data structuring benchmarks.

8K Context

Handles Long Inputs

Supports 8K max input length for classification, extraction, and entity resolution.

Examples

See what Refuel LLM V2 Small can create

Copy any prompt below and try it yourself in the playground.

Invoice Extraction

“Extract structured fields from this invoice text: date, amount, vendor, items list. Input: Invoice dated 2023-10-01 from Acme Corp, total $1500, items: widgets x10 $100 each, gadgets x5 $200 each. Output as JSON.”

Sentiment Classify

“Classify sentiment of customer review as positive, negative, neutral with confidence score. Input: Product arrived fast but quality poor. Output JSON: {sentiment: negative, confidence: 0.85}.”

Entity Resolution

“Resolve entities in email: names, organizations, dates. Input: Meeting with John Doe from Google on Friday 2023-10-06. Output JSON list of resolved entities.”

Data Cleaning

“Clean and standardize addresses from messy list. Input: 123 Main St, NY; 456 broadway nyc. Output standardized JSON array.”

For Developers

A few lines of code.
Data extraction. 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

API Documentation

import requests

response = requests.post(
    "https://modelslab.com/api/v7/llm/chat/completions",
    json={
  "key": "YOUR_API_KEY",
  "prompt": "",
  "model_id": ""
}
)
print(response.json())

FAQ

Common questions about Refuel LLM V2 Small

Read the docs

Ready to create?

Start generating with Refuel LLM V2 Small on ModelsLab.