Llama-3-Refueled
| Property | Value |
|---|---|
| Parameter Count | 8.03B |
| Model Type | Text Generation / Data Labeling |
| Architecture | Llama-3 Base with Instruction Tuning |
| License | CC BY-NC 4.0 |
| Release Date | May 8, 2024 |
What is Llama-3-Refueled?
Llama-3-Refueled is a specialized language model developed by Refuel AI, built on top of the Llama-3-8B architecture and fine-tuned specifically for data labeling tasks. This model represents a significant advancement in automated data labeling, showing impressive performance metrics that compete with much larger models like GPT-4-Turbo and Claude-3-Opus.
Implementation Details
The model utilizes an optimized transformer architecture and has been trained on over 4 billion tokens across 2,750+ NLP tasks. The training data encompasses human-annotated datasets (Flan, Task Source, Aya collection), synthetic datasets (OpenOrca, OpenHermes, WizardLM), and proprietary datasets from Refuel AI.
- Architecture: Based on Llama-3-8B-instruct with specialized fine-tuning
- Training Scope: 2,750+ datasets covering various NLP tasks
- Format Support: Handles both input and output in text format
- Deployment: Compatible with HuggingFace Transformers library
Core Capabilities
- Classification: 81.72% accuracy
- Reading Comprehension: 70.04% accuracy
- Structure Extraction: 84.28% accuracy
- Entity Matching: 92.00% accuracy
- Overall Performance: 79.67% accuracy across all tasks
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized focus on data labeling tasks and its ability to achieve performance metrics comparable to much larger models while maintaining a relatively compact 8B parameter size. It's particularly noteworthy that it outperforms GPT-3.5-Turbo in specific data labeling scenarios.
Q: What are the recommended use cases?
The model is specifically designed for text data labeling tasks including classification, reading comprehension, structured attribute extraction, and entity resolution. It's particularly well-suited for automated data annotation pipelines and large-scale data labeling projects where human annotation would be time-consuming or costly.
