RWKV v5-Eagle-7B
| Property | Value |
|---|---|
| Parameter Count | 7.52B |
| License | Apache 2.0 |
| Training Tokens | 1.1 Trillion |
| Context Length | 4096 |
What is v5-Eagle-7B-pth?
Eagle-7B represents a breakthrough in AI model efficiency, implementing the RWKV-v5 architecture as a linear transformer that achieves unprecedented inference cost reduction. This foundation model has been trained on a diverse dataset spanning over 100 languages, with a distribution of 70% English, 15% multilingual content, and 15% code.
Implementation Details
Built on the innovative RWKV-v5 architecture, Eagle-7B delivers transformer-like performance without traditional attention mechanisms, resulting in 10-100x lower inference costs. The model supports a context length of 4096 tokens and is designed for flexible deployment across various applications.
- Linear transformer architecture with revolutionary efficiency
- Comprehensive training across multiple languages and code
- Optimized for both performance and computational efficiency
- Foundation model ready for specific fine-tuning
Core Capabilities
- Multi-lingual processing with superior performance in benchmarks
- Competitive performance with larger models like Falcon and LLaMA2
- Efficient code processing and generation
- Environmentally conscious design with minimal carbon footprint
Frequently Asked Questions
Q: What makes this model unique?
Eagle-7B stands out for its attention-free transformer architecture, achieving comparable performance to larger models while maintaining significantly lower computational requirements. It's particularly notable for being the most environmentally friendly 7B model in terms of per-token processing.
Q: What are the recommended use cases?
As a foundation model, Eagle-7B is suitable for various applications after fine-tuning, including multilingual text processing, code generation, and general language understanding tasks. It's particularly valuable for deployments where computational efficiency is crucial.
