RWKV-4 Raven
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Training Data | The Pile, Alpaca, CodeAlpaca, Guanaco, GPT4All, ShareGPT |
| Architecture | 100% RNN-based Language Model |
| Available Sizes | 1.5B, 3B, 7B, 14B parameters |
What is rwkv-4-raven?
RWKV-4 Raven is an innovative language model series that combines the efficiency of RNNs with transformer-like performance. It's uniquely designed as a 100% RNN architecture while maintaining competitive capabilities with transformer models. The series includes multiple variants trained for different language distributions, from English-focused to multilingual models.
Implementation Details
The model implements a novel architecture that enables both efficient training and inference. It's available in various sizes from 1.5B to 14B parameters, with specific adaptations for both CUDA (GPU) and CPU inference through different implementations.
- Multiple language variants (Eng99%-Other1%, Eng86%-Chn10%-JpnEspKor2%-Other2%, etc.)
- Optimized inference implementations for both CPU and GPU
- Supports Q8_0 quantization for efficient CPU deployment
- Specific prompt format optimization for chat applications
Core Capabilities
- Text generation and chat functionality
- Multilingual support with various language ratio models
- Code generation capabilities
- Zero-shot and in-context learning
- Efficient inference with both CPU and GPU support
Frequently Asked Questions
Q: What makes this model unique?
RWKV-4 Raven uniquely combines RNN architecture with transformer-like capabilities, offering efficient inference while maintaining high performance. Even the 1.5B parameter version shows impressive capabilities for its size.
Q: What are the recommended use cases?
The model excels in text generation, chat applications, and code generation. It's particularly useful when you need efficient inference or multilingual capabilities, with specific versions optimized for different language distributions.
