RWKV-4 14B
| Property | Value |
|---|---|
| Architecture | RWKV-4 (L40-D5120) |
| Training Data | The Pile |
| License | Apache 2.0 |
| Context Length | 8192 tokens |
What is rwkv-4-pile-14b?
RWKV-4 14B is a large-scale causal language model that represents a significant advancement in the RWKV architecture family. Built with 40 layers and 5120 embedding dimensions, this model has been extensively trained on The Pile dataset for 331B tokens, achieving impressive benchmark results including a LAMBADA perplexity of 3.81 and accuracy of 71.05%.
Implementation Details
The model implements a unique architecture combining the best aspects of transformer and RNN designs. It can be deployed using the ChatRWKV framework and supports context lengths up to 8192 tokens in its fine-tuned versions.
- 40 layers with 5120 embedding dimensions
- Trained on The Pile dataset for 331B tokens
- Supports both general text generation and chat capabilities
- Compatible with Alpaca and Vicuna-style instructions
Core Capabilities
- Strong performance on multiple benchmarks (PIQA: 77.42%, SC2016: 75.57%, Hellaswag: 70.24%)
- Supports chat functionality through the "Raven" variant
- Handles context lengths up to 8192 tokens
- Efficient text generation and processing
Frequently Asked Questions
Q: What makes this model unique?
The RWKV-4 14B combines the efficiency of RNNs with transformer-like performance, offering a unique architecture that can process long sequences efficiently while maintaining high accuracy on various language tasks.
Q: What are the recommended use cases?
The model excels in general text generation, chat applications (especially when using the Raven variant), and can handle complex language understanding tasks. It's particularly well-suited for applications requiring both high performance and efficient processing of long contexts.
