The RWKV (pronounced RwaKuv) Language Model is an innovative approach to language modelling, merging the strengths of both RNN (Recurrent Neural Network) and Transformer architectures. It stands out by achieving LLM (Large Language Model) performance while maintaining the parallelizability of GPT transformers. The current version is RWKV-7 "Goose".
Key Features of the RWKV Language Model
- Performance: Exhibits great LLM performance.
- Efficiency: Achieves linear time complexity and constant space requirements due to the absence of KV-cache.
- Speed: Allows for fast training.
- Context Length: Supports infinite context length.
- Embedding: Enables free text embedding.
- Attention-Free: Operates without attention mechanisms.
How to Use RWKV
RWKV offers a range of tools and resources that cater to different needs and levels of expertise:
- RWKV-Runner: A GUI that offers one-click install and API.
- RWKV pip package: The official RWKV pip package.
- RWKV-PEFT: Used for finetuning RWKV (9GB VRAM can finetune 7B).
- RWKV-server: Allows fast WebGPU inference (NVIDIA/AMD/Intel), nf4/int8/fp16.
- HuggingFace-compatible RWKV weights: RWKV weights compatible with HuggingFace.
RWKV Projects
- There are over 400 RWKV projects.
Conclusion
RWKV Language Model combines the advantages of both RNN and Transformer architectures. Its efficient design, linear time complexity, and constant space requirements make it a promising solution for various natural language processing tasks. The availability of tools like RWKV-Runner and RWKV-PEFT, along with the active development within the Linux Foundation AI project, positions RWKV as a noteworthy advancement in the field.