#RWKV

Leveraging a unique attention-free architecture, RWKV combines the strengths of RNNs and Transformers to deliver exceptional language model performance. It supports rapid inference, low VRAM usage, and efficient training. RWKV's parallelization capabilities facilitate GPT-style computation, making it adaptable for various AI applications such as text generation and image processing. This model is compatible with edge devices, ensuring resource efficiency and offering diverse training and fine-tuning options for tailored outputs across different data scales.

The project ports RWKV language model architecture to ggml, supporting FP32, FP16, and various quantized inferences like INT4, INT5, and INT8. Primarily CPU-focused, it includes both a C library and a Python wrapper, with optional cuBLAS support. It supports RWKV versions 5 and 6, providing competitive alternatives to Transformer models, especially for extensive contexts, and accommodates LoRA checkpoint integration, offering detailed performance metrics for efficient computations.

Access LLaMa and RWKV models in ONNX format to enhance inference efficiency on devices with limited memory. This project bypasses the need for torch or transformers, supports memory pooling, and is compatible with FPGA/NPU/GPGPU hardware, enabling streamlined conversion to fp16 or TVM.

Discover the efficiency and scalability of the RWKV-6, an RNN language model that competes with transformer models in quality and performance, reducing VRAM usage and increasing speed. ChatRWKV offers cutting-edge demos and multi-platform inference options, supported by Stability EleutherAI. This model provides rich community resources and developer tools, including GPU optimization and various project backends, suitable for developers involved in innovative AI research or constructing effective chatbot systems.