Besides the usual FP32, it supports FP16, quantized INT4, INT5 and INT8 inference. This project is focused on CPU, but cuBLAS is also supported. RWKV is a novel large language model architecture, with the largest model in the family having 14B parameters. In contrast to Transformer with O(n^2) attention, RWKV requires only state from the previous step to calculate logits. This makes RWKV very CPU-friendly on large context lengths.
Features
- Windows / Linux / MacOS
- Build the library yourself
- Get an RWKV model
- Requirements: Python 3.x with PyTorch and tokenizers
- ggml moves fast, and can occasionally break compatibility with older file formats
- Requirements: Python 3.x with PyTorch
License
MIT LicenseFollow rwkv.cpp
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