  We introduce GoldFinch, a hybrid Linear Attention/Transformer sequence model
that uses a new technique to efficiently generate a highly compressed and
reusable KV-Cache in linear time and space with respect to sequence length.
GoldFinch stacks our new GOLD transformer on top of an enhanced version of the
Finch (RWKV-6) architecture. We train up to 1.5B parameter class models of the
Finch, Llama, and GoldFinch architectures, and find dramatically improved
modeling performance relative to both Finch and Llama. Our cache size savings
increase linearly with model layer count, ranging from 756-2550 times smaller
than the traditional transformer cache for common sizes, enabling inference of
extremely large context lengths even on limited hardware. Although
autoregressive generation has O(n) time complexity per token because of
attention, pre-fill computation of the entire initial cache state for a
submitted context costs only O(1) time per token due to the use of a recurrent
neural network (RNN) to generate this cache. We release our trained weights and
training code under the Apache 2.0 license for community use.
