Expand description
RWKV v6 model implementation.
The RWKV model is a recurrent neural network model with performance on par with transformer architectures. Several variants are available, candle implements the v5 and v6 versions and can be used with Eagle 7B(blog post).
Key characteristics:
- Linear attention mechanism
- Time-mixing for temporal dependencies
- Group normalization
- Feed forward gating
- State recycling for efficient inference
§Example
cargo run --example rwkv --release -- \
--prompt "The smallest prime is "
> avx: true, neon: false, simd128: false, f16c: true
> temp: 0.00 repeat-penalty: 1.10 repeat-last-n: 64
> The smallest prime is ϕ(2) = 2.
> The smallest composite is ϕ(3) = 3.
> The smallest perfect number is ϕ(5) = 5.
> The smallest perfect square is ϕ(4) = 4.
> The smallest perfect cube is ϕ(6) = 6.Re-exports§
pub use crate::models::rwkv_v5::Config;pub use crate::models::rwkv_v5::State;pub use crate::models::rwkv_v5::Tokenizer;