ROSA+: RWKV's ROSA implementation with fallback statistical predictor (github.com)

ROSA+ is a compact Python implementation (rosaplus.py) that extends BlinkDL’s ROSA statistical next-token predictor for RWKV-style work by adding an easy API, persistent model I/O, and a Witten–Bell fallback predictor for unknown sequences. In practice you train it on raw text (default character tokenization), call build_lm() to create the fallback, and generate or sample next-token probability distributions via get_dist(). The author demonstrates Shakespeare experiments (example uses max_order=1,048,576), shows saving/loading JSON (orjson optional for speed), and notes ROSA handles ~99% of predictions while the lightweight fallback only triggers on novel contexts; forcing always_fallback produces coherent but novel, collage-like outputs. For the AI/ML community ROSA+ is significant as a fast, self-contained statistical LM that exposes interpretable probability outputs and a pragmatic fallback mechanism—useful for autocompletion, toy LMs, or as a surface-feature extractor. Crucially it’s purely statistical (no neural continuous state), so it captures local syntax and phrase patterns but lacks deep context, few-shot or transfer capabilities of neural models and can fall into attractor loops. The implementation suggests hybrid directions: feeding ROSA-style embeddings into a small NN (e.g., GRU) or adding statistical attention/continuous state could yield much more efficient models—an active research avenue for building lightweight alternatives to large NNs. Note: ROSA+ itself does not integrate with RWKV out of the box; contact BlinkDL or RWKV channels for upstream integration.