Run a transformer forward pass and return the logits

Assembles the GGML compute graph for a llama-architecture forward pass (RMSNorm, RoPE, causal self-attention, SwiGLU feed-forward) over the model's memory-mapped weights and computes it on the GGML CPU backend. Quantized weights are contracted natively through the SIMD-dispatched quantized kernels - they are never decoded to double.

Usage

rllm_forward(model, tokens, cache = NULL)

Arguments

model

An rllm_model from rllm_gguf_model().

tokens

Integer vector of 0-based token ids (as in the GGUF vocab).

cache

Optional rllm_kv_cache() for incremental decoding.

Value

A numeric matrix of logits, dim c(n_vocab, length(tokens)): column i scores the token following position i.

Details

Without a cache, the graph attends over the whole token batch with a causal mask (prompt scoring). With a rllm_kv_cache(), the pass appends the new tokens' keys/values to the cache and attends over everything cached so far, advancing cache$n_past - the incremental-decoding path: prefill once with the prompt, then feed one token at a time.