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llama.cpp fork for Ternary Bonsai 2 27B: MTP speculation, suffix cache reuse (SCR), q8 KV, two-slot fairness, fused DeltaNet kernels
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C++
at main
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354// ref: https://github.com/ggml-org/llama.cpp/issues/25937// only works reliably when run with a large model that occupies 3GB+ of wired memory// thus, this test is not run by default// example model to run with: google/gemma-4-E4B-it-qat-q4_0-gguf
#include "llama.h"#include "common.h"
#include <cstdint>#include <mach/mach.h>#include <mach/mach_host.h>#include <unistd.h>
static uint64_t wired_memory() { vm_statistics64_data_t vmstat; mach_msg_type_number_t count = HOST_VM_INFO64_COUNT; if (host_statistics64(mach_host_self(), HOST_VM_INFO64, (host_info64_t)&vmstat, &count) != KERN_SUCCESS) { return UINT64_MAX; } return static_cast<uint64_t>(vmstat.wire_count) * vm_kernel_page_size;}
int main(int argc, char ** argv) { auto * model_path = common_get_model_or_exit(argc, argv);
llama_backend_init();
const uint64_t wired_initial = wired_memory();
llama_model_params params = llama_model_default_params(); params.load_mode = LLAMA_LOAD_MODE_NONE; struct llama_model* model = llama_model_load_from_file(model_path, params);
const uint64_t wired_loaded = wired_memory(); const uint64_t wired_delta = wired_loaded - wired_initial; // system memory fluctuates, so we need to allocate enough to reliably detect the release GGML_ASSERT(wired_delta > 2'000'000'000); // 2GB
llama_model_free(model);
const uint64_t t_start_ms = ggml_time_ms();
// expect most of the allocated memory to be released within 10 seconds // we allow for some tolerance due to system-wide memory fluctuations while (wired_memory() > wired_loaded - 0.75 * wired_delta) { GGML_ASSERT(ggml_time_ms() - t_start_ms < 10'000); usleep(100'000); // 100ms }
llama_backend_free();
return 0;}