int32_t n_vocab = 32000;

int32_t n_ctx = 512; // this is provided as user input?

int32_t n_embd = 4096;

int32_t n_mult = 256;

int32_t n_head = 32;

int32_t n_layer = 32;

int32_t n_rot = 64;

int32_t f16 = 1;

// normalization

struct ggml_tensor * attention_norm;

// attention

struct ggml_tensor * wq;

struct ggml_tensor * wk;

struct ggml_tensor * wv;

struct ggml_tensor * wo;

// normalization

struct ggml_tensor * ffn_norm;

// ff

struct ggml_tensor * w1;

struct ggml_tensor * w2;

struct ggml_tensor * w3;

llama_hparams hparams;

struct ggml_tensor * tok_embeddings;

struct ggml_tensor * norm;

struct ggml_tensor * output;

std::vector<llama_layer> layers;

// key + value memory

struct ggml_tensor * memory_k;

struct ggml_tensor * memory_v;

//

struct ggml_context * ctx;

std::map<std::string, struct ggml_tensor *> tensors;

printf("%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str());

auto fin = std::ifstream(fname, std::ios::binary);

if (!fin) {

fprintf(stderr, "%s: failed to open '%s'\n", __func__, fname.c_str());

return false;

}

// verify magic

{

uint32_t magic;

fin.read((char *) &magic, sizeof(magic));

if (magic != 0x67676d6c) {

fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str());

return false;

}

}

int n_ff = 0;

// load hparams

{

auto & hparams = model.hparams;

fin.read((char *) &hparams.n_vocab, sizeof(hparams.n_vocab));

//fin.read((char *) &hparams.n_ctx, sizeof(hparams.n_ctx));

fin.read((char *) &hparams.n_embd, sizeof(hparams.n_embd));

fin.read((char *) &hparams.n_mult, sizeof(hparams.n_mult));

fin.read((char *) &hparams.n_head, sizeof(hparams.n_head));

fin.read((char *) &hparams.n_layer, sizeof(hparams.n_layer));

fin.read((char *) &hparams.n_rot, sizeof(hparams.n_rot));

fin.read((char *) &hparams.f16, sizeof(hparams.f16));

hparams.n_ctx = n_ctx;

n_ff = ((2*(4*hparams.n_embd)/3 + hparams.n_mult - 1)/hparams.n_mult)*hparams.n_mult;

printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);

printf("%s: n_ctx = %d\n", __func__, hparams.n_ctx);

printf("%s: n_embd = %d\n", __func__, hparams.n_embd);

printf("%s: n_mult = %d\n", __func__, hparams.n_mult);

printf("%s: n_head = %d\n", __func__, hparams.n_head);

printf("%s: n_layer = %d\n", __func__, hparams.n_layer);

printf("%s: n_rot = %d\n", __func__, hparams.n_rot);

printf("%s: f16 = %d\n", __func__, hparams.f16);

printf("%s: n_ff = %d\n", __func__, n_ff);

}

// load vocab

{

const int32_t n_vocab = model.hparams.n_vocab;

if (n_vocab != model.hparams.n_vocab) {

fprintf(stderr, "%s: invalid model file '%s' (bad vocab size %d != %d)\n",

__func__, fname.c_str(), n_vocab, model.hparams.n_vocab);

return false;

}

std::string word;

for (int i = 0; i < n_vocab; i++) {

uint32_t len;

fin.read((char *) &len, sizeof(len));

word.resize(len);

fin.read((char *) word.data(), len);

vocab.token_to_id[word] = i;

vocab.id_to_token[i] = word;

//if (i < 30000) {

// printf("%s: vocab[%d] = '%s'\n", __func__, i, word.c_str());

//}

}

}

// for the big tensors, we have the option to store the data in 16-bit floats or quantized

// in order to save memory and also to speed up the computation

ggml_type wtype = GGML_TYPE_COUNT;

switch (model.hparams.f16) {

case 0: wtype = GGML_TYPE_F32; break;

case 1: wtype = GGML_TYPE_F16; break;

case 2: wtype = GGML_TYPE_Q4_0; break;

case 3: wtype = GGML_TYPE_Q4_1; break;

default:

{

fprintf(stderr, "%s: invalid model file '%s' (bad f16 value %d)\n",

__func__, fname.c_str(), model.hparams.f16);

return false;

}

}

const ggml_type wtype2 = GGML_TYPE_F32;

auto & ctx = model.ctx;

size_t ctx_size = 0;

{

const auto & hparams = model.hparams;

const int n_embd = hparams.n_embd;

const int n_layer = hparams.n_layer;

const int n_ctx = hparams.n_ctx;

const int n_vocab = hparams.n_vocab;

ctx_size += n_embd*n_vocab*ggml_type_sizef(wtype); // tok_embeddings

ctx_size += n_embd*ggml_type_sizef(GGML_TYPE_F32); // norm

ctx_size += n_embd*n_vocab*ggml_type_sizef(wtype); // output

ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // attention_norm

ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // wq

ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // wk

ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // wv

ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // wo

ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ffn_norm

ctx_size += n_layer*(n_ff*n_embd*ggml_type_sizef(wtype)); // w1

ctx_size += n_layer*(n_ff*n_embd*ggml_type_sizef(wtype)); // w2

ctx_size += n_layer*(n_ff*n_embd*ggml_type_sizef(wtype)); // w3

ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(GGML_TYPE_F32); // memory_k

ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(GGML_TYPE_F32); // memory_v

ctx_size += (5 + 10*n_layer)*256; // object overhead

printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));

}

// create the ggml context

{

struct ggml_init_params params = {

.mem_size = ctx_size,

.mem_buffer = NULL,

};

model.ctx = ggml_init(params);

if (!model.ctx) {

fprintf(stderr, "%s: ggml_init() failed\n", __func__);

return false;

}

}

// prepare memory for the weights

{

const auto & hparams = model.hparams;

const int n_embd = hparams.n_embd;

const int n_layer = hparams.n_layer;

const int n_ctx = hparams.n_ctx;

const int n_vocab = hparams.n_vocab;

model.layers.resize(n_layer);

model.tok_embeddings = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);

model.norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);

model.output = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);

// map by name

model.tensors["tok_embeddings.weight"] = model.tok_embeddings;

model.tensors["norm.weight"] = model.norm;

model.tensors["output.weight"] = model.output;

for (int i = 0; i < n_layer; ++i) {

auto & layer = model.layers[i];

layer.attention_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);

layer.wq = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);

layer.wk = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);

layer.wv = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);

layer.wo = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);

layer.ffn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);

layer.w1 = ggml_new_tensor_2d(ctx, wtype, n_embd, n_ff);

layer.w2 = ggml_new_tensor_2d(ctx, wtype, n_ff, n_embd);

layer.w3 = ggml_new_tensor_2d(ctx, wtype, n_embd, n_ff);

// map by name

model.tensors["layers." + std::to_string(i) + ".attention_norm.weight"] = layer.attention_norm;

model.tensors["layers." + std::to_string(i) + ".attention.wq.weight"] = layer.wq;

model.tensors["layers." + std::to_string(i) + ".attention.wk.weight"] = layer.wk;

model.tensors["layers." + std::to_string(i) + ".attention.wv.weight"] = layer.wv;

model.tensors["layers." + std::to_string(i) + ".attention.wo.weight"] = layer.wo;

model.tensors["layers." + std::to_string(i) + ".ffn_norm.weight"] = layer.ffn_norm;

model.tensors["layers." + std::to_string(i) + ".feed_forward.w1.weight"] = layer.w1;

model.tensors["layers." + std::to_string(i) + ".feed_forward.w2.weight"] = layer.w2;

model.tensors["layers." + std::to_string(i) + ".feed_forward.w3.weight"] = layer.w3;

}

}

// key + value memory

{

const auto & hparams = model.hparams;

const int n_embd = hparams.n_embd;

const int n_layer = hparams.n_layer;

const int n_ctx = hparams.n_ctx;

const int n_mem = n_layer*n_ctx;

const int n_elements = n_embd*n_mem;

model.memory_k = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements);

model.memory_v = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements);

const size_t memory_size = ggml_nbytes(model.memory_k) + ggml_nbytes(model.memory_v);

printf("%s: memory_size = %8.2f MB, n_mem = %d\n", __func__, memory_size/1024.0/1024.0, n_mem);

}

// load weights

{

int n_tensors = 0;

size_t total_size = 0;

printf("%s: ", __func__);

while (true) {

int32_t n_dims;

int32_t length;

int32_t ftype;

fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));

fin.read(reinterpret_cast<char *>(&length), sizeof(length));

fin.read(reinterpret_cast<char *>(&ftype), sizeof(ftype));

if (fin.eof()) {

break;

}

int32_t nelements = 1;

int32_t ne[2] = { 1, 1 };

for (int i = 0; i < n_dims; ++i) {

fin.read(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));

nelements *= ne[i];

}

std::string name(length, 0);

fin.read(&name[0], length);

if (model.tensors.find(name.data()) == model.tensors.end()) {

fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.data());

return false;

}

auto tensor = model.tensors[name.data()];

if (ggml_nelements(tensor) != nelements) {

fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());

return false;

}

if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1]) {

fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n",

__func__, name.data(), tensor->ne[0], tensor->ne[1], ne[0], ne[1]);

return false;

}

if (0) {

static const char * ftype_str[] = { "f32", "f16", "q4_0", "q4_1", };

printf("%24s - [%5d, %5d], type = %6s, %6.2f MB, %9zu bytes\n", name.data(), ne[0], ne[1], ftype_str[ftype], ggml_nbytes(tensor)/1024.0/1024.0, ggml_nbytes(tensor));

}

size_t bpe = 0;

switch (ftype) {

case 0: bpe = ggml_type_size(GGML_TYPE_F32); break;

case 1: bpe = ggml_type_size(GGML_TYPE_F16); break;

case 2: bpe = ggml_type_size(GGML_TYPE_Q4_0); assert(ne[0] % 64 == 0); break;

case 3: bpe = ggml_type_size(GGML_TYPE_Q4_1); assert(ne[0] % 64 == 0); break;

default:

{

fprintf(stderr, "%s: unknown ftype %d in model file\n", __func__, ftype);

return false;

}

};

if ((nelements*bpe)/ggml_blck_size(tensor->type) != ggml_nbytes(tensor)) {

fprintf(stderr, "%s: tensor '%s' has wrong size in model file: got %zu, expected %zu\n",

__func__, name.data(), ggml_nbytes(tensor), nelements*bpe);

return false;

}

fin.read(reinterpret_cast<char *>(tensor->data), ggml_nbytes(tensor));

//printf("%42s - [%5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], ftype == 0 ? "float" : "f16", ggml_nbytes(tensor)/1024.0/1024.0);

total_size += ggml_nbytes(tensor);

if (++n_tensors % 8 == 0) {

printf(".");

fflush(stdout);

}

}

printf(" done\n");

printf("%s: model size = %8.2f MB / num tensors = %d\n", __func__, total_size/1024.0/1024.0, n_tensors);

}

fin.close();

return true;

const llama_model & model,

const int n_threads,

const int n_past,

const std::vector<gpt_vocab::id> & embd_inp,

std::vector<float> & embd_w,

size_t & mem_per_token) {

const int N = embd_inp.size();

const auto & hparams = model.hparams;

const int n_embd = hparams.n_embd;

const int n_layer = hparams.n_layer;

const int n_ctx = hparams.n_ctx;

const int n_head = hparams.n_head;

const int n_vocab = hparams.n_vocab;

const int n_rot = hparams.n_embd/hparams.n_head;

const int d_key = n_embd/n_head;

static size_t buf_size = 256u*1024*1024;

static void * buf = malloc(buf_size);

if (mem_per_token > 0 && mem_per_token*N > buf_size) {

const size_t buf_size_new = 1.1*(mem_per_token*N); // add 10% to account for ggml object overhead

//printf("\n%s: reallocating buffer from %zu to %zu bytes\n", __func__, buf_size, buf_size_new);

// reallocate

buf_size = buf_size_new;

buf = realloc(buf, buf_size);

if (buf == nullptr) {

fprintf(stderr, "%s: failed to allocate %zu bytes\n", __func__, buf_size);

return false;

}

}

struct ggml_init_params params = {

.mem_size = buf_size,

.mem_buffer = buf,

};

struct ggml_context * ctx0 = ggml_init(params);

struct ggml_cgraph gf = { .n_threads = n_threads };

struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);

memcpy(embd->data, embd_inp.data(), N*ggml_element_size(embd));

struct ggml_tensor * inpL = ggml_get_rows(ctx0, model.tok_embeddings, embd);

for (int il = 0; il < n_layer; ++il) {

struct ggml_tensor * inpSA = inpL;

struct ggml_tensor * cur;

// norm

{

cur = ggml_norm(ctx0, inpL);

// cur = attention_norm*cur

cur = ggml_mul(ctx0,

ggml_repeat(ctx0, model.layers[il].attention_norm, cur),

cur);

}

// self-attention

{

struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);

struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);

struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);

// store key and value to memory

if (N >= 1) {

struct ggml_tensor * k = ggml_view_1d(ctx0, model.memory_k, N*n_embd, (ggml_element_size(model.memory_k)*n_embd)*(il*n_ctx + n_past));

struct ggml_tensor * v = ggml_view_1d(ctx0, model.memory_v, N*n_embd, (ggml_element_size(model.memory_v)*n_embd)*(il*n_ctx + n_past));

ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k));

ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v));

}

// Q = Qcur.contiguous().view(n_embd/n_head, n_head, N).permute(0, 2, 1, 3)

struct ggml_tensor * Q =

ggml_permute(ctx0,

ggml_rope(ctx0,

ggml_cpy(ctx0,

Qcur,

ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_embd/n_head, n_head, N)),

n_past, n_rot, 0),

0, 2, 1, 3);

// K = Kmem.view(n_embd/n_head, n_head, n_past + N).permute(0, 2, 1, 3)

struct ggml_tensor * K =

ggml_permute(ctx0,

ggml_rope(ctx0,

ggml_reshape_3d(ctx0,

ggml_view_1d(ctx0, model.memory_k, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.memory_k)*n_embd),

n_embd/n_head, n_head, n_past + N),

n_past, n_rot, 1),

0, 2, 1, 3);

// K * Q

struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);

// KQ_scaled = KQ / sqrt(n_embd/n_head)

struct ggml_tensor * KQ_scaled =

ggml_scale(ctx0,

KQ,

ggml_new_f32(ctx0, 1.0f/sqrt(float(n_embd)/n_head))

);

// KQ_masked = mask_past(KQ_scaled)

struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past);

// KQ = soft_max(KQ_masked)

struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);

// V_trans = Vmem.view(n_embd/n_head, n_head, n_past + N).permute(1, 2, 0, 3).contiguous()

struct ggml_tensor * V_trans =

ggml_permute(ctx0,

ggml_reshape_3d(ctx0,

ggml_view_1d(ctx0, model.memory_v, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.memory_v)*n_embd),

n_embd/n_head, n_head, n_past + N),

1, 2, 0, 3);

// KQV = transpose(V) * KQ_soft_max

struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_trans, KQ_soft_max);

// KQV_merged = KQV.permute(0, 2, 1, 3)

struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);

// cur = KQV_merged.contiguous().view(n_embd, N)

cur = ggml_cpy(ctx0,

KQV_merged,

ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));

// projection (no bias)

cur = ggml_mul_mat(ctx0,

model.layers[il].wo,

cur);

}

struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA);

// feed-forward network

{

// norm

{

cur = ggml_norm(ctx0, inpFF);

// cur = ffn_norm*cur

cur = ggml_mul(ctx0,

ggml_repeat(ctx0, model.layers[il].ffn_norm, cur),

cur);

}

struct ggml_tensor * tmp = ggml_mul_mat(ctx0,

model.layers[il].w3,

cur);

cur = ggml_mul_mat(ctx0,

model.layers[il].w1,

cur);

// SILU activation

cur = ggml_silu(ctx0, cur);

cur = ggml_mul(ctx0, cur, tmp);

cur = ggml_mul_mat(ctx0,

model.layers[il].w2,

cur);

}

cur = ggml_add(ctx0, cur, inpFF);

// input for next layer

inpL = cur;

}

// norm

{

inpL = ggml_norm(ctx0, inpL);

// inpL = norm*inpL

inpL = ggml_mul(ctx0,

ggml_repeat(ctx0, model.norm, inpL),

inpL);

}

// lm_head

{

inpL = ggml_mul_mat(ctx0, model.output, inpL);

}

// logits -> probs

//inpL = ggml_soft_max(ctx0, inpL);

// run the computation

ggml_build_forward_expand(&gf, inpL);

ggml_graph_compute (ctx0, &gf);

//if (n_past%100 == 0) {

// ggml_graph_print (&gf);

// ggml_graph_dump_dot(&gf, NULL, "gpt-2.dot");

//}

//embd_w.resize(n_vocab*N);

//memcpy(embd_w.data(), ggml_get_data(inpL), sizeof(float)*n_vocab*N);

// return result for just the last token

embd_w.resize(n_vocab);

memcpy(embd_w.data(), (float *) ggml_get_data(inpL) + (n_vocab*(N-1)), sizeof(float)*n_vocab);

if (mem_per_token == 0) {

mem_per_token = ggml_used_mem(ctx0)/N;

}

//printf("used_mem = %zu\n", ggml_used_mem(ctx0));

ggml_free(ctx0);

return true;

const int64_t t_main_start_us = ggml_time_us();

gpt_params params;

params.model = "models/llama-7B/ggml-model.bin";

if (gpt_params_parse(argc, argv, params) == false) {

return 1;

}

if (params.seed < 0) {

params.seed = time(NULL);

}

printf("%s: seed = %d\n", __func__, params.seed);

std::mt19937 rng(params.seed);

if (params.prompt.empty()) {

params.prompt = gpt_random_prompt(rng);

}

int64_t t_load_us = 0;

gpt_vocab vocab;

llama_model model;

// load the model

{

const int64_t t_start_us = ggml_time_us();

if (!llama_model_load(params.model, model, vocab, 512)) { // TODO: set context from user input ??

fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.c_str());

return 1;

}

t_load_us = ggml_time_us() - t_start_us;

}

int n_past = 0;

int64_t t_sample_us = 0;

int64_t t_predict_us = 0;

std::vector<float> logits;

// tokenize the prompt

std::vector<gpt_vocab::id> embd_inp = ::llama_tokenize(vocab, params.prompt, true);

params.n_predict = std::min(params.n_predict, model.hparams.n_ctx - (int) embd_inp.size());

printf("\n");

printf("%s: prompt: '%s'\n", __func__, params.prompt.c_str());

printf("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());

for (int i = 0; i < (int) embd_inp.size(); i++) {

printf("%6d -> '%s'\n", embd_inp[i], vocab.id_to_token.at(embd_inp[i]).c_str());

}

printf("\n");

printf("sampling parameters: temp = %f, top_k = %d, top_p = %f\n", params.temp, params.top_k, params.top_p);

printf("\n\n");

std::vector<gpt_vocab::id> embd;

// determine the required inference memory per token:

size_t mem_per_token = 0;

llama_eval(model, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token);

for (int i = embd.size(); i < embd_inp.size() + params.n_predict; i++) {

// predict

if (embd.size() > 0) {

const int64_t t_start_us = ggml_time_us();

if (!llama_eval(model, params.n_threads, n_past, embd, logits, mem_per_token)) {

printf("Failed to predict\n");

return 1;

}

t_predict_us += ggml_time_us() - t_start_us;

}

n_past += embd.size();

embd.clear();

if (i >= embd_inp.size()) {

// sample next token

const float top_p = params.top_p;

const float temp = params.temp;

const int n_vocab = model.hparams.n_vocab;

gpt_vocab::id id = 0;

{

const int64_t t_start_sample_us = ggml_time_us();

id = llama_sample_top_p(vocab, logits.data() + (logits.size() - n_vocab), top_p, temp, rng);

t_sample_us += ggml_time_us() - t_start_sample_us;

}

// add it to the context

embd.push_back(id);

} else {

// if here, it means we are still processing the input prompt

for (int k = i; k < embd_inp.size(); k++) {

embd.push_back(embd_inp[k]);

if (embd.size() > params.n_batch) {

break;

}

}

i += embd.size() - 1;

}

// display text

for (auto id : embd) {

printf("%s", vocab.id_to_token[id].c_str());

}

fflush(stdout);

// end of text token

if (embd.back() == 2) {

printf(" [end of text]\n");

break;

}

}

// report timing

{

const int64_t t_main_end_us = ggml_time_us();

printf("\n\n");

printf("%s: mem per token = %8zu bytes\n", __func__, mem_per_token);

printf("%s: load time = %8.2f ms\n", __func__, t_load_us/1000.0f);

printf("%s: sample time = %8.2f ms\n", __func__, t_sample_us/1000.0f);

printf("%s: predict time = %8.2f ms / %.2f ms per token\n", __func__, t_predict_us/1000.0f, t_predict_us/1000.0f/n_past);

printf("%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0f);

}

ggml_free(model.ctx);

return 0;