diff --git a/Makefile b/Makefile
index fb7c27cd9..10343a4f8 100644
--- a/Makefile
+++ b/Makefile
@@ -1,5 +1,5 @@
 # Define the default target now so that it is always the first target
-BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch simple server embd-input-test
+BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch convert-llama2c simple server embd-input-test
 
 # Binaries only useful for tests
 TEST_TARGETS = tests/test-double-float tests/test-grad0 tests/test-opt tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0
@@ -330,7 +330,7 @@ libllama.so: llama.o ggml.o $(OBJS)
 	$(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS)
 
 clean:
-	rm -vf *.o *.so *.dll main quantize quantize-stats perplexity embedding benchmark-matmult save-load-state server simple vdot train-text-from-scratch embd-input-test build-info.h $(TEST_TARGETS)
+	rm -vf *.o *.so *.dll main quantize quantize-stats perplexity embedding benchmark-matmult save-load-state server simple vdot train-text-from-scratch convert-llama2c embd-input-test build-info.h $(TEST_TARGETS)
 
 #
 # Examples
@@ -373,6 +373,10 @@ embd-input-test: $(LIB_PRE)embdinput$(DSO_EXT) examples/embd-input/embd-input-te
 train-text-from-scratch: examples/train-text-from-scratch/train-text-from-scratch.cpp    build-info.h ggml.o llama.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
+convert-llama2c: examples/convert-llama2c/convert-lamma-2c.cpp    build-info.h ggml.o llama.o $(OBJS)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+
+
 build-info.h: $(wildcard .git/index) scripts/build-info.sh
 	@sh scripts/build-info.sh > $@.tmp
 	@if ! cmp -s $@.tmp $@; then \
diff --git a/examples/convert-llama2c/convert-lamma-2c.cpp b/examples/convert-llama2c/convert-lamma-2c.cpp
new file mode 100644
index 000000000..88b0619f0
--- /dev/null
+++ b/examples/convert-llama2c/convert-lamma-2c.cpp
@@ -0,0 +1,576 @@
+#include <stdio.h>
+#include <stdlib.h>
+#include <time.h>
+#include <math.h>
+#include <string.h>
+#include <sys/time.h>
+#include <cstdlib>
+#include <cstdio>
+#include <stdexcept>
+#include <cstring>
+#include <string>
+
+#include "llama.h"
+#include "ggml.h"
+
+typedef struct {
+    int dim; // transformer dimension
+    int hidden_dim; // for ffn layers
+    int n_layers; // number of layers
+    int n_heads; // number of query heads
+    int n_kv_heads; // number of key/value heads (can be < query heads because of multiquery)
+    int vocab_size; // vocabulary size, usually 256 (byte-level)
+    int seq_len; // max sequence length
+} Config;
+
+typedef struct {
+    // token embedding table
+    float* token_embedding_table;    // (vocab_size, dim)
+    // weights for rmsnorms
+    float* rms_att_weight; // (layer, dim) rmsnorm weights
+    float* rms_ffn_weight; // (layer, dim)
+    // weights for matmuls
+    float* wq; // (layer, dim, dim)
+    float* wk; // (layer, dim, dim)
+    float* wv; // (layer, dim, dim)
+    float* wo; // (layer, dim, dim)
+    // weights for ffn
+    float* w1; // (layer, hidden_dim, dim)
+    float* w2; // (layer, dim, hidden_dim)
+    float* w3; // (layer, hidden_dim, dim)
+    // final rmsnorm
+    float* rms_final_weight; // (dim,)
+    // freq_cis for RoPE relatively positional embeddings
+    float* freq_cis_real; // (seq_len, dim/2)
+    float* freq_cis_imag; // (seq_len, dim/2)
+} TransformerWeights;
+
+#ifdef __GNUC__
+#ifdef __MINGW32__
+__attribute__((format(gnu_printf, 1, 2)))
+#else
+__attribute__((format(printf, 1, 2)))
+#endif
+#endif
+static std::string format(const char * fmt, ...) {
+    va_list ap, ap2;
+    va_start(ap, fmt);
+    va_copy(ap2, ap);
+    int size = vsnprintf(NULL, 0, fmt, ap);
+    GGML_ASSERT(size >= 0 && size < INT_MAX);
+    std::vector<char> buf(size + 1);
+    int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
+    GGML_ASSERT(size2 == size);
+    va_end(ap2);
+    va_end(ap);
+    return std::string(buf.data(), size);
+}
+struct llama_file {
+    // use FILE * so we don't have to re-open the file to mmap
+    FILE * fp;
+    size_t size;
+
+    llama_file(const char * fname, const char * mode) {
+        fp = std::fopen(fname, mode);
+        if (fp == NULL) {
+            size = 0;
+        } else {
+            seek(0, SEEK_END);
+            size = tell();
+            seek(0, SEEK_SET);
+        }
+    }
+
+    size_t tell() const {
+#ifdef _WIN32
+        __int64 ret = _ftelli64(fp);
+#else
+        long ret = std::ftell(fp);
+#endif
+        GGML_ASSERT(ret != -1); // this really shouldn't fail
+        return (size_t) ret;
+    }
+
+    void seek(size_t offset, int whence) {
+#ifdef _WIN32
+        int ret = _fseeki64(fp, (__int64) offset, whence);
+#else
+        int ret = std::fseek(fp, (long) offset, whence);
+#endif
+        GGML_ASSERT(ret == 0); // same
+    }
+
+    void read_raw(void * ptr, size_t size) {
+        if (size == 0) {
+            return;
+        }
+        errno = 0;
+        std::size_t ret = std::fread(ptr, size, 1, fp);
+        if (ferror(fp)) {
+            throw std::runtime_error(format("read error: %s", strerror(errno)));
+        }
+        if (ret != 1) {
+            throw std::runtime_error(std::string("unexpectedly reached end of file"));
+        }
+    }
+
+    std::uint32_t read_u32() {
+        std::uint32_t ret;
+        read_raw(&ret, sizeof(ret));
+        return ret;
+    }
+
+    std::string read_string(std::uint32_t len) {
+        std::vector<char> chars(len);
+        read_raw(chars.data(), len);
+        return std::string(chars.data(), len);
+    }
+
+    void write_raw(const void * ptr, size_t size) {
+        if (size == 0) {
+            return;
+        }
+        errno = 0;
+        size_t ret = std::fwrite(ptr, size, 1, fp);
+        if (ret != 1) {
+            throw std::runtime_error(format("write error: %s", strerror(errno)));
+        }
+    }
+
+    void write_u32(std::uint32_t val) {
+        write_raw(&val, sizeof(val));
+    }
+
+    ~llama_file() {
+        if (fp) {
+            std::fclose(fp);
+        }
+    }
+};
+
+struct my_llama_hparams {
+    uint32_t n_vocab = 32000;
+    uint32_t n_ctx   = 512;   // this is provided as user input?
+    uint32_t n_embd  = 4096;
+    uint32_t n_mult  = 4;
+    uint32_t n_head  = 32;
+    uint32_t n_layer = 32;
+    uint32_t n_rot   = 64;
+
+    bool operator!=(const my_llama_hparams& other) const {
+        return memcmp(this, &other, sizeof(my_llama_hparams));
+    }
+};
+struct my_llama_layer {
+    // 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;
+};
+struct my_llama_model {
+    struct ggml_context * ctx = NULL;
+
+    my_llama_hparams hparams;
+
+    struct ggml_tensor * tok_embeddings;
+
+    struct ggml_tensor * norm;
+    struct ggml_tensor * output;
+
+    std::vector<my_llama_layer> layers;
+
+    uint32_t train_its = 0;
+    uint32_t train_samples = 0;
+    uint32_t train_tokens = 0;
+};
+struct llama_vocab {
+    using id    = int32_t;
+    using token = std::string;
+
+    struct token_score {
+        token tok;
+        float score;
+    };
+
+    std::unordered_map<token, id> token_to_id;
+    std::vector<token_score> id_to_token;
+};
+
+struct train_params {
+    const char * fn_vocab_model;
+    const char * fn_train_data;
+    const char * fn_checkpoint_in;
+    const char * fn_checkpoint_out;
+    const char * fn_model_out;
+
+    uint32_t seed;
+
+    int n_ctx;
+    int n_embd;
+    int n_mult;
+    int n_head;
+    int n_layer;
+    int n_rotmax;
+
+    int n_threads;
+    int n_batch;
+    int n_examples;
+    int n_predict;
+
+    int print_info_interval;
+    int print_details_interval;
+
+    bool samples_start_after_nl;
+    bool use_adam;
+    bool use_flash;
+    bool use_scratch;
+
+    // only adam
+    int   warmup;
+    int   cos_decay_steps;
+    float cos_decay_restart;
+    float cos_decay_alpha;
+
+    int   lbfgs_n_iter;
+    int   adam_n_iter;
+    float adam_alpha;
+    float adam_decay;
+
+    int mem_model_gb;
+    int mem_compute_gb;
+    int mem_compute0_gb;
+    int mem_compute1_gb;
+};
+
+struct train_params get_default_train_params() {
+    struct train_params params;
+    params.fn_vocab_model    = "ggml-vic7b-uncensored-q4_0.bin";
+    params.fn_train_data     = "shakespeare.txt";
+    params.fn_checkpoint_in  = "checkpoint.bin";
+    params.fn_checkpoint_out = "checkpoint.bin";
+    params.fn_model_out      = "ggml-checkpoint-f32.bin";
+
+    params.seed       =   -1;
+
+    params.n_ctx      =  128;
+    params.n_embd     =  256;
+    params.n_mult     =  256;
+    params.n_head     =    8;
+    params.n_layer    =   16;
+    params.n_rotmax   =   64;
+
+    params.n_threads  =    6;
+    params.n_batch    =    8;
+    params.n_examples =    8;
+    params.n_predict  = 1024;
+
+    params.print_info_interval    = 1;
+    params.print_details_interval = 2;
+
+    params.samples_start_after_nl = false;
+    params.use_adam               = true;
+    params.use_flash              = true;
+    params.use_scratch            = true;
+
+    // only adam
+    params.warmup            =  100;
+    params.cos_decay_steps   = 1000;
+    params.cos_decay_restart = 1.1f;
+    params.cos_decay_alpha   = 0.0f;
+
+    params.lbfgs_n_iter      = 16;
+    params.adam_n_iter       = 16;
+    params.adam_alpha        = 1e-3f;
+    params.adam_decay        = 1e-3f;
+
+    params.mem_model_gb   = 2;
+    params.mem_compute_gb = 24;
+    params.mem_compute0_gb = 8;
+    params.mem_compute1_gb = 2;
+
+    return params;
+}
+
+void write_tensor(struct llama_file * file, struct ggml_tensor * tensor) {
+    if (tensor == NULL) {
+        file->write_u32(0);
+        file->write_u32(0);
+        file->write_u32(GGML_TYPE_F32);
+        file->seek((0-file->tell()) & 31, SEEK_CUR);
+        return;
+    }
+    const char * name = ggml_get_name(tensor);
+    uint32_t name_len = strlen(name);
+    uint32_t nd = tensor->n_dims;
+    uint32_t ne[4] = { (uint32_t)tensor->ne[0],
+                       (uint32_t)tensor->ne[1],
+                       (uint32_t)tensor->ne[2],
+                       (uint32_t)tensor->ne[3] };
+    file->write_u32(nd);
+    file->write_u32(name_len);
+    file->write_u32(tensor->type);
+    file->write_raw(ne, sizeof(ne[0]) * nd);
+    file->write_raw(name, name_len);
+    file->seek((0-file->tell()) & 31, SEEK_CUR);
+    file->write_raw(tensor->data, ggml_nbytes(tensor));
+}
+
+void save_as_llama_model(struct llama_vocab * vocab, struct my_llama_model * model, const char * filename) {
+    struct llama_file file(filename, "wb");
+    if (file.fp == NULL) {
+        return;
+    }
+
+    // write_magic
+    file.write_u32(LLAMA_FILE_MAGIC);   // magic
+    file.write_u32(LLAMA_FILE_VERSION); // version
+    // write_hparams
+    file.write_u32(model->hparams.n_vocab);
+    file.write_u32(model->hparams.n_embd);
+    file.write_u32(model->hparams.n_mult);
+    file.write_u32(model->hparams.n_head);
+    file.write_u32(model->hparams.n_layer);
+    file.write_u32(model->hparams.n_rot);
+    file.write_u32(LLAMA_FTYPE_ALL_F32);
+    // write_vocab
+    uint32_t n_vocab = model->hparams.n_vocab;
+    for (uint32_t i = 0; i < n_vocab; i++) {
+        const auto & token_score = vocab->id_to_token.at(i);
+        file.write_u32((uint32_t) token_score.tok.size());
+        file.write_raw(token_score.tok.data(), token_score.tok.size());
+        file.write_raw(&token_score.score, sizeof(token_score.score));
+    }
+    // write tensors
+    write_tensor(&file, model->tok_embeddings);
+    write_tensor(&file, model->norm);
+    write_tensor(&file, model->output);
+    for (uint32_t i = 0; i < model->hparams.n_layer; ++i) {
+        auto & layer = model->layers[i];
+
+        write_tensor(&file, layer.attention_norm);
+        write_tensor(&file, layer.wq);
+        write_tensor(&file, layer.wk);
+        write_tensor(&file, layer.wv);
+        write_tensor(&file, layer.wo);
+        write_tensor(&file, layer.ffn_norm);
+        write_tensor(&file, layer.w1);
+        write_tensor(&file, layer.w2);
+        write_tensor(&file, layer.w3);
+    }
+}
+
+void print_config(Config* p){
+    printf("----- Configs extracted from the header -------\n");
+    printf("config.dim %d\n", p->dim);
+    printf("config.hidden_dim %d\n", p->hidden_dim);
+    printf("config.n_layers %d\n", p->n_layers);
+    printf("config.n_heads %d\n", p->n_heads );
+    printf("config.n_kv_heads %d\n", p->n_kv_heads);
+    printf("config.vocab_size %d\n", p->vocab_size);
+    printf("config.seq_len %d\n", p->seq_len);
+    printf("----------------------------------------------\n");
+}
+
+void print_sample_weights(TransformerWeights *w){
+    printf("----- Quick print of first of the weight vales of all the variables\n");
+    printf("%f\n", w->token_embedding_table[0]);
+    printf("%f\n", w->rms_att_weight[0]);
+    printf("%f\n", w->rms_ffn_weight[0]);
+
+    printf("%f\n", w->wq[0]);
+    printf("%f\n", w->wk[0]);
+    printf("%f\n", w->wv[0]);
+    printf("%f\n", w->wo[0]);
+    printf("%f\n", w->w1[0]);
+    printf("%f\n", w->w2[0]);
+    printf("%f\n", w->w3[0]);
+    printf("%f\n", w->rms_att_weight[0]);
+    printf("%f\n", w->freq_cis_real[0]);
+    printf("%f\n", w->freq_cis_imag[0]);
+    printf("------------------------------------------------------------------\n");
+
+    
+}
+void malloc_weights(TransformerWeights* w, Config* p) {
+    // we calloc instead of malloc to keep valgrind happy
+    w->token_embedding_table = new float[p->vocab_size * p->dim]();//calloc(p->vocab_size * p->dim, sizeof(float));
+    w->rms_att_weight = new float[p->n_layers * p->dim](); //calloc(p->n_layers * p->dim, sizeof(float));
+    w->rms_ffn_weight = new float[p->n_layers * p->dim](); //calloc(p->n_layers * p->dim, sizeof(float));
+    w->wq = new float[p->n_layers * p->dim * p->dim](); //calloc(p->n_layers * p->dim * p->dim, sizeof(float));
+    w->wk = new float[p->n_layers * p->dim * p->dim](); //calloc(p->n_layers * p->dim * p->dim, sizeof(float));
+    w->wv = new float[p->n_layers * p->dim * p->dim](); //calloc(p->n_layers * p->dim * p->dim, sizeof(float));
+    w->wo = new float[p->n_layers * p->dim * p->dim](); //calloc(p->n_layers * p->dim * p->dim, sizeof(float));
+    w->w1 = new float[p->n_layers * p->hidden_dim * p->dim](); //calloc(p->n_layers * p->hidden_dim * p->dim, sizeof(float));
+    w->w2 = new float[p->n_layers * p->hidden_dim * p->dim](); //calloc(p->n_layers * p->dim * p->hidden_dim, sizeof(float));
+    w->w3 = new float[p->n_layers * p->hidden_dim * p->dim](); //calloc(p->n_layers * p->hidden_dim * p->dim, sizeof(float));
+    w->rms_final_weight = new float[p->dim](); //calloc(p->dim, sizeof(float));
+    w->freq_cis_real = new float[p->seq_len * p->dim / 2](); //calloc(p->seq_len * p->dim / 2, sizeof(float));
+    w->freq_cis_imag = new float[p->seq_len * p->dim / 2](); //calloc(p->seq_len * p->dim / 2, sizeof(float));
+    // ensure all mallocs went fine
+    // if (!w->token_embedding_table || !w->rms_att_weight || !w->rms_ffn_weight 
+    //  || !w->wq || !w->wk || !w->wv || !w->wo || !w->w1 || !w->w2 || !w->w3 || 
+    //     !w->rms_final_weight || !w->freq_cis_real || !w->freq_cis_imag) {
+    //     printf("malloc failed!\n");
+    //     exit(1);
+    // }
+}
+
+void free_weights(TransformerWeights* w) {
+    free(w->token_embedding_table);
+    free(w->rms_att_weight);
+    free(w->rms_ffn_weight);
+    free(w->wq);
+    free(w->wk);
+    free(w->wv);
+    free(w->wo);
+    free(w->w1);
+    free(w->w2);
+    free(w->w3);
+    free(w->rms_final_weight);
+    free(w->freq_cis_real);
+    free(w->freq_cis_imag);
+}
+
+int checkpoint_init_weights(TransformerWeights *w, Config* p, FILE* f) {
+    if (fread(w->token_embedding_table, sizeof(float), p->vocab_size * p->dim, f) != static_cast<size_t>(p->vocab_size * p->dim)) return 1;
+    if (fread(w->rms_att_weight, sizeof(float), p->n_layers * p->dim, f) != static_cast<size_t>(p->n_layers * p->dim)) return 1;
+    if (fread(w->wq, sizeof(float), p->n_layers * p->dim * p->dim, f) != static_cast<size_t>(p->n_layers * p->dim * p->dim)) return 1;
+    if (fread(w->wk, sizeof(float), p->n_layers * p->dim * p->dim, f) != static_cast<size_t>(p->n_layers * p->dim * p->dim)) return 1;
+    if (fread(w->wv, sizeof(float), p->n_layers * p->dim * p->dim, f) != static_cast<size_t>(p->n_layers * p->dim * p->dim)) return 1;
+    if (fread(w->wo, sizeof(float), p->n_layers * p->dim * p->dim, f) != static_cast<size_t>(p->n_layers * p->dim * p->dim)) return 1;
+    if (fread(w->rms_ffn_weight, sizeof(float), p->n_layers * p->dim, f) != static_cast<size_t>(p->n_layers * p->dim)) return 1;
+    if (fread(w->w1, sizeof(float), p->n_layers * p->dim * p->hidden_dim, f) != static_cast<size_t>(p->n_layers * p->dim * p->hidden_dim)) return 1;
+    if (fread(w->w2, sizeof(float), p->n_layers * p->hidden_dim * p->dim, f) != static_cast<size_t>(p->n_layers * p->hidden_dim * p->dim)) return 1;
+    if (fread(w->w3, sizeof(float), p->n_layers * p->dim * p->hidden_dim, f) != static_cast<size_t>(p->n_layers * p->dim * p->hidden_dim)) return 1;
+    if (fread(w->rms_final_weight, sizeof(float), p->dim, f) != static_cast<size_t>(p->dim)) return 1;
+    int head_size = p->dim / p->n_heads;
+    if (fread(w->freq_cis_real, sizeof(float), p->seq_len * head_size / 2, f) != static_cast<size_t>(p->seq_len * head_size / 2)) return 1;
+    if (fread(w->freq_cis_imag, sizeof(float), p->seq_len * head_size / 2, f) != static_cast<size_t>(p->seq_len * head_size / 2)) return 1;
+    return 0;
+}
+
+int main(int argc, char *argv[]) {
+
+    // poor man's C argparse
+    char *checkpoint = NULL;
+    char *tokenizer = NULL;
+    // float temperature = 0.9f;
+    // 'checkpoint' is necessary arg
+    if (argc < 3) {
+        printf("Usage: %s <checkpoint_file> <tokenizer_file>\n", argv[0]);
+        return 1;
+    }
+    checkpoint = argv[1];
+    tokenizer = argv[2];
+    // if (argc < 3) {
+    //     printf("Usage: %s <checkpoint_file>\n", argv[0]);
+    //     return 1;
+    // }
+    // temperature is optional
+    // if (argc >= 3) {
+    //     temperature = atof(argv[2]);
+    // }
+    // seed is optional
+    // if (argc >= 4) {
+    //     unsigned int seed = atoi(argv[3]);
+    //     srand(seed);
+    // } else {
+    //     time_t current_time; 
+    //     time(&current_time);
+    //     srand((unsigned int)current_time);
+    // }
+
+    // read in the Karpathy model.bin file
+    Config config; // Configs are stashed in the bin file as header
+    TransformerWeights weights;
+
+    {
+        FILE *file = fopen(checkpoint, "rb");
+        if (!file) {
+            printf("Unable to open the checkpoint file %s!\n", checkpoint);
+            return 1;
+        }
+        else{
+            printf("model file opened for reading...\n");
+        }
+        // read in the config header
+        if(fread(&config, sizeof(Config), 1, file) != 1) { return 1; }
+        printf("config file read..\n");
+        print_config(&config);
+        // read in the Transformer weights
+        malloc_weights(&weights, &config);
+        printf("reading the opened model file...\n");
+        if(checkpoint_init_weights(&weights, &config, file)) { return 1; }
+        print_sample_weights(&weights);
+        printf("Closing model file..bye...\n");
+        fclose(file);
+    }
+
+    // read in the tokenizer.bin file
+    char** vocab_ak = (char**)malloc(config.vocab_size * sizeof(char*));
+    {
+        FILE *file = fopen(tokenizer, "rb");
+        if (!file) {
+            printf("Unable to open the tokenizer file tokenizer.bin! Run "
+            "python tokenizer.py to convert tokenizer.model -> tokenizer.bin\n");
+            return 1;
+        }
+        int len;
+        printf("karpathy vocab size = %d\n", config.vocab_size);
+
+        for (int i = 0; i < config.vocab_size; i++) {
+            if(fread(&len, sizeof(int), 1, file) != 1) { return 1; }
+            vocab_ak[i] = (char *)malloc(len + 1);
+            if(fread(vocab_ak[i], len, 1, file) != 1) { return 1; }
+            vocab_ak[i][len] = '\0'; // add the string terminating token
+            printf("len = %d, %s\n", len, vocab_ak[i]);
+
+        }
+        fclose(file);
+    }
+
+    //TODO:-------------------------------------------------------------------------------
+    struct my_llama_model model;
+    struct train_params params = get_default_train_params();
+    struct llama_context_params llama_params = llama_context_default_params();
+    struct llama_model * lmodel = llama_load_model_from_file(params.fn_vocab_model, llama_params);
+    struct llama_context * lctx = llama_new_context_with_model(lmodel, llama_params);
+    struct llama_vocab vocab;
+    {
+        std::vector<const char *> strings;
+        std::vector<float> scores;
+        int n_vocab = llama_n_vocab(lctx);
+        strings.resize(n_vocab, NULL);
+        scores.resize(n_vocab, 0);
+        n_vocab = llama_get_vocab(lctx, strings.data(), scores.data(), n_vocab);
+        GGML_ASSERT(n_vocab == llama_n_vocab(lctx));
+        vocab.id_to_token.resize(n_vocab);
+        for (int i=0; i<n_vocab; ++i) {
+            std::string tok   = std::string(strings[i]);
+            float       score = scores[i];
+            vocab.id_to_token[i].tok   = tok;
+            vocab.id_to_token[i].score = score;
+            vocab.token_to_id.emplace(tok, i);
+        }
+    }
+
+    save_as_llama_model(&vocab, &model, params.fn_model_out);
+
+    printf("\n");
+    free_weights(&weights);
+    free(vocab_ak);
+    return 0;
+
+}
\ No newline at end of file