diff --git a/examples/merge/merge.cpp b/examples/merge/merge.cpp
new file mode 100644
index 000000000..4091eb918
--- /dev/null
+++ b/examples/merge/merge.cpp
@@ -0,0 +1,118 @@
+#include "common.h"
+#include "llama.h"
+
+#include <cstdio>
+#include <cstring>
+#include <vector>
+#include <string>
+#include <unordered_map>
+#include <fstream>
+#include <cmath>
+#include <algorithm>
+
+
+int32_t merge(
+    const std::string & fname_inp1,
+    const std::vector<float> scale1,
+    const std::string & fname_inp2,
+    const std::vector<float> scale2,
+    const int n_layers,
+    const std::string & fname_out) {
+#if defined(__linux__) || defined(_WIN32)
+    constexpr bool use_mmap = true;
+#else
+    constexpr bool use_mmap = false;
+#endif
+
+    llama_model_loader ml(fname_inp1, use_mmap, NULL);
+    ml.init_mapping(false); // no prefetching?
+
+    llama_model model;
+    llm_load_arch(ml, model);
+    llm_load_hparams(ml, model);
+
+    struct gguf_context * ctx_out = gguf_init_empty();
+    // copy the KV pairs from the input file
+    gguf_set_kv(ctx_out, ml.ctx_gguf);
+    
+    // populate the original tensors so we get an initial meta data
+    for (int i = 0; i < ml.n_tensors; ++i) {
+        struct ggml_tensor * meta = ml.get_tensor_meta(i);
+        gguf_add_tensor(ctx_out, meta);
+    }
+
+    std::ofstream fout(fname_out, std::ios::binary);
+    fout.exceptions(std::ofstream::failbit); // fail fast on write errors
+
+    const size_t meta_size = gguf_get_meta_size(ctx_out);
+
+    LLAMA_LOG_INFO("%s: meta size = %zu bytes\n", __func__, meta_size);
+
+    // placeholder for the meta data
+    ::zeros(fout, meta_size);
+
+    std::vector<no_init<uint8_t>> read_data;
+
+    for (int i = 0; i < ml.n_tensors; ++i) {
+        struct ggml_tensor * tensor = ml.get_tensor_meta(i);
+
+        const std::string name = ggml_get_name(tensor);
+
+        if (!ml.use_mmap) {
+            if (read_data.size() < ggml_nbytes(tensor)) {
+                read_data.resize(ggml_nbytes(tensor));
+            }
+            tensor->data = read_data.data();
+        }
+        ml.load_data_for(tensor);
+
+        size_t new_size = ggml_nbytes(tensor);
+        void * new_data = tensor->data;
+
+        LLAMA_LOG_INFO("[%4d/%4d] %36s - [%s], type = %6s, ",
+               ++idx, ml.n_tensors,
+               ggml_get_name(tensor),
+               llama_format_tensor_shape(tensor).c_str(),
+               ggml_type_name(tensor->type));
+
+        // update the gguf meta data as we go
+        gguf_set_tensor_type(ctx_out, name.c_str(), new_type);
+        gguf_set_tensor_data(ctx_out, name.c_str(), new_data, new_size);
+
+        // write tensor data + padding
+        fout.write((const char *) new_data, new_size);
+        zeros(fout, GGML_PAD(new_size, GGUF_DEFAULT_ALIGNMENT) - new_size);
+    }
+
+    // go back to beginning of file and write the updated meta data
+    {
+        fout.seekp(0);
+        std::vector<uint8_t> data(gguf_get_meta_size(ctx_out));
+        gguf_get_meta_data(ctx_out, data.data());
+        fout.write((const char *) data.data(), data.size());
+    }
+
+    fout.close();
+
+    gguf_free(ctx_out);
+}
+
+
+// usage:
+//  ./merge ./path/model_1 LAYERS_1 ./path/model_2 LAYERS_2
+//
+[[noreturn]]
+static void usage(const char * executable) {
+    printf("usage: %s ./path/model_1 LAYERS_1 ./path/model_2 LAYERS_2\n\n", executable);
+    printf("  LAYERS must be in format: p0-p1,p2-p3,p4,... Example: 0-5,7,8-12\n");
+    //printf("  Optionally, you can specify the scaling for a range of layers, for example: 0-5*0.5,6-7*1\n");
+    printf("  The embedding layer of the first model will be used");
+    exit(1);
+}
+
+int main(int argc, char ** argv) {
+    llama_backend_init();
+    llama_model_params model_params = llama_model_default_params();
+    llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params);
+    llama_backend_free();
+}
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