diff --git a/common/common.cpp b/common/common.cpp
index dbb724fb..c26fe6ee 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -2087,14 +2087,27 @@ std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_par
     for (unsigned int i = 0; i < params.lora_adapter.size(); ++i) {
         const std::string & lora_adapter = std::get<0>(params.lora_adapter[i]);
         float lora_scale = std::get<1>(params.lora_adapter[i]);
+
+        // try to load as gguf
         auto adapter = llama_lora_adapter_init(model, lora_adapter.c_str());
         if (adapter == nullptr) {
-            fprintf(stderr, "%s: error: failed to apply lora adapter\n", __func__);
-            llama_free(lctx);
-            llama_free_model(model);
-            return std::make_tuple(nullptr, nullptr);
+            fprintf(stderr, "%s: error: failed to apply lora adapter, trying ggla\n", __func__);
+
+            // if that fails, try loading as ggla for compatibility
+            int err = llama_model_apply_lora_from_file(model,
+                                                    lora_adapter.c_str(),
+                                                    lora_scale,
+                                                    nullptr,
+                                                    params.n_threads);
+            if (err != 0) {
+                fprintf(stderr, "%s: error: failed to apply lora adapter\n", __func__);
+                llama_free(lctx);
+                llama_free_model(model);
+                return std::make_tuple(nullptr, nullptr);
+            }
+        } else {
+            llama_lora_adapter_set(lctx, adapter, lora_scale);
         }
-        llama_lora_adapter_set(lctx, adapter, lora_scale);
     }
 
     if (params.ignore_eos) {
diff --git a/include/llama.h b/include/llama.h
index 93fd77ca..b0fb37a6 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -1160,6 +1160,20 @@ extern "C" {
 
     LLAMA_API void llama_dump_timing_info_yaml(FILE * stream, const struct llama_context * ctx);
 
+    // Apply a LoRA adapter to a loaded model
+    // path_base_model is the path to a higher quality model to use as a base for
+    // the layers modified by the adapter. Can be NULL to use the current loaded model.
+    // The model needs to be reloaded before applying a new adapter, otherwise the adapter
+    // will be applied on top of the previous one
+    // Returns 0 on success
+    LLAMA_API int32_t llama_model_apply_lora_from_file(
+            const struct llama_model * model,
+                            const char * path_lora,
+                                float   scale,
+                            const char * path_base_model,
+                                int32_t   n_threads);
+
+
 #ifdef __cplusplus
 }
 #endif
diff --git a/src/llama.cpp b/src/llama.cpp
index 80a0dd0f..9d7b0e17 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -21880,3 +21880,290 @@ static void llama_log_callback_default(ggml_log_level level, const char * text,
     fputs(text, stderr);
     fflush(stderr);
 }
+
+static int llama_apply_lora_from_file_internal(
+    const struct llama_model & model, const char * path_lora, float scale, const char * path_base_model, int n_threads
+) {
+    LLAMA_LOG_INFO("%s: applying lora adapter from '%s' - please wait ...\n", __func__, path_lora);
+
+    const int64_t t_start_lora_us = ggml_time_us();
+
+    llama_file fin(path_lora, "rb");
+
+    // verify magic and version
+    {
+        uint32_t magic = fin.read_u32();
+        if (magic != LLAMA_FILE_MAGIC_GGLA) {
+            LLAMA_LOG_ERROR("%s: bad file magic\n", __func__);
+            return 1;
+        }
+
+        uint32_t format_version = fin.read_u32();
+        if (format_version != 1) {
+            LLAMA_LOG_ERROR("%s: unsupported file version\n", __func__ );
+            return 1;
+        }
+    }
+
+    int32_t lora_r = fin.read_u32();
+    int32_t lora_alpha = fin.read_u32();
+    float scaling = scale * (float)lora_alpha / (float)lora_r;
+
+    LLAMA_LOG_INFO("%s: r = %d, alpha = %d, scaling = %.2f\n", __func__, lora_r, lora_alpha, scaling);
+
+    // load base model
+    std::unique_ptr<llama_model_loader> ml;
+    if (path_base_model) {
+        LLAMA_LOG_INFO("%s: loading base model from '%s'\n", __func__, path_base_model);
+        ml.reset(new llama_model_loader(path_base_model, /*use_mmap*/ true, /*check_tensors*/ false, /*kv_overrides*/ nullptr));
+        ml->init_mappings(/*prefetch*/ false); // no prefetching
+    }
+
+    struct tensor_meta {
+        std::string name;
+        ggml_type type;
+        int32_t ne[2];
+        size_t offset;
+    };
+    std::map<std::string, tensor_meta> tensor_meta_map;
+
+    // load all tensor meta
+    while (true) {
+        if (fin.tell() == fin.size) {
+            // eof
+            break;
+        }
+
+        int32_t n_dims;
+        int32_t name_len;
+        int32_t ftype;
+
+        fin.read_raw(&n_dims, sizeof(n_dims));
+        fin.read_raw(&name_len, sizeof(name_len));
+        fin.read_raw(&ftype, sizeof(ftype));
+
+        if (n_dims != 1 && n_dims != 2) {
+            LLAMA_LOG_ERROR("%s: unsupported tensor dimension %d\n", __func__, n_dims);
+            return 1;
+        }
+
+        int32_t ne[2] = { 1, 1 };
+        for (int i = 0; i < n_dims; ++i) {
+            fin.read_raw(&ne[i], sizeof(ne[i]));
+        }
+
+        std::string name;
+        {
+            GGML_ASSERT(name_len < GGML_MAX_NAME);
+            char buf[GGML_MAX_NAME];
+            fin.read_raw(buf, name_len);
+            name = std::string(buf, name_len);
+        }
+
+        // check for lora suffix
+        std::string lora_suffix;
+        if (name.length() > 6) {
+            lora_suffix = name.substr(name.length() - 6);
+        }
+        if (lora_suffix != ".loraA" && lora_suffix != ".loraB") {
+            LLAMA_LOG_ERROR("%s: error: '%s' is not a lora tensor\n", __func__, name.c_str());
+            return 1;
+        }
+
+        // tensor type
+        ggml_type wtype;
+        switch (ftype) {
+            case 0: wtype = GGML_TYPE_F32;  break;
+            case 1: wtype = GGML_TYPE_F16;  break;
+            default:
+                    {
+                        LLAMA_LOG_ERROR("%s: invalid tensor data type '%d'\n",
+                                __func__, ftype);
+                        return 1;
+                    }
+        }
+
+        // data offset
+        size_t offset = fin.tell();
+        offset = (offset + 31) & -32;
+
+        // skip tensor data
+        fin.seek(offset + ggml_row_size(wtype, ne[0]) * ne[1], SEEK_SET);
+
+        tensor_meta_map.emplace(name, tensor_meta{ name, wtype, { ne[0], ne[1] }, offset });
+    }
+
+    bool warned = false;
+    int n_tensors = 0;
+
+    // apply
+    ggml_backend_t backend_cpu = ggml_backend_cpu_init();
+    if (backend_cpu == nullptr) {
+        LLAMA_LOG_ERROR("%s: error: failed to initialize cpu backend\n", __func__);
+        return 1;
+    }
+    ggml_backend_cpu_set_n_threads(backend_cpu, n_threads);
+
+    std::vector<no_init<uint8_t>> read_buf;
+    for (const auto & it : model.tensors_by_name) {
+        const std::string & base_name = it.first;
+        ggml_tensor * model_t = it.second;
+
+        if (tensor_meta_map.find(base_name + ".loraA") == tensor_meta_map.end() ||
+            tensor_meta_map.find(base_name + ".loraB") == tensor_meta_map.end()) {
+            continue;
+        }
+
+        tensor_meta & metaA = tensor_meta_map.at(base_name + ".loraA");
+        tensor_meta & metaB = tensor_meta_map.at(base_name + ".loraB");
+
+        ggml_init_params lora_init_params = {
+            /* .mem_size   */ ggml_tensor_overhead()*128 + ggml_graph_overhead(),
+            /* .mem_buffer */ nullptr,
+            /* .no_alloc   */ true,
+        };
+        ggml_context * lora_ctx = ggml_init(lora_init_params);
+        if (lora_ctx == nullptr) {
+            LLAMA_LOG_ERROR("%s: error: failed to initialize lora context\n", __func__);
+            ggml_backend_free(backend_cpu);
+            return 1;
+        }
+
+        // create tensors
+        ggml_tensor * loraA = ggml_new_tensor_2d(lora_ctx, metaA.type, metaA.ne[0], metaA.ne[1]);
+        ggml_tensor * loraB = ggml_new_tensor_2d(lora_ctx, metaB.type, metaB.ne[0], metaB.ne[1]);
+        ggml_set_name(loraA, metaA.name.c_str());
+        ggml_set_name(loraB, metaB.name.c_str());
+
+        ggml_tensor * base_t;
+        if (ml) {
+            if (!ml->get_tensor_meta(base_name.c_str())) {
+                LLAMA_LOG_ERROR("%s: error: tensor '%s' not found in base model\n", __func__, base_name.c_str());
+                return 1;
+            }
+            base_t = ggml_dup_tensor(lora_ctx, ml->get_tensor_meta(base_name.c_str()));
+        } else {
+            base_t = ggml_dup_tensor(lora_ctx, model_t);
+        }
+        ggml_set_name(base_t, base_name.c_str());
+
+        // allocate in backend buffer
+        ggml_backend_buffer_t lora_buf = ggml_backend_alloc_ctx_tensors_from_buft(lora_ctx, ggml_backend_cpu_buffer_type());
+        if (lora_buf == nullptr) {
+            LLAMA_LOG_ERROR("%s: error: failed to allocate lora tensors\n", __func__);
+            return 1;
+        }
+
+        // load tensor data
+        auto load_tensor = [&read_buf, &fin](const tensor_meta & tensor_meta, ggml_tensor * tensor) {
+            read_buf.resize(ggml_nbytes(tensor));
+            fin.seek(tensor_meta.offset, SEEK_SET);
+            fin.read_raw(read_buf.data(), ggml_nbytes(tensor));
+            ggml_backend_tensor_set(tensor, read_buf.data(), 0, read_buf.size());
+        };
+        load_tensor(metaA, loraA);
+        load_tensor(metaB, loraB);
+
+        // load base model tensor data
+        if (ml) {
+            ml->load_data_for(base_t);
+        } else {
+            ggml_backend_tensor_copy(model_t, base_t);
+        }
+
+        if (ggml_is_quantized(base_t->type) && !warned) {
+            LLAMA_LOG_WARN("%s: warning: using a lora adapter with a quantized model may result in poor quality, "
+                            "use a f16 or f32 base model with --lora-base\n", __func__);
+            warned = true;
+        }
+
+        if (base_t->ne[0] != loraA->ne[1] || base_t->ne[1] != loraB->ne[1]) {
+            LLAMA_LOG_ERROR("%s: incompatible tensor dimensions (%" PRId64 " and %" PRId64 ");"
+                            " are you sure that this adapter is for this model?\n", __func__, base_t->ne[0], loraA->ne[1]);
+            ggml_free(lora_ctx);
+            ggml_backend_buffer_free(lora_buf);
+            ggml_backend_free(backend_cpu);
+            return 1;
+        }
+
+        auto build_lora_graph = [&]() {
+            // w = w + BA*s
+            ggml_tensor * BA = ggml_mul_mat(lora_ctx, loraA, loraB);
+            ggml_set_name(BA, "BA");
+
+            if (scaling != 1.0f) {
+                BA = ggml_scale(lora_ctx, BA, scaling);
+                ggml_set_name(BA, "BA_scaled");
+            }
+
+            ggml_tensor * r;
+            r = ggml_add_inplace(lora_ctx, base_t, BA);
+            ggml_set_name(r, "r_add");
+
+            if (base_t->type != model_t->type) {
+                // convert the result to the model type
+                r = ggml_cast(lora_ctx, r, model_t->type);
+                ggml_set_name(r, "r_cast");
+            }
+
+            return r;
+        };
+
+        ggml_cgraph * gf = ggml_new_graph(lora_ctx);
+        ggml_tensor * r = build_lora_graph();
+        ggml_build_forward_expand(gf, r);
+
+        ggml_backend_buffer_t graph_buf = ggml_backend_alloc_ctx_tensors_from_buft(lora_ctx, ggml_backend_cpu_buffer_type());
+        if (graph_buf == nullptr) {
+            LLAMA_LOG_ERROR("%s: error: failed to allocate graph tensors\n", __func__);
+            ggml_free(lora_ctx);
+            ggml_backend_buffer_free(lora_buf);
+            ggml_backend_free(backend_cpu);
+            return 1;
+        }
+
+        ggml_backend_graph_compute(backend_cpu, gf);
+
+        ggml_backend_tensor_set(model_t, r->data, 0, ggml_nbytes(r));
+
+#if 0
+        // TODO: use scheduler with fallback to CPU for less copies between CPU and GPU
+        //ggml_backend_sched_t sched = ggml_backend_sched_new(backends.data(), backends.size(), GGML_DEFAULT_GRAPH_SIZE);
+
+        // sched compute
+        ggml_build_forward_expand(gf, build_graph());
+        ggml_backend_sched_init_measure(sched, gf);
+
+        // create the graph again, since the previous one was destroyed by the measure
+        ggml_graph_clear(gf);
+        ggml_build_forward_expand(gf, build_graph());
+        ggml_backend_sched_graph_compute(sched, gf);
+        ggml_backend_sched_free(sched);
+#endif
+
+        ggml_backend_buffer_free(lora_buf);
+        ggml_backend_buffer_free(graph_buf);
+        ggml_free(lora_ctx);
+
+        n_tensors++;
+        if (n_tensors % 4 == 0) {
+            LLAMA_LOG_INFO(".");
+        }
+    }
+
+    ggml_backend_free(backend_cpu);
+
+    const int64_t t_lora_us = ggml_time_us() - t_start_lora_us;
+    LLAMA_LOG_INFO(" done (%.2f ms)\n", t_lora_us / 1000.0);
+
+    return 0;
+}
+
+int32_t llama_model_apply_lora_from_file(const struct llama_model * model, const char * path_lora, float scale, const char * path_base_model, int32_t n_threads) {
+    try {
+        return llama_apply_lora_from_file_internal(*model, path_lora, scale, path_base_model, n_threads);
+    } catch (const std::exception & err) {
+        LLAMA_LOG_ERROR("%s: failed to apply lora adapter: %s\n", __func__, err.what());
+        return 1;
+    }
+}
\ No newline at end of file
