diff --git a/convert-lora-to-ggml.py b/convert-lora-to-ggml.py
index ef1aa5305d56d1..535381ecbda726 100644
--- a/convert-lora-to-ggml.py
+++ b/convert-lora-to-ggml.py
@@ -1,3 +1,4 @@
+import json
 import os
 import re
 import struct
@@ -14,8 +15,10 @@
 class UnquantizedDataType:
     name: str
 
-DT_F16 = UnquantizedDataType('F16')
-DT_F32 = UnquantizedDataType('F32')
+
+DT_F16 = UnquantizedDataType("F16")
+DT_F32 = UnquantizedDataType("F32")
+
 
 @dataclass(frozen=True)
 class QuantizedDataType:
@@ -23,6 +26,7 @@ class QuantizedDataType:
     have_addends: bool
     have_g_idx: bool
 
+
 DataType = UnquantizedDataType
 
 DATA_TYPE_TO_FTYPE: dict[DataType, int] = {
@@ -35,17 +39,28 @@ class QuantizedDataType:
     DT_F32: np.dtype(np.float32),
 }
 
-NUMPY_TYPE_TO_DATA_TYPE: dict[np.dtype[Any], DataType] = {dtype: data_type for (data_type, dtype) in DATA_TYPE_TO_NUMPY.items()}
+NUMPY_TYPE_TO_DATA_TYPE: dict[np.dtype[Any], DataType] = {
+    dtype: data_type for (data_type, dtype) in DATA_TYPE_TO_NUMPY.items()
+}
 
 HF_SUBLAYER_TO_GGML = {
     "self_attn.q_proj": "attention.wq.weight",
     "self_attn.k_proj": "attention.wk.weight",
     "self_attn.v_proj": "attention.wv.weight",
     "self_attn.o_proj": "attention.wo.weight",
+    # "embed_tokens.weight": "tok_embeddings.weight",
+    # "norm.weight": "norm.weight",
+    # "lm_head.weight": "output.weight",
+    # "mlp.gate_proj": "feed_forward.w1.weight",
+    # "mlp.down_proj": "feed_forward.w2.weight",
+    # "mlp.up_proj": "feed_forward.w3.weight",
+    # "input_layernorm": "attention_norm.weight",
+    # "post_attention_layernorm": "ffn_norm.weight",
 }
 
+
 def translate_tensor_name(t):
-    match = re.match(r'.*layers\.(\d+)\.(\w+\.\w+)\.lora_(A|B)\.weight', t)
+    match = re.match(r".*layers\.(\d+)\.(\w+\.\w+)\.lora_(A|B)\.weight", t)
     if match:
         nn = match.group(1)
         sub_layer = match.group(2)
@@ -54,50 +69,85 @@ def translate_tensor_name(t):
         sub_layer_renamed = HF_SUBLAYER_TO_GGML.get(sub_layer)
         if sub_layer_renamed is None:
             print(f"Error: unrecognized sub-layer {sub_layer} in tensor {t}")
-            exit(1)
+            sys.exit(1)
 
         output_string = f"layers.{nn}.{HF_SUBLAYER_TO_GGML[sub_layer]}.lora{lora_type}"
         return output_string
     else:
         print(f"Error: unrecognized tensor {t}")
-        exit(1)
+        sys.exit(1)
 
-def write_file_header(fout):
-    fout.write(b"ggla"[::-1]) # magic (ggml lora)
-    fout.write(struct.pack("i", 1)) # file version
+
+def write_file_header(fout, params):
+    fout.write(b"ggla"[::-1])  # magic (ggml lora)
+    fout.write(struct.pack("i", 1))  # file version
+    fout.write(struct.pack("ii", params["r"], params["lora_alpha"]))
 
 
 def write_tensor_header(self, name: str, shape: Sequence[int], data_type: 1) -> None:
-    sname = name.encode('utf-8')
-    fout.write(struct.pack("iii", len(shape), len(sname), DATA_TYPE_TO_FTYPE[NUMPY_TYPE_TO_DATA_TYPE[data_type]]))
+    sname = name.encode("utf-8")
+    fout.write(
+        struct.pack(
+            "iii",
+            len(shape),
+            len(sname),
+            DATA_TYPE_TO_FTYPE[NUMPY_TYPE_TO_DATA_TYPE[data_type]],
+        )
+    )
     fout.write(struct.pack("i" * len(shape), *shape[::-1]))
     fout.write(sname)
     fout.seek((fout.tell() + 31) & -32)
-    
 
-if len(sys.argv) < 2:
-    print(f"Usage: python {sys.argv[0]} adapter_model.bin [ggml_adapter_model.bin]")
+
+if len(sys.argv) != 2:
+    print(f"Usage: python {sys.argv[0]} <path>")
+    print(
+        "Path must contain HuggingFace PEFT LoRA files 'adapter_config.json' and 'adapter_model.bin'"
+    )
     sys.exit(1)
 
-input_path = sys.argv[1]
-if len(sys.argv) > 2:
-    output_path = sys.argv[2]
-else:
-    output_filename = f"ggml_{os.path.basename(input_path)}"
-    output_path = os.path.join(os.path.dirname(input_path), output_filename)
+input_json = os.path.join(sys.argv[1], "adapter_config.json")
+input_model = os.path.join(sys.argv[1], "adapter_model.bin")
+output_path = os.path.join(sys.argv[1], "ggml-adapter-model.bin")
 
-model = torch.load(input_path, map_location="cpu")
+model = torch.load(input_model, map_location="cpu")
+
+with open(input_json, "r") as f:
+    params = json.load(f)
+
+if params["peft_type"] != "LORA":
+    print(f"Error: unsupported adapter type {params['peft_type']}, expected LORA")
+    sys.exit(1)
+
+if params["fan_in_fan_out"] == True:
+    print("Error: param fan_in_fan_out is not supported")
+    sys.exit(1)
+
+if params["bias"] is not None and params["bias"] != "none":
+    print("Error: param bias is not supported")
+    sys.exit(1)
+
+# TODO: these seem to be layers that have been trained but without lora.
+# doesn't seem widely used but eventually should be supported
+if params["modules_to_save"] is not None and len(params["modules_to_save"]) > 0:
+    print("Error: param modules_to_save is not supported")
+    sys.exit(1)
 
 with open(output_path, "wb") as fout:
-    write_file_header(fout)
+    fout.truncate()
+
+    write_file_header(fout, params)
     for k, v in model.items():
         # since ggml doesn't always support other types for the second operand,
         # the tensors are always converted and exported as f32
-        t = v.float().numpy()
+        v = v.float()
+        t = v.numpy()
         if "lora_A" in k:
             t = t.T
-        print(f"{k} => {translate_tensor_name(k)} {t.shape} {t.dtype} {t.nbytes/1024/1024:.2f}MB")
+        print(
+            f"{k} => {translate_tensor_name(k)} {t.shape} {t.dtype} {t.nbytes/1024/1024:.2f}MB"
+        )
         write_tensor_header(fout, translate_tensor_name(k), t.shape, t.dtype)
         t.tofile(fout)
 
-print(f"Converted {input_path} to {output_path}")
\ No newline at end of file
+print(f"Converted {input_json} and {input_model} to {output_path}")
diff --git a/llama.cpp b/llama.cpp
index da5ec9475564ed..aedc31fc4272e7 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -1714,6 +1714,15 @@ int llama_apply_lora_from_file(struct llama_context * ctx, const char * path_lor
         }
     }
 
+    int32_t lora_r;
+    int32_t lora_alpha;
+    fin.read((char *) &lora_r, sizeof(lora_r));
+    fin.read((char *) &lora_alpha, sizeof(lora_alpha));
+    float scaling = (float)lora_alpha / (float)lora_r;
+
+    fprintf(stderr, "%s: r = %d, alpha = %d, scaling = %.2f\n", __func__, lora_r, lora_alpha, scaling);
+
+
     // create a temporary ggml context to store the lora tensors
     std::vector<uint8_t> buf(1024 * 1024 * 100);
     struct ggml_init_params params;
@@ -1815,11 +1824,13 @@ int llama_apply_lora_from_file(struct llama_context * ctx, const char * path_lor
             // w = w + BA*s
             ggml_tensor * BA = ggml_mul_mat(lora_ctx, loraB, loraA);
 
-            //if (true) {
-            //    ggml_tensor * scale_tensor = ggml_new_f32(lora_ctx, 1.0f);
-            //    BA = ggml_scale(lora_ctx, BA, scale_tensor);
-            //}
-            ggml_tensor * r = ggml_add(lora_ctx, tensor, BA);
+            if (scaling != 1.0f) {
+                ggml_tensor * scale_tensor = ggml_new_f32(lora_ctx, scaling);
+                BA = ggml_scale(lora_ctx, BA, scale_tensor);
+            }
+
+            ggml_tensor * r = ggml_add_inplace(lora_ctx, tensor, BA);
+            //ggml_tensor * r = ggml_add(lora_ctx, tensor, BA);
             //r = ggml_cpy(lora_ctx, r, tensor);
 
             struct ggml_cgraph gf = ggml_build_forward(r);
@@ -1827,7 +1838,7 @@ int llama_apply_lora_from_file(struct llama_context * ctx, const char * path_lor
             ggml_graph_compute(lora_ctx, &gf);
 
             // hack until ggml_cpy supports quantized tensors
-            memcpy(tensor->data, r->data, ggml_nbytes(tensor));
+            // memcpy(tensor->data, r->data, ggml_nbytes(tensor));
 
             // we won't need these tensors again, reset the context to save memory
             ggml_free(lora_ctx);