diff --git a/ggml.c b/ggml.c
index fbc66f65b..c94006e51 100644
--- a/ggml.c
+++ b/ggml.c
@@ -470,6 +470,19 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .type_size                = sizeof(int32_t),
         .is_quantized             = false,
     },
+    [GGML_TYPE_I64] = {
+        .type_name                = "i64",
+        .blck_size                = 1,
+        .type_size                = sizeof(int64_t),
+        .is_quantized             = false,
+    },
+    [GGML_TYPE_F64] = {
+        .type_name                = "f64",
+        .blck_size                = 1,
+        .type_size                = sizeof(double),
+        .is_quantized             = false,
+        .nrows                    = 1,
+    },
     [GGML_TYPE_F32] = {
         .type_name                = "f32",
         .blck_size                = 1,
@@ -12418,6 +12431,8 @@ static void ggml_compute_forward_alibi(
         case GGML_TYPE_I8:
         case GGML_TYPE_I16:
         case GGML_TYPE_I32:
+        case GGML_TYPE_I64:
+        case GGML_TYPE_F64:
         case GGML_TYPE_COUNT:
             {
                 GGML_ASSERT(false);
@@ -12504,6 +12519,8 @@ static void ggml_compute_forward_clamp(
         case GGML_TYPE_I8:
         case GGML_TYPE_I16:
         case GGML_TYPE_I32:
+        case GGML_TYPE_I64:
+        case GGML_TYPE_F64:
         case GGML_TYPE_COUNT:
             {
                 GGML_ASSERT(false);
diff --git a/ggml.h b/ggml.h
index ab26c8f59..c937d4a53 100644
--- a/ggml.h
+++ b/ggml.h
@@ -366,6 +366,8 @@ extern "C" {
         GGML_TYPE_I8      = 24,
         GGML_TYPE_I16     = 25,
         GGML_TYPE_I32     = 26,
+        GGML_TYPE_I64     = 27,
+        GGML_TYPE_F64     = 28,
         GGML_TYPE_COUNT,
     };
 
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index 2d7cf16c1..458a641dc 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -665,6 +665,8 @@ class GGMLQuantizationType(IntEnum):
     I8      = 24
     I16     = 25
     I32     = 26
+    I64     = 27
+    F64     = 28
 
 
 class GGUFEndian(IntEnum):
@@ -734,6 +736,8 @@ GGML_QUANT_SIZES = {
     GGMLQuantizationType.I8:      (1, 1),
     GGMLQuantizationType.I16:     (1, 2),
     GGMLQuantizationType.I32:     (1, 4),
+    GGMLQuantizationType.I64:     (1, 8),
+    GGMLQuantizationType.F64:     (1, 8),
 }
 
 
diff --git a/gguf-py/gguf/gguf_reader.py b/gguf-py/gguf/gguf_reader.py
index 1c10f5753..33afac552 100644
--- a/gguf-py/gguf/gguf_reader.py
+++ b/gguf-py/gguf/gguf_reader.py
@@ -242,12 +242,15 @@ class GGUFReader:
             n_bytes = n_elems * type_size // block_size
             data_offs = int(start_offs + offset_tensor[0])
             item_type: npt.DTypeLike
-            if ggml_type == GGMLQuantizationType.F32:
-                item_count = n_elems
-                item_type = np.float32
-            elif ggml_type == GGMLQuantizationType.F16:
+            if ggml_type == GGMLQuantizationType.F16:
                 item_count = n_elems
                 item_type = np.float16
+            elif ggml_type == GGMLQuantizationType.F32:
+                item_count = n_elems
+                item_type = np.float32
+            elif ggml_type == GGMLQuantizationType.F64:
+                item_count = n_elems
+                item_type = np.float64
             elif ggml_type == GGMLQuantizationType.I8:
                 item_count = n_elems
                 item_type = np.int8
@@ -257,6 +260,9 @@ class GGUFReader:
             elif ggml_type == GGMLQuantizationType.I32:
                 item_count = n_elems
                 item_type = np.int32
+            elif ggml_type == GGMLQuantizationType.I64:
+                item_count = n_elems
+                item_type = np.int64
             else:
                 item_count = n_bytes
                 item_type = np.uint8
diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py
index 81b2eb884..1967b633c 100644
--- a/gguf-py/gguf/gguf_writer.py
+++ b/gguf-py/gguf/gguf_writer.py
@@ -204,18 +204,22 @@ class GGUFWriter:
         for i in range(n_dims):
             self.ti_data += self._pack("Q", tensor_shape[n_dims - 1 - i])
         if raw_dtype is None:
-            if tensor_dtype == np.float32:
-                dtype = GGMLQuantizationType.F32
-            elif tensor_dtype == np.float16:
+            if tensor_dtype == np.float16:
                 dtype = GGMLQuantizationType.F16
+            elif tensor_dtype == np.float32:
+                dtype = GGMLQuantizationType.F32
+            elif tensor_dtype == np.float64:
+                dtype = GGMLQuantizationType.F64
             elif tensor_dtype == np.int8:
                 dtype = GGMLQuantizationType.I8
             elif tensor_dtype == np.int16:
                 dtype = GGMLQuantizationType.I16
             elif tensor_dtype == np.int32:
                 dtype = GGMLQuantizationType.I32
+            elif tensor_dtype == np.int64:
+                dtype = GGMLQuantizationType.I64
             else:
-                raise ValueError("Only F32, F16, I8, I16, I32 tensors are supported for now")
+                raise ValueError("Only F16, F32, F64, I8, I16, I32, I64 tensors are supported for now")
         else:
             dtype = raw_dtype
         self.ti_data += self._pack("I", dtype)