diff --git a/constants.py b/constants.py
index 7c7456403..3f4e42b10 100644
--- a/constants.py
+++ b/constants.py
@@ -1,5 +1,6 @@
 GGUF_MAGIC = 0x47475546
 GGUF_VERSION = 1
+GGUF_DEFAULT_ALIGNMENT = 32
 
 # general
 KEY_GENERAL_ARCHITECTURE = "general.architecture"
diff --git a/gguf.py b/gguf.py
index 991bbe2f3..ec1ee4751 100644
--- a/gguf.py
+++ b/gguf.py
@@ -7,7 +7,7 @@
 
 import struct
 from enum import IntEnum
-from typing import List, Any
+from typing import List, Any, Sequence
 import constants
 
 
@@ -57,14 +57,15 @@ class GGUFValueType(IntEnum):
 
 
 class GGUFWriter:
-    def __init__(self, buffered_writer):
-        self.buffered_writer = buffered_writer
+    def __init__(self, fout):
+        self.fout = fout
+        self.offset_tensor = 0
 
     def write_header(self, tensor_count: int, metadata_kv_count: int):
-        self.buffered_writer.write(struct.pack("<I", constants.GGUF_MAGIC))
-        self.buffered_writer.write(struct.pack("<I", constants.GGUF_VERSION))
-        self.buffered_writer.write(struct.pack("<I", tensor_count))
-        self.buffered_writer.write(struct.pack("<I", metadata_kv_count))
+        self.fout.write(struct.pack("<I", constants.GGUF_MAGIC))
+        self.fout.write(struct.pack("<I", constants.GGUF_VERSION))
+        self.fout.write(struct.pack("<I", tensor_count))
+        self.fout.write(struct.pack("<I", metadata_kv_count))
 
     @classmethod
     def open(cls, path: str) -> "GGUFWriter":
@@ -150,11 +151,23 @@ class GGUFWriter:
         else:
             raise ValueError("Invalid GGUF metadata value type")
 
+    def write_tensor_info(self, name: str, shape: Sequence[int], dtype: GGMLQuantizationType):
+        self.write_value(name, GGUFValueType.STRING)
+        n_dims = len(shape)
+        self.write_value(n_dims, GGUFValueType.INT32)
+        for i in range(n_dims):
+            self.write_value(shape[n_dims - 1 - i], GGUFValueType.INT32)
+
+        self.fout.write(struct.pack("<Q", self.offset_tensor))
+
+        # TODO: update offset with alignment
+        # probably we need a dict as a class attribute to hold tensor data while writing
+
     def flush(self):
-        self.buffered_writer.flush()
+        self.fout.flush()
 
     def close(self):
-        self.buffered_writer.close()
+        self.fout.close()
 
     def write_architecture(self, architecture: str):
         self.write_string(constants.KEY_GENERAL_ARCHITECTURE,