header: Dict[str, Dict[str, Any]] = json.loads(fp.read(header_size))
# Use mmap for the actual data to avoid race conditions with the file offset.
mapped = memoryview(mmap.mmap(fp.fileno(), 0, access=mmap.ACCESS_READ))
- byte_buf = mapped[fp.tell():]
+ byte_buf = mapped[8 + header_size:]
def convert(info: Dict[str, Any]) -> LazyTensor:
data_type = SAFETENSORS_DATA_TYPES[info['dtype']]
return ret
-def lazy_load_ggml_file(fp: IO[bytes], path: Path) -> ModelPlus:
+def lazy_load_ggml_file(fp: io.BufferedReader, path: Path) -> ModelPlus:
magic = must_read(fp, 4)[::-1]
if magic in (b'ggmf', b'ggjt'):
version, = struct.unpack("i", must_read(fp, 4))
model: LazyModel = {}
# Use mmap for the actual data to avoid race conditions with the file offset.
+ off = fp.raw.tell()
mapped = memoryview(mmap.mmap(fp.fileno(), 0, access=mmap.ACCESS_READ))
+ fp.raw.seek(off) # needed on Windows
def read_tensor() -> None: # this is a function so that variables captured in `load` don't change
shape_len, name_len, ftype = struct.unpack("iii", must_read(fp, 12))