vbatts/llama.cpp
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convert_hf_to_gguf.py: rebase error correction

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brian khuu 2024-07-07 18:52:52 +10:00
parent 5011eefeaf
commit 2f23927d37
1 changed files with 3 additions and 18 deletions
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21
convert_hf_to_gguf.py
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@ -67,7 +67,7 @@ class Model:
model_arch: gguf.MODEL_ARCH
def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, is_big_endian: bool, use_temp_file: bool, eager: bool, metadata: gguf.Metadata,
model_name: str | None, split_max_tensors: int = 0, split_max_size: int = 0, dry_run: bool = False, small_first_shard: bool = False):
split_max_tensors: int = 0, split_max_size: int = 0, dry_run: bool = False, small_first_shard: bool = False):
if type(self) is Model:
raise TypeError(f"{type(self).__name__!r} should not be directly instantiated")
@ -107,21 +107,6 @@ class Model:
self.gguf_writer = gguf.GGUFWriter(path=None, arch=gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=self.use_temp_file,
split_max_tensors=split_max_tensors, split_max_size=split_max_size, dry_run=dry_run, small_first_shard=small_first_shard)
# Update any missing authorship metadata with HuggingFace parameters or model card frontmatter
if self.metadata is not None:
# Source Hugging Face Repository
if self.metadata.source_hf_repo is None:
if self.hparams is not None and "_name_or_path" in self.hparams:
self.metadata.source_hf_repo = self.hparams["_name_or_path"]
# Model License
if self.metadata.license is None:
if self.model_card is not None and "license" in self.model_card:
self.metadata.source_hf_repo = self.model_card["license"]
self.model_name = Model.get_model_name(self.metadata, self.hparams, self.dir_model, self.model_arch)
# Fallback to model architecture name if metadata name is still missing
if self.metadata.name is None:
self.metadata.name = gguf.MODEL_ARCH_NAMES[self.model_arch]
@ -3708,8 +3693,8 @@ def main() -> None:
logger.error(f"Model {hparams['architectures'][0]} is not supported")
sys.exit(1)
model_instance = model_class(dir_model, output_type, fname_out, args.bigendian, args.use_temp_file, args.no_lazy,
metadata, args.model_name, split_max_tensors=args.split_max_tensors,
model_instance = model_class(dir_model, output_type, fname_out, args.bigendian, args.use_temp_file,
args.no_lazy, metadata, split_max_tensors=args.split_max_tensors,
split_max_size=split_str_to_n_bytes(args.split_max_size), dry_run=args.dry_run,
small_first_shard=args.no_tensor_first_split)