Skip to content

Fix ONNX model loading when torch file is absent #317

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
urchade merged 1 commit into from
Dec 20, 2025
Merged

Fix ONNX model loading when torch file is absent #317

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
21 changes: 13 additions & 8 deletions gliner/model.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -588,14 +588,6 @@ def from_pretrained(
model_id, revision, cache_dir, force_download, proxies, resume_download, token, local_files_only
)

# Find model file
model_file = model_dir / "model.safetensors"
if not model_file.exists():
model_file = model_dir / "pytorch_model.bin"

if not model_file.exists():
raise FileNotFoundError(f"No model file found in {model_dir}")

# Load config
config_file = model_dir / "gliner_config.json"
if not config_file.exists():
Expand All @@ -618,6 +610,14 @@ def from_pretrained(
tokenizer = cls._load_tokenizer(config, model_dir, cache_dir)

if not load_onnx_model:
# Find model file
model_file = model_dir / "model.safetensors"
if not model_file.exists():
model_file = model_dir / "pytorch_model.bin"

if not model_file.exists():
raise FileNotFoundError(f"No model file found in {model_dir}")

# Create model instance
instance = cls(
config, tokenizer=tokenizer, backbone_from_pretrained=False, cache_dir=cache_dir, **model_kwargs
Expand Down Expand Up @@ -876,6 +876,11 @@ def export_to_onnx(
opset,
)

# Save Config file
self.config.to_json_file(save_dir / "gliner_config.json")
# Save Tokenizer file
self.data_processor.transformer_tokenizer.save_pretrained(save_dir)

# Quantize if requested
q_path = self._maybe_quantize_onnx(onnx_path, save_dir / quantized_filename, quantize)

Expand Down