| """Load the open guard: GLiNER2.5-Decide with the adapter folded in (W + alpha/r * B@A). Usage in code: | |
| from load_guard import load; m = load("model") # then m.classify_text(text, {"injection": {"labels": ["injection", "benign"]}})""" | |
| import json, os, tempfile | |
| import torch | |
| from gliner2 import AutoExtractor | |
| from safetensors.torch import load_file, save_file | |
| BASE = "fastino/GLiNER2.5-Decide" | |
| def load(adapter_dir: str, device: str | None = None): | |
| cfg = json.load(open(os.path.join(adapter_dir, "lora_config.json"))) | |
| scale = cfg["lora_alpha"] / cfg["lora_r"] | |
| lora = load_file(os.path.join(adapter_dir, "adapter.safetensors")) | |
| m = AutoExtractor.from_pretrained(BASE) | |
| state = m.state_dict() | |
| for key in [k for k in lora if ".lora_A." in k]: | |
| pre = key.split(".lora_A.")[0] | |
| target = pre + ".weight" | |
| a, b = lora[key].float(), lora[key.replace(".lora_A.", ".lora_B.")].float() | |
| state[target] = (state[target].float() + scale * (b @ a)).to(state[target].dtype) | |
| m.load_state_dict(state) | |
| device = device or ("cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu") | |
| return m.to(device).eval() | |