pytorch_to_safetensors.py

import os
import torch

# --- AGGRESSIVE FIX: Bypass Security Check ---
# We must import these modules specifically to patch the function where it is used
import transformers.modeling_utils
import transformers.utils.import_utils

# Disable the check in both locations
transformers.modeling_utils.check_torch_load_is_safe = lambda: None
transformers.utils.import_utils.check_torch_load_is_safe = lambda: None
# ---------------------------------------------

from transformers import AutoModelForCausalLM, AutoTokenizer

# 1. Path to your local PyTorch model
input_path = r"B:\7B\!models--Gryphe--Tiamat-7b" 

# 2. Path where you want the SafeTensors version
output_path = r"B:\7B\!models--Gryphe--Tiamat-7b\safe"

print(f"Loading model from {input_path}...")

# Load the model
model = AutoModelForCausalLM.from_pretrained(
    input_path,
    torch_dtype=torch.bfloat16,
    device_map="cpu",
    low_cpu_mem_usage=True
)

# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(input_path)

print(f"Saving to {output_path}...")

if not os.path.exists(output_path):
    os.makedirs(output_path)

# 3. Save with safe_serialization=True
model.save_pretrained(
    output_path, 
    safe_serialization=True, 
    max_shard_size="5GB"
)

tokenizer.save_pretrained(output_path)

print("Conversion complete.")