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import sys
import os
from safetensors.torch import save_file
import json

# Add the directory containing your modeling.py and configuration.py to the Python path
model_dir = "/Users/gokdenizgulmez/Desktop/mlx-lm/MiniMaxM1-Dev"
sys.path.append(model_dir)

# Import your custom model and configuration classes
from modeling_minimax_m1 import MiniMaxM1ForCausalLM
from configuration_minimax_m1 import MiniMaxM1Config

# Load the configuration
config_path = os.path.join(model_dir, "config.json")
with open(config_path, 'r') as f:
    config_dict = json.load(f)

# Create the configuration object
config = MiniMaxM1Config(**config_dict)

# Create the model
small_model = MiniMaxM1ForCausalLM(config)

# Print parameter count to verify
param_count = sum(p.numel() for p in small_model.parameters())
print(f"Model has {param_count:,} parameters")

# Convert model to state dict
model_state_dict = small_model.state_dict()

# Save as safetensors
save_file(model_state_dict, os.path.join(model_dir, "model.safetensors"))

print("Model saved in safetensors format")

	import sys
	import os
	from safetensors.torch import save_file
	import json

	# Add the directory containing your modeling.py and configuration.py to the Python path
	model_dir = "/Users/gokdenizgulmez/Desktop/mlx-lm/MiniMaxM1-Dev"
	sys.path.append(model_dir)

	# Import your custom model and configuration classes
	from modeling_minimax_m1 import MiniMaxM1ForCausalLM
	from configuration_minimax_m1 import MiniMaxM1Config

	# Load the configuration
	config_path = os.path.join(model_dir, "config.json")
	with open(config_path, 'r') as f:
	config_dict = json.load(f)

	# Create the configuration object
	config = MiniMaxM1Config(**config_dict)

	# Create the model
	small_model = MiniMaxM1ForCausalLM(config)

	# Print parameter count to verify
	param_count = sum(p.numel() for p in small_model.parameters())
	print(f"Model has {param_count:,} parameters")

	# Convert model to state dict
	model_state_dict = small_model.state_dict()

	# Save as safetensors
	save_file(model_state_dict, os.path.join(model_dir, "model.safetensors"))

	print("Model saved in safetensors format")