from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    AutoTokenizer,
)

import torch

d_map = {"": torch.cuda.current_device()} if torch.cuda.is_available() else None
merged_model_path = "outputs/merged"     # Path to the combined weights
repo_name = "Financial_Analyst"            # HuggingFace repo name


model = AutoModelForCausalLM.from_pretrained(
    merged_model_path,
    ignore_mismatched_sizes=True,
    from_tf=True,
    trust_remote_code=True, 
    device_map=d_map, 
    torch_dtype=torch.float16,
).eval()

tokenizer = AutoTokenizer.from_pretrained(merged_model_path)


model.push_to_hub(repo_name, token=hf_token)
tokenizer.push_to_hub(repo_name, token=hf_token)