app/model_loader.py

import os
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

def load_model():
    hf_token = os.getenv("hf_token")
    if not hf_token:
        raise RuntimeError("hf_token not set.")
    
    # Use a user-writable cache directory (important for Docker non-root)
    HF_CACHE = os.path.expanduser("~/.cache/huggingface")
    os.makedirs(HF_CACHE, exist_ok=True)

    os.environ["TRANSFORMERS_CACHE"] = HF_CACHE
    os.environ["HF_HOME"] = HF_CACHE

    base_model = AutoModelForCausalLM.from_pretrained(
        "meta-llama/Llama-2-7b-chat-hf",
        token=hf_token,
        cache_dir="/tmp/hf_cache",
        torch_dtype="auto",
        device_map="auto"
    )
    model = PeftModel.from_pretrained(
        base_model,
        "BrainGPT/BrainGPT-7B-v0.1",
        token=hf_token,
        cache_dir="/tmp/hf_cache"
    )
    tokenizer = AutoTokenizer.from_pretrained(
        "meta-llama/Llama-2-7b-chat-hf",
        token=hf_token,
        cache_dir="/tmp/hf_cache"
    )
    return model, tokenizer

## GPT 2 Model
# import os
# from transformers import AutoModelForCausalLM, AutoTokenizer

# def load_model():
#     # Use a user-writable cache directory (important for Docker non-root)
#     HF_CACHE = os.path.expanduser("~/.cache/huggingface")
#     os.makedirs(HF_CACHE, exist_ok=True)

#     os.environ["TRANSFORMERS_CACHE"] = HF_CACHE
#     os.environ["HF_HOME"] = HF_CACHE

#     model_name = "gpt2"

#     tokenizer = AutoTokenizer.from_pretrained(
#         model_name,
#         cache_dir=HF_CACHE
#     )

#     model = AutoModelForCausalLM.from_pretrained(
#         model_name,
#         cache_dir=HF_CACHE
#     )

#     return model, tokenizer