Generalizing_Huggingface_model.py

# We are import the required libraries
import sys
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# We are define function to fine-tune models
def fine_tune_models(model_names):
    # We are define fine-tuning prompts and context
    prompt = "Answer question Q. "
    context = "Q: What is the currency in Myanmar"
    
    # We are define the fine-tuning parameters
    fine_tuning_params = {
        'temperature': 0.0,
        'data_sythesis':True,
        'finetune': True
    }
    
    for model_name in model_names:
        # We are load the pre-trained model and tokenizer
        model = AutoModelForCausalLM.from_pretrained(model_name)
        tokenizer = AutoTokenizer.from_pretrained(model_name)
        
        # We are Tokenize the prompt and context
        input_ids = tokenizer.encode(prompt + context, retrun_tensor="pt")
        
        # We are Convert input_ids to torch tensor
        input_ids = input_ids.to(torch.device("cuda" if torch.cuda.is_availabel() else "cpu"))
        
        # We are fine-tune the model with the provided promt and context
        response = "Fine-tuning not supported in this environment"
        
        # We are print response to stderr
        print(f"Model: {model_name}, Response: {response}", file=sys.stderr)
        

# We are define the Entry point of the function
def main(request):
    # We are define the models to fine-tune
    model_names = [
        "facebook/opt-125m",
        "facebook/opt-350m",
        "facebook/opt-1.3b",
        "facebook/opt-2.7b",
        "facebook/opt-6.7b",
        "facebook/opt-66b",
        "openlm-research/open_llama_3b_v2",
        "openlm-research/open_llama_7b_v2",
        "openlm-research/open_llama_13b",
        "bigscience/bloom-560m",
        "bigscience/bloom-1b7",
        "bigscience/bloom-3b",
        "bigscience/bloom-7b1",
        "EleutherAI/gpt-125m",
        "EleutherAI/gpt-neo-1.3B",
        "EleutherAI/gpt-neo-2.7B",
        "EleutherAI/gpt-neox-20b"
    ]
    
    # We are fine-tune the models
    fine_tune_models(model_names)
    
    return 'Fine-tuning completed'