def generate_response(model_name, input_text, max_new_tokens=50):
    # Load the tokenizer and model from Hugging Face Hub
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForCausalLM.from_pretrained(model_name)
    
    # Tokenize the input text
    input_ids = tokenizer(input_text, return_tensors='pt').input_ids
    
    # Generate a response using the model
    with torch.no_grad():
        generated_ids = model.generate(input_ids, max_new_tokens=max_new_tokens)
    
    # Decode the generated tokens into text
    generated_text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
    
    return generated_text

if __name__ == "__main__":
    # Set the model name from Hugging Face Hub
    model_name = "AINovice2005/Le-Empereur_70-Base" 
    input_text = "Hello, how are you?"

    # Generate and print the model's response
    output = generate_response(model_name, input_text)
    
    print(f"Input: {input_text}")
    print(f"Output: {output}")

𝐑𝐞𝐬𝐮𝐥𝐭𝐬: Firstly, a higher learning rate was required for the model to train the model on a dataset, training methods such as SFT and ORPO with PEFT failed to restore convergence.Secondly, training only using PEFT helped to restore partial model convergence.

Lastly, future experiments to restore model convergence will require a systematic training and eval strategy.

RichardErkhov/AINovice2005_-_LeEmpereur_70-Base-awq

Original model description:

Model Overview

Model Description:

Inference Script:

RichardErkhov/
AINovice2005_-_LeEmpereur_70-Base-awq