Damped Resonance Alignment 테스트 79: 압력 다이얼 비교와 Qwen2.5-1.5B 스티어링 로그

import torch
from transformers import AutoTokenizer, AutoModelWithLMHead
# Load pre-trained model and tokenizer from Hugging Face Transformers Library
model_name = "your_model_name" # Replace with actual model name
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelWithLMHead.from_pretrained(model_name).cuda()

def generate_text(prompt):
    """ Generates text given prompt information
    Parameters: prompt (str): Input string containing query details
    Returns: str: Generated text """
    inputs = tokenizer.encode(prompt, return_tensors='pt').to('cuda')
    outputs = model.generate(
        input_ids=inputs, max_length=50, # Adjusted max length according to needs
        num_return_sequences=1, do_sample=True, top_k=50, # Controls diversity factor
        temperature=1.2, # Temperature controls sampling randomness
        early_stopping=False, pad_token_id=tokenizer.eos_token_id,
    )
    generated_text = [tokenizer.decode(output, skip_special_tokens=True) for output in outputs]
    final_output = generated_text[0] # Selects only first possible sequence
    return final_output

if __name__ == "__main__":
    print(generate_text("What was the most important event"))

import torch

# Initialize Model Parameters
model = YourModelClass()

def forward(model):
    # Forward pass through the model
    outputs = model(input_tensor)
    return outputs

class CustomOptimizer(torch.optim.Optimizer):
    def __init__(self, params):
        super().__init__(params)

    def step(self, closure=None):
        """Performs a single optimization step."""
        loss = None
        if_cuda = next(iter(params)).is_cuda
        if_gpu = False
        if_cpu = True
        for param_group in self.param_groups:
            if_gpu |= bool(param_group['lr'])
            if_cpu &= ~bool(both_gpu)
            if_cuda &= ~bool(not_both_gpu)
        if_gpu |= cuda
        if_cpu |= cpu
        total_params = sum(p.numel() for p in model.parameters())
        print(f"Total number of parameters : {total_params}")
        assert(total_params == 87692984)
        output = forward(model)