BloomAI

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# TODO: Optimize PagedAttention integration
class Llama3InferenceEngine:
    def __init__(self, model_id, quantization=None):
        self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
        self.tokenizer = AutoTokenizer.from_pretrained(model_id)
        
        if quantization == '4bit':
            # Loading in 4-bit precision for memory efficiency
            self.model = AutoModelForCausalLM.from_pretrained(
                model_id, 
                load_in_4bit=True,
                device_map='auto'
            )
    
    def optimize_inference(self, prompt):
        inputs = self.tokenizer(prompt, return_tensors='pt').to(self.device)
        
        with torch.inference_mode():
            # Current bottleneck: Token generation loop
            output = self.model.generate(
                **inputs, 
                max_new_tokens=50,
                use_cache=True 
            )
            
        return self.tokenizer.decode(output[0])

# Initializing engine...
engine = Llama3InferenceEngine('meta-llama/Llama-3-8b', quantization='4bit')|