GPT-2-gguf

Gargaz/GPT-2-gguf

Gargaz/GPT-2-gguf is a highly optimized, stable, and efficient version of GPT-2, designed for fast and reliable language generation. Leveraging the GGUF format, this model minimizes memory usage while maximizing performance, making it ideal for a wide range of natural language processing tasks. Whether you're building conversational AI, generating text, or exploring NLP research, this model delivers consistent, high-quality results.

Features

• Optimized for Performance: Utilizes the GGUF format for reduced memory footprint and faster inference.
• GPU Acceleration: Offloads model layers to the GPU for significantly improved processing times.
• Large Context Handling: Supports up to 16,000 tokens, enabling it to manage lengthy conversations or documents effectively.
• Stable and Reliable: Provides a robust and consistent output across various NLP tasks, ensuring high stability in deployment.

Requirements

• Python 3.7+
• llama_cpp for running the model
• huggingface_hub for downloading the model
• A machine with a capable GPU is recommended for best performance.

Installation

Install the necessary dependencies with:

pip install llama-cpp-python huggingface_hub

Load the model with:

import logging
import os
import time  # Make sure to import time for measuring durations
from huggingface_hub import hf_hub_download
from llama_cpp import Llama

# Set up logging
logging.basicConfig(level=logging.INFO)  # Set to INFO to reduce overhead
logger = logging.getLogger()

# Download the GGUF model
model_name = "Gargaz/GPT-2-gguf"
model_file = "llama3.1-Q4_K_M.gguf"
model_path = hf_hub_download(model_name, filename=model_file)

# Instantiate the model from the downloaded file
llm = Llama(
    model_path=model_path,
    n_ctx=16000,  # Context length to use
    n_threads=64,  # Number of CPU threads
    n_gpu_layers=32  # Number of model layers to offload to GPU
)

# System instructions for the AI
system_instructions = (
    "You are a friendly conversational AI designed to respond clearly and concisely to user inquiries. "
    "Stay on topic by answering questions directly, use a warm tone and acknowledge gratitude, ask for "
    "clarification on vague questions, provide brief and helpful recommendations, and encourage users "
    "to ask more questions to keep the conversation flowing."
    "don't speak alone always respond just to the user input"
)

def chat():
    """Start a chat session with the model."""
    print("Introduceti 'exit' pentru a iesi din chat.")
    while True:
        user_input = input("Tu: ")
        if user_input.lower() == 'exit':
            print("Iesire din chat.")
            break
        
        # Prepare the prompt
        full_prompt = f"{system_instructions}\nUser: {user_input}\nAI:"

        # Limit AI responses to a maximum of 500 tokens for faster responses
        generation_kwargs = {
            "max_tokens": 40,  # Reduced max tokens for faster inference
            "stop": ["AI:"],  # Change the stop token to ensure clarity
            "echo": False,
        }

        try:
            # Start measuring time for response generation
            load_start_time = time.time()
            res = llm(full_prompt, **generation_kwargs)  # Res is a dictionary
            load_time = (time.time() - load_start_time) * 1000  # Convert to ms

            # Log load time
            load_message = f"llama_perf_context_print: load time = {load_time:.2f} ms"
            logger.info(load_message)

            generated_text = res["choices"][0]["text"].strip()
            print(f"AI: {generated_text}")

            # Log prompt evaluation time and other metrics
            num_tokens = len(full_prompt.split())
            eval_message = f"llama_perf_context_print: prompt eval time = {load_time:.2f} ms / {num_tokens} tokens"
            logger.info(eval_message)

        except Exception as e:
            logger.error(f"Error generating response: {e}")
            print("Eroare la generarea răspunsului.")

if __name__ == "__main__":
    chat()