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""" |
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Script de utilidades para GPT Local |
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Manejo de modelos, limpieza, actualización |
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""" |
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import sys |
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import os |
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import shutil |
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import subprocess |
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from pathlib import Path |
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def check_environment(): |
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"""Verificar el entorno y dependencias""" |
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print("🔍 Verificando entorno...") |
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try: |
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import torch |
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import transformers |
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import gradio as gr |
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print(f"✅ PyTorch: {torch.__version__}") |
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print(f"✅ Transformers: {transformers.__version__}") |
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print(f"✅ Gradio: {gr.__version__}") |
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print(f"✅ CUDA disponible: {torch.cuda.is_available()}") |
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print(f"✅ MPS disponible: {torch.backends.mps.is_available() if hasattr(torch.backends, 'mps') else 'N/A'}") |
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return True |
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except ImportError as e: |
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print(f"❌ Error de importación: {e}") |
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return False |
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def clean_cache(): |
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"""Limpiar cache de modelos""" |
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print("🧹 Limpiando cache...") |
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cache_dirs = [ |
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Path.home() / ".cache" / "huggingface", |
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Path("models_cache"), |
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Path("__pycache__"), |
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] |
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for cache_dir in cache_dirs: |
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if cache_dir.exists(): |
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size = sum(f.stat().st_size for f in cache_dir.rglob('*') if f.is_file()) |
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print(f"📁 {cache_dir}: {size / (1024*1024*1024):.2f} GB") |
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response = input(f"¿Eliminar {cache_dir}? (y/N): ") |
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if response.lower() == 'y': |
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shutil.rmtree(cache_dir) |
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print(f"✅ {cache_dir} eliminado") |
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def install_dependencies(): |
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"""Instalar dependencias""" |
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print("📦 Instalando dependencias...") |
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subprocess.run([sys.executable, "-m", "pip", "install", "-r", "requirements.txt"]) |
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def update_dependencies(): |
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"""Actualizar dependencias""" |
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print("⬆️ Actualizando dependencias...") |
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subprocess.run([sys.executable, "-m", "pip", "install", "--upgrade", "-r", "requirements.txt"]) |
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def list_models(): |
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"""Listar modelos disponibles localmente""" |
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print("📋 Modelos disponibles:") |
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cache_dir = Path.home() / ".cache" / "huggingface" / "hub" |
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if cache_dir.exists(): |
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models = [d.name for d in cache_dir.iterdir() if d.is_dir()] |
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for model in models: |
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print(f" - {model}") |
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else: |
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print(" No hay modelos en cache") |
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def download_model(model_name): |
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"""Descargar un modelo específico""" |
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print(f"⬇️ Descargando modelo: {model_name}") |
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try: |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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print("Descargando tokenizer...") |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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print("Descargando modelo...") |
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model = AutoModelForCausalLM.from_pretrained(model_name) |
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print(f"✅ Modelo {model_name} descargado exitosamente") |
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except Exception as e: |
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print(f"❌ Error al descargar: {e}") |
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def show_usage(): |
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"""Mostrar información de uso""" |
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print(""" |
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🤖 GPT Local - Utilidades |
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Comandos disponibles: |
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check - Verificar entorno |
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clean - Limpiar cache |
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install - Instalar dependencias |
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update - Actualizar dependencias |
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models - Listar modelos |
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download - Descargar modelo específico |
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Ejemplos: |
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python3 utils.py check |
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python3 utils.py download microsoft/DialoGPT-small |
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python3 utils.py clean |
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""") |
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def main(): |
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if len(sys.argv) < 2: |
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show_usage() |
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return |
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command = sys.argv[1] |
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if command == "check": |
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check_environment() |
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elif command == "clean": |
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clean_cache() |
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elif command == "install": |
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install_dependencies() |
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elif command == "update": |
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update_dependencies() |
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elif command == "models": |
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list_models() |
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elif command == "download": |
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if len(sys.argv) < 3: |
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print("❌ Especifica el nombre del modelo") |
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return |
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download_model(sys.argv[2]) |
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else: |
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print(f"❌ Comando desconocido: {command}") |
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show_usage() |
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if __name__ == "__main__": |
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main() |
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