commit 7ca6ae3dd421ae58f1a8237253949402a4d64848
Author: Egutierrez <egutierrez@dead.dd>
Date:   Mon Nov 3 00:20:04 2025 +0100

    iniciado con modelo qwen

diff --git a/.gitignore b/.gitignore
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+# Python-generated files
+__pycache__/
+*.py[oc]
+build/
+dist/
+wheels/
+*.egg-info
+
+# Virtual environments
+.venv
+
+models
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diff --git a/.python-version b/.python-version
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+3.13
diff --git a/README.md b/README.md
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+# Llama.cpp OpenAI Compatible API
+
+API compatible con OpenAI para servir modelos GGUF usando llama.cpp.
+
+## Caracter�sticas
+
+- =� API compatible con OpenAI Chat Completions
+- > Soporte para modelos GGUF
+- � Construido con FastAPI y llama-cpp-python
+- =� Documentaci�n autom�tica con Swagger
+- =' Configuraci�n flexible de par�metros
+
+## Instalaci�n
+
+### Instalaci�n b�sica (solo CPU):
+```bash
+pip install -e .[cpu]
+```
+
+### Instalaci�n con soporte CUDA (recomendado para GPU):
+```bash
+pip install -e .[cuda]
+```
+
+### Instalaci�n manual con CUDA:
+```bash
+# Opci�n 1: Usando CMAKE
+CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python[cublas] --force-reinstall --no-cache-dir
+
+# Opci�n 2: Usando variables de entorno
+export CMAKE_ARGS="-DLLAMA_CUBLAS=on"
+pip install llama-cpp-python[cublas] --force-reinstall --no-cache-dir
+
+# Luego instalar el resto de dependencias
+pip install -e .
+```
+
+### Verificar instalaci�n de CUDA:
+```bash
+nvidia-smi  # Verificar que CUDA est� disponible
+python -c "import llama_cpp; print('llama-cpp-python instalado correctamente')"
+```
+
+2. Descargar un modelo GGUF (ejemplo):
+```bash
+# Ejemplo con un modelo peque�o
+wget https://huggingface.co/microsoft/DialoGPT-medium/resolve/main/pytorch_model.bin
+```
+
+## Uso
+
+### Iniciar el servidor
+
+```bash
+python main.py --model-path /ruta/a/tu/modelo.gguf
+```
+
+### Opciones disponibles
+
+```bash
+python main.py --help
+```
+
+- `--model-path` / `-m`: Ruta al archivo del modelo GGUF (requerido)
+- `--host`: Host del servidor (default: 0.0.0.0)
+- `--port`: Puerto del servidor (default: 8000)
+- `--n-ctx`: Tama�o del contexto (default: 4096)
+- `--n-batch`: Tama�o del batch (default: 512)
+- `--n-threads`: N�mero de threads (default: auto)
+- `--n-gpu-layers`: N�mero de capas a cargar en GPU (-1 = todas, 0 = ninguna, default: -1)
+- `--main-gpu`: GPU principal a usar (default: 0)
+- `--split-mode`: Modo de divisi�n entre GPUs (default: 1)
+
+### Ejemplo de uso
+
+```bash
+# Ejemplo recomendado con GPU (CUDA) - M�ximo rendimiento
+uv run python main.py --model-path ./models/Qwen3-4B-Instruct-2507-Q4_K_M.gguf --port 8000 --n-ctx 4096 --n-gpu-layers -1
+
+# Ejemplo b�sico con CPU solamente
+uv run python main.py --model-path ./models/Qwen3-4B-Instruct-2507-Q4_K_M.gguf --port 8000 --n-ctx 4096 --n-gpu-layers 0
+
+# Ejemplo con GPU - Solo algunas capas en GPU (para modelos grandes o GPUs con poca memoria)
+uv run python main.py --model-path ./models/Qwen3-4B-Instruct-2507-Q4_K_M.gguf --port 8000 --n-ctx 4096 --n-gpu-layers 20
+
+# Ejemplo con configuraci�n personalizada para GPU m�ltiples
+uv run python main.py --model-path ./models/Qwen3-4B-Instruct-2507-Q4_K_M.gguf --port 8000 --n-ctx 4096 --n-gpu-layers -1 --main-gpu 0 --split-mode 1
+```
+
+## API Endpoints
+
+### GET `/v1/models`
+Lista los modelos disponibles
+
+### POST `/v1/chat/completions`
+Crea una completion de chat
+
+#### Ejemplo con curl:
+```bash
+curl -X POST http://localhost:8000/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "llama",
+    "messages": [
+      {"role": "user", "content": "Hola, �c�mo est�s?"}
+    ],
+    "max_tokens": 100,
+    "temperature": 0.7
+  }'
+```
+
+#### Ejemplo con Python (usando openai):
+```python
+import openai
+
+# Configurar el cliente para usar tu servidor local
+openai.api_base = "http://localhost:8000/v1"
+openai.api_key = "no-key-needed"  # No se requiere clave real
+
+response = openai.ChatCompletion.create(
+    model="llama",
+    messages=[
+        {"role": "user", "content": "Expl�came qu� es la inteligencia artificial"}
+    ],
+    max_tokens=200,
+    temperature=0.7
+)
+
+print(response.choices[0].message.content)
+```
+
+## Documentaci�n de la API
+
+Una vez que el servidor est� ejecut�ndose, puedes acceder a la documentaci�n interactiva en:
+- Swagger UI: http://localhost:8000/docs
+- ReDoc: http://localhost:8000/redoc
+
+## Par�metros de la API
+
+### ChatCompletionRequest
+
+- `model`: Nombre del modelo (string)
+- `messages`: Lista de mensajes del chat
+- `max_tokens`: M�ximo n�mero de tokens a generar (default: 2048)
+- `temperature`: Temperatura para el muestreo (default: 0.7)
+- `top_p`: Top-p para el muestreo (default: 0.9)
+- `stream`: Respuesta en streaming (no implementado a�n)
+
+### Formato de mensajes
+
+```json
+{
+  "role": "user|assistant|system",
+  "content": "Contenido del mensaje"
+}
+```
+
+## Notas importantes
+
+- La API estima el conteo de tokens dividiendo la longitud del texto por 4
+- Los modelos GGUF deben ser compatibles con llama.cpp
+- El formateo de prompts puede variar seg�n el modelo usado
+- Para mejor rendimiento, ajusta `n_ctx`, `n_batch` y `n_threads` seg�n tu hardware
+
+## Troubleshooting
+
+### Error: "El archivo del modelo no existe"
+- Verifica que la ruta al modelo GGUF sea correcta
+- Aseg�rate de que el archivo tenga extensi�n .gguf
+
+### Error de memoria
+- Reduce `n_ctx` para modelos grandes
+- Usa un modelo m�s peque�o
+- Aumenta la memoria disponible
+
+### Respuestas lentas
+- Aumenta `n_threads` si tienes m�s CPU cores disponibles
+- Ajusta `n_batch` para tu hardware
+- Considera usar un modelo m�s peque�o
+- **Para GPU**: Usa `--n-gpu-layers -1` para cargar todas las capas en GPU
+- **Para GPU**: Verifica que CUDA est� instalado correctamente con `nvidia-smi`
+
+### Problemas con GPU/CUDA
+- **Error "CUDA not found"**: Reinstala llama-cpp-python con soporte CUDA:
+  ```bash
+  CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python[cublas] --force-reinstall --no-cache-dir
+  ```
+- **Memoria GPU insuficiente**: Reduce `--n-gpu-layers` a un n�mero menor (ej: 20, 10)
+- **GPU no detectada**: Verifica que `nvidia-smi` funcione y muestre tu GPU
+- **Rendimiento lento con GPU**: Aseg�rate de usar `--n-gpu-layers -1` para cargar todas las capas
+- **Error de compatibilidad**: Verifica que tu GPU tenga compute capability >= 3.5
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diff --git a/cuda-keyring_1.1-1_all.deb b/cuda-keyring_1.1-1_all.deb
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diff --git a/docs/WSL2_CUDA_Installation.md b/docs/WSL2_CUDA_Installation.md
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+# Instalación de CUDA y llama-cpp-python en WSL2
+
+Esta guía explica cómo instalar CUDA Toolkit y llama-cpp-python con soporte GPU en WSL2 (Windows Subsystem for Linux 2).
+
+## Prerrequisitos
+
+- WSL2 instalado y funcionando
+- GPU NVIDIA compatible con CUDA
+- Drivers de NVIDIA instalados en Windows (versión 452.39 o superior)
+
+## Verificación inicial
+
+### 1. Verificar GPU disponible
+
+```bash
+nvidia-smi
+```
+
+Esto debería mostrar tu GPU y la versión de CUDA disponible. Por ejemplo:
+```
++-----------------------------------------------------------------------------------------+
+| NVIDIA-SMI 580.76.04              Driver Version: 580.97         CUDA Version: 13.0     |
++-----------------------------------------+------------------------+----------------------+
+| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
+|   0  NVIDIA GeForce RTX 3070        On  |   00000000:01:00.0  On |                  N/A |
+```
+
+### 2. Verificar distribución de Linux
+
+```bash
+cat /etc/os-release
+```
+
+## Instalación de CUDA Toolkit
+
+### 1. Descargar e instalar el keyring de CUDA
+
+```bash
+# Descargar keyring oficial de NVIDIA
+wget https://developer.download.nvidia.com/compute/cuda/repos/wsl-ubuntu/x86_64/cuda-keyring_1.1-1_all.deb
+
+# Instalar keyring
+sudo dpkg -i cuda-keyring_1.1-1_all.deb
+```
+
+### 2. Actualizar repositorios e instalar CUDA
+
+```bash
+# Actualizar lista de paquetes
+sudo apt update
+
+# Instalar CUDA Toolkit (versión 12.0 recomendada para compatibilidad)
+sudo apt install cuda-toolkit-12-0
+
+# Opcional: Instalar paquetes adicionales
+sudo apt install cuda cuda-toolkit cuda-tools
+```
+
+### 3. Verificar instalación de CUDA
+
+```bash
+# Verificar que nvcc esté disponible
+/usr/local/cuda/bin/nvcc --version
+
+# Debería mostrar algo como:
+# nvcc: NVIDIA (R) Cuda compiler driver
+# Copyright (c) 2005-2023 NVIDIA Corporation
+# Built on Fri_Jan__6_16:45:21_PST_2023
+# Cuda compilation tools, release 12.0, V12.0.140
+```
+
+## Instalación de compiladores necesarios
+
+### 1. Instalar GCC-12 (requerido para WSL2)
+
+```bash
+sudo apt install gcc-12 g++-12
+```
+
+### 2. Verificar versiones de compiladores
+
+```bash
+gcc-12 --version
+g++-12 --version
+```
+
+## Configuración de variables de entorno
+
+### 1. Agregar variables al .bashrc
+
+```bash
+# Editar .bashrc
+nano ~/.bashrc
+
+# Agregar al final del archivo:
+# CUDA Configuration for llama-cpp-python
+export PATH=/usr/local/cuda/bin:$PATH
+export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH
+export CC=/usr/bin/gcc-12
+export CXX=/usr/bin/g++-12
+export CUDAHOSTCXX=/usr/bin/g++-12
+export CMAKE_ARGS="-DGGML_CUDA=on -DCMAKE_CUDA_ARCHITECTURES=all-major -DCMAKE_CUDA_COMPILER=/usr/local/cuda/bin/nvcc"
+export FORCE_CMAKE=1
+```
+
+### 2. Recargar configuración
+
+```bash
+source ~/.bashrc
+```
+
+### 3. Verificar variables
+
+```bash
+echo $CMAKE_ARGS
+echo $PATH | grep cuda
+nvcc --version
+```
+
+## Instalación de llama-cpp-python con CUDA
+
+### Método 1: Con uv (recomendado si usas uv)
+
+```bash
+# Configurar variables en la sesión actual
+export CC=/usr/bin/gcc-12
+export CXX=/usr/bin/g++-12
+export CUDAHOSTCXX=/usr/bin/g++-12
+export CMAKE_ARGS="-DGGML_CUDA=on -DCMAKE_CUDA_ARCHITECTURES=all-major"
+export FORCE_CMAKE=1
+
+# Desinstalar versión anterior (si existe)
+uv pip uninstall llama-cpp-python
+
+# Instalar con soporte CUDA
+CC=gcc-12 CXX=g++-12 uv pip install llama-cpp-python --no-cache --force-reinstall --verbose
+```
+
+### Método 2: Con pip estándar
+
+```bash
+# Configurar variables
+export CC=/usr/bin/gcc-12
+export CXX=/usr/bin/g++-12
+export CUDAHOSTCXX=/usr/bin/g++-12
+export CMAKE_ARGS="-DGGML_CUDA=on -DCMAKE_CUDA_ARCHITECTURES=all-major"
+export FORCE_CMAKE=1
+
+# Instalar
+pip install llama-cpp-python --no-cache-dir --force-reinstall --upgrade --verbose
+```
+
+### Método 3: Configuración específica para WSL2 (más confiable)
+
+```bash
+CMAKE_ARGS="-DGGML_CUDA=on -DCMAKE_CUDA_ARCHITECTURES=all-major" \
+CC=gcc-12 \
+CXX=g++-12 \
+CUDAHOSTCXX=/usr/bin/g++-12 \
+FORCE_CMAKE=1 \
+pip install llama-cpp-python --no-cache-dir --force-reinstall --upgrade
+```
+
+## Verificación de la instalación
+
+### 1. Verificar que llama-cpp-python tiene soporte CUDA
+
+```bash
+python -c "
+import llama_cpp
+print('llama-cpp-python version:', llama_cpp.__version__)
+print('Build info:', llama_cpp.llama_print_system_info())
+"
+```
+
+**Sin CUDA** verás solo:
+```
+CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | ...
+```
+
+**Con CUDA** deberías ver:
+```
+CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | ...
+GPU : CUDA = 1 | ...
+```
+
+### 2. Probar carga de modelo con GPU
+
+```python
+from llama_cpp import Llama
+
+# Intentar cargar modelo con GPU
+llama = Llama(
+    model_path="./models/tu-modelo.gguf",
+    n_ctx=512,
+    n_gpu_layers=-1,  # Todas las capas en GPU
+    verbose=True
+)
+
+# Si funciona, deberías ver en los logs:
+# "load_tensors: layer X assigned to device GPU"
+```
+
+## Uso con la API
+
+### Comandos de ejemplo
+
+```bash
+# Máximo rendimiento (todas las capas en GPU)
+python main.py --model-path ./models/modelo.gguf --port 8000 --n-ctx 4096 --n-gpu-layers -1
+
+# Solo algunas capas en GPU (para GPUs con poca memoria)
+python main.py --model-path ./models/modelo.gguf --port 8000 --n-ctx 4096 --n-gpu-layers 20
+
+# Solo CPU
+python main.py --model-path ./models/modelo.gguf --port 8000 --n-ctx 4096 --n-gpu-layers 0
+```
+
+## Troubleshooting
+
+### Problema: "CUDA not found" durante compilación
+
+**Solución:**
+1. Verificar que nvcc esté en el PATH: `which nvcc`
+2. Reinstalar CUDA Toolkit si es necesario
+3. Verificar variables de entorno: `echo $CMAKE_ARGS`
+
+### Problema: Capas se asignan a CPU en lugar de GPU
+
+**Posibles causas:**
+1. llama-cpp-python compilado sin soporte CUDA
+2. Memoria GPU insuficiente
+3. Modelo no compatible
+
+**Soluciones:**
+1. Recompilar con variables CUDA correctas
+2. Reducir `--n-gpu-layers` a un número menor
+3. Verificar compatibilidad del modelo
+
+### Problema: Error "libcudart.so not found"
+
+**Solución:**
+```bash
+# Agregar librerías CUDA al path
+export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH
+sudo ldconfig
+```
+
+### Problema: Compilación muy lenta o falla
+
+**Soluciones:**
+1. Usar `FORCE_CMAKE=1` para forzar recompilación
+2. Limpiar caché: `--no-cache-dir`
+3. Verificar espacio en disco disponible
+4. Instalar ccache para compilaciones más rápidas:
+   ```bash
+   sudo apt install ccache
+   ```
+
+### Problema: Error de compute capability
+
+**Solución:**
+Verificar que tu GPU sea compatible:
+```bash
+# Para RTX 30xx series: compute capability 8.6
+# Para RTX 40xx series: compute capability 8.9
+# Mínimo requerido: 3.5
+```
+
+## Verificación final
+
+### Script de prueba completo
+
+```python
+#!/usr/bin/env python3
+"""Script de verificación CUDA para llama-cpp-python"""
+
+import llama_cpp
+from pathlib import Path
+
+def test_cuda():
+    print("=== Verificación CUDA ===")
+    print(f"llama-cpp-python version: {llama_cpp.__version__}")
+    print(f"Build info: {llama_cpp.llama_print_system_info()}")
+
+    # Verificar si hay referencias a CUDA/GPU
+    build_info = str(llama_cpp.llama_print_system_info())
+    if 'CUDA' in build_info or 'GPU' in build_info:
+        print("✅ CUDA support detected!")
+        return True
+    else:
+        print("❌ No CUDA support found")
+        return False
+
+if __name__ == "__main__":
+    has_cuda = test_cuda()
+    if has_cuda:
+        print("\n🎉 ¡Configuración CUDA exitosa!")
+    else:
+        print("\n⚠️ Recompilación necesaria con soporte CUDA")
+```
+
+## Referencias
+
+- [NVIDIA CUDA WSL Guide](https://docs.nvidia.com/cuda/wsl-user-guide/index.html)
+- [llama-cpp-python GitHub](https://github.com/abetlen/llama-cpp-python)
+- [WSL2 CUDA Installation Gist](https://gist.github.com/chrisbu/687cafefb87e0ddb3cb2d73301a9c64d)
+
+## Notas importantes
+
+1. **WSL2 específico**: Usar GCC-12 es crucial para WSL2
+2. **Variables de entorno**: Deben estar configuradas antes de la compilación
+3. **Paciencia**: La compilación puede tardar 10-30 minutos
+4. **Memoria**: Asegúrate de tener suficiente RAM y espacio en disco
+5. **Versiones**: CUDA 12.0 es la más estable para WSL2 con llama.cpp
+
+## Comandos útiles de diagnóstico
+
+```bash
+# Verificar GPU
+nvidia-smi
+
+# Verificar CUDA
+nvcc --version
+
+# Verificar compiladores
+gcc-12 --version
+g++-12 --version
+
+# Verificar variables
+env | grep -E "(CUDA|CMAKE|CC|CXX)"
+
+# Verificar librerías CUDA
+ldd /usr/local/cuda/lib64/libcudart.so
+
+# Verificar llama-cpp-python
+python -c "import llama_cpp; print(llama_cpp.llama_print_system_info())"
+```
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diff --git a/main.py b/main.py
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+import sys
+import time
+import uuid
+from typing import List, Optional
+from dataclasses import dataclass
+from pathlib import Path
+
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel, Field
+import uvicorn
+from llama_cpp import Llama
+
+
+# Modelos de datos para la API compatible con OpenAI
+class ChatCompletionMessage(BaseModel):
+    role: str = Field(..., description="El rol del mensaje: 'system', 'user', o 'assistant'")
+    content: str = Field(..., description="El contenido del mensaje")
+
+
+class ChatCompletionRequest(BaseModel):
+    model: str = Field(default="llama", description="Nombre del modelo")
+    messages: List[ChatCompletionMessage] = Field(..., description="Lista de mensajes")
+    max_tokens: Optional[int] = Field(default=2048, description="Máximo número de tokens a generar")
+    temperature: Optional[float] = Field(default=0.7, description="Temperatura para el muestreo")
+    top_p: Optional[float] = Field(default=0.9, description="Top-p para el muestreo")
+    stream: Optional[bool] = Field(default=False, description="Si devolver respuesta en streaming")
+
+
+class ChatCompletionChoice(BaseModel):
+    index: int
+    message: ChatCompletionMessage
+    finish_reason: str
+
+
+class ChatCompletionUsage(BaseModel):
+    prompt_tokens: int
+    completion_tokens: int
+    total_tokens: int
+
+
+class ChatCompletionResponse(BaseModel):
+    id: str
+    object: str = "chat.completion"
+    created: int
+    model: str
+    choices: List[ChatCompletionChoice]
+    usage: ChatCompletionUsage
+
+
+class ModelInfo(BaseModel):
+    id: str
+    object: str = "model"
+    created: int
+    owned_by: str = "llama-cpp"
+
+
+class ModelsResponse(BaseModel):
+    object: str = "list"
+    data: List[ModelInfo]
+
+
+@dataclass
+class LlamaAPI:
+    """Clase para manejar la API de Llama"""
+
+    def __init__(self, model_path: str, **kwargs):
+        if not Path(model_path).exists():
+            raise FileNotFoundError(f"El archivo del modelo no existe: {model_path}")
+
+        print(f"Cargando modelo desde: {model_path}")
+
+        # Parámetros por defecto para llama.cpp
+        default_params = {
+            "n_ctx": 4096,
+            "n_batch": 512,
+            "n_threads": None,  # Usar todos los threads disponibles
+            "verbose": False,
+            "n_gpu_layers": -1,  # Usar todas las capas en GPU (-1 = auto)
+            "main_gpu": 0,      # GPU principal a usar
+            "split_mode": 1,     # Modo de división entre GPUs
+        }
+
+        # Actualizar con parámetros personalizados
+        default_params.update(kwargs)
+
+        try:
+            self.llama = Llama(model_path=model_path, **default_params)
+            print("Modelo cargado exitosamente!")
+            print(f"GPU layers: {default_params.get('n_gpu_layers', 'N/A')}")
+            print(f"Main GPU: {default_params.get('main_gpu', 'N/A')}")
+        except Exception as e:
+            print(f"Error al cargar el modelo: {e}")
+            print("Nota: Si tienes problemas con CUDA, intenta instalar: pip install llama-cpp-python[cublas]")
+            raise
+
+    def generate_chat_completion(self, messages: List[ChatCompletionMessage],
+                               max_tokens: int = 2048,
+                               temperature: float = 0.7,
+                               top_p: float = 0.9) -> str:
+        """Genera una respuesta de chat usando el modelo"""
+
+        # Formatear los mensajes en un prompt
+        prompt = self._format_messages_to_prompt(messages)
+
+        try:
+            # Generar respuesta usando llama.cpp
+            output = self.llama(
+                prompt,
+                max_tokens=max_tokens,
+                temperature=temperature,
+                top_p=top_p,
+                echo=False,
+                stop=["</s>", "<|im_end|>", "<|endoftext|>"]
+            )
+
+            return output['choices'][0]['text'].strip()
+
+        except Exception as e:
+            print(f"Error durante la generación: {e}")
+            raise HTTPException(status_code=500, detail=f"Error en la generación: {str(e)}")
+
+    def _format_messages_to_prompt(self, messages: List[ChatCompletionMessage]) -> str:
+        """Convierte la lista de mensajes en un prompt formateado"""
+
+        prompt_parts = []
+
+        for message in messages:
+            if message.role == "system":
+                prompt_parts.append(f"Sistema: {message.content}")
+            elif message.role == "user":
+                prompt_parts.append(f"Usuario: {message.content}")
+            elif message.role == "assistant":
+                prompt_parts.append(f"Asistente: {message.content}")
+
+        prompt_parts.append("Asistente:")
+        return "\n".join(prompt_parts)
+
+
+# Instancia global de la API
+llama_api = None
+
+# Crear la aplicación FastAPI
+app = FastAPI(
+    title="Llama.cpp OpenAI Compatible API",
+    description="API compatible con OpenAI para modelos GGUF usando llama.cpp",
+    version="1.0.0"
+)
+
+# Configurar CORS
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+@app.get("/")
+async def root():
+    """Endpoint raíz"""
+    return {"message": "Llama.cpp OpenAI Compatible API", "status": "running"}
+
+
+@app.get("/v1/models", response_model=ModelsResponse)
+async def list_models():
+    """Lista los modelos disponibles"""
+    return ModelsResponse(
+        data=[
+            ModelInfo(
+                id="llama",
+                created=int(time.time()),
+                owned_by="llama-cpp"
+            )
+        ]
+    )
+
+
+@app.post("/v1/chat/completions", response_model=ChatCompletionResponse)
+async def create_chat_completion(request: ChatCompletionRequest):
+    """Crea una completion de chat"""
+
+    if llama_api is None:
+        raise HTTPException(status_code=503, detail="Modelo no cargado")
+
+    if not request.messages:
+        raise HTTPException(status_code=400, detail="Se requiere al menos un mensaje")
+
+    try:
+        # Generar respuesta
+        response_text = llama_api.generate_chat_completion(
+            messages=request.messages,
+            max_tokens=request.max_tokens or 2048,
+            temperature=request.temperature or 0.7,
+            top_p=request.top_p or 0.9
+        )
+
+        # Crear respuesta en formato OpenAI
+        completion_id = f"chatcmpl-{uuid.uuid4().hex[:29]}"
+
+        response = ChatCompletionResponse(
+            id=completion_id,
+            created=int(time.time()),
+            model=request.model,
+            choices=[
+                ChatCompletionChoice(
+                    index=0,
+                    message=ChatCompletionMessage(
+                        role="assistant",
+                        content=response_text
+                    ),
+                    finish_reason="stop"
+                )
+            ],
+            usage=ChatCompletionUsage(
+                prompt_tokens=len(str(request.messages)) // 4,  # Estimación aproximada
+                completion_tokens=len(response_text) // 4,      # Estimación aproximada
+                total_tokens=(len(str(request.messages)) + len(response_text)) // 4
+            )
+        )
+
+        return response
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error procesando la solicitud: {str(e)}")
+
+
+def main():
+    """Función principal para iniciar el servidor"""
+    import argparse
+
+    parser = argparse.ArgumentParser(description="Servidor API compatible con OpenAI usando llama.cpp")
+    parser.add_argument("--model-path", "-m", type=str, required=True,
+                       help="Ruta al archivo del modelo GGUF")
+    parser.add_argument("--host", type=str, default="0.0.0.0",
+                       help="Host para el servidor (default: 0.0.0.0)")
+    parser.add_argument("--port", type=int, default=8000,
+                       help="Puerto para el servidor (default: 8000)")
+    parser.add_argument("--n-ctx", type=int, default=4096,
+                       help="Tamaño del contexto (default: 4096)")
+    parser.add_argument("--n-batch", type=int, default=512,
+                       help="Tamaño del batch (default: 512)")
+    parser.add_argument("--n-threads", type=int, default=None,
+                       help="Número de threads (default: auto)")
+    parser.add_argument("--n-gpu-layers", type=int, default=-1,
+                       help="Número de capas a cargar en GPU (-1 = todas, 0 = ninguna, default: -1)")
+    parser.add_argument("--main-gpu", type=int, default=0,
+                       help="GPU principal a usar (default: 0)")
+    parser.add_argument("--split-mode", type=int, default=1,
+                       help="Modo de división entre GPUs (default: 1)")
+
+    args = parser.parse_args()
+
+    # Validar que el archivo del modelo existe
+    if not Path(args.model_path).exists():
+        print(f"Error: El archivo del modelo no existe: {args.model_path}")
+        sys.exit(1)
+
+    # Configurar parámetros del modelo
+    model_params = {
+        "n_ctx": args.n_ctx,
+        "n_batch": args.n_batch,
+        "n_gpu_layers": args.n_gpu_layers,
+        "main_gpu": args.main_gpu,
+        "split_mode": args.split_mode,
+    }
+
+    if args.n_threads:
+        model_params["n_threads"] = args.n_threads
+
+    try:
+        # Inicializar la API de Llama
+        global llama_api
+        llama_api = LlamaAPI(args.model_path, **model_params)
+
+        print(f"\n🚀 Servidor iniciado en http://{args.host}:{args.port}")
+        print(f"📚 Documentación disponible en http://{args.host}:{args.port}/docs")
+        print(f"🤖 Modelo cargado desde: {args.model_path}")
+        print(f"🎮 GPU layers: {args.n_gpu_layers} (usa -1 para todas las capas)")
+        print(f"🎯 Main GPU: {args.main_gpu}")
+        print("\n💡 Ejemplo de uso con curl:")
+        print(f"""
+curl -X POST http://{args.host}:{args.port}/v1/chat/completions \\
+  -H "Content-Type: application/json" \\
+  -d '{{
+    "model": "llama",
+    "messages": [
+      {{"role": "user", "content": "Hola, ¿cómo estás?"}}
+    ],
+    "max_tokens": 100,
+    "temperature": 0.7
+  }}'
+        """)
+
+        # Iniciar el servidor
+        uvicorn.run(
+            app,
+            host=args.host,
+            port=args.port,
+            log_level="info"
+        )
+
+    except KeyboardInterrupt:
+        print("\n👋 Servidor detenido por el usuario")
+    except Exception as e:
+        print(f"❌ Error al iniciar el servidor: {e}")
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..6189c8e
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,20 @@
+[project]
+name = "llama-models"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.13"
+dependencies = [
+    "fastapi>=0.104.0",
+    "uvicorn[standard]>=0.24.0",
+    "pydantic>=2.0.0",
+    "python-multipart>=0.0.6"
+]
+
+[project.optional-dependencies]
+cuda = [
+    "llama-cpp-python[cublas]>=0.2.20"
+]
+cpu = [
+    "llama-cpp-python>=0.2.20"
+]
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000..d1c71c9
--- /dev/null
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