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from fastapi import FastAPI, HTTPException, Request
import uvicorn
import requests
import os
import io
import time
import asyncio
from typing import List, Dict, Any
from tqdm import tqdm
from llama_cpp import Llama
app = FastAPI()
# Configuración de los modelos
model_configs = [
{"repo_id": "Ffftdtd5dtft/gpt2-xl-Q2_K-GGUF", "filename": "gpt2-xl-q2_k.gguf", "name": "GPT-2 XL"},
{"repo_id": "Ffftdtd5dtft/Meta-Llama-3.1-8B-Instruct-Q2_K-GGUF", "filename": "meta-llama-3.1-8b-instruct-q2_k.gguf", "name": "Meta Llama 3.1-8B Instruct"},
{"repo_id": "Ffftdtd5dtft/gemma-2-9b-it-Q2_K-GGUF", "filename": "gemma-2-9b-it-q2_k.gguf", "name": "Gemma 2-9B IT"},
{"repo_id": "Ffftdtd5dtft/gemma-2-27b-Q2_K-GGUF", "filename": "gemma-2-27b-q2_k.gguf", "name": "Gemma 2-27B"},
{"repo_id": "Ffftdtd5dtft/Phi-3-mini-128k-instruct-Q2_K-GGUF", "filename": "phi-3-mini-128k-instruct-q2_k.gguf", "name": "Phi-3 Mini 128K Instruct"},
{"repo_id": "Ffftdtd5dtft/Meta-Llama-3.1-8B-Q2_K-GGUF", "filename": "meta-llama-3.1-8b-q2_k.gguf", "name": "Meta Llama 3.1-8B"},
{"repo_id": "Ffftdtd5dtft/Qwen2-7B-Instruct-Q2_K-GGUF", "filename": "qwen2-7b-instruct-q2_k.gguf", "name": "Qwen2 7B Instruct"},
{"repo_id": "Ffftdtd5dtft/starcoder2-3b-Q2_K-GGUF", "filename": "starcoder2-3b-q2_k.gguf", "name": "Starcoder2 3B"},
{"repo_id": "Ffftdtd5dtft/Qwen2-1.5B-Instruct-Q2_K-GGUF", "filename": "qwen2-1.5b-instruct-q2_k.gguf", "name": "Qwen2 1.5B Instruct"},
{"repo_id": "Ffftdtd5dtft/starcoder2-15b-Q2_K-GGUF", "filename": "starcoder2-15b-q2_k.gguf", "name": "Starcoder2 15B"},
{"repo_id": "Ffftdtd5dtft/gemma-2-2b-it-Q2_K-GGUF", "filename": "gemma-2-2b-it-q2_k.gguf", "name": "Gemma 2-2B IT"},
{"repo_id": "Ffftdtd5dtft/sarvam-2b-v0.5-Q2_K-GGUF", "filename": "sarvam-2b-v0.5-q2_k.gguf", "name": "Sarvam 2B v0.5"},
{"repo_id": "Ffftdtd5dtft/WizardLM-13B-Uncensored-Q2_K-GGUF", "filename": "wizardlm-13b-uncensored-q2_k.gguf", "name": "WizardLM 13B Uncensored"},
{"repo_id": "Ffftdtd5dtft/Qwen2-Math-72B-Instruct-Q2_K-GGUF", "filename": "qwen2-math-72b-instruct-q2_k.gguf", "name": "Qwen2 Math 72B Instruct"},
{"repo_id": "Ffftdtd5dtft/WizardLM-7B-Uncensored-Q2_K-GGUF", "filename": "wizardlm-7b-uncensored-q2_k.gguf", "name": "WizardLM 7B Uncensored"},
{"repo_id": "Ffftdtd5dtft/Qwen2-Math-7B-Instruct-Q2_K-GGUF", "filename": "qwen2-math-7b-instruct-q2_k.gguf", "name": "Qwen2 Math 7B Instruct"}
]
class ModelManager:
def __init__(self):
self.models = {}
self.model_parts = {}
self.load_lock = asyncio.Lock()
self.index_lock = asyncio.Lock()
self.part_size = 1024 * 1024 # Tamaño de cada parte en bytes (1 MB)
async def download_model_to_memory(self, model_config):
url = f"https://huggingface.co/{model_config['repo_id']}/resolve/main/{model_config['filename']}"
print(f"Descargando modelo desde {url}")
try:
start_time = time.time()
response = requests.get(url)
response.raise_for_status()
end_time = time.time()
download_duration = end_time - start_time
print(f"Descarga completa para {model_config['name']} en {download_duration:.2f} segundos")
return io.BytesIO(response.content)
except requests.RequestException as e:
raise HTTPException(status_code=500, detail=f"Error al descargar el modelo: {e}")
async def save_model_to_temp_file(self, model_config):
model_file = await self.download_model_to_memory(model_config)
temp_filename = f"/tmp/{model_config['filename']}"
print(f"Guardando el modelo en {temp_filename}")
with open(temp_filename, 'wb') as f:
f.write(model_file.getvalue())
print(f"Modelo guardado en {temp_filename}")
return temp_filename
async def load_model(self, model_config):
async with self.load_lock:
try:
temp_filename = await self.save_model_to_temp_file(model_config)
start_time = time.time()
print(f"Cargando modelo desde {temp_filename}")
llama = Llama.load(temp_filename)
end_time = time.time()
load_duration = end_time - start_time
if load_duration > 0.5:
print(f"Modelo {model_config['name']} tardó {load_duration:.2f} segundos en cargar, dividiendo automáticamente")
await self.handle_large_model(temp_filename, model_config)
else:
print(f"Modelo {model_config['name']} cargado correctamente en {load_duration:.2f} segundos")
tokenizer = llama.tokenizer
model_data = {
'model': llama,
'tokenizer': tokenizer,
'pad_token': tokenizer.pad_token,
'pad_token_id': tokenizer.pad_token_id,
'eos_token': tokenizer.eos_token,
'eos_token_id': tokenizer.eos_token_id,
'bos_token': tokenizer.bos_token,
'bos_token_id': tokenizer.bos_token_id,
'unk_token': tokenizer.unk_token,
'unk_token_id': tokenizer.unk_token_id
}
self.models[model_config['name']] = model_data
except Exception as e:
print(f"Error al cargar el modelo: {e}")
async def handle_large_model(self, model_filename, model_config):
total_size = os.path.getsize(model_filename)
num_parts = (total_size + self.part_size - 1) // self.part_size
print(f"Modelo {model_config['name']} dividido en {num_parts} partes")
with open(model_filename, 'rb') as file:
for i in tqdm(range(num_parts), desc=f"Indexando {model_config['name']}"):
start = i * self.part_size
end = min(start + self.part_size, total_size)
file.seek(start)
model_part = io.BytesIO(file.read(end - start))
await self.index_model_part(model_part, i)
async def index_model_part(self, model_part, part_index):
async with self.index_lock:
part_name = f"part_{part_index}"
print(f"Indexando parte {part_index}")
temp_filename = f"/tmp/{part_name}.gguf"
with open(temp_filename, 'wb') as f:
f.write(model_part.getvalue())
print(f"Parte {part_index} indexada y guardada")
async def generate_response(self, user_input):
results = []
for model_name, model_data in self.models.items():
print(f"Generando respuesta con el modelo {model_name}")
try:
tokenizer = model_data['tokenizer']
input_ids = tokenizer(user_input, return_tensors="pt").input_ids
outputs = model_data['model'].generate(input_ids)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Dividir el texto generado en partes
parts = []
while len(generated_text) > 1000:
part = generated_text[:1000]
parts.append(part)
generated_text = generated_text[1000:]
parts.append(generated_text)
results.append({
'model_name': model_name,
'generated_text_parts': parts
})
except Exception as e:
print(f"Error al generar respuesta con el modelo {model_name}: {e}")
results.append({'model_name': model_name, 'error': str(e)})
return results
@app.post("/generate/")
async def generate(request: Request):
data = await request.json()
user_input = data.get('input', '')
if not user_input:
raise HTTPException(status_code=400, detail="Se requiere una entrada de usuario.")
try:
model_manager = ModelManager()
tasks = [model_manager.load_model(config) for config in model_configs]
await asyncio.gather(*tasks)
responses = await model_manager.generate_response(user_input)
return {"responses": responses}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
def start_uvicorn():
uvicorn.run(app, host="0.0.0.0", port=7860)
if __name__ == "__main__":
asyncio.run(start_uvicorn())
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