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