File size: 4,009 Bytes
2da32e3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 |
import os
import torch
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel, Field
from typing import List, Optional, Literal
from transformers import AutoTokenizer, AutoModelForCausalLM
import uvicorn
app = FastAPI(title="OpenAI-compatible API")
# --- Configuración del Modelo ---
# Puedes cambiar esto por variables de entorno en HF Spaces
MODEL_ID = os.getenv("MODEL_ID", "Qwen/Qwen2.5-0.5B-Instruct")
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Cargando modelo: {MODEL_ID} en {device}...")
try:
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(
MODEL_ID,
torch_dtype=torch.float16 if device == "cuda" else torch.float32,
device_map="auto" if device == "cuda" else None,
low_cpu_mem_usage=True
)
if device == "cpu":
model.to(device)
print("¡Modelo cargado exitosamente!")
except Exception as e:
print(f"Error cargando el modelo: {e}")
raise e
# --- Modelos Pydantic (Formato OpenAI) ---
class ChatMessage(BaseModel):
role: str
content: str
class ChatCompletionRequest(BaseModel):
model: str = "default-model"
messages: List[ChatMessage]
max_tokens: Optional[int] = 512
temperature: Optional[float] = 0.7
top_p: Optional[float] = 0.9
stream: Optional[bool] = False
class Choice(BaseModel):
index: int
message: ChatMessage
finish_reason: str
class Usage(BaseModel):
prompt_tokens: int
completion_tokens: int
total_tokens: int
class ChatCompletionResponse(BaseModel):
id: str
object: Literal["chat.completion"] = "chat.completion"
created: int
model: str
choices: List[Choice]
usage: Usage
# --- Endpoint API ---
@app.post("/v1/chat/completions", response_model=ChatCompletionResponse)
async def chat_completions(request: ChatCompletionRequest):
try:
# 1. Aplicar el Chat Template (convierte la lista de mensajes en el string que entiende el modelo)
# Esto hace que funcione con Llama, Mistral, Qwen, etc. automáticamente.
input_text = tokenizer.apply_chat_template(
[{"role": m.role, "content": m.content} for m in request.messages],
tokenize=False,
add_generation_prompt=True
)
inputs = tokenizer(input_text, return_tensors="pt").to(device)
# 2. Generar respuesta
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=request.max_tokens,
temperature=request.temperature,
top_p=request.top_p,
do_sample=True
)
# 3. Decodificar solo la parte nueva (la respuesta)
generated_ids = outputs[0][inputs.input_ids.shape[1]:]
response_text = tokenizer.decode(generated_ids, skip_special_tokens=True)
# 4. Calcular tokens (aproximado)
prompt_tokens = len(inputs.input_ids[0])
completion_tokens = len(generated_ids)
# 5. Formatear como OpenAI
return ChatCompletionResponse(
id="chatcmpl-123",
created=1234567890,
model=MODEL_ID,
choices=[
Choice(
index=0,
message=ChatMessage(role="assistant", content=response_text),
finish_reason="stop"
)
],
usage=Usage(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
total_tokens=prompt_tokens + completion_tokens
)
)
except Exception as e:
print(f"Error en generación: {e}")
raise HTTPException(status_code=500, detail=str(e))
@app.get("/")
def home():
return {"message": "OpenAI-compatible API is running", "model": MODEL_ID}
# Permite ejecutar con `python main.py` para pruebas locales
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=7860) |