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from fastapi import FastAPI
import uvicorn
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
model_name = "Tap-M/Luna-AI-Llama2-Uncensored"
bnb_config = BitsAndBytesConfig(
load_in_4bit=True, # Enable 4-bit quantization
bnb_4bit_compute_dtype=torch.float16
)
model = AutoModelForCausalLM.from_pretrained(
model_name, # Example model
device_map="auto", # Auto-distribute across GPU/CPU
quantization_config=bnb_config,
offload_folder="./offload", # Temporary directory
low_cpu_mem_usage=True, # Reduces CPU memory spikes
trust_remote_code=True # Required for some models
)
# load tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token
app = FastAPI()
@app.get("/")
def greet_json():
return {"Hello": "World!"}
@app.get("/message")
async def message(input: str):
prompt = "USER:" + input + "\nASSISTANT:"
inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True)
output = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
max_new_tokens=100,
)
response = tokenizer.decode(output[0], skip_special_tokens=True)
return response
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=7860) |