File size: 2,280 Bytes
cd55ee8 |
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 |
from fastapi import FastAPI
from pydantic import BaseModel
from transformers import AutoTokenizer, AutoModelForCausalLM
from fastapi.middleware.cors import CORSMiddleware
import torch
import os
# Ensure Hugging Face cache uses a writable path
os.environ["TRANSFORMERS_CACHE"] = "./.cache"
os.environ["HF_HOME"] = "./.cache"
app = FastAPI()
# ✅ Allow all origins
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
class ChatRequest(BaseModel):
message: str
max_tokens: int = 200 # default shorter responses for speed
# 🔹 Choose a model (smaller = faster on CPU)
#model_name = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
#model_name = "Qwen/Qwen2.5-1.5B-Instruct"
model_name = "deepseek-ai/deepseek-coder-1.3b-base"
print("🚀 Loading model... this may take a minute ⏳")
try:
if torch.cuda.is_available():
# ✅ GPU with quantization
from transformers import BitsAndBytesConfig
quant_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.float16,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
device_map="auto",
quantization_config=quant_config,
)
else:
# ✅ CPU fallback (no quantization)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float32,
device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
print("✅ Model loaded successfully!")
except Exception as e:
print("❌ Model loading failed:", str(e))
raise
@app.get("/")
def root():
return {"status": "ok"}
@app.post("/chat")
def chat(request: ChatRequest):
"""Chat endpoint"""
inputs = tokenizer(request.message, return_tensors="pt").to(model.device)
outputs = model.generate(
**inputs,
max_new_tokens=request.max_tokens,
do_sample=True,
top_p=0.9,
temperature=0.7
)
# 🔹 Only decode new tokens
reply_tokens = outputs[0][inputs["input_ids"].shape[1]:]
reply = tokenizer.decode(reply_tokens, skip_special_tokens=True)
return {"reply": reply}
|