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99bc3f3 c4f5b88 2a0877d c4f5b88 ce5482c c4f5b88 ce5482c c4f5b88 99bc3f3 ce5482c 2a0877d 99bc3f3 2a0877d ce5482c c4f5b88 99bc3f3 c4f5b88 99bc3f3 | 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 | import spaces
import gradio as gr
import torch
BASE_MODEL = "deepseek-ai/deepseek-coder-1.3b-instruct"
ADAPTER = "praveends/migration-copilot-deepseek-coder-1-3b-instruct"
# Global variables — loaded lazily inside GPU function
tokenizer = None
model = None
def load_model():
global tokenizer, model
if model is not None:
return
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
print("Loading model...")
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token
model = AutoModelForCausalLM.from_pretrained(
BASE_MODEL,
torch_dtype=torch.float16,
trust_remote_code=True,
device_map="auto",
)
model = PeftModel.from_pretrained(model, ADAPTER)
model = model.merge_and_unload()
model.eval()
print("Model loaded!")
@spaces.GPU(duration=60)
def generate(prompt: str) -> str:
load_model()
inputs = tokenizer(
prompt, return_tensors="pt",
truncation=True, max_length=512
).to("cuda")
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=300,
temperature=0.1,
do_sample=True,
pad_token_id=tokenizer.eos_token_id,
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
return response[len(prompt):].strip()
demo = gr.Interface(
fn=generate,
inputs=gr.Textbox(label="Prompt", lines=10),
outputs=gr.Textbox(label="Generated PySpark", lines=10),
title="Migration Copilot Inference",
api_name="generate",
)
demo.launch() |