|
|
import os, torch, gradio as gr |
|
|
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig |
|
|
from peft import PeftModel |
|
|
|
|
|
BASE_MODEL = os.getenv("BASE_MODEL", "mistralai/Mistral-7B-Instruct-v0.2") |
|
|
LORA_REPO = os.getenv("LORA_REPO", "YOUR_USERNAME/DSAN-5800-LoRA-mistral7b-r8") |
|
|
HF_TOKEN = os.getenv("HF_TOKEN") |
|
|
|
|
|
def load_model(): |
|
|
tok = AutoTokenizer.from_pretrained(BASE_MODEL, use_fast=True, token=HF_TOKEN) |
|
|
if tok.pad_token is None and tok.eos_token is not None: |
|
|
tok.pad_token = tok.eos_token; tok.padding_side = "left" |
|
|
quant = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_use_double_quant=True, |
|
|
bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.float16) |
|
|
base = AutoModelForCausalLM.from_pretrained(BASE_MODEL, device_map="auto", |
|
|
torch_dtype=torch.float16, quantization_config=quant, |
|
|
token=HF_TOKEN) |
|
|
model = PeftModel.from_pretrained(base, LORA_REPO, device_map="auto", token=HF_TOKEN) |
|
|
model.eval() |
|
|
return model, tok |
|
|
|
|
|
model, tokenizer = load_model() |
|
|
|
|
|
def build_prompt(instruction: str) -> str: |
|
|
msgs = [{"role":"system","content":"You are a Python coding assistant. Produce correct, clean, efficient Python."}, |
|
|
{"role":"user","content":instruction}] |
|
|
try: |
|
|
return tokenizer.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True) |
|
|
except Exception: |
|
|
return f"System: You are a Python coding assistant.\nUser: {instruction}\nAssistant:" |
|
|
|
|
|
def infer(instruction, max_new_tokens, temperature, top_p): |
|
|
prompt = build_prompt(instruction) |
|
|
inputs = tokenizer(prompt, return_tensors="pt").to(model.device) |
|
|
with torch.no_grad(): |
|
|
out = model.generate(**inputs, do_sample=True, temperature=float(temperature), |
|
|
top_p=float(top_p), max_new_tokens=int(max_new_tokens), |
|
|
pad_token_id=tokenizer.eos_token_id, eos_token_id=tokenizer.eos_token_id) |
|
|
text = tokenizer.decode(out[0], skip_special_tokens=True) |
|
|
return text[len(prompt):].strip() if text.startswith(prompt) else text |
|
|
|
|
|
demo = gr.Interface( |
|
|
fn=infer, |
|
|
inputs=[gr.Textbox(label="Instruction", lines=8), |
|
|
gr.Slider(32, 2048, value=512, step=32, label="max_new_tokens"), |
|
|
gr.Slider(0.0, 1.0, value=0.2, step=0.05, label="temperature"), |
|
|
gr.Slider(0.1, 1.0, value=0.9, step=0.05, label="top_p")], |
|
|
outputs=gr.Code(label="Model output (Python)", language="python"), |
|
|
title="DSAN-5800 LoRA Demo", |
|
|
description="Mistral 7B + LoRA adapter with 4-bit inference." |
|
|
) |
|
|
|
|
|
if __name__ == "__main__": |
|
|
demo.launch() |
