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import torch
import gradio as gr
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
peft_model_id = "kimmeoungjun/qlora-koalpaca"
config = PeftConfig.from_pretrained(peft_model_id)
model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path)
model = PeftModel.from_pretrained(model, peft_model_id).to(device)
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
def generate(q):
inputs = tokenizer(f"### 질문: {q}\n\n### 답변:", return_tensors='pt', return_token_type_ids=False)
outputs = model.generate(
**{k: v.to(device) for k, v in inputs.items()},
max_new_tokens=256,
do_sample=True,
eos_token_id=2,
)
result = tokenizer.decode(outputs[0])
answer_idx = result.find("### 답변:")
answer = result[answer_idx + 7:].strip()
return answer
gr.Interface(generate, "text", "text").launch(share=True) |