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from threading import Thread | |
import torch | |
import gradio as gr | |
from transformers import pipeline,AutoTokenizer, AutoModelForCausalLM, BertTokenizer, BertForSequenceClassification, StoppingCriteria, StoppingCriteriaList | |
from peft import PeftModel, PeftConfig | |
import re | |
from kobert_transformers import get_tokenizer | |
torch_device = "cuda" if torch.cuda.is_available() else "cpu" | |
print("Running on device:", torch_device) | |
print("CPU threads:", torch.get_num_threads()) | |
peft_model_id = "ldhldh/polyglot-ko-1.3b_lora_big_8kstep" | |
#18k > ์๋์ ๋ง๊น์ง ํ๋ ์ด์๊ฐ ์์ | |
#8k > ์ฝ๊ฐ ์์ฌ์ด๊ฐ? | |
config = PeftConfig.from_pretrained(peft_model_id) | |
base_model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path) | |
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path) | |
#base_model = AutoModelForCausalLM.from_pretrained("EleutherAI/polyglot-ko-3.8b") | |
#tokenizer = AutoTokenizer.from_pretrained("EleutherAI/polyglot-ko-3.8b") | |
base_model.eval() | |
#base_model.config.use_cache = True | |
model = PeftModel.from_pretrained(base_model, peft_model_id, device_map="auto") | |
model.eval() | |
#model.config.use_cache = True | |
mbti_bert_model_name = "Lanvizu/fine-tuned-klue-bert-base_model_11" | |
mbti_bert_model = BertForSequenceClassification.from_pretrained(mbti_bert_model_name) | |
mbti_bert_model.eval() | |
mbti_bert_tokenizer = BertTokenizer.from_pretrained("bert-base-uncased") | |
bert_model_name = "ldhldh/bert_YN_small" | |
bert_model = BertForSequenceClassification.from_pretrained(bert_model_name) | |
bert_model.eval() | |
bert_tokenizer = get_tokenizer() | |
def mbti_classify(x): | |
classifier = pipeline("text-classification", model=mbti_bert_model, tokenizer=mbti_bert_tokenizer, return_all_scores=True) | |
result = classifier([x]) | |
return result[0] | |
def classify(x): | |
input_list = bert_tokenizer.batch_encode_plus([x], truncation=True, padding=True, return_tensors='pt') | |
input_ids = input_list['input_ids'].to(bert_model.device) | |
attention_masks = input_list['attention_mask'].to(bert_model.device) | |
outputs = bert_model(input_ids, attention_mask=attention_masks, return_dict=True) | |
return outputs.logits.argmax(dim=1).cpu().tolist()[0] | |
def gen(x, top_p, top_k, temperature, max_new_tokens, repetition_penalty): | |
gened = model.generate( | |
**tokenizer( | |
f"{x}", | |
return_tensors='pt', | |
return_token_type_ids=False | |
), | |
#bad_words_ids = bad_words_ids , | |
max_new_tokens=max_new_tokens, | |
min_new_tokens = 5, | |
exponential_decay_length_penalty = (max_new_tokens/2, 1.1), | |
top_p=top_p, | |
top_k=top_k, | |
temperature = temperature, | |
early_stopping=True, | |
do_sample=True, | |
eos_token_id=2, | |
pad_token_id=2, | |
#stopping_criteria = stopping_criteria, | |
repetition_penalty=repetition_penalty, | |
no_repeat_ngram_size = 2 | |
) | |
model_output = tokenizer.decode(gened[0]) | |
return model_output | |
def reset_textbox(): | |
return gr.update(value='') | |
with gr.Blocks() as demo: | |
duplicate_link = "https://huggingface.co/spaces/beomi/KoRWKV-1.5B?duplicate=true" | |
gr.Markdown( | |
"duplicated from beomi/KoRWKV-1.5B, baseModel:EleutherAI/polyglot-ko-1.3b" | |
) | |
with gr.Row(): | |
with gr.Column(scale=4): | |
user_text = gr.Textbox( | |
placeholder='\\nfriend: ์ฐ๋ฆฌ ์ฌํ ๊ฐ๋? \\nyou:', | |
label="User input" | |
) | |
model_output = gr.Textbox(label="Model output", lines=10, interactive=False) | |
button_submit = gr.Button(value="Submit") | |
button_bert = gr.Button(value="bert_Sumit") | |
button_mbti_bert = gr.Button(value="mbti_bert_Sumit") | |
with gr.Column(scale=1): | |
max_new_tokens = gr.Slider( | |
minimum=1, maximum=200, value=20, step=1, interactive=True, label="Max New Tokens", | |
) | |
top_p = gr.Slider( | |
minimum=0.05, maximum=1.0, value=0.8, step=0.05, interactive=True, label="Top-p (nucleus sampling)", | |
) | |
top_k = gr.Slider( | |
minimum=5, maximum=100, value=30, step=5, interactive=True, label="Top-k (nucleus sampling)", | |
) | |
temperature = gr.Slider( | |
minimum=0.1, maximum=2.0, value=0.5, step=0.1, interactive=True, label="Temperature", | |
) | |
repetition_penalty = gr.Slider( | |
minimum=1.0, maximum=3.0, value=1.2, step=0.1, interactive=True, label="repetition_penalty", | |
) | |
button_submit.click(gen, [user_text, top_p, top_k, temperature, max_new_tokens, repetition_penalty], model_output) | |
button_bert.click(classify, [user_text], model_output) | |
button_mbti_bert.click(mbti_classify, [user_text], model_output) | |
demo.queue(max_size=32).launch(enable_queue=True) |