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from threading import Thread
import torch
import gradio as gr
import re
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/1.3_40kstep"
#peft_model_id = "ldhldh/polyglot-ko-1.3b_lora_big_tern_30kstep"
# 20k or 30k
#18k > 상대의 말까지 하는 이슈가 있음
#8k > 약간 아쉬운가?
base_model = AutoModelForCausalLM.from_pretrained("Skyranch/KoAlpaca-Polyglot-12.8B-ggml-model-f16", hf=True, model_type='gpt_neox')
tokenizer = AutoTokenizer.from_pretrained("beomi/KoAlpaca-Polyglot-12.8B")
#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
def gen(x, top_p, top_k, temperature, max_new_tokens, repetition_penalty):
gened = base_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: 우리 여행 갈래? \\nyou:',
label="User input"
)
model_output = gr.Textbox(label="Model output", lines=10, interactive=False)
button_submit = gr.Button(value="Submit")
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)
demo.queue(max_size=32).launch(enable_queue=True) |