mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-10-30 06:00:15 +01:00
118 lines
4.1 KiB
Python
118 lines
4.1 KiB
Python
import time
|
|
import re
|
|
import torch
|
|
import gradio as gr
|
|
import transformers
|
|
from transformers import AutoTokenizer
|
|
from transformers import GPTJForCausalLM, AutoModelForCausalLM, AutoModelForSeq2SeqLM, OPTForCausalLM, T5Tokenizer, T5ForConditionalGeneration, GPTJModel, AutoModel
|
|
|
|
#model_name = "bloomz-7b1-p3"
|
|
#model_name = 'gpt-j-6B-float16'
|
|
#model_name = "opt-6.7b"
|
|
#model_name = 'opt-13b'
|
|
#model_name = "gpt4chan_model_float16"
|
|
model_name = 'galactica-6.7b'
|
|
#model_name = 'gpt-neox-20b'
|
|
#model_name = 'flan-t5'
|
|
#model_name = 'OPT-13B-Erebus'
|
|
|
|
def load_model(model_name):
|
|
print(f"Loading {model_name}")
|
|
|
|
t0 = time.time()
|
|
if model_name in ['gpt-neox-20b', 'opt-13b', 'OPT-13B-Erebus']:
|
|
model = AutoModelForCausalLM.from_pretrained(f"models/{model_name}", device_map='auto', load_in_8bit=True)
|
|
elif model_name in ['gpt-j-6B']:
|
|
model = AutoModelForCausalLM.from_pretrained(f"models/{model_name}", low_cpu_mem_usage=True, torch_dtype=torch.float16).cuda()
|
|
elif model_name in ['flan-t5']:
|
|
model = T5ForConditionalGeneration.from_pretrained(f"models/{model_name}").cuda()
|
|
else:
|
|
model = torch.load(f"torch-dumps/{model_name}.pt").cuda()
|
|
|
|
if model_name in ['gpt4chan_model_float16']:
|
|
tokenizer = AutoTokenizer.from_pretrained("models/gpt-j-6B/")
|
|
elif model_name in ['flan-t5']:
|
|
tokenizer = T5Tokenizer.from_pretrained(f"models/{model_name}/")
|
|
else:
|
|
tokenizer = AutoTokenizer.from_pretrained(f"models/{model_name}/")
|
|
|
|
print(f"Loaded the model in {time.time()-t0} seconds.")
|
|
return model, tokenizer
|
|
|
|
def fix_gpt4chan(s):
|
|
for i in range(10):
|
|
s = re.sub("--- [0-9]*\n>>[0-9]*\n---", "---", s)
|
|
s = re.sub("--- [0-9]*\n *\n---", "---", s)
|
|
s = re.sub("--- [0-9]*\n\n\n---", "---", s)
|
|
|
|
return s
|
|
|
|
def fn(question, temperature, max_length, inference_settings, selected_model):
|
|
global model, tokenizer, model_name
|
|
|
|
if selected_model != model_name:
|
|
model_name = selected_model
|
|
model = None
|
|
tokenier = None
|
|
torch.cuda.empty_cache()
|
|
model, tokenizer = load_model(model_name)
|
|
|
|
torch.cuda.empty_cache()
|
|
input_text = question
|
|
input_ids = tokenizer.encode(str(input_text), return_tensors='pt').cuda()
|
|
|
|
if inference_settings == 'Default':
|
|
output = model.generate(
|
|
input_ids,
|
|
do_sample=True,
|
|
max_new_tokens=max_length,
|
|
#max_length=max_length+len(input_ids[0]),
|
|
top_p=1,
|
|
typical_p=0.3,
|
|
temperature=temperature,
|
|
).cuda()
|
|
elif inference_settings == 'Verbose':
|
|
output = model.generate(
|
|
input_ids,
|
|
num_beams=10,
|
|
min_length=max_length,
|
|
max_new_tokens=max_length,
|
|
length_penalty =1.4,
|
|
no_repeat_ngram_size=2,
|
|
early_stopping=True,
|
|
temperature=0.7,
|
|
top_k=150,
|
|
top_p=0.92,
|
|
repetition_penalty=4.5,
|
|
).cuda()
|
|
|
|
reply = tokenizer.decode(output[0], skip_special_tokens=True)
|
|
if model_name.startswith('gpt4chan'):
|
|
reply = fix_gpt4chan(reply)
|
|
|
|
return reply
|
|
|
|
model, tokenizer = load_model(model_name)
|
|
if model_name.startswith('gpt4chan'):
|
|
default_text = "-----\n--- 865467536\nInput text\n--- 865467537\n"
|
|
else:
|
|
default_text = "Common sense questions and answers\n\nQuestion: \nFactual answer:"
|
|
|
|
interface = gr.Interface(
|
|
fn,
|
|
inputs=[
|
|
gr.Textbox(value=default_text, lines=15),
|
|
gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Temperature', value=0.7),
|
|
gr.Slider(minimum=1, maximum=2000, step=1, label='max_length', value=200),
|
|
gr.Dropdown(choices=["Default", "Verbose"], value="Default"),
|
|
gr.Dropdown(choices=["gpt4chan_model_float16", "galactica-6.7b", "opt-6.7b", "opt-13b", "gpt-neox-20b", "gpt-j-6B-float16", "flan-t5", "bloomz-7b1-p3", "OPT-13B-Erebus"], value=model_name),
|
|
],
|
|
outputs=[
|
|
gr.Textbox(placeholder="", lines=15),
|
|
],
|
|
title="Text generation lab",
|
|
description=f"Generate text using Large Language Models. Currently working with {model_name}",
|
|
)
|
|
|
|
interface.launch(share=False, server_name="0.0.0.0")
|