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from transformers import AutoTokenizer, AutoModelForCausalLM
from unidecode import unidecode
from collections import Counter
import torch
import os
import gradio as gr
import numpy as np
import re
import string
from peft import PeftModel, PeftConfig
tokenizer = AutoTokenizer.from_pretrained("osiria/primo")
model = AutoModelForCausalLM.from_pretrained("osiria/primo")
model = PeftModel.from_pretrained(model, "osiria/primo")
class Prime:
def __init__(self, tokenizer, model):
self.tokenizer = tokenizer
self.model = model
def _check_sublist(self, lst, sub_lst, sep = " "):
l_type = type(lst[0])
lst = sep.join(list(map(str, lst)))
sub_lst = sep.join(list(map(str, sub_lst)))
return sub_lst in lst
def _exclude_sublist(self, lst, sub_lst, sep = " "):
l_type = type(lst[0])
lst = sep.join(list(map(str, lst)))
sub_lst = sep.join(list(map(str, sub_lst)))
lst = re.sub("\s+", " ", lst.replace(sub_lst, "")).strip().split(sep)
lst = list(map(l_type, lst))
return lst
def generate(self, prompt, message = "", sep = " [AI]", max_tokens = 100, excluded = [[40, 19]],
lookback = 5, resample_tokens = [27793], replace_tokens = {11302: 23318},
stop_tokens = [239],
sample = False,
top_k = 5):
if message:
prompt = message + ". " + prompt
prompt = prompt.replace("“", '"').replace("”", '"').replace("’", "'")
if not sample:
top_k = 2
tokens = tokenizer.encode("[HUMAN] " + prompt + sep)
tokens_generated = []
checkpoint = 0
while tokens[-1] not in stop_tokens and len(tokens_generated) < max_tokens:
output = model.forward(input_ids=torch.tensor([tokens]).to(device)).logits[0,-1]
output = torch.softmax(output, dim = 0)
candidates = torch.topk(output, k = top_k)
if sample:
indices = candidates.indices
scores = candidates.values
next_token = indices[torch.multinomial(scores, 1)[0].item()]
else:
next_token = candidates.indices[0]
next_token = next_token.item()
sub_tokens = tokens_generated[-lookback:] + [next_token]
if next_token in resample_tokens:
next_token = candidates.indices[1]
next_token = next_token.item()
if len(tokens_generated) >= (lookback + 1) and next_token in tokens_generated[-2:]:
next_token = candidates.indices[1]
next_token = next_token.item()
elif len(tokens_generated) >= lookback and self._check_sublist(tokens_generated, sub_tokens):
if checkpoint:
tokens = tokens[:checkpoint]
break
else:
next_token = candidates.indices[1]
next_token = next_token.item()
sample = True
if next_token in replace_tokens:
next_token = replace_tokens[next_token]
tokens = tokens + [next_token]
tokens_generated = tokens_generated + [next_token]
if next_token == 5:
checkpoint = len(tokens)
for ex_lst in excluded:
tokens = self._exclude_sublist(tokens, ex_lst)
output = tokenizer.decode(tokens, skip_special_tokens=True)
output = output.split(sep)[-1].strip()
output = output[0].upper() + output[1:]
if output[-1] == tokenizer.decode(stop_tokens[0]):
output = output[:-1]
if len(re.findall("\d\.", output)) > 1:
output = re.sub("\d\.", "<br>•", output)
return output
model.eval()
device = torch.device("cuda")
prime = Prime(tokenizer = tokenizer, model = model)
def process_input(user_input, max_tokens, sample, top_k, message):
return prime.generate(prompt = user_input, message = message,
max_tokens = max_tokens, sample = sample,
top_k = top_k)
header = '''--------------------------------------------------------------------------------------------------
<style>
.vertical-text {
writing-mode: vertical-lr;
text-orientation: upright;
background-color:red;
}
</style>
<center>
<body>
<span class="vertical-text" style="background-color:lightgreen;border-radius: 3px;padding: 3px;"> </span>
<span class="vertical-text" style="background-color:orange;border-radius: 3px;padding: 3px;"> </span>
<span class="vertical-text" style="background-color:lightblue;border-radius: 3px;padding: 3px;"> </span>
<span class="vertical-text" style="background-color:tomato;border-radius: 3px;padding: 3px;"> </span>
<span class="vertical-text" style="background-color:lightgrey;border-radius: 3px;padding: 3px;"> </span>
<span class="vertical-text" style="background-color:#CF9FFF;border-radius: 3px;padding: 3px;"> </span>
</body>
</center>
<br>
<center><img src="file/prime.png" width="100"></center>
'''
import gradio as gr
import random
import time
with gr.Blocks(title="primo", css="footer {visibility: hidden}", theme=gr.themes.Default(text_size="md", spacing_size="md")) as interface:
gr.Markdown(header)
with gr.Row():
with gr.Column(scale=1):
gr.Markdown("<b>options</b>")
max_tokens = gr.Slider(1, 250, value=150, label="max tokens", info="choose a limit between 1 and 250")
sample = gr.Checkbox(label="sampling")
top_k = gr.Slider(1, 5, value=1, label="creativity", info="choose a level between 1 and 5")
message = gr.Textbox(label="system message", value = "")
clear = gr.Button("clear chat")
with gr.Column(scale=8):
chatbot = gr.Chatbot(label = "prime").style(height=600)
msg = gr.Textbox(label = "query")
def user(user_message, history):
return gr.update(value="", interactive=False), history + [[user_message, None]]
def bot(history, message, max_tokens, sample, top_k):
bot_message = process_input(history[-1][0], message = message, max_tokens = max_tokens,
sample = sample, top_k = top_k)
history[-1][1] = ""
for character in bot_message:
history[-1][1] += character
time.sleep(0.05)
yield history
response = msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
bot, [chatbot, message, max_tokens, sample, top_k], chatbot
)
response.then(lambda: gr.update(interactive=True), None, [msg], queue=False)
clear.click(lambda: None, None, chatbot, queue=False)
with gr.Column(scale=1):
gr.Markdown("<b>warning</b>")
gr.Markdown("the model might behave erratically when presented with prompts which are too far away from its pre-training or fine-tuning and, because of the probabilistic nature of its generation mechanism, it might occasionally produce biased or offensive content with respect to gender, race, ideologies, and political or religious beliefs<br><br>these limitations imply that the model and its outputs should be used with caution, and should not be involved in situations that require the generated text to be fair or true")
interface.queue()
interface.launch() |