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\.", "
•", output) output = re.sub("^\", "", 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 = '''--------------------------------------------------------------------------------------------------
                 

''' 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("opzioni") max_tokens = gr.Slider(1, 250, value=150, label="massimo numero di token", info="scegli un limite tra 1 e 250") sample = gr.Checkbox(label="campionamento") top_k = gr.Slider(1, 5, step=1, value=1, label="creatività", info="scegli un livello tra 1 e 5") message = gr.Textbox(label="messaggio di sistema", value = "") clear = gr.Button("pulisci conversazione") with gr.Column(scale=8): chatbot = gr.Chatbot(label = "prime").style(height=600) msg = gr.Textbox(label = "richiesta") 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("attenzione") gr.Markdown("il modello potrebbe comportarsi in maniera imprevista nel caso in cui riceva prompt troppo lontani dal suo pre-training o fine-tuning e, per via della natura probabilistica del meccanismo di generazione, potrebbe occasionalmente produrre contenuti distorti o offensivi in relazione a tematiche come il genere, le etnie, le ideologie, e le convinzioni politiche o religiose

per via di queste limitazioni, il modello e i suoi output dovrebbero essere usati con cautela, e non dovrebbero essere coinvolti in contesti che richiedono che il testo generato sia corretto o veritiero") interface.queue() interface.launch()