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import gradio as gr
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
from transformers import GPT2TokenizerFast,GPT2LMHeadModel
tokenizer = GPT2TokenizerFast.from_pretrained("AlexWortega/instruct_rugptlarge")
special_tokens_dict = {'additional_special_tokens': ['<code>', '</code>', '<instructionS>', '<instructionE>', '<next>']}
tokenizer.add_special_tokens(special_tokens_dict)
device = 'cpu' # мэх дорога
model = GPT2LMHeadModel.from_pretrained("AlexWortega/instruct_rugptlarge")
#
model.resize_token_embeddings(len(tokenizer))
def generate_prompt(instruction, input=None):
if input:
return f"{input}:"
return f"{instruction}"
def generate_seqs(q, temp, topp, topk, nb, maxtok):
k=1
gen_kwargs = {
"min_length": 20,
"max_new_tokens": maxtok,
"top_k": topk,
"top_p": topp,
"do_sample": True,
"early_stopping": True,
"no_repeat_ngram_size": 2,
"temperature":temp,
"eos_token_id": tokenizer.eos_token_id,
"pad_token_id": tokenizer.eos_token_id,
"use_cache": True,
"repetition_penalty": 1.5,
"length_penalty": 0.8,
"num_beams": nb,
"num_return_sequences": k
}
if len(q)>0:
q = q + '<instructionS>'
else:
q = 'Как зарабатывать денег на нейросетях ?' + '<instructionS>'
t = tokenizer.encode(q, return_tensors='pt').to(device)
g = model.generate(t, **gen_kwargs)
generated_sequences = tokenizer.batch_decode(g, skip_special_tokens=False)
#print(generated_sequences)
# Add </s></s>A: after the question and before each generated sequence
#sequences = [f"H:{q}</s></s>A:{s.replace(q, '')}" for s in generated_sequences]
# Compute the reward score for each generated sequence
#cores = [reward_model.reward_score(q, s.split('</s></s>A:')[-1]) for s in sequences]
# Return the k sequences with the highest score and their corresponding scores
# results = [(s, score) for score, s in sorted(zip(scores, sequences), reverse=True)[:k]]
ans = generated_sequences[0].replace('<instructionS>','\n').replace('<instructionE>','').replace('<|endoftext|>','')
return ans
description_html = '''
<p>Обучена на 2v100, коллективом авторов:</p>
<ul>
<li><a href="https://t.me/YallenGusev" target="_blank">@YallenGusev</a></li>
<li><a href="https://t.me/lovedeathtransformers" target="_blank">@lovedeathtransformers</a></li>
<li><a href="https://t.me/alexkuk" target="_blank">@alexkuk</a></li>
<li><a href="https://t.me/chckdskeasfsd" target="_blank">@chckdskeasfsd</a></li>
<li><a href="https://t.me/dno5iq" target="_blank">@dno5iq</a></li>
</ul>
'''
g = gr.Interface(
fn=generate_seqs,
inputs=[
gr.components.Textbox(
lines=2, label="Впишите сюда задачу, а я попробую решить", placeholder="Как зарабатывать денег на нейросетях?"
),
#gr.components.Textbox(lines=2, label="Вход", placeholder="Нет"),
gr.components.Slider(minimum=0.1, maximum=2, value=1.0, label="Temperature"),
gr.components.Slider(minimum=0, maximum=1, value=0.9, label="Top p"),
gr.components.Slider(minimum=0, maximum=100, value=50, label="Top k"),
gr.components.Slider(minimum=0, maximum=5, step=1, value=4, label="Beams"),
gr.components.Slider(
minimum=1, maximum=256, step=1, value=100, label="Max tokens"
),
],
outputs=[
gr.inputs.Textbox(
lines=5,
label="Output",
)
],
title="ruInstructlarge",
description=description_html)
g.queue(concurrency_count=5)
g.launch() |