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import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
from peft import PeftModel, PeftConfig
MODEL_NAME = "IlyaGusev/llama_7b_ru_turbo_alpaca_lora"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
config = PeftConfig.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(
config.base_model_name_or_path,
load_in_8bit=True,
device_map="auto"
)
model = PeftModel.from_pretrained(model, MODEL_NAME)
model.eval()
def generate_prompt(instruction, input=None):
if input:
return f"Задание: {instruction}\nВход: {input}\nОтвет:"
return f"Задание: {instruction}\n\nОтвет:"
def evaluate(
instruction,
input=None,
temperature=1.0,
top_p=1.0,
top_k=40,
num_beams=3,
max_new_tokens=256,
**kwargs,
):
prompt = generate_prompt(instruction, input)
inputs = tokenizer(prompt, return_tensors="pt")
input_ids = inputs["input_ids"].to(model.device)
generation_config = GenerationConfig(
temperature=temperature,
top_p=top_p,
top_k=top_k,
num_beams=num_beams,
**kwargs,
)
with torch.no_grad():
generation_output = model.generate(
input_ids=input_ids,
generation_config=generation_config,
return_dict_in_generate=True,
output_scores=True,
max_new_tokens=max_new_tokens
)
s = generation_output.sequences[0]
output = tokenizer.decode(s, skip_special_tokens=True)
return output.strip()
g = gr.Interface(
fn=evaluate,
inputs=[
gr.components.Textbox(
lines=2, label="Задание", placeholder="Почему трава зеленая?"
),
gr.components.Textbox(lines=2, label="Вход", placeholder="Нет"),
gr.components.Slider(minimum=0, maximum=2, value=1.0, label="Temperature"),
gr.components.Slider(minimum=0, maximum=1, value=0.8, label="Top p"),
gr.components.Slider(minimum=0, maximum=100, value=40, label="Top k"),
gr.components.Slider(minimum=1, maximum=5, step=1, value=4, label="Beams"),
gr.components.Slider(
minimum=1, maximum=256, step=1, value=256, label="Max tokens"
),
],
outputs=[
gr.inputs.Textbox(
lines=5,
label="Output",
)
],
title="LLaMA 7B Ru Turbo Alpaca",
description="",
)
g.queue(concurrency_count=1)
g.launch() |