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# import pathlib | |
import gradio as gr | |
# import transformers | |
# from transformers import AutoTokenizer | |
# from transformers import ModelForCausalLM | |
# from transformers import GenerationConfig | |
# from typing import List, Dict, Union | |
# from typing import Any, TypeVar | |
# Pathable = Union[str, pathlib.Path] | |
# def load_model(name: str) -> Any: | |
# return ModelForCausalLM.from_pretrained(name) | |
# def load_tokenizer(name: str) -> Any: | |
# return AutoTokenizer.from_pretrained(name) | |
# def create_generator(): | |
# return GenerationConfig( | |
# temperature=1.0, | |
# top_p=0.75, | |
# num_beams=4, | |
# ) | |
# def generate_prompt(instruction, input=None): | |
# if input: | |
# return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. | |
# ### Instruction: | |
# {instruction} | |
# ### Input: | |
# {input} | |
# ### Response:""" | |
# else: | |
# return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request. | |
# ### Instruction: | |
# {instruction} | |
# ### Response:""" | |
# def evaluate(instruction, input=None): | |
# prompt = generate_prompt(instruction, input) | |
# inputs = tokenizer(prompt, return_tensors="pt") | |
# input_ids = inputs["input_ids"].cuda() | |
# generation_output = model.generate( | |
# input_ids=input_ids, | |
# generation_config=generation_config, | |
# return_dict_in_generate=True, | |
# output_scores=True, | |
# max_new_tokens=256 | |
# ) | |
# for s in generation_output.sequences: | |
# output = tokenizer.decode(s) | |
# print("Response:", output.split("### Response:")[1].strip()) | |
# def inference(text): | |
# output = evaluate(instruction = instruction, input = input) | |
# return output | |
# io = gr.Interface( | |
# inference, | |
# gr.Textbox( | |
# lines = 3, max_lines = 10, | |
# placeholder = "Add question here", | |
# interactive = True, | |
# show_label = False | |
# ), | |
# gr.Textbox( | |
# lines = 3, | |
# max_lines = 25, | |
# placeholder = "add context here", | |
# interactive = True, | |
# show_label = False | |
# ), | |
# outputs =[ | |
# gr.Textbox(lines = 2, label = 'Pythia410m output', interactive = False) | |
# ] | |
# ), | |
# title = title, | |
# description = description, | |
# article = article, | |
# examples = examples, | |
# cache_examples = False, | |
# ) | |
# io.launch() | |
gr.Interface.load("models/s3nh/pythia-410m-70k-steps-self-instruct-polish").launch() | |