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##### `💁 Open_Assistant.py`
##### https://huggingface.co/spaces/olivierdehaene/chat-llm-streaming/blob/main/README.md
##### https://huggingface.co/OpenAssistant/oasst-sft-1-pythia-12b
##### Please reach out to ben@benbox.org for any questions
#### Loading needed Python libraries
import streamlit as st
import os
from text_generation import Client, InferenceAPIClient
from text_generation import InferenceAPIClient
st.header('💁 Open Assistant LLM')
st.write('This is the first iteration English supervised-fine-tuning (SFT) model of the Open-Assistant project. It is based on a Pythia 12B that was fine-tuned on ~22k human demonstrations of assistant conversations collected through the https://open-assistant.io/ human feedback web app before March 7, 2023.')
st.write('Question: :green[Why is the sky blue?]')
client = InferenceAPIClient("OpenAssistant/oasst-sft-1-pythia-12b")
text = client.generate("<|prompter|>Why is the sky blue?<|endoftext|><|assistant|>").generated_text
st.write('Answer: :green[' + str(text) + ']')
# Token Streaming
#text = ""
#for response in client.generate_stream("<|prompter|>Why is the sky blue?<|endoftext|><|assistant|>"):
# if not response.token.special:
# print(response.token.text)
# text += response.token.text
#st.write(text)
#
# openchat_preprompt = (
# "\n<human>: Hi!\n<bot>: My name is Bot, model version is 0.15, part of an open-source kit for "
# "fine-tuning new bots! I was created by Together, LAION, and Ontocord.ai and the open-source "
# "community. I am not human, not evil and not alive, and thus have no thoughts and feelings, "
# "but I am programmed to be helpful, polite, honest, and friendly.\n"
# )
#
#
# def get_client(model: str):
# if model == "togethercomputer/GPT-NeoXT-Chat-Base-20B":
# return Client(os.getenv("OPENCHAT_API_URL"))
# return InferenceAPIClient(model, token = os.getenv("HF_TOKEN", None))
#
#
# def get_usernames(model: str):
# """
# Returns:
# (str, str, str, str): pre-prompt, username, bot name, separator
# """
# if model == "OpenAssistant/oasst-sft-1-pythia-12b":
# return "", "<|prompter|>", "<|assistant|>", "<|endoftext|>"
# if model == "togethercomputer/GPT-NeoXT-Chat-Base-20B":
# return openchat_preprompt, "<human>: ", "<bot>: ", "\n"
# return "", "User: ", "Assistant: ", "\n"
#
#
# def predict(
# model: str,
# inputs: str,
# typical_p: float,
# top_p: float,
# temperature: float,
# top_k: int,
# repetition_penalty: float,
# watermark: bool,
# chatbot,
# history,
# ):
# client = get_client(model)
# preprompt, user_name, assistant_name, sep = get_usernames(model)
#
# history.append(inputs)
#
# past = []
# for data in chatbot:
# user_data, model_data = data
#
# if not user_data.startswith(user_name):
# user_data = user_name + user_data
# if not model_data.startswith(sep + assistant_name):
# model_data = sep + assistant_name + model_data
#
# past.append(user_data + model_data.rstrip() + sep)
#
# if not inputs.startswith(user_name):
# inputs = user_name + inputs
#
# total_inputs = preprompt + "".join(past) + inputs + sep + assistant_name.rstrip()
#
# partial_words = ""
#
# if model == "OpenAssistant/oasst-sft-1-pythia-12b":
# iterator = client.generate_stream(
# total_inputs,
# typical_p = typical_p,
# truncate = 1000,
# watermark = watermark,
# max_new_tokens = 500,
# )
# else:
# iterator = client.generate_stream(
# total_inputs,
# top_p = top_p if top_p < 1.0 else None,
# top_k = top_k,
# truncate = 1000,
# repetition_penalty = repetition_penalty,
# watermark = watermark,
# temperature = temperature,
# max_new_tokens = 500,
# stop_sequences = [user_name.rstrip(), assistant_name.rstrip()],
# )
#
# for i, response in enumerate(iterator):
# if response.token.special:
# continue
#
# partial_words = partial_words + response.token.text
# if partial_words.endswith(user_name.rstrip()):
# partial_words = partial_words.rstrip(user_name.rstrip())
# if partial_words.endswith(assistant_name.rstrip()):
# partial_words = partial_words.rstrip(assistant_name.rstrip())
#
# if i == 0:
# history.append(" " + partial_words)
# elif response.token.text not in user_name:
# history[-1] = partial_words
#
# chat = [
# (history[i].strip(), history[i + 1].strip())
# for i in range(0, len(history) - 1, 2)
# ]
# yield chat, history
#
#
# def reset_textbox():
# return gr.update(value = "")
#
#
# def radio_on_change(
# value: str,
# disclaimer,
# typical_p,
# top_p,
# top_k,
# temperature,
# repetition_penalty,
# watermark,
# ):
# if value == "OpenAssistant/oasst-sft-1-pythia-12b":
# typical_p = typical_p.update(value = 0.2, visible = True)
# top_p = top_p.update(visible = False)
# top_k = top_k.update(visible = False)
# temperature = temperature.update(visible = False)
# disclaimer = disclaimer.update(visible = False)
# repetition_penalty = repetition_penalty.update(visible = False)
# watermark = watermark.update(False)
# elif value == "togethercomputer/GPT-NeoXT-Chat-Base-20B":
# typical_p = typical_p.update(visible = False)
# top_p = top_p.update(value = 0.25, visible = True)
# top_k = top_k.update(value = 50, visible = True)
# temperature = temperature.update(value = 0.6, visible = True)
# repetition_penalty = repetition_penalty.update(value = 1.01, visible = True)
# watermark = watermark.update(False)
# disclaimer = disclaimer.update(visible = True)
# else:
# typical_p = typical_p.update(visible = False)
# top_p = top_p.update(value = 0.95, visible = True)
# top_k = top_k.update(value = 4, visible = True)
# temperature = temperature.update(value = 0.5, visible = True)
# repetition_penalty = repetition_penalty.update(value = 1.03, visible = True)
# watermark = watermark.update(True)
# disclaimer = disclaimer.update(visible = False)
# return (
# disclaimer,
# typical_p,
# top_p,
# top_k,
# temperature,
# repetition_penalty,
# watermark,
# )
#
#
# title = """<h1 align="center">🔥Large Language Model API 🚀Streaming🚀</h1>"""
# description = """Language models can be conditioned to act like dialogue agents through a conversational prompt that typically takes the form:
# ```
# User: <utterance>
# Assistant: <utterance>
# User: <utterance>
# Assistant: <utterance>
# ...
# ```
# In this app, you can explore the outputs of multiple LLMs when prompted in this way.
# """
#
# openchat_disclaimer = """
# <div align="center">Checkout the official <a href=https://huggingface.co/spaces/togethercomputer/OpenChatKit>OpenChatKit feedback app</a> for the full experience.</div>
# """
#
# with gr.Blocks(
# css = """#col_container {margin-left: auto; margin-right: auto;}
# #chatbot {height: 520px; overflow: auto;}"""
# ) as demo:
# gr.HTML(title)
# with gr.Column(elem_id = "col_container"):
# model = gr.Radio(
# value = "OpenAssistant/oasst-sft-1-pythia-12b",
# choices = [
# "OpenAssistant/oasst-sft-1-pythia-12b",
# # "togethercomputer/GPT-NeoXT-Chat-Base-20B",
# "google/flan-t5-xxl",
# "google/flan-ul2",
# "bigscience/bloom",
# "bigscience/bloomz",
# "EleutherAI/gpt-neox-20b",
# ],
# label = "Model",
# interactive = True,
# )
#
# chatbot = gr.Chatbot(elem_id = "chatbot")
# inputs = gr.Textbox(
# placeholder = "Hi there!", label = "Type an input and press Enter"
# )
# disclaimer = gr.Markdown(openchat_disclaimer, visible = False)
# state = gr.State([])
# b1 = gr.Button()
#
# with gr.Accordion("Parameters", open = False):
# typical_p = gr.Slider(
# minimum = -0,
# maximum = 1.0,
# value = 0.2,
# step = 0.05,
# interactive = True,
# label = "Typical P mass",
# )
# top_p = gr.Slider(
# minimum = -0,
# maximum = 1.0,
# value = 0.25,
# step = 0.05,
# interactive = True,
# label = "Top-p (nucleus sampling)",
# visible = False,
# )
# temperature = gr.Slider(
# minimum = -0,
# maximum = 5.0,
# value = 0.6,
# step = 0.1,
# interactive = True,
# label = "Temperature",
# visible = False,
# )
# top_k = gr.Slider(
# minimum = 1,
# maximum = 50,
# value = 50,
# step = 1,
# interactive = True,
# label = "Top-k",
# visible = False,
# )
# repetition_penalty = gr.Slider(
# minimum = 0.1,
# maximum = 3.0,
# value = 1.03,
# step = 0.01,
# interactive = True,
# label = "Repetition Penalty",
# visible = False,
# )
# watermark = gr.Checkbox(value = False, label = "Text watermarking")
#
# model.change(
# lambda value: radio_on_change(
# value,
# disclaimer,
# typical_p,
# top_p,
# top_k,
# temperature,
# repetition_penalty,
# watermark,
# ),
# inputs = model,
# outputs = [
# disclaimer,
# typical_p,
# top_p,
# top_k,
# temperature,
# repetition_penalty,
# watermark,
# ],
# )
#
# inputs.submit(
# predict,
# [
# model,
# inputs,
# typical_p,
# top_p,
# temperature,
# top_k,
# repetition_penalty,
# watermark,
# chatbot,
# state,
# ],
# [chatbot, state],
# )
# b1.click(
# predict,
# [
# model,
# inputs,
# typical_p,
# top_p,
# temperature,
# top_k,
# repetition_penalty,
# watermark,
# chatbot,
# state,
# ],
# [chatbot, state],
# )
# b1.click(reset_textbox, [], [inputs])
# inputs.submit(reset_textbox, [], [inputs])
#
# gr.Markdown(description)
# demo.queue(concurrency_count = 16).launch(debug = True)
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