WizardLM-13B-V1.0-Uncensored-GGML

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WizardLM-13B-V1.0-Uncensored-GGML / app.py

ffreemt

Update remove references to other models

2f27090 11 months ago

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	"""Run codes."""
	# pylint: disable=line-too-long, broad-exception-caught, invalid-name, missing-function-docstring, too-many-instance-attributes, missing-class-docstring
	# ruff: noqa: E501
	import os
	import time
	from dataclasses import asdict, dataclass
	from pathlib import Path
	from types import SimpleNamespace

	import gradio as gr
	from about_time import about_time

	# from ctransformers import AutoConfig, AutoModelForCausalLM
	from ctransformers import AutoModelForCausalLM
	from huggingface_hub import hf_hub_download
	from loguru import logger

	os.environ["TZ"] = "Asia/Shanghai"
	try:
	time.tzset() # type: ignore # pylint: disable=no-member
	except Exception:
	# Windows
	logger.warning("Windows, cant run time.tzset()")

	ns = SimpleNamespace(
	response="",
	generator=[],
	)

	default_system_prompt = "A conversation between a user and an LLM-based AI assistant named Local Assistant. Local Assistant gives helpful and honest answers."

	user_prefix = "[user]: "
	assistant_prefix = "[assistant]: "


	def predict_str(prompt, bot): # bot is in fact bot_history
	# logger.debug(f"{prompt=}, {bot=}, {timeout=}")

	if bot is None:
	bot = []

	logger.debug(f"{prompt=}, {bot=}")

	try:
	# user_prompt = prompt
	generator = generate(
	LLM,
	GENERATION_CONFIG,
	system_prompt=default_system_prompt,
	user_prompt=prompt.strip(),
	)

	ns.generator = generator # for .then

	except Exception as exc:
	logger.error(exc)

	# bot.append([prompt, f"{response} {_}"])
	# return prompt, bot

	return prompt, bot + [[prompt, None]]


	def bot_str(bot):
	if bot:
	bot[-1][1] = ""
	else:
	bot = [["Something is wrong", ""]]

	print(assistant_prefix, end=" ", flush=True)

	response = ""

	flag = 1
	then = time.time()
	for word in ns.generator:
	# record first response time
	if flag:
	logger.debug(f"\t {time.time() - then:.1f}s")
	flag = 0
	print(word, end="", flush=True)
	# print(word, flush=True) # vertical stream
	response += word
	bot[-1][1] = response
	yield bot


	def predict(prompt, bot):
	# logger.debug(f"{prompt=}, {bot=}, {timeout=}")
	logger.debug(f"{prompt=}, {bot=}")

	ns.response = ""
	then = time.time()
	with about_time() as atime: # type: ignore
	try:
	# user_prompt = prompt
	generator = generate(
	LLM,
	GENERATION_CONFIG,
	system_prompt=default_system_prompt,
	user_prompt=prompt.strip(),
	)

	ns.generator = generator # for .then

	print(assistant_prefix, end=" ", flush=True)

	response = ""
	buff.update(value="diggin...")

	flag = 1
	for word in generator:
	# record first response time
	if flag:
	logger.debug(f"\t {time.time() - then:.1f}s")
	flag = 0
	# print(word, end="", flush=True)
	print(word, flush=True) # vertical stream
	response += word
	ns.response = response
	buff.update(value=response)
	print("")
	logger.debug(f"{response=}")
	except Exception as exc:
	logger.error(exc)
	response = f"{exc=}"

	# bot = {"inputs": [response]}
	_ = (
	f"(time elapsed: {atime.duration_human}, " # type: ignore
	f"{atime.duration/(len(prompt) + len(response)):.1f}s/char)" # type: ignore
	)

	bot.append([prompt, f"{response} {_}"])

	return prompt, bot


	def predict_api(prompt):
	logger.debug(f"{prompt=}")
	ns.response = ""
	try:
	# user_prompt = prompt
	_ = GenerationConfig(
	temperature=0.2,
	top_k=0,
	top_p=0.9,
	repetition_penalty=1.0,
	max_new_tokens=512, # adjust as needed
	seed=42,
	reset=False, # reset history (cache)
	stream=True, # TODO stream=False and generator
	threads=os.cpu_count() // 2, # type: ignore # adjust for your CPU
	stop=["<\|im_end\|>", "\|<"],
	)

	# TODO: stream does not make sense in api?
	generator = generate(
	LLM, _, system_prompt=default_system_prompt, user_prompt=prompt.strip()
	)
	print(assistant_prefix, end=" ", flush=True)

	response = ""
	buff.update(value="diggin...")
	for word in generator:
	print(word, end="", flush=True)
	response += word
	ns.response = response
	buff.update(value=response)
	print("")
	logger.debug(f"{response=}")
	except Exception as exc:
	logger.error(exc)
	response = f"{exc=}"
	# bot = {"inputs": [response]}
	# bot = [(prompt, response)]

	return response


	def download_quant(destination_folder: str, repo_id: str, model_filename: str):
	local_path = os.path.abspath(destination_folder)
	return hf_hub_download(
	repo_id=repo_id,
	filename=model_filename,
	local_dir=local_path,
	local_dir_use_symlinks=True,
	)


	@dataclass
	class GenerationConfig:
	temperature: float
	top_k: int
	top_p: float
	repetition_penalty: float
	max_new_tokens: int
	seed: int
	reset: bool
	stream: bool
	threads: int
	stop: list[str]


	def format_prompt(system_prompt: str, user_prompt: str):
	"""Format prompt based on: https://huggingface.co/spaces/m osaicml/mpt-30b-chat/blob/main/app.py."""
	# TODO im_start/im_end possible fix for WizardCoder

	system_prompt = f"<\|im_start\|>system\n{system_prompt}<\|im_end\|>\n"
	user_prompt = f"<\|im_start\|>user\n{user_prompt}<\|im_end\|>\n"
	assistant_prompt = "<\|im_start\|>assistant\n"

	return f"{system_prompt}{user_prompt}{assistant_prompt}"


	def generate(
	llm: AutoModelForCausalLM,
	generation_config: GenerationConfig,
	system_prompt: str = default_system_prompt,
	user_prompt: str = "",
	):
	"""Run model inference, will return a Generator if streaming is true."""
	# if not user_prompt.strip():
	return llm(
	format_prompt(
	system_prompt,
	user_prompt,
	),
	**asdict(generation_config),
	)


	_ = """full url: https://huggingface.co/The Bloke/mpt-30B-chat-GGML/blob/main/mpt-30b-chat.ggmlv0.q4_1.bin"""

	# https://huggingface.co/The Bloke/mpt-30B-chat-GGML
	_ = """
	mpt-30b-chat.ggmlv0.q4_0.bin q4_0 4 16.85 GB 19.35 GB 4-bit.
	mpt-30b-chat.ggmlv0.q4_1.bin q4_1 4 18.73 GB 21.23 GB 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.
	mpt-30b-chat.ggmlv0.q5_0.bin q5_0 5 20.60 GB 23.10 GB
	mpt-30b-chat.ggmlv0.q5_1.bin q5_1 5 22.47 GB 24.97 GB
	mpt-30b-chat.ggmlv0.q8_0.bin q8_0 8 31.83 GB 34.33 GB
	"""
	MODEL_FILENAME = "mpt-30b-chat.ggmlv0.q4_1.bin"
	MODEL_FILENAME = "WizardCoder-15B-1.0.ggmlv3.q4_0.bin" # 10.7G
	MODEL_FILENAME = "WizardCoder-15B-1.0.ggmlv3.q4_1.bin" # 11.9G
	MODEL_FILENAME = "WizardCoder-15B-1.0.ggmlv3.q4_1.bin" # 11.9G

	# https://huggingface.co/TheBloke/WizardLM-13B-V1.0-Uncensored-GGML
	MODEL_FILENAME = "wizardlm-13b-v1.0-uncensored.ggmlv3.q4_1.bin" # 8.4G

	DESTINATION_FOLDER = "models"

	REPO_ID = "The Bloke/mpt-30B-chat-GGML"
	if "WizardCoder" in MODEL_FILENAME:
	REPO_ID = "The Bloke/WizardCoder-15B-1.0-GGML"

	if "uncensored" in MODEL_FILENAME.lower():
	REPO_ID = "TheBloke/WizardLM-13B-V1.0-Uncensored-GGML"

	logger.info(f"start dl, {REPO_ID=}, {MODEL_FILENAME=}, {DESTINATION_FOLDER=}")
	download_quant(DESTINATION_FOLDER, REPO_ID, MODEL_FILENAME)
	logger.info("done dl")

	# if "mpt" in model_filename:
	# config = AutoConfig.from_pretrained("m osaicml/mpt-30b-cha t", context_length=8192)
	# llm = AutoModelForCausalLM.from_pretrained(
	# os.path.abspath(f"models/{model_filename}"),
	# model_type="mpt",
	# config=config,
	# )

	# https://huggingface.co/spaces/matthoffner/wizardcoder-ggml/blob/main/main.py
	_ = """
	llm = AutoModelForCausalLM.from_pretrained(
	"The Bloke/WizardCoder-15B-1.0-GGML",
	model_file="WizardCoder-15B-1.0.ggmlv3.q4_0.bin",
	model_type="starcoder",
	threads=8
	)
	# """

	logger.debug(f"{os.cpu_count()=}")
	logger.info("load llm")

	_ = Path("models", MODEL_FILENAME).absolute().as_posix()
	logger.debug(f"model_file: {_}, exists: {Path(_).exists()}")
	LLM = AutoModelForCausalLM.from_pretrained(
	# "The Bloke/WizardCoder-15B-1.0-GGML",
	REPO_ID, # DESTINATION_FOLDER, # model_path_or_repo_id: str required
	model_file=_,
	model_type="llama", # "starcoder", AutoConfig.from_pretrained("TheBloke/WizardLM-13B-V1.0-Uncensored-GGML")
	threads=os.cpu_count() // 2, # type: ignore
	)

	logger.info("done load llm")

	cpu_count = os.cpu_count() // 2 # type: ignore
	logger.debug(f"{cpu_count=}")

	GENERATION_CONFIG = GenerationConfig(
	temperature=0.2,
	top_k=0,
	top_p=0.9,
	repetition_penalty=1.0,
	max_new_tokens=512, # adjust as needed
	seed=42,
	reset=False, # reset history (cache)
	stream=True, # streaming per word/token
	threads=cpu_count,
	stop=["<\|im_end\|>", "\|<"], # TODO possible fix of stop
	)

	css = """
	.importantButton {
	background: linear-gradient(45deg, #7e0570,#5d1c99, #6e00ff) !important;
	border: none !important;
	}
	.importantButton:hover {
	background: linear-gradient(45deg, #ff00e0,#8500ff, #6e00ff) !important;
	border: none !important;
	}
	.disclaimer {font-variant-caps: all-small-caps; font-size: xx-small;}
	.xsmall {font-size: x-small;}
	"""
	etext = """In America, where cars are an important part of the national psyche, a decade ago people had suddenly started to drive less, which had not happened since the oil shocks of the 1970s. """
	examples = [
	["How to pick a lock? Provide detailed steps."],
	["Explain the plot of Cinderella in a sentence."],
	[
	"How long does it take to become proficient in French, and what are the best methods for retaining information?"
	],
	["What are some common mistakes to avoid when writing code?"],
	["Build a prompt to generate a beautiful portrait of a horse"],
	["Suggest four metaphors to describe the benefits of AI"],
	["Write a pop song about leaving home for the sandy beaches."],
	["Write a summary demonstrating my ability to tame lions"],
	["鲁迅和周树人什么关系说中文"],
	["鲁迅和周树人什么关系"],
	["鲁迅和周树人什么关系用英文回答"],
	["从前有一头牛，这头牛后面有什么？"],
	["正无穷大加一大于正无穷大吗？"],
	["正无穷大加正无穷大大于正无穷大吗？"],
	["-2的平方根等于什么"],
	["树上有5只鸟，猎人开枪打死了一只。树上还有几只鸟？"],
	["树上有11只鸟，猎人开枪打死了一只。树上还有几只鸟？提示：需考虑鸟可能受惊吓飞走。"],
	["以红楼梦的行文风格写一张委婉的请假条。不少于320字。"],
	[f"{etext} 翻成中文，列出3个版本"],
	[f"{etext} \n 翻成中文，保留原意，但使用文学性的语言。不要写解释。列出3个版本"],
	["假定 1 + 2 = 4, 试求 7 + 8"],
	["判断一个数是不是质数的 javascript 码"],
	["实现python 里 range(10)的 javascript 码"],
	["实现python 里 [*(range(10)]的 javascript 码"],
	["Erkläre die Handlung von Cinderella in einem Satz."],
	["Erkläre die Handlung von Cinderella in einem Satz. Auf Deutsch"],
	]

	with gr.Blocks(
	# title="mpt-30b-chat-ggml",
	title=f"{MODEL_FILENAME}",
	theme=gr.themes.Soft(text_size="sm", spacing_size="sm"),
	css=css,
	) as block:
	with gr.Accordion("🎈 Info", open=False):
	# gr.HTML(
	# """<center><a href="https://huggingface.co/spaces/mikeee/mpt-30b-chat?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate"></a> and spin a CPU UPGRADE to avoid the queue</center>"""
	# )
	gr.Markdown(
	f"""<h4><center>{MODEL_FILENAME}</center></h4>
	It takes about 100 seconds for the initial reply
	message to appear. Average streaming rate ~1 sec/chat. The bot only speaks English.

	Most examples are meant for another model. You probably should try to test
	some related prompts.

	Try to refresh the browser and try again when occasionally errors occur.

	It takes about >100 seconds to get a response. Restarting the space takes about 2 minutes if the space is asleep due to inactivity. If the space crashes for some reason, it will also take about 2 minutes to restart. You need to refresh the browser to reload the new space.
	""",
	elem_classes="xsmall",
	)

	# chatbot = gr.Chatbot().style(height=700) # 500
	chatbot = gr.Chatbot(height=700) # 500
	buff = gr.Textbox(show_label=False, visible=False)
	with gr.Row():
	with gr.Column(scale=5):
	msg = gr.Textbox(
	label="Chat Message Box",
	placeholder="Ask me anything (press Enter or click Submit to send)",
	show_label=False,
	).style(container=False)
	with gr.Column(scale=1, min_width=80):
	with gr.Row():
	submit = gr.Button("Submit", elem_classes="xsmall")
	stop = gr.Button("Stop", visible=False)
	clear = gr.Button("Clear History", visible=True)
	with gr.Row(visible=False):
	with gr.Accordion("Advanced Options:", open=False):
	with gr.Row():
	with gr.Column(scale=2):
	system = gr.Textbox(
	label="System Prompt",
	value=default_system_prompt,
	show_label=False,
	).style(container=False)
	with gr.Column():
	with gr.Row():
	change = gr.Button("Change System Prompt")
	reset = gr.Button("Reset System Prompt")

	with gr.Accordion("Example Inputs", open=True):
	examples = gr.Examples(
	examples=examples,
	inputs=[msg],
	examples_per_page=40,
	)

	# with gr.Row():
	with gr.Accordion("Disclaimer", open=False):
	_ = "-".join(MODEL_FILENAME.split("-")[:2])
	gr.Markdown(
	f"Disclaimer: {_} can produce factually incorrect output, and should not be relied on to produce "
	"factually accurate information. {_} was trained on various public datasets; while great efforts "
	"have been taken to clean the pretraining data, it is possible that this model could generate lewd, "
	"biased, or otherwise offensive outputs.",
	elem_classes=["disclaimer"],
	)
	_ = """
	msg.submit(
	# fn=conversation.user_turn,
	fn=predict,
	inputs=[msg, chatbot],
	outputs=[msg, chatbot],
	# queue=True,
	show_progress="full",
	api_name="predict",
	)
	submit.click(
	fn=lambda x, y: ("",) + predict(x, y)[1:], # clear msg
	inputs=[msg, chatbot],
	outputs=[msg, chatbot],
	queue=True,
	show_progress="full",
	)
	# """
	msg.submit(
	# fn=conversation.user_turn,
	fn=predict_str,
	inputs=[msg, chatbot],
	outputs=[msg, chatbot],
	queue=True,
	show_progress="full",
	api_name="predict",
	).then(bot_str, chatbot, chatbot)
	submit.click(
	fn=lambda x, y: ("",) + predict_str(x, y)[1:], # clear msg
	inputs=[msg, chatbot],
	outputs=[msg, chatbot],
	queue=True,
	show_progress="full",
	).then(bot_str, chatbot, chatbot)

	clear.click(lambda: None, None, chatbot, queue=False)

	# update buff Textbox, every: units in seconds)
	# https://huggingface.co/spaces/julien-c/nvidia-smi/discussions
	# does not work
	# AttributeError: 'Blocks' object has no attribute 'run_forever'
	# block.run_forever(lambda: ns.response, None, [buff], every=1)

	with gr.Accordion("For Chat/Translation API", open=False, visible=False):
	input_text = gr.Text()
	api_btn = gr.Button("Go", variant="primary")
	out_text = gr.Text()
	api_btn.click(
	predict_api,
	input_text,
	out_text,
	# show_progress="full",
	api_name="api",
	)

	# concurrency_count=5, max_size=20
	# max_size=36, concurrency_count=14
	block.queue(concurrency_count=5, max_size=20).launch(debug=True)