Joseph Cheng indiejoseph

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Exploring the Vision of AI Smart Watch

Here is some thoughts of mine on the vision of AI Smart Watch. I hope you enjoy it.

Introduction

Running LLM(Large language model) on a embedding device is a trendy topic in the field of AI. The AI Smart Watch is a typical example of such devices. It is also a challenging task because of the limited resources of the device. In this article, I will explore the vision and the solution of AI Smart Watch.

	分析以下幾篇文章內容，以分析員角度分析，歸納成一份學術性文章。

	- 畀出主題、主體同概念
	- 必須用廣東話書寫
	- 為主體人物或概念提供簡介
	- 為主題作總結

	文章內容：

	# 香港電影業新聞與動態

	import queue
	import time
	import threading
	from transformers.generation.logits_process import (
	TopPLogitsWarper,
	RepetitionPenaltyLogitsProcessor,
	)

	@torch.inference_mode()
	def inference_v2(

	version: "3.9"
	services:
	app:
	image: heartexlabs/label-studio:latest
	restart: unless-stopped
	depends_on:
	- db
	expose:
	- "8080"
	environment:

	$ conda activate ml
	(ml) $ conda install ipykernel
	(ml) $ ipython kernel install --user --name=<any_name_for_kernel>
	(ml) $ conda deactivate

	import torchaudio

	def eval(audio, text):
	# convert audio to 16000 sample rate
	audio = torchaudio.transforms.Resample(orig_freq=44100, new_freq=16000)(torch.tensor(audio).unsqueeze(0)).squeeze()
	# process text
	tokenized_seq = torch.tensor([processor.tokenizer(text, add_special_tokens=True).input_ids]).to(device)
	decoder_input_ids = tokenized_seq[:, 1:]
	decoder_input_ids_right_shifted = tokenized_seq[:, :-1]
	# process audio

	const resampling = (audioBuffer: AudioBuffer, targetSampleRate: number): Promise<AudioBuffer> => {
	const offlineAudioContext = new OfflineAudioContext(1, audioBuffer.length, targetSampleRate);

	const source = offlineAudioContext.createBufferSource();
	source.buffer = audioBuffer;

	source.connect(offlineAudioContext.destination);
	source.start();

	return offlineAudioContext.startRendering();

	You are asked to come up with a set of 20 diverse and detailed roleplay scenarios. These scenarios will be given to a large language model and we will evaluate the large language model for completing the roleplay effectively.

	Here are the requirements:

	1. Ensure the roles and scenarios are diverse, covering different contexts, professions, and situations.
	2. The language used for the scenarios should also be diverse. For example, include both formal and informal dialogues.
	3. The type of scenarios should be diverse. Include diverse types of interactions like customer service, medical consultations, casual conversations, educational settings, etc.
	4. A large language model should be able to complete the roleplay. For example, do not include scenarios that require real-time actions or physical interactions.
	5. The scenarios and dialogues should be in Cantonese.
	6. Each scenario should be detailed, providing a clear context and background. Include relevant information such as the setting, characters involv