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Basic AI terminology includes: Large language models (LLMs) Deep learning Supervised learning Transformers Foundation models Language Self-supervised learning Generative AI It is important to democratize AI, leveraging the energy and the transparency of open science and open-source AI so that we all have a voice in: What AI is What AI does How AI is used How AI impacts society How AI integrates with your business To help you effectively, safely, and responsibly put AI to work, you must: Protect your data Embrace principles of transparency and trust Make sure that your AI is implemented ethically Empower yourself with platforms and processes to control your AI destiny Foundation models are trained using self-supervised learning. If you give the model a few words in a prompt, it can mathematically predict the likelihood of words in the response. There are basically three models of AI consumption, including: Embedded AI APIs Platform models The future of AI is not about one model, it'...

  pexel To develop a Telegram bot for monitoring, summarizing, and sending periodic overviews of channel posts, follow these steps: Step 1: Set Up Your Environment 1. Install Python : Ensure you have Python installed on your system. 2. Install Required Libraries :     ```python     pip install python-telegram-bot requests beautifulsoup4     ``` Step 2: Create the Telegram Bot 1. Create a Bot on Telegram : Talk to [@BotFather](https://telegram.me/BotFather) to create a new bot. Note the API token provided. Step 3: Develop the Bot 1. Monitor Telegram Channels :     ```python     from telegram import Bot, Update     from telegram.ext import Updater, CommandHandler, MessageHandler, Filters, CallbackContext     import requests     from bs4 import BeautifulSoup     TOKEN = 'YOUR_TELEGRAM_BOT_TOKEN'     CHANNELS = ['@example_channel_1', '@example_channel_2']     SUMMARY_PERIOD = 6...

Understanding Free-threaded CPython and Parallel Execution The new feature in Python 3.13, allowing CPython to run without the Global Interpreter Lock (GIL), is significant for improving parallelism in Python programs. Here’s a detailed explanation along with a code example to illustrate how it works and the benefits it brings: Key Points 1. Disabling the GIL : CPython can be built with the `--disable-gil` option, allowing threads to run in parallel across multiple CPU cores. 2. Parallel Execution : This enables full utilization of multi-core processors, leading to potential performance improvements for multi-threaded programs. 3. Experimental Feature : This is still experimental and may have bugs and performance trade-offs in single-threaded contexts. 4. Optional GIL : The GIL can still be enabled or disabled at runtime using the `PYTHON_GIL` environment variable or the `-X gil` command-line option. 5. C-API Extensions : Extensions need to be adapted to work without the GIL. Demo Code...

  I am trying to preparing a Dataset for Fine-Tuning on Pathology Lab Data. 1. Dataset Collection    - Sources:  Gather data from pathology lab reports, medical journals, and any other relevant medical documents.    - Format:  Ensure that the data is in a readable format like CSV, JSON, or text files. 2. Data Preprocessing    - Cleaning:  Remove any irrelevant data, correct typos, and handle missing values.    - Formatting:  Convert the data into a format suitable for fine-tuning, usually pairs of input and output texts.    - Example Format:      - Input:  "Patient exhibits symptoms of hyperglycemia."      - Output:  "Hyperglycemia" 3. Tokenization    - Tokenize the text using the tokenizer that corresponds to the model you intend to fine-tune. Example Code for Dataset Preparation Using Pandas and Transformers for Preprocessing 1. Install Required Libraries: ...

  Photo by MART PRODUCTION at pexel If you’re interested in customizing an agent for a specific task, one way to do this is to fine-tune the models on your dataset.  For preparing dataset you can see this article . 1. Curate the Dataset - Using NeMo Curator:   - Install NVIDIA NeMo: `pip install nemo_toolkit`   - Use NeMo Curator to prepare your dataset according to your specific requirements. 2. Fine-Tune the Model - Using NeMo Framework:   1. Setup NeMo:      ```python      import nemo      import nemo.collections.nlp as nemo_nlp      ```   2. Prepare the Data:      ```python      # Example to prepare dataset      from nemo.collections.nlp.data.text_to_text import TextToTextDataset      dataset = TextToTextDataset(file_path="path_to_your_dataset")      ```   3. Fine-Tune the Model:      ```python ...

  Photo by energepic.com at pexel You can create your own coding auto-completion co-pilot using Hugging Face LangChain and Phi3 SLM ! Here's a breakdown of the steps involved: 1. Setting Up the Environment: Install the required libraries: Bash pip install langchain transformers datasets phi3 Download the Phi3 SLM model: Bash from transformers import AutoModelForSeq2SeqLM model_name = "princeton-ml/ph3_base" model = AutoModelForSeq2SeqLM.from_pretrained(model_name) 2. Preprocessing Code for LangChain: LangChain provides a AutoTokenizer class to preprocess code. Identify the programming language you want to support and install the corresponding tokenizer from Hugging Face. For example, for Python: Bash from langchain.llms import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained( "openai/gpt-code-code" ) Define a function to preprocess code into LangChain format. This might involve splitting the code into tokens, adding special tokens (e.g., start/e...