Alonso Silva

@alonsosilva.bsky.social

250 Followers 296 Following

AI Researcher @ Nokia Bell Labs. Interested on Large Language Models (LLMs), Machine Learning and Data Analysis.

The image shows the following Python code: ```python from graphviz import Digraph from gvrender import render_graphviz dot = Digraph() dot.edge("Hello", "World") dot.edge("Hello", "Name") render_graphviz(dot) ```
Code to generate a Mermaid diagram Input cell 1: %pip install mermaid-nb Input cell 2: from mermaid_nb import Mermaid Mermaid( diagram=""" flowchart LR A[Start] --> B{Decision} B -->|Yes| C[Continue] B -->|No| D[Stop] """, theme="forest", look="handDrawn", theme_variables={"nodeTextColor": "#0000ff", "fontFamily": "Caveat, cursive"} )
Screenshot of Awesome LLM constrained decoding repo.
The following code: ```python from fastlite import database, diagram from graphviz import Source from pathlib import Path from fastcore.net import urlsave url = 'https://github.com/lerocha/chinook-database/raw/master/ChinookDatabase/DataSources/Chinook_Sqlite.sqlite’ path = Path('chinook.sqlite') if not path.exists(): urlsave(url, path) db = database("chinook.sqlite") diagram(db.t) ``` displays the database diagram.
The following code: ```python import graphviz dot = graphviz.Digraph() dot.edge("hello", "world") dot.edge("hello", "name") dot ``` displays a graph with three nodes: 'hello', 'world', 'name', and two directed edges: from hello to world and from hello to name.
Qwen logo in Chinese.
How to define a logits processor and generate a structured response.
A snippet of the article that reads: "The lab is also dipping into AI. One team is working on integrating large language models with robots for industrial applications. These robots have access to a digital twin model of the space they are in and have a semantic representation of certain objects in their surroundings. In a demo, a robot was verbally asked to identify missing boxes in a rack, and it successfully pointed out which box wasn’t found in its intended place, and when prompted travelled to the storage area and identified the replacement. The key is to build robots that can “reason about the physical world,” says Matthew Andrews, a researcher in the AI lab. A test system will be deployed in a warehouse in the United Arab Emirates in the next six months."
An app showing a user prompt "Hi" and the LLM probabilities to the reply word.
<think> Okay, I need to help the user by providing timestamps and chapters for their given text. The text is a transcript of someone explaining how they set up a vector database using Milvus or Qdrant with song lyrics. They mentioned creating a table, inserting data, and querying it. First, I'll read through the entire text to understand the flow. It starts by talking about two types of queries: one about a song with nightmares (Inter Sandman) and another about a guy with wounds (Stairway to Heaven). Then they mention that the results aren't perfect but still good. I should break this down into logical chapters. Maybe start with an introduction, then setting up the database, inserting data, querying for the first song, querying for the second song, discussing imperfections, and a conclusion. Looking at the timestamps provided earlier, each chapter is about 30 seconds to a minute long. I'll need to assign timestamps based on where each section starts in the original text. I should make sure the chapters are clear and cover all main points without overlapping too much. Also, the timestamps should be accurate based on the user's initial example. Finally, I'll present this in a structured way, using bold for chapter titles and italics for descriptions, along with the start and end times. </think> Here’s how you can structure the content into chapters with their respective timestamps: Chapter 1: Introduction to Vector Databases Timestamp: 00:00:00 - 00:01:00 Introduction to vector databases like Milvus or Qdrant and their relevance in handling text data.
Air Katakana @airkatakana "¡ do not know javascript, i do not know how the internet works, and i have no idea how to make an app" "so how are you building all this?" "through my ability to tell claude what i want in a way it understands, and my ability to evaluate whether or not its output makes sense" 4:07 AM • 12/31/24 • 495K Views
It reads: Everything’s high risk if you’re a p*ssy
It reads: “Death has never been closer”.
Picture of the speaker presenting.
Anuncio de la charla
Afiche de anuncio de la charla.
Anuncio de charla: Potenciando aplicaciones RAG con grafos de conocimiento.
E-mail of acceptance of the talk. The text reads: PyData Global 2024 Your proposal: Building Knowledge Graph-Based Agents with Structured Text Generation and Open-Weights Models Hello Alonso Silva, We are pleased to inform you that your Talk proposal, Building Knowledge Graph-Based Agents with Structured Text Generation and Open-Weights Models, has been accepted! Thank you for submitting and for your interest in speaking at PyData Global 2024. We’re looking forward to an incredible event!
Overview of the slides of the talk: uv+marimo
Xavier Niel, Founder of the Illiad Group , presenting at ai-PULSE
Annoucement by Outlines about the new added example.
Outlines describe the new Knowledge Graph extraction example