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Notebook by Stefano Fiorucci In this experiment, we will use Large Language Models to perform Information Extraction from textual data. 🎯 Goal: create an application that, given a text (or URL) and a specific structure provided by the user, extracts information from the source.
We introduce NeoMME, a family of 260M and 800M multilingual multimodal encoders. Unlike many generative visual language models, NeoMME does not use a separate pretrained vision tower or a causal language model.
TensorRT LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and supports state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs.
A short video on how conventional LLMs relate to reasoning models and agents, along with Python and PyTorch setup using uv.
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training. - Release Release: v5.16.0 · huggingface/transformers
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
TL;DR: Granite 4.2 is our first family of dense, decoder-only reasoning LLMs, released in three sizes: 3B, 8B, and 30B. These models are post-trained from Granite-4.1 base models.
MTIA 300 is the first of Meta’s family of in-house training and inference accelerators optimized for training ranking and recommendation models. We’re sharing how MTIA 300’s built-in NIC chiplets allow it to meet the communication needs associated with training recommendation models with superior…
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A 48-minute video walkthrough of token sampling, watermark detection, and removal
We read the AGENTS.md files of the 100 most-starred repos on GitHub that have one. Here's what they instruct their coding agents, what they ban, and a copy-paste template distilled from all of it
🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch. - Release Diffusers 0.40.0: New pipelines, tensor-parallel support, improved CLI, and more · huggingface/diffusers
OpenRecall failing with a torch and torchvision conflict or a PyObjC error? Here are the four workarounds from the open issues that actually install it.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
An End-to-End Project With Dataset Construction, Model Training, Local Deployment, and RLVR
You have an agent that can already record a demonstration and push it to the Hugging Face Hub. Now you want to run that loop continuously: collect episodes through the day, train a policy on the growing dataset, deploy it, and pull the next batch back to improve it.
NVIDIA Nemotron 3.5 Lightning is now available in Cline, bringing a 30B MoE model with 3B active parameters, up to 1M context, and high throughput for agentic coding workflows.
CodeRabbit worked with NVIDIA and Baseten to post-train NVIDIA Nemotron 3.5 Lightning for one of CodeRabbit's highest-volume routing tasks, improving route agreement while reducing estimated inference cost.
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