In this tutorial, we demonstrate how to design a contract-first agentic decision system using PydanticAI, treating structured schemas as non-negotiable governance contracts rather than optional output formats. We show how we define a strict decision model that encodes policy compliance,…
NVIDIA AI research team released NitroGen, an open vision action foundation model for generalist gaming agents that learns to play commercial games directly from pixels and gamepad actions using internet video at scale. NitroGen is trained on 40,000 hours of…
Liquid AI has introduced LFM2-2.6B-Exp, an experimental checkpoint of its LFM2-2.6B language model that is trained with pure reinforcement learning on top of the existing LFM2 stack. The goal is simple, improve instruction following, knowledge tasks, and math for a…
In this tutorial, we build an end-to-end, production-style agentic workflow using GraphBit that demonstrates how graph-structured execution, tool calling, and optional LLM-driven agents can coexist in a single system. We start by initializing and inspecting the GraphBit runtime, then define…
5 Agentic Coding Tips & TricksImage by Editor Introduction Agentic coding only feels “smart” when it ships correct diffs, passes tests, and leaves a paper trail you can trust. The fastest way to get there is to stop asking an…
In this article, you will learn how to fine-tune open-source large language models for customer support using Unsloth and QLoRA, from dataset preparation through training, testing, and comparison. Topics we will cover include: Setting up a Colab environment and installing…
Rotary Position Embeddings (RoPE) is a technique for encoding token positions in a sequence. It is widely used in many models and works well for standard context lengths. However, it requires adaptation for longer contexts. In this article, you will…
import dataclasses import os import datasets import tqdm import tokenizers import torch import torch.nn as nn import torch.nn.functional as F import torch.optim.lr_scheduler as lr_scheduler from torch import Tensor # Load the tokenizer tokenizer = tokenizers.Tokenizer.from_file(“bpe_50K.json”) # Load…
In this article, you will learn three reliable techniques — ordinal encoding, one-hot encoding, and target (mean) encoding — for turning categorical features into model-ready numbers while preserving their meaning. Topics we will cover include: When and how to apply…
A language model is a probability distribution over sequences of tokens. When you train a language model, you want to measure how accurately it predicts human language use. This is a difficult task, and you need a metric to evaluate…
