v0.19.0
Go ahead and axolotl questions. Contribute to axolotl-ai-cloud/axolotl development by creating an account on GitHub.
Axolotl11 min read
PyTorch
194 stories
Release 5.17.0
🤗 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 5.17.0 · huggingface/transformers
Transformers15 min read
PyTorch Conference China 2026: Advancing the Open Source AI Stack
PyTorch Conference China 2026 brought the PyTorch community together in Shanghai on September 8–9 alongside KubeCon + CloudNativeCon and OpenInfra Summit, following sponsor-hosted co-located events on September 7. Technical discussions...
PyTorch Blog6 min readDeepSeek Flash v4.1 🤖, Siri AI 📱, Anthropic AI economics 📈
Bad tool calls, malformed output, and token spend you cannot account for. Agent tracing shows all of it. Serge from Sentry instruments three agents live: a chatbot in an ecommerce store, a custom Slack agent, and a GitHub Action that reviews PRs.
TLDR AI5 min read
Introducing preemptible compute: the same compute, half the price
Together GPU Clusters now supports preemptible compute: the same GPU capacity at a flat 50% of the on-demand rate, with a five-minute drain window.
Together AI Blog4 min read
CUDA Toolkit 13.4 Adds Windows on Arm Support and Greater Control over Shared GPUs
Every NVIDIA CUDA Toolkit release adds functionality and performance improvements that help developers get more from NVIDIA GPUs and the broader NVIDIA software...
NVIDIA Developer Blog13 min read
v1.3.0rc26
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.
TensorRT-LLM22 min read
How to Improve Deep Learning Skills and Become Job-Ready
You can finish three deep learning courses, build a CNN, and still find yourself stuck when the same model stops working on your own dataset. Maybe changing a few parameters does nothing, the validation loss starts behaving strangely, or the model simply refuses to learn.
AI and Machine Learning Roadmaps Blog38 min read
RAG with Llama 3.1
Simple RAG example on the Oscars using Llama 3.1 open models and the Haystack LLM framework. Installation ! pip install transformers-haystack sentence-transformers-haystack bitsandbytes Authorization you need an Hugging Face account you need to accept Meta conditions here:…
Haystack by deepset7 min read🦍 Information Extraction with Gorilla
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.
Haystack by deepset12 min read
NeoMME: an efficient Multimodal-native and Multilingual Encoder
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.
Hugging Face Blog11 min read
v1.3.0rc25
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.
TensorRT-LLM44 min read
Reasoning Models From Scratch: Code Setup
A short video on how conventional LLMs relate to reasoning models and agents, along with Python and PyTorch setup using uv.
Sebastian Raschka1 min read
Release: v5.16.0
🤗 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
Transformers17 min read
Training and Finetuning Multi-Vector Embedding Models with Sentence Transformers
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Hugging Face Blog27 min read
Granite 4.2 LLMs: How They're Built
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.
Hugging Face Blog21 min read
MTIA 300: Meta’s First Training Chip with Built-in NICs and Communication-Offloading Engines
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…
Meta Engineering5 min read
Import AI 470: No rights for machines; automating environment generation with SPADE; and building better GPU kernels with Hawkeye
Welcome to Import AI, a newsletter about AI research. Import AI runs on arXiv, cappuccinos, and feedback from readers. If you’d like to support this, please subscribe.
Import AI (Jack Clark)18 min read
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