v0.20.0
Gemma 4 Effective 2B (E2B) ollama run gemma4:e2b Effective 4B (E4B) ollama run gemma4:e4b 26B (Mixture of Experts model with 4B active parameters) ollama run gemma4:26b 31B (Dense) ollama run gemma4:31b What's Changed docs: update pi docs by @ParthSareen in #15152 mlx: respect tokenizer add_bos_token setting in pipeline by @dhiltgen in #15185 tokenizer: add SentencePiece-style BPE support by @dhiltgen in #15162 Full Changelog : v0.19.0...v0.20.0-rc0
Gemma 4
Effective 2B (E2B)
ollama run gemma4:e2b
Effective 4B (E4B)
ollama run gemma4:e4b
26B (Mixture of Experts model with 4B active parameters)
ollama run gemma4:26b
31B (Dense)
ollama run gemma4:31b
What's Changed
-
docs: update pi docs by @ParthSareen in #15152
-
mlx: respect tokenizer add_bos_token setting in pipeline by @dhiltgen in #15185
-
tokenizer: add SentencePiece-style BPE support by @dhiltgen in #15162
Full Changelog: v0.19.0...v0.20.0-rc0
Sign in to highlight and annotate this article
Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
llamamodelupdate
Takedown is not a ticket, but a campaign-suppression system
Most security teams still talk about takedown as if it were one workflow: detect a phishing page, file an abuse report, wait for the host or registrar, close the ticket, move on. That model was always too simple, and it is getting weaker. The better way to think about takedown is this: takedown is the process of reducing attacker operating time across the assets, channels, and trust surfaces a campaign depends on . If your process only removes one URL but leaves the spoofed number, the cloned social profile, the fake app listing, the paid ad, or the next domain in the chain untouched, you did not really suppress the campaign. You trimmed one branch. That distinction matters because modern phishing and scam operations are not domain-only problems. APWG recorded 892,494 phishing attacks in Q
UniCon: A Unified System for Efficient Robot Learning Transfers
arXiv:2601.14617v2 Announce Type: replace Abstract: Deploying learning-based controllers across heterogeneous robots is challenging due to platform differences, inconsistent interfaces, and inefficient middleware. To address these issues, we present UniCon, a lightweight framework that standardizes states, control flow, and instrumentation across platforms. It decomposes workflows into execution graphs with reusable components, separating system states from control logic to enable plug-and-play deployment across various robot morphologies. Unlike traditional middleware, it prioritizes efficiency through batched, vectorized data flow, minimizing communication overhead and improving inference latency. This modular, data-oriented approach enables seamless sim-to-real transfer with minimal re-
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Models
An Open-Source LiDAR and Monocular Off-Road Autonomous Navigation Stack
arXiv:2604.03096v1 Announce Type: new Abstract: Off-road autonomous navigation demands reliable 3D perception for robust obstacle detection in challenging unstructured terrain. While LiDAR is accurate, it is costly and power-intensive. Monocular depth estimation using foundation models offers a lightweight alternative, but its integration into outdoor navigation stacks remains underexplored. We present an open-source off-road navigation stack supporting both LiDAR and monocular 3D perception without task-specific training. For the monocular setup, we combine zero-shot depth prediction (Depth Anything V2) with metric depth rescaling using sparse SLAM measurements (VINS-Mono). Two key enhancements improve robustness: edge-masking to reduce obstacle hallucination and temporal smoothing to mit
FSUNav: A Cerebrum-Cerebellum Architecture for Fast, Safe, and Universal Zero-Shot Goal-Oriented Navigation
arXiv:2604.03139v1 Announce Type: new Abstract: Current vision-language navigation methods face substantial bottlenecks regarding heterogeneous robot compatibility, real-time performance, and navigation safety. Furthermore, they struggle to support open-vocabulary semantic generalization and multimodal task inputs. To address these challenges, this paper proposes FSUNav: a Cerebrum-Cerebellum architecture for fast, safe, and universal zero-shot goal-oriented navigation, which innovatively integrates vision-language models (VLMs) with the proposed architecture. The cerebellum module, a high-frequency end-to-end module, develops a universal local planner based on deep reinforcement learning, enabling unified navigation across heterogeneous platforms (e.g., humanoid, quadruped, wheeled robots
Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!