b8608
llama : refactor llama_model_quantize_params to expose a pure C interface ( #20346 ) Refactor llama_model_quantize_params to expose a pure C interface Restore comment and cleanup struct def Code review refactoring Co-authored-by: Georgi Gerganov [email protected] Code review refactoring Co-authored-by: Georgi Gerganov [email protected] macOS/iOS: macOS Apple Silicon (arm64) macOS Intel (x64) iOS XCFramework Linux: Ubuntu x64 (CPU) Ubuntu arm64 (CPU) Ubuntu s390x (CPU) Ubuntu x64 (Vulkan) Ubuntu arm64 (Vulkan) Ubuntu x64 (ROCm 7.2) Ubuntu x64 (OpenVINO) Windows: Windows x64 (CPU) Windows arm64 (CPU) Windows x64 (CUDA 12) - CUDA 12.4 DLLs Windows x64 (CUDA 13) - CUDA 13.1 DLLs Windows x64 (Vulkan) Windows x64 (SYCL) Windows x64 (HIP) openEuler: openEuler x86 (310p) openEuler x86 (910b, A
Provide feedback
Saved searches
Use saved searches to filter your results more quickly
Sign up
Appearance settings
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
llamamodelreview
Cloud Cost Anomaly Detection: How to Catch Surprise Bills Before They Hit
Cloud Cost Anomaly Detection: How to Catch Surprise Bills Before They Hit Cloud bills don't spike gradually. They spike overnight. A misconfigured NAT gateway starts routing all inter-AZ traffic inefficiently on a Friday. A data pipeline job enters an infinite retry loop on Saturday. A developer spins up a p3.8xlarge for a test and forgets to terminate it over a long weekend. By the time you find out, you've already burned through budget that wasn't allocated for it. The problem isn't that anomalies happen. The problem is the detection lag: most teams don't discover a cost spike until the invoice arrives 30 days later. With the right alerting in place, you catch the same spike in under 6 hours. This is the practical guide to setting that up. Why Cloud Bills Spike (And Why You Don't Find Ou
Cloud Observability vs Monitoring: What's the Difference and Why It Matters
Cloud Observability vs Monitoring: What's the Difference and Why It Matters Your alerting fires at 2 AM. CPU is at 94%, error rate is at 6.2%, and latency is climbing. You page the on-call engineer. They open the dashboard. They see the numbers going up. What they cannot see is why — because the service throwing errors depends on three upstream services, one of which depends on a database that is waiting on a connection pool that was quietly exhausted by a batch job that ran 11 minutes ago. Monitoring told you something was wrong. Observability would have told you what. This is not a semantic argument. Teams with mature observability resolve incidents 2.8x faster than teams that rely on monitoring alone, according to DORA research. The gap matters in production. Understanding why the gap e
How to Write Custom Semgrep Rules: Complete Tutorial
Why write custom Semgrep rules Semgrep ships with over 2,800 community rules and 20,000+ Pro rules that cover common security vulnerabilities, best practice violations, and correctness issues across more than 30 programming languages. For many teams, these pre-built rule sets are enough to catch the most critical problems. But every codebase has patterns, APIs, and conventions that are unique to its organization - and that is where custom rules become essential. Custom Semgrep rules let you codify institutional knowledge into automated checks. When a senior engineer discovers a subtle misuse of an internal API, they can write a rule that catches that mistake everywhere it appears and prevents it from being introduced again. When your security team identifies a vulnerability pattern specifi
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!