A neural-symbolic AI agent system for biomedical concept mapping
npj Digital Medicine, Published online: 04 April 2026; doi:10.1038/s41746-026-02594-6 A neural-symbolic AI agent system for biomedical concept mapping
Could not retrieve the full article text.
Read on nature.com →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
agentpublished
Gemma4 26B A4B runs easily on 16GB Macs
Typically, models in the 26B-class range are difficult to run on 16GB macs because any GPU acceleration requires the accelerated layers to sit entirely within wired memory. It's possible with aggressive quants (2 bits, or maybe a very lightweight IQ3_XXS), but quality degrades significantly by doing so. However, if run entirely on the CPU instead (which is much more feasible with MoE models), it's possible to run really good quants even when the models end up being larger than the entire available system RAM. There is some performance loss from swapping in and out experts, but I find that the performance loss is much less than I would have expected. I was able to easily achieve 6-10 tps with a context window of 8-16K on my M2 Macbook Pro (tested using IQ4_NL and Q5_K_S). Far from fast, but
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Products
![[P] MCGrad: fix calibration of your ML model in subgroups](https://d2xsxph8kpxj0f.cloudfront.net/310419663032563854/konzwo8nGf8Z4uZsMefwMr/default-img-earth-satellite-QfbitDhCB2KjTsjtXRYcf9.webp)
[P] MCGrad: fix calibration of your ML model in subgroups
Hi r/MachineLearning , We’re open-sourcing MCGrad , a Python package for multicalibration–developed and deployed in production at Meta. This work will also be presented at KDD 2026. The Problem: A model can be globally calibrated yet significantly miscalibrated within identifiable subgroups or feature intersections (e.g., "users in region X on mobile devices"). Multicalibration aims to ensure reliability across such subpopulations. The Solution: MCGrad reformulates multicalibration using gradient boosted decision trees. At each step, a lightweight booster learns to predict residual miscalibration of the base model given the features, automatically identifying and correcting miscalibrated regions. The method scales to large datasets, and uses early stopping to preserve predictive performanc
b8663
common : respect specified tag, only fallback when tag is empty ( #21413 ) Signed-off-by: Adrien Gallouët [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, ACL Graph) openEuler aarch64 (310p) openEuler aarch64 (910b, ACL Graph)
I can't use the service anymore
I get this message while having a pro subscription: Error: Failed to perform inference: You have depleted your monthly included credits. Purchase pre-paid credits to continue using Inference Providers. Can you help me? Thank you Louis 1 post - 1 participant Read full topic
Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!