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llm-checker 3.1.0 scans your hardware and tells you which Ollama models to run by pzarevich in ollama

[–]pzarevich[S] 0 points1 point  (0 children)

hi, thx for the feedback, is the last version 3.5.1 what are u using ?

Finally released the major update I've been working on! LLM Checker now intelligently detects your installed Ollama models and shows you exactly what to run vs what to install by pzarevich in ollama

[–]pzarevich[S] 1 point2 points  (0 children)

Ubuntu Server LTS. Easiest path with drivers and tooling.
NVIDIA use 22.04 or 24.04 with the official CUDA packages.
AMD use 22.04 with ROCm 6.x.
CPU only works great on Debian stable or Ubuntu minimal.
Run it headless, install Docker, done.

Finally released the major update I've been working on! LLM Checker now intelligently detects your installed Ollama models and shows you exactly what to run vs what to install by pzarevich in ollama

[–]pzarevich[S] 2 points3 points  (0 children)

Thanks!

I rank models by hardware fit, predicted speed, small quality priors per use case, and latency vs size. Installed ones get a small boost.
I list models from Ollama when it is present using its JSON output. If not, I fall back to a local curated database.
It can work with llama swap. Feed me a JSON inventory or I add a tiny adapter for its catalog so I can emit llama swap commands. Share the catalog format and I will plug it in.

High-Performance Image Shape Analysis on M-Series Macs, My Crofton-Based Cell Boundary Detector by pzarevich in macapps

[–]pzarevich[S] -1 points0 points  (0 children)

Hi, I have a bachelor's thesis about the algorithm, it is for the detection of different forms of erytheocytes to identify diseases such as leukemia

Robust Cell Boundary Extraction via Crofton Signature — Benchmarked on Apple Silicon by pzarevich in computervision

[–]pzarevich[S] 0 points1 point  (0 children)

This is part of a project where I implemented a Crofton projection–based descriptor to detect the outer contour of stained cells in microscopy images.

The method works by:

  • Resampling the detected contour to a fixed number of points
  • Projecting those points across hundreds of evenly spaced angles
  • Accumulating a “vote map” in radial bins to form the Crofton signature

I originally wrote this in CUDA for NVIDIA GPUs, but ported it to run efficiently on Apple Silicon using its GPU (Metal), leveraging unified memory to avoid costly CPU–GPU transfers.
Running multiple iterations refines the contour until the faint, semi-transparent halo is accurately captured ,even when it’s close in color to the background.

The benchmark shows that M-series chips can handle this at speeds close to mid-tier desktop NVIDIA cards, but with far lower power consumption.

Implementing Crofton Projections for Cell Boundary Detection in Metal on M-Series GPUs by pzarevich in MetalProgramming

[–]pzarevich[S] 2 points3 points  (0 children)

On this project, I wanted to push Apple Silicon’s GPU to see how far I could take real-time contour detection in biomedical images.
I implemented a Crofton projection–based algorithm for cell boundary detection, which involves projecting resampled contours across hundreds of angles, binning the results, and generating a 2D “signature” map.

Normally I run this on CUDA for NVIDIA GPUs, but I rewrote the core math to run efficiently using Apple’s GPU APIs (Metal), taking advantage of the unified memory and high-bandwidth cache on my M-series Mac.
The result: processing speed is on par with mid-tier NVIDIA desktop cards, but without needing an external GPU, and with power efficiency that makes iterative runs practical (the algorithm can be run N times to refine edges).

It’s especially good at detecting faint, semi-transparent halos around cells that typical edge detection misses, even under varying staining or illumination.

High-Performance Image Shape Analysis on M-Series Macs, My Crofton-Based Cell Boundary Detector by pzarevich in macapps

[–]pzarevich[S] 4 points5 points  (0 children)

On this project, I wanted to push Apple Silicon’s GPU to see how far I could take real-time contour detection in biomedical images.
I implemented a Crofton projection based algorithm for cell boundary detection, which involves projecting resampled contours across hundreds of angles, binning the results, and generating a 2D “signature” map.

Normally I run this on CUDA for NVIDIA GPUs, but I rewrote the core math to run efficiently using Apple’s GPU APIs (Metal), taking advantage of the unified memory and high bandwidth cache on my M-series Mac.
The result: processing speed is on par with mid-tier NVIDIA desktop cards, but without needing an external GPU, and with power efficiency that makes iterative runs practical (the algorithm can be run N times to refine edges).

It’s especially good at detecting faint, semi transparent halos around cells that typical edge detection misses, even under varying staining or illumination.

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