I thought that the best option to run Frigate is to run bare metal and skip virtualization and system containers. However now situation changed a little bit as I was able to fire up Frigate on LXC container on Proxmox with little help of AMD ROCm hardware assisted video decryption. And yes, detection crashes on ONNX and need to run on CPU instead… but video decryption works well. And even more, detection on 16 x AMD Ryzen 7 255 w/ Radeon 780M Graphics (1 Socket) works very well for almost 20 video streams (mixed H264 and H265). You can switch
Quick overview of LLM MLX LORA training parameters. weight_decay A regularization technique that adds a small penalty to the weights during training to prevent them from growing too large, helping to reduce overfitting. Often implemented as L2 regularization.examples: 0.00001 – 0.01 grad_clip Short for gradient clipping — a method that limits (clips) the size of gradients during backpropagation to prevent exploding gradients and stabilize training.examples: 0.1 – 1.0 rank Refers to the dimensionality or the number of independent directions in a matrix or tensor. In low-rank models, it controls how much the model compresses or approximates the original data.examples: 4,
I have done over 500 training sessions using Qwen2.5, Qwen3, Gemma and plenty other LLM publicly available to inject domain specific knowledge into the model’s low rank adapters (LORA). However, instead of giving you tons of unimportant facts I will just stick to the most important things. Starting with the fact that I have used MLX on my Mac Studio M2 Ultra as well as on MacBook Pro M1 Pro. Both fit well to this task in terms of BF16 speed as well as unified memory capacity and speed (up to 800GB/s). Memory speed is the most important factor comparing
Theory: running AI workload spreaded across various devices using pipeline parallel inference In theory Exo provides a way to run memory heavy AI/LLM models workload onto many different devices spreading memory and computations across. They say: “Unify your existing devices into one powerful GPU: iPhone, iPad, Android, Mac, NVIDIA, Raspberry Pi, pretty much any device!“ People say: “It requires mlx but it is an Apple silicon-only library as far as I can tell. How is it supposed to be (I quote) “iPhone, iPad, Android, Mac, Linux, pretty much any device” ? Has it been tested on anything else than the
What is Qwen? This is the organization of Qwen, which refers to the large language model family built by Alibaba Cloud. In this organization, we continuously release large language models (LLM), large multimodal models (LMM), and other AGI-related projects. Check them out and enjoy! What models do they provide? They provide wide range of models, since 2023. Original model was just called Qwen and can be still found on GitHub. The current model Qwen2.5 has its own repository, also on GitHub. General purpose models are just Qwen, but there are also code specific models. There are also Math, Audio and
Well, in one of the previous articles I described how to invoke Ollama/moondream:1.8b using cURL, however I forgot to tell how to even run it in Docker container. So here you go: You can specify to run particular model in background (-d) or in foreground (without parameter -d). You can also define parallelism and maximum queue in Ollama server: One important one regarding stability of Ollama server. Once it runs for over few hours there might be issue with GPU driver which requires restart, so Ollama needs to be monitored for such scenario. Moreover after minutes of idle time it
What is OpenVINO? “Open-source software toolkit for optimizing and deploying deep learning models.” It is developed by Intel since 2018. It supports LLM, computer vision and generative AI. It runs on Windows, Linux and MacOS. As for Ubuntu, it is recommended to run on 22.04 LTS and higher. It utilizes OpenCL drivers. In theory, libraries using OpenCL (such as OpenVINO) should be cross platform contrary to vendor-locked similar solutions like CUDA. In theory OpenVINO should then work on both Intel and AMD hardware. Internet says that is works, but as for now I need to order some additional hardware to
Given this image: You would like to describe it using Ollama and moondream:1.8b model you can try cURL. First encode image in base64: Then prepare request: And finally invoke cURL pointing at your Ollama server running: In response you could get somehing like this:
These are two majors which allow to run object detection models. Google Coral TPU is a physical module which can be in a form of USB stick. TensorRT is a feature of GPU runtime. Both allows to run detection models on them. Coral TPU: And TensorRT: Compute Capabilities requirements CC 5.0 is required to run DeepStack and TensorRT, but 7.0 to run Ollama moondream:1.8b. Even having GPU with CC 5.0 which is minimum required to run for instance TensorRT might be not enough due to some minor differences in implementation. It is better to run on GPU with higher CC.