#go #gemma #gemma2 #go #golang #llama #llama2 #llama3 #llava #llm #llms #mistral #ollama #phi3

Ollama is a tool that lets you use large language models on your own computer. You can download and install it for macOS, Windows, or Linux. It supports various models like Llama 3.2, Phi 3, and others, which you can run locally using simple commands. For example, to run the Llama 3.2 model, you just need to type `ollama run llama3.2`.

The benefit to you is that you can use powerful language models without relying on cloud services, ensuring your data stays private and secure. You can also customize the models with specific prompts and settings to fit your needs. Additionally, there are many community integrations and libraries available to extend its functionality in various applications.

https://github.com/ollama/ollama

#python #cuda #deepseek #deepseek_llm #deepseek_v3 #inference #llama #llama2 #llama3 #llama3_1 #llava #llm #llm_serving #moe #pytorch #transformer #vlm

SGLang is a tool that makes working with large language models and vision language models much faster and more manageable. It has a fast backend runtime that optimizes model performance with features like prefix caching, continuous batching, and quantization. The frontend language is flexible and easy to use, allowing for complex tasks like chained generation calls and multi-modal inputs. SGLang supports many different models and has an active community behind it. This means you can get your models running quickly and efficiently, saving time and resources. Additionally, the extensive documentation and community support make it easier to get started and resolve any issues.

https://github.com/sgl-project/sglang

#python #apple_silicon #florence2 #idefics #llava #llm #local_ai #mlx #molmo #paligemma #pixtral #vision_framework #vision_language_model #vision_transformer

MLX-VLM lets you run, chat with, and fine-tune Vision Language Models (VLMs) plus audio/video models on your Mac using MLX—install easily with `pip install -U mlx-vlm`. Use CLI for quick text/image/audio generation (e.g., `mlx_vlm.generate --model ... --image photo.jpg`), Gradio UI for chats, Python scripts, or a FastAPI server with OpenAI-compatible endpoints supporting multi-images/videos. Features like TurboQuant cut KV cache memory by 76%, and LoRA/QLoRA fine-tuning works on consumer hardware. You benefit by experimenting with powerful multimodal AI locally—fast, memory-efficient, no cloud costs, perfect for Mac users tweaking models affordably.

https://github.com/Blaizzy/mlx-vlm