#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

#python #ai_agents #amd #comfyui #docker #llama_cpp #llm #local_ai #n8n #nvidia #open_webui #rag #self_hosted #speech_to_text #strix_halo #text_to_speech #workflow_automation

Dream Server lets you run AI on your own machine instead of renting it from a cloud service. It works on Linux, Windows, and macOS, and it can set up chat, voice, agents, search, image tools, and privacy tools with one command. The main benefit is more control: your data stays with you, costs can be lower, and you can keep using AI even without a cloud account.

https://github.com/Light-Heart-Labs/DreamServer

#python #cli #consumer_gpu #dpo #fine_tuning #gguf #huggingface #llm #llmops #local_ai #local_llm #lora #low_vram #machine_learning #ollama #peft #python #pytorch #qlora #sft #transformers

Soup is a tool that helps you fine-tune and post-train large language models with one command, using one config file and little setup. It can work on a local GPU, even a 4 GB laptop GPU for an 8B model with layer streaming, so you can train without SSH or cloud hassle. This saves time, reduces setup pain, and makes model training easier to start and manage.

https://github.com/MakazhanAlpamys/Soup