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Retrieval Augmented Generation (RAG) helps large language models (LLMs) answer questions using up-to-date or private information by connecting them to external data sources, unlike fine-tuning which retrains the model on specific data. RAG is useful when you need current, dynamic information without costly retraining, making it ideal for tasks like customer support or knowledge management. Fine-tuning is better for deep expertise in a specialized field but requires more data and effort. Using RAG lets you get accurate, relevant answers quickly by combining the model’s language skills with fresh, specific data, improving usefulness and reliability.
https://github.com/langchain-ai/rag-from-scratch
Пока весь мир ждет доступа к новой модели со зрением GPT-4V(ision), опенсорс команда (пара азитов со степенью PhD из американских вузов) уже выпустили свой аналог и бесплатную версию #LLaVA (Large Language and Vision Assistant), которая выдает результат (не) хуже GPT4V и может работать локально.
Вот такая скорость развития и конкуренции в этом новом #AI рынке.
🧠LLava - вебсайт
📄WhitePaper
🧬Github code
🔋Demo для потестить на своих дикпиках
🦒Colab (для запуска у себя на серваке)
#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