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Timestamps 00:00 - Intro 01:19 - First Look 03:29 - Model Considerations 05:30 - Local Install & Setup 08:36 - First Test 08:54 - Testing Prelude 10:27 - General Testing 11:48 - Increasing Token Limit 12:19 - Python Game Test 13:11 - Refusal Test 13:58 - HTML Test 15:30 - Closing Thoughts In this video, we take a look at a new and unique release from Microsoft: BitNet b1.58, a 1-bit quantized LLM designed for efficient inference on low-power and edge devices. Specifically, we’re testing the 2B 4T GGUF model, which brings bit-level efficiency to the world of local LLMs. We begin with a brief technical overview, discussing how BitNet differs from traditional models and what its 1-bit approach offers in terms of speed, memory usage, and scalability. After that, we walk through the local install and setup, and then run a series of real-world tests to get a feel for how well it performs. These include general chat, Python game generation, refusal handling, and basic HTML output—giving us a rounded look at how BitNet handles different types of prompts, despite its unusually compressed architecture. HuggingFace Repo: https://huggingface.co/microsoft/bitn...