2-bit Quantized Muse Glimmer Calls 100+ Tools on 14GB RAM
danielhanchen · x · 2026-08-10
UnslothAI demonstrated how to efficiently run Meta's newly released open-source model, Muse Glimmer. Using 2-bit GGUF quantization, the model successfully executed over 100 tool calls while consuming only 14GB of RAM.
In practical tests, the quantized model autonomously performed a codebase bug hunt for 5 minutes continuously, outputting a complete engineering workflow that included evidence, reproduction steps, code fixes, test cases, and a PR writeup. Users can run and train the model directly via the Unsloth platform.
Related event: Meta Open-Sources 30B Muse Glimmer for On-Device Agents(108 posts)→
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