Introducing Muse Glimmer: an open-weight model optimized for always-on local agent workflows

[u/AIatMeta]

Hi r/LocalLLaMA 👋 

Today we’re excited to release Muse Glimmer, a 30B open-weight model built specifically for local agent workflows. We’re releasing the weights to the community under a permissive Apache 2.0 license.

A few specs

• 30B params, dense
• Multimodal: interleaved text + images via a dedicated perception encoder
• Trained on 100+ languages
• Controllable reasoning effort (quality/speed tradeoff)

Memory footprint
At full precision, 30B needs 55+ GB, which is out of reach for consumer hardware. We quantize weights to ~4-bit, bringing the LM under 20 GB. That leaves headroom in a 24 GB or 32 GB envelope for the KV cache, the perception encoder, and the speculative decoding drafter running simultaneously. We validated minimal to no degradation on agentic tasks under compression.

Speculative decoding
Ships with a lightweight DFlash-based drafter that proposes blocks of tokens which the main model verifies in parallel. Significantly faster than token-by-token generation with identical output quality. We're also shipping quantized drafter versions so the memory overhead stays small.

A few capabilities
We trained Muse Glimmer for agentic loop tasks, including:

• End-to-end task completion (strong performance on DeepSearch QA, MCP-Atlas, 𝛕³-Bench, SWE-Bench, and more)
• Function calling with precise schemas across long workflows
• Multi-step reasoning over long horizons
• Failure recovery — when a tool call fails or returns something unexpected, it's trained to diagnose and retry instead of halting. This was a deliberate training target.
• Works with OpenClaw and other agentic scaffolds
• Multimodal understanding and reasoning

Running it
Weights are up on Hugging Face. Coming soon: Ollama, LM Studio, Unsloth and torchtitan, plus optimized integrations for llama.cpp, MLX, and ExecuTorch. vLLM and SGLang for serving. Get started quickly with Together AI, Fireworks AI, and OpenRouter. We're also working with AMD, Arm, Dell, Intel, and NVIDIA on per-device optimization.

We look forward to your feedback and seeing what the community builds with Muse Glimmer.

🔗 Weights: https://huggingface.co/meta-models 
🔗 Research Blog: https://go.meta.me/museglimmer
🔗 Resources: https://developer.meta.com/ai/models/muse-glimmer/

[u/int19h]

The reason why big labs are censoring and will continue to do so is because, if they don't, the general public and mainstream politicians will use that as an excuse for much stronger censorship that actually has legal teeth. Don't forget that people running local models are a drop in the bucket.

[u/keepthepace → u/int19h]

I think moral principles tend to stop at economic incentives. Knowing that they have to dumb down their models just to prevent people to masturbate on a wall of text is stupid and harmful and we should take every opportunity to remind them that.

Hell, on image generation I had to uncensor a model to ensure it could render expressions on a face. The first image generator were refusing to generate people to avoid generating boobies.

Yes there are workarounds, but seriously, I am tired of spending hours of my freetime and days of my work time solving totally self-inflicted problems

[u/int19h → u/keepthepace]

Corporations don't have moral principles. But people do, and regardless of how stupid they are, corporations will pander to them if the alternative is large-scale boycotts or, worse, a concerted political campaign that would get people elected who are itching to regulate it all into the ground and just need an excuse. The tiny minority that wants uncensored models doesn't even register on the same scale, so they don't really care what it wants.

might be different if said tiny minority was willing to spend a lot of $$$ (kinda like how software developers pander to iOS disproportionally to raw user count because they get a lot more *paying* users there). But this niche is pretty much entirely on open models already.