RT by @huggingface: 1/ big announcement today: we will be releasing an open weight version of muse…
Ranking
Overall
68
Content
75
Popularity
N/A
No observed public metrics; popularity remains neutral/archived.
Merged summary
TL;DR - A company announcement thread stating that an open-weight version of Muse Spark 1.2 is coming, alongside the immediate release of Muse Glimmer, a 30B agentic model under Apache 2.0. It matters because it puts a permissively licensed agent-focused model in reach of single-GPU users.
- Two releases described: an open-weight Muse Spark 1.2 (promised "soon") and Muse Glimmer (30B parameters, open weights, released now).
- Muse Glimmer ships under Apache 2.0, a permissive license allowing commercial use and redistribution.
- Claimed to run within 24GB of VRAM — consumer/prosumer single-GPU territory — "without losing agentic reliability," implying quantization or other compression with retained tool-use/agent performance.
- Content is thin: this is the first post of a thread with no benchmarks, architecture details, training data, or evaluation methodology provided, so the reliability claim is unverified here.
Sources (1)
RT by @huggingface: 1/ big announcement today: we will be releasing an open weight version of muse…
Public signals
N/A
TL;DR - A company announcement thread stating that an open-weight version of Muse Spark 1.2 is coming, alongside the immediate release of Muse Glimmer, a 30B agentic model under Apache 2.0. It matters because it puts a permissively licensed agent-focused model in reach of single-GPU users.
- Two releases described: an open-weight Muse Spark 1.2 (promised "soon") and Muse Glimmer (30B parameters, open weights, released now).
- Muse Glimmer ships under Apache 2.0, a permissive license allowing commercial use and redistribution.
- Claimed to run within 24GB of VRAM — consumer/prosumer single-GPU territory — "without losing agentic reliability," implying quantization or other compression with retained tool-use/agent performance.
- Content is thin: this is the first post of a thread with no benchmarks, architecture details, training data, or evaluation methodology provided, so the reliability claim is unverified here.