Users experiment with Meta’s new Muse-Glimmer model
20 posts across 3 communities in the last 90 days; 90% from r/LocalLLaMA.
“What is the issue? Running Hugging Face Muse Glimmer models produce no response: It might be because these models have internal jinja templates. The Model card for Meta Models specifically states jinja is not optional. Relevant log output OS Linux GPU Intel, Nvidia CPU Intel…”
“https://huggingface.co/ggml-org/Muse-Spark-Open-816B-A42B-GGUF merged after 4h”
“Honestly Muse Glimmer has been very respectable for repo level work for such a moderately sized model. It’s very performant too. So any mistakes it makes are quick to iterate though and improve. [re: Best open-weight coding model + harness for an on-prem multi-agent setup?…”
Posts highlight the release and availability of Meta's Muse-Glimmer 30B model, emphasizing its open-source nature and compatibility with llama.cpp. Users are actively exploring and sharing experiences with this on-device language model.
SOURCES (27)
“https://huggingface.co/ggml-org/Muse-Spark-Open-816B-A42B-GGUF merged after 4h submitted by /u/jacek2023 [link] [comments]”
“Honestly Muse Glimmer has been very respectable for repo level work for such a moderately sized model. It’s very performant too. So any mistakes it makes are quick to iterate though and improve. [re: Best open-weight coding model + harness for an on-prem multi-agent setup? (80-200+ GB VRAM, 2-3 concurrent users)]”
“What is the issue? Running Hugging Face Muse Glimmer models produce no response: It might be because these models have internal jinja templates. The Model card for Meta Models specifically states jinja is not optional. Relevant log output OS Linux GPU Intel, Nvidia CPU Intel Ollama version 0.35.0”
“I'm like you guys and am constantly experimenting with new models, seeing what they're all good at, how I can make use of them for certain projects and goals. I've been using Qwen 3.8 27b for minor coding work and it has been impressive. But with just regular chatting I have been impressed with Muse Glimmer. It seems to be able to have the ability to follow and hold good, deep and meaningful conversations without coming off as a typical chatbot. No repeated statements like "I he”
“Fingers crosses, this model seems really good when it comes to coding, and the speeds are nice. They talked about making it open source but what happened? I still hope they release 1.2/1.3. submitted by /u/formatme [link] [comments]”
“Can we expect a Muse Glimmer 1.1 or was it one-time only? Or does it have like a normal release schedule, not what I'm accustomed to with the summer of AI this year? submitted by /u/Aggravating-Push-207 [link] [comments]”
“Lemonade llama.cpp supports dflash but you have to manually update the llama.cpp version and flag the model for it”
“Muse Glimmer Cookbook Clone it, run it on the GPU you already own, and ship a working agent — one that plans, calls tools, and self-corrects — in a single sitting, fully offline. Muse Glimmer is an open-weight model built for local agentic work on a single GPU. This cookbook is how you go from weights to a running agent. Previously shared Liquid AI's Cookbook . submitted by /u/pmttyji [link] [comments]”
“It should have been a moe at 30b - they are known for smaller models (llama 3 models we're honestly pretty decent) not dropping a dense model at 12b range (or near) was a mistake as well.”
“Its easily the best in everything except multilingual/translation, where Gemma 4 31B QAT is ahead, if you're limited to this sizs class.”
“I ran benchmark on Meta's Muse Glimmer 30B Unsloth GGUF model in TensorSharp, and compare its performance with llama.cpp. Here is the results. Test setup GPU 1x NVIDIA RTX PRO 6000 Blackwell Server Edition (97,887 MiB), driver 580.126.20, PCIe 5.0 x16. The host has two; every row except Two GPUs pins CUDA_VISIBLE_DEVICES=0 . CPU / RAM 2x Intel Xeon 6952P (384 threads), 1.5 TiB Model Muse-Glimmer-30B-Q8_0.gguf (27.6 GiB) Drafter dflash-kquant.gguf (1.5 GiB) TensorSharp commit 5098e3f , vendor”
“Hey everyone, Non-native speaker, writing my post by hand, let me know if I make mistakes (can only learn from it!) Muse Glimmer 30B is so far quite nice, but I haven't found a clear-cut case yet what I can use it for over Gemma 4 31B QAT (my go-to model) or Qwen3.6 27B. I really appreciate that they have openai style toggable reasoning effort like gpt-oss 20b has. I don't like the caveman speak style reasoning, makes it harder for me to pick up what it's doing or why it goes wrong.”
“Heeeey all! I just completed some fun tests with Muse Glimmer, I thought I'd let you know. In fact, the summary below was written by Muse itself! I ran a 2× DGX Spark cluster and got Meta's day-old Muse Glimmer 30B running the day after release — then pushed its context from the trained 131K all the way to 1M with YaRN, verifying retrieval at every rung. Sharing config + results since the "131,072+" hint in the model card turned out to be very real. Setup Hardware: 2× NVIDIA DG”
“${llama-server} -c ${128k} --model ${models}/unsloth/Muse-Glimmer-30B-GGUF/Muse-Glimmer-30B-UD-Q4_K_XL.gguf --mmproj ${models}/unsloth/Muse-Glimmer-30B-GGUF/mmproj-Muse-Glimmer-30B-BF16.gguf --model-draft ${models}/unsloth/Muse-Glimmer-30B-GGUF/dflash-kquant.gguf --spec-type draft-dflash,ngram-mod --spec-draft-n-max 6 --spec-ngram-mod-n-match 24 --spec-ngram-mod-n-min 48 --spec-ngram-mod-n-max 64 --temperature 1.0 --top-p 0.95 --top-k 64 --image-min-tokens 560 --image-max-tokens 1120 --kv-unifie”
“Same here, their latest binaries don't recognize the model even though they were released a few hours after the commit that added support. Must have been built off an older commit for some reason.”
“I can’t believe it!! When Muse Spark just came out, I was already thinking they might consider open sourcing this. And now they’re actually gonna open source it!! And ever since Alexandr Wang took over, they’d be releasing anything but Llama 4 Behemoth! What’s next? Llama 5 release before Llama 4 Behemoth? submitted by /u/aero-spike [link] [comments]”
“If you're on a Mac with unified memory, the 4-bit path at about 18GB is the realistic entry point. Full precision at 64GB VRAM is a different class of machine entirely. Before you pull Unsloth's Dynamic 2. 0 iMatrix GGUFs, check free RAM under load, not idle. Browser plus Docker plus desktop will eat the headroom that the 17GB floor assumes. On Apple Silicon I'd leave DFlash speculative decoding on first and measure tokens/s yourself. Their 25 tok/s on M5 Max is a best case ceiling,”
“Hello everyone! Meta today just released Muse Glimmer, a new 30B open-weight model that you can run locally. It brings ChatGPT like multimodal agent capabilities to your own device, including reasoning, coding, tool use and image understanding. The model was made from their larger model and is mostly designed for coding workflows but you can also use it for chat with vision support. To run Muse Glimmer locally, we at Unsloth converted and optimized the model into GGUFs that fit consumer hardware”
Back in the instant day 0 support on llama.cpp, meta is back boys !
Day 0 support submitted by /u/jacek2023 [link] [comments]
“Guide: https://unsloth.ai/docs/models/muse-glimmer submitted by /u/Nunki08 [link] [comments]”
“29.6B dense (including vision encoder) Apache 2.0 GGUF: https://huggingface.co/meta-models/Muse-Glimmer-30B-GGUF llama.cpp PR: https://github.com/ggml-org/llama.cpp/pull/26841 Meta blog: https://research.meta.ai/blog/introducing-muse-glimmer-open-agentic-model submitted by /u/rerri [link] [comments]”