Meta is back with Muse Glimmer: local, agentic, multimodal, and open source

Great news from the OGs of open source LLMs! Muse Glimmer, released today, is Meta’s new multimodal model, especially designed for local agentic use cases. Distilled from Muse to 30B parameters, and released under the Apache 2.0 license , it’s ideal deploying locally for privacy, reducing costs, or just hacking around. It’s intended for privacy-aware applications such as coding, document analysis, personal assistants, Claw- or Hermes-like setups.

To celebrate, we are shipping with Meta day-0 support in transformers , llama.cpp , vLLM , Inference Endpoints, and other libraries. We built a few cool things and explain our findings in this blog.

Scores are reported as published. Bold indicates the best result among the compared models; ↓ indicates lower is better.

The language model uses the following architecture components:

Muse Glimmer uses one image encoder to handle both images and videos. Unlike the relatively small vision encoders used in other VLMs, this is a sizable 2B ViT-like model designed after the Perception Encoder architecture. Perception Encoder was previously introduced by Meta as a backbone for various downstream spatial and multimodal tasks . The encoder patchifies images to a shape of 2 frames x 3 channels x 14 x 14, and passes them through a linear layer for projection. An interpolated absolute position embedding from a learned position table is then added to these embeddings. These are then sent to the vision tower which consist of 50 layers and GELU MLPs. Similar to the language model, the attention pattern consists of three window attention layers followed by one full attention layer. Inside the attention layers, 2D RoPE is applied to the queries and keys.

After transformer, pixel shuffle concatenates 2x2 groups of neighboring spatial tokens which reduces the number of image tokens 4x without discarding their channels. The merged features are then projected to the shared embedding space of the text decoder.

Videos go through the same encoder frame by frame, where each frame is converted into patches (of shape [batch, temporal groups, grid height, grid width, 2 frames, 3 channels, 14, 14]). The processor targets 2 frames per second and caps the clip at 96 frames sampled evenly across video. The processor creates timestamped video placeholders, interleaving text with frame e.g. “Time: 0.0s <|video|> x N” in which the final video embeddings are replaced before the final projection layer.

Muse Glimmer comes with day-0 support in transformers, both for the main model and the speculative decoding drafter. You can use AutoModelForMultimodalLM and AutoProcessor classes to load the model and the processor.

Muse Glimmer comes with day-0 llama.cpp support. Meta has distributed calibrated quants in this repo , and Unsloth is releasing optimized quants as well. DFlash speculative decoding is supported as well. You can use a pre-built llama binary to start a llama server or a CLI. To install llama.cpp, run:

DFlash uses a lightweight block-diffusion drafter model to provide same output with extra speed-ups in decoding phase. Transformers and llama.cpp ship support for DFlash drafter of Muse Glimmer day-0.

In addition to the main VLM, there’s also a speculative decoding drafter implemented on DFlash. Usage of this module is optional, and it can provide much faster generation in exchange for some memory cost. We found this drafter to be particularly well suited to structured content generation such as coding.

The same snippet runs unchanged on NVIDIA (CUDA), AMD (ROCm) and Intel (XPU) GPUs, device_map="auto" places the model on whichever accelerator is available.

Muse Glimmer can answer complex questions about videos without audio . You can do video inference as follows, here’s an example from VideoMME2, which is the most popular video question answering benchmark.