Entries matching “official”

Zach Holman

@holman

Still really enjoying @muse. I use Things.app for all of my todos, and though it has cloud sync they (still) haven’t added an official API. I told muse to build it and a minute or two later I have ongoing todo sync from my LLM across all my Apple devices. Wild.

Meta's own GGUF conversions of Muse Glimmer 30B for llama.cpp.

@luckeyfaraday

@luckeyfaraday

An unofficial loopback shim that lets Meta Muse Code use OpenAI Responses APIs, OpenRouter, Codex OAuth and the official Claude Code CLI, switchable from Muse's /model menu.

Xuan-Son Nguyen

@ngxson

We are happy to announce that Muse Glimmer is day-0 supported on llama.cpp. Meta also provides an official GGUF quant:

松xR

@matsu_vr

muse glimmer 30Bを公式の17GBに収まるよバージョンのggufでLM Studioで動かしてみました。写真の批評もバッチリしてくれる。これは僕が撮った写真なのでネットにないのでちゃんと写真を見ているはず。コンテクスト長64000にして、VRAM20GB以内に収まっているので、かなり実用的かもしれない！

@cezaronx

@cezaronx

A reference CUDA worker that serves Meta's official Muse Glimmer 30B GGUF through llama-server on Runpod Serverless load-balancing endpoints or manual Pods, exposing a real OpenAI-compatible API.

@johnhalloran321

@johnhalloran321

Scripts that run Muse-Glimmer-30B with vision and tool calling on a 32GB M2 MacBook Pro via llama.cpp and Metal, without admin or sudo access, pulling Meta's official GGUFs.

@harris-ryder

@harris-ryder

Dowser is a read-only MCP connector for Muse that surfaces open class-action settlements, product recalls with refunds, unused card credits, expiring points and birthday freebies, each with an official link.

@arananet

@arananet

A CLI that opens a shop in headless Chromium to check whether an AI shopping agent can discover and act on the page, and can ask a real Muse Code agent to shop it through the official @muse-code/sdk.

@MiaAI-Lab

@MiaAI-Lab

A one-script vLLM setup that serves the roughly 19 GB NVFP4 Muse Glimmer 30B with its vision encoder kept, DFlash speculative decoding using the official drafter head, and up to 256K context on GB10, RTX 5090 or RTX PRO 6000.

Muse found that i was owed $88.92 PROMPT: Research every state’s official unclaimed property database for money owed to me. Use my full name and every city I’ve lived in. For anything you find, tell me what it is, who owes it, and how much. My government name is _______

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 DGX Spark (GB10, 128 GB unified each, ~273 GB/s), ConnectX-7 direct link between them • Engine: llama.cpp master (day-1 muse_glimmer support), built from source with CUDA sm_121 + GGML_RPC • Model: official Muse-Glimmer-30B-GGUF K-Quant-Dynamic (~18.3 GiB) + official mmproj (vision) + official DFlash drafter • Spec decode: --spec-type draft-dflash --spec-draft-n-max 15 (block-diffusion drafter) • Context extension: --rope-scaling yarn --rope-scale <2/4/8> --yarn-orig-ctx 131072 plus --override-kv muse-glimmer.context_length=int:<N> (llama.cpp caps at trained length otherwise) • Yes, we also ran it split across both Sparks with llama.cpp RPC — no reason beyond liking to cluster things for fun. Our daily dri

Alex Cornell walks through how his Muse buys movie tickets, plans family weekends and helps his chess game, covering the chat tab, feed, proactive ideas, goal tracking and library.

I'm running Muse Glimmer 30B EXL3-SC 3.00bpw H4, fully resident on my 12GB VRAM GPU at 100K context with Q8\_O KV cache. It's a joy to use a dense 30B model at this size and still get \~30 tok/s on a VRAM-constrained laptop. It's supposed to be only slightly worse than the official 17GB K-quant at a much smaller footprint, and for my Hermes Agent use case I don't notice a quality difference. It's just much faster. I've tried Qwen 3.8 27B at SC2.20bpw H3 too. Definitely usable but I'm sticking with Unsloth UD\_Q4\_K\_XL for Qwen 3.8 27B because it's mainly for coding.

Cristian Uibar

@cristianuibar

@theo please add support for Muse Code in T3. I've opened a PR here for it using the official SDK. github.com/pingdotgg/t3co…

Meta's lightweight DFlash block-diffusion drafter for Muse Glimmer 30B that predicts blocks of 16 tokens per forward pass for speculative decoding.

Muse

@Muse

Get more done with Muse with two new connectors: ✅ @meetgranola: your meeting notes ✅ @NotionHQ: your docs …and more coming soon! Ask Muse or connect in settings.

@emman27

@emman27

MCP server built for the Muse connector directory that answers Singapore bus arrival times and MRT/LRT service questions using the official LTA DataMall API.

Trapit Bansal

@TrapitBansal

We entered Meta models in five international STEM Olympiads, as an uncontaminated eval of their reasoning capabilities. Three of these were live participations and graded officially. The models achieved gold-medal results in all five! 1/5

@itsgoharr

@itsgoharr

Threadwell Slack is an open-source Muse connector that bridges to Slack's official MCP server via a local OAuth bridge, submitted to the Muse Connector Platform with a full submission pack.

@sxuff

@sxuff

A pinned llama.cpp recipe for Muse Glimmer 30B on one NVIDIA GB10 that verifies Meta's official GGUFs by SHA-256 and reproduces text, tool-call, coding, vision and throughput checks.

Postiz's Nevo David shows how to hook Postiz's OAuth MCP server into Muse through the custom-connector path, so Muse can schedule posts across 30+ platforms before an official directory listing.

@meta-models

@meta-models

Meta's official recipes for building agents and coding tools on the Model API with Muse Spark 1.3, covering API basics, multi-agent patterns and end-to-end use cases via OpenAI- and Anthropic-compatible SDKs.

Setup the latest (master) version of llama.cpp server with the guide and the official GGUF weights by Meta AI: https://huggingface.co/meta-models/Muse-Glimmer-30B-GGUF and connected the Hermes Agent to the llama.cpp endpoint. Getting about 22t/s (+3-4t/s) on M5 Pro, using ~24GB including the drafter (provided by Meta). The model did correct tool calls and actually did some useful work inside the Hermes Agent. Moreover, the resulting coding task/project works, which was not the case when running the model with OpenCode. Watch more: https://www.youtube.com/watch?v=cmENEolUtM4

A quantization-aware-trained Q4_0 GGUF of Muse Glimmer 30B for llama.cpp. On held-out tokens it measures closer to BF16 than Meta's official Q4_K_M: 0.0213 vs 0.0228 KL and 95.9% vs 95.6% top-token agreement.

Meta's official Muse-Glimmer-30B repo: ~29.6B dense model with a 1.8B vision encoder, 131K context, Apache 2.0, with vLLM and SGLang serve commands.