Entries matching “recipe”

@mgaruccio

@mgaruccio

A vLLM-XPU and DFlash recipe for Muse Glimmer 30B on a single Intel Arc Pro B70, reporting 278 aggregate tok/s across eight clients and an 840.8 tok/s burst peak at concurrency 96.

@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.

filipe

@filicroval

got Muse Glimmer 30B running on a single DGX Spark / GX10 at 29.6 tok/s with DFlash, up from 11.9 tok/s baseline, 2.49× raw decode throughput. packaged the whole thing into a reproducible GB10 recipe: - pinned weights + SHA-256s - llama.cpp compiled for GB10 / SM121 - baseline

merve

@mervenoyann

this is super easy to run install llama binary: curl -LsSf llama.app/install.sh | sh run: llama serve -hf meta-models/muse-glimmer-30b --spec-type draft-dflash -fa on --jinja please spread the word

dario

@DarioVillescass

Bought all the ingredients for this date on my Walmart account , scheduled the delivery of them for when I got out of work, made a site with the instructions, and edited this video. Super cool!

Meta's developer post on running Muse Glimmer on a single consumer GPU with vLLM, llama.cpp and ExecuTorch, with quantized builds in 24-32 GB and cookbook recipes.

A prebuilt macOS arm64 bundle for the Muse Glimmer voice-agent recipe in meta-oss-cookbook: Parakeet speech helper, Muse Glimmer worker and Supertonic TTS executables built from one pinned ExecuTorch checkout, plus the shared MLX Metal library.

@CumulativeWebInc

@CumulativeWebInc

A 60-second recipe and connector brief for connecting Meta Muse to the CWI music catalog as a custom connector, re-verified every 30 days.

Mixed-precision NVFP4/MXFP8 checkpoint of Muse Glimmer packed for SGLang, keeping v_proj, down_proj and lm_head at MXFP8.

@vcruz305

@vcruz305

Tested SGLang and vLLM launch recipes for an NVFP4 export of a Hermes-agentic fine-tune of Muse-Glimmer-30B on NVIDIA DGX Spark (GB10).

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.

@AIwork4me

@AIwork4me

A reproducible RDNA reference that adapts MI-series ROCm recipes to run Muse-Glimmer-30B on Ryzen AI (Radeon 8060S) hardware, measuring 2.2–2.5x single-stream speedups from DFlash.

@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.

Meta's developer guide to first calls, coding primitives and agentic patterns; its multi-agent recipe turns a one-line idea into an app plus launch package, and Spark fixed all 5 planted bugs in 7.6 turns on average.

D I G I

@digicodermon

Connected @muse with my @NotionHQ meal planner. Now I can take a picture of my grocery receipt have each purchase registered to an ingredient in my pantry, Updating my pantry stock, tracking per item cost, and suggests meals based on what I actually have in stock vs my recipes.