Entries matching “json”

LoRA adapter for Muse Glimmer 30B that makes responses predictable machine-readable JSON with a stable API-style envelope.

@SoundDeal

@SoundDeal

dealscan is an open-source agent built on Muse Glimmer 30B that reads producer, distribution and publishing agreements, extracts the payment terms and flags risky clauses in a markdown and JSON report.

@codemarc

@codemarc

A VS Code and Cursor sidebar chat that drives Meta's Muse Code CLI through muse exec --json, keeping Muse as the agent harness; its build is described in the post 'Inception: A Muse Inside Muse'.

@develate

@develate

A small PHP 8.2+ SDK for controlling the Muse Code CLI, running one muse exec --json process per turn and resuming sessions by ID.

@Pascapone

@Pascapone

DeepSeek Harness bundle that exposes an authenticated Muse Code CLI as a one-shot subagent_muse tool running muse exec --json in the parent workspace.

@fengyiqicoder

@fengyiqicoder

A serverless, read-only Muse connector hosted on GitHub Pages: an OpenAPI document plus JSON conversation playbooks for declining, apologising, negotiating, following up and other hard messages.

@bentedesco

@bentedesco

A shared Socket Mode transport between Slack and Muse bots: one listener daemon appends Slack events to a JSONL log, a CLI posts replies back, and each bot runs its own consumer with no public HTTP endpoint.

Hi all, I'm a newbie and trying to assess the performance of some LLMs I'm running locally via oMLX on my MacBook Pro M5pro CPU 15 cores (5 Super and 10 Performance), GPU 16 cores and 48 GB of LPDDR5 RAM. I asked chatGPT guidance to run some tests and check whether the DFlash-based drafter Muse-Glimmer-30B-Assistant might somewhat speedup the base model Muse-Glimmer-30B-4bit. The results show no or negligible improvement with active DFlash acceleration (speedup between 0.90% and 1.16%). The test was structured with three different prompts fed to both the baseline and the dflash-capable model profiles: Technical prose; Python code; Structured JSON a cap of 2048 tokens, no cache, temperature=0. Each inference was repeated three times. Anyone have similar experience? can we simply dump the Assistant as not useful in this hw/sw configuration?