Entries matching “tool-calling”

Unsloth AI

@UnslothAI

2-bit Muse Glimmer GGUF managed to call 100+ tools on just 14GB RAM. 🔥 Muse Glimmer did a complete repo bug hunt for 5 mins nonstop with: evidence, repro, fix, tests and a PR writeup. Run and train it in Unsloth. GitHub repo: github.com/unslothai/unsl…

LoRA adapter that makes Muse Glimmer 30B reliably commit to tool calls when it already knows the correct function.

kwindla

@kwindla

Exciting to see Meta releasing new open weights this week. Meta trained the new 30B dense model Muse Glimmer with "agentic" use cases in mind. This generally means task-oriented, multi-turn, and heavy use of tool calling. I ran Muse Glimmer in the GGUF quant through a bunch of

@accretional

@accretional

Unofficial knowledge base and tools for Meta's Muse Spark, starting with a root-cause write-up of Codex tool-calling failures with Muse Spark 1.1.

Simon Willison reads the Muse Code launch as more evidence that long-sequence agentic tool calling is now the key model trait.

Simon Willison notes Muse Spark 1.1 is the first Spark model with an API and that Meta claims big gains in agentic tool calling and computer use.

@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.

@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.

@emonibnmustafa

@emonibnmustafa

A local translation gateway and profile manager that plugs Meta Muse Spark (or DeepSeek, Groq, Ollama) into OpenAI Codex and ChatGPT Desktop, translating tool calls and SSE streams, with a one-command switch between providers.

AI at Meta

@AIatMeta

Muse Glimmer can complete multi-step agentic tasks end-to-end from a single natural language prompt. In this demo, it autonomously discovers a local Home Assistant instance via network tool calls, queries device APIs, writes a responsive HTML/CSS/JS dashboard from scratch, and

Ran the model with quants (Q4) by Unsloth with latest (build from master) llama.cpp server. It takes ~20GB ram running on M5 Pro with 48GB at about 17t/s. Didn't do any reasoning loops/overthinking. Overall, sits below Qwen3.6 27B, wasn't able to get good code (frontend and backend) results. On the positive side, it didn't fail any tool calls. Your opinions/findings? Watch more: https://www.youtube.com/watch?v=_5wKhkUT438

@starsoldier420

@starsoldier420

An unofficial, open-source (MIT) VS Code extension that runs Meta's Muse Code (Muse Spark 1.3) in a side panel. Every tool call shows as a row with its diff, permission modes (Manu

@bex-co

@bex-co

muse-code-acp is an unofficial Node adapter that exposes Meta's Muse Code through the Agent Client Protocol, so it can be driven from Zed or VS Code with streaming, tool calls and MCP servers.

@rickyzzzzz

@rickyzzzzz

A controlled local benchmark on an M1 Max comparing Muse Glimmer 30B with Qwen 3.6 35B and Qwen 3.8 27B on tool calling and data-science tasks; Glimmer passed 24/30 versus Qwen 3.8's 30/30.

@SamuelAlexander

@SamuelAlexander

Samuel Alexander ran Muse Glimmer 30B entirely on a Qualcomm Dragonwing IQ-9075 board for zero-shot PCB defect inspection and tool calling, measuring 21.6 GB resident with full 131K context and 2.84 tokens/s generation.

A static Space showing a Hermes-agentic fine-tune of Muse Glimmer 30B proposing one or two tool calls or answering directly; the model card reports 16/20 on a simulated loop gate.

@atameric

@atameric

An unofficial macOS desktop client that speaks MSP over muse serve, offering streaming markdown, visible tool calls, session history shared with the terminal and screenshot attachments.

AICodeKing reviews Muse Glimmer for local agent setups, finding it strong at tool calling, multi-step tasks and failure recovery but weaker on general benchmarks.

Cloud Codes tests whether Muse Glimmer on a single 24GB VRAM GPU can compete with cloud coding agents in multi-step tool-calling loops.

Cloud Codes runs Unsloth's 2-bit dynamic quant of Muse Glimmer 30B in about 14 GB of laptop memory and tests 100+ autonomous tool calls.

@agentic-control-plane

@agentic-control-plane

A Muse Code plugin that policy-checks every tool call against an Agentic Control Plane workspace before it runs, returning allow, ask or deny and logging each decision with its reason.

@carlosrymer

@carlosrymer

A browser-only agent console for Muse Glimmer 30B that renders the model's ATEM tool-call format client-side, streams from a local server and runs every tool inside the tab across seven capability tracks.

Nadia Dubois walks through API key setup, auth, tool-calling config and cost control for Muse Spark 1.3 at $1.25/M input and $4.25/M output tokens.

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

@wonder-soft

@wonder-soft

A benchmark that tests whether Muse Glimmer 30B on a single RTX 5090 works as an OpenCode backend, using the same tasks as earlier DeepSeek and Qwen runs; first results show 18/18 episodes with zero malformed tool calls.

Meta's announcement of Muse Spark 1.3 for Muse Code and the Meta Model API, claiming ~20% fewer tool calls and ~25% fewer tokens than 1.2, with a max reasoning mode.