Curated with by@mohithkumar808

№ 0484GitHub

Hermes-agentic Glimmer NVFP4 on DGX Spark

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).

Open source github.com
Post on X ↗LinkedIn ↗Reddit ↗Hacker News ↗
vcruz305/muse-glimmer-nvfp4-spark-serveREADME ↗

Muse Glimmer 30B Hermes Agentic — NVFP4 Spark serve recipes

Verified SGLang and vLLM launch recipes for the NVFP4 export of
vcruz305/Muse-Glimmer-30B-Hermes-Agentic-NVFP4
on NVIDIA DGX Spark (GB10 / SM 12.1).

Hub artifact Role
vcruz305/Muse-Glimmer-30B-Hermes-Agentic BF16 / FP16 merge (~56 GB)
vcruz305/Muse-Glimmer-30B-Hermes-Agentic-NVFP4 NVFP4 compressed-tensors (~22 GB) — this recipe
vcruz305/Muse-Glimmer-30B-Hermes-Agentic-GGUF llama.cpp GGUF ladder

Base model: meta-models/Muse-Glimmer-30B (Apache-2.0).
SFT data: vcruz305/hermes-agentic-tool-sft.

Hardware / software assumptions

  • DGX Spark GB10 (Blackwell, compute capability 12.1), ~128 GB unified memory
  • aarch64 Linux
  • CUDA 13.x host stack with working /dev/nvidia* nodes
  • Podman or Docker for the SGLang path
  • OpenAI-compatible clients against http://127.0.0.1:8201/v1

Preferred path: SGLang (measured)

Image: docker.io/lmsysorg/sglang:latest-cu130
(image env on the measured host: SGLANG_IMAGE_TAG=lmsysorg/sglang:v0.5.18, FlashInfer 0.6.17)

Why these flags (SM 12.1):

Flag Why
--fp4-gemm-backend flashinfer_cutlass Auto can pick Marlin “on SM120” and die: mm_fp4 does not support Marlin on cap 121. Force CUTLASS.
--cuda-graph-backend-decode full Avoids breakable-graph crash: Unsupported BCG output type LogitsProcessorOutput.
--cuda-graph-backend-prefill disabled Prefill graphs not used on this path.
--kv-cache-dtype fp8_e4m3 Memory headroom on Spark.
--language-model-only Text/tools student; vision frozen in the SFT merge.
--reasoning-parser muse + --tool-call-parser muse Muse chat / tool protocol.

Launch

# download once
hf download vcruz305/Muse-Glimmer-30B-Hermes-Agentic-NVFP4 \
  --local-dir $HOME/models/Muse-Glimmer-30B-Hermes-Agentic-NVFP4

export MODEL=$HOME/models/Muse-Glimmer-30B-Hermes-Agentic-NVFP4
bash scri
Hermes-agentic Glimmer NVFP4 on DGX Spark on github.com

Also filed under Local & open models

See all →
  1. 0805

    Muse drives a rover with a custom connector★

    Took your custom-connector idea to the physical world: the "service with an API" was my robot. Muse wrote the connector for my rover's API, then installed PyTorch & Depth Anything V2 in the VM because the camera is 2D, drove to the black ball and stopped a few inches short.

    @hrhraj

    X post

    Local & open models

  2. 0490

    Muse Glimmer on one Arc Pro B70★

    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.

    @mgaruccio

    GitHub

    Local & open models

  3. 0458

    Muse Glimmer 30B stretched to 512K context★

    My fun weekend project was to try to make the new Muse Glimmer 30B work with a longer context, deciding to go for 512k first. I had expected the usual YaRN shenanigans and maybe a LoRA. I couldn't have been wrong more. Upon closer look, Glimmer turned out to be rather unusual architecturally. The thing that make long-context adaptations painful in other models, full attention layers with token position encoding, it simply not there. Instead, only 2048 tokens-wide SWA layers have RoPE, and full GQA attention layers have no position encoding at all. It appears the model is trained to work with long-distance token relationships inferred from the context and SWA layers. It's a rather bold architecture bet, but it seems Meta managed to pull it off. As a result, the model architecture appears to be uniquely suited for context extension by simple mechanical means. To change model context length from stock 128k to, say, 512k, you need only to change “max_position_embeddings” config setting from 131072 to 524288. What confuses other models, like Qwen3.5 family, Glimmer just takes into its stride. I spent close to 70h of compute on DGX Spark to test stock model with extended context on a

    u/mr_il

    Reddit post

    Local & open models

  4. 0435

    Local credit card statement analysis with Glimmer★

    Using @AIatMeta's Muse Glimmer all locally to process personal monthly credit card statements. Your data belongs to you! Try different agent tasks using your favorite apps / harnesses with Ollama.

    @ollama

    X post

    Local & open models

More GitHub

See all →
  1. 1091

    RedMuse

    Muse-native pentesting skill pack for Muse.ai. 13 skills, 4 scan modes (quick, standard, deep, diff), gate-first, localhost vulnerable app plus passive scans only, no active probes on live sites without a separate yes. Strix-inspired with original writing. Proven on localhost (IDOR, XSS, open redirect) and on live targets with anonymized totals: 0 critical, 2 high, 4 medium, 8 low, 14 info. MIT.

    Anonymous

    GitHub

    Coding & dev tools

  2. 1089

    Gig Driver Profit Checker — real delivery profit in minutes

    A working mobile-friendly web app built by the Meta Muse personal agent from a real courier's detailed requirements. It compares DoorDash/Uber Eats delivery offers after total miles (including empty return), waiting time, all-in vehicle costs and tolls. The surprising demo: a $9 offer can yield $19.08 net per hour while a $20 offer only yields $16.09 under stated assumptions. Muse created the standalone offline HTML and logic tests (49/49 self-reported); I downloaded it, independently verified actual browser inputs/outputs, published the source and proof screenshot to GitHub, and deployed a no-signup live demo. Live app: https://boyhoangxattpt2-blip.github.io/gig-driver-profit-checker/ . GitHub README includes exact math, source code, build story, and reproducible prompt.

    Anonymous

    GitHub

    Apps & websites

  3. 1087

    Zouroboros Workshop

    Muse is your personal assistant. Zouroboros Workshop is the workshop behind it. Workshop is an MCP service that runs on a private, single-tenant VPS provisioned for each customer. From a normal Muse conversation, a user can dispatch real software work to a coordinated team of seven AI coding executors — Claude Code, Codex, Gemini, OpenCode, Kimi, Hermes, and Pi — routed by health and capability, with automatic fallback when one executor is unavailable. Three things make it different from a single-vendor hosted builder. First, it runs on the subscriptions the user already pays for: there are no Workshop model credits and no second meter. Second, its memory is shared across every executor and survives across sessions, so a decision recorded by one tool is retrieved by another in a later Muse conversation — memory that compounds instead of resetting. Third, work is verified before it is delivered: builds run through a factory lane with deterministic review, and swarm results return with per-task evidence, not just an agent's claim that it is done. The customer-facing promise is simple: turn your existing AI tools into a workshop that delivers tested, reviewed work — a finished pull request, not a wall of code in chat.

    @Xmarlandoj

    GitHub

    Connectors & MCP

  4. 1086

    Self-Healing Agent VM

    A self-healing watchdog pattern for AI agent VMs: byte-exact restore caches in persistent storage, sentinel probes, no-clobber atomic restores, and verified heals. When the VM's root filesystem is rebuilt with pieces missing (Python stdlib, timezone data, Node), the watchdog restores them in minutes and reports only repairs and failures. Includes an AGENTS.md so any agent can set it up.

    @TMTrevisan

    GitHub

    Agents & automation

Curator picks

  1. 1057

    Muse Mail: your agent gets an email address★

    MUSE FEATURE ALERT: y'all have been asking for this one. your @muse is getting its own email address. add it to a thread or forward it stuff and it’ll handle it for you. muse mail!

    @alexandr_wang

    X post

    Agents & automation

  2. 1054

    Kevin Roose gives his Muse $100 and a Kalshi account★

    gave my Muse $100 and a kalshi account, let's see how frontier this thing is

    @kevinroose

    X post

    Agents & automation

  3. 1046

    Medical bills audited line by line, $4,000 saved★

    Got Muse logged in to my medical provider’s portal, he pulled the itemized bills, and questioned every line. So far he’s found several times I’d been double billed, asked for some discounts and has saved me over $4,000. If your moat is bureaucracy, you’re cooked.

    @Ryan_Holdaway

    X post

    Errands & personal agent

  4. 1042

    GitHub connector for Muse★

    GitHub 🤝 @Muse A new way to connect your GitHub account to review PRs, catch up on issues and notifications, and leave comments without switching tabs.

    @github

    X post

    Connectors & MCP

Advertise hereAdvertise hereAdvertise hereAdvertise hereAdvertise hereAdvertise hereAdvertise hereAdvertise hereAdvertise hereAdvertise here
Want more AI builds?artificialbuilds.com