monitor
Skill5.6k starsChanged 3 days agoApache-2.0View on GitHub
Compatibility name for modelopt-monitor. Monitor submitted ModelOpt jobs on SLURM.
31 other projects use a version of this.
Trending
More talk lately.about the whole repository
Most recently taken up by
- adelrodriguez/skillstheir own version, first seen Nov 15, 2026
- Mahiler1909/burnlogtheir own version, first seen Nov 8, 2026
- cihantopac/searvextheir own version, first seen Nov 8, 2026
- gspencergoog/skillstheir own version, first seen Nov 1, 2026
- neurospin-projects/2026_bdrabczuk_champollion_mcptheir own version, first seen Nov 1, 2026
- GuanceCloud/ai-skillstheir own version, first seen Oct 25, 2026
Who's talking about NVIDIA/Model-Optimizer
- @github-trending.bsky.social“📦 NVIDIA / Model-Optimizer ⭐ 3,886 (+22) 🗒 Python A unified library of SOTA model optimization techniques …”Bluesky · Sep 25, 2026 · 5 likes
- onnxsim/onnxsim“Maintaining status of this project”GitHub · Jan 6, 2026 · 5 reactions
- vllm-project/vllm“Add LM head quantization support for ModelOpt”GitHub · May 8, 2026 · 4 reactions
- @chrisshort.net“to optimize inference speed. #devopsish”Bluesky · Sep 27, 2026 · 1 likes
- @dailygithubtrends.bsky.social“今日のGitHubトレンド NVIDIA/Model-Optimizer NVIDIA Model Optimizerは、量子化やプルーニング、蒸留などの手法を用いてモデルを高速化するためのライブラリです。 …”Bluesky · Sep 26, 2026 · 1 likes
The biggest 5 of 54 mentions linking to the repository that our crawl found. Each line is their own headline.
See all 54
Talked about on GitHub and Bluesky (54 times).
On GitHub · 50
- Jan 6, 2026Maintaining status of this project — onnxsim/onnxsim5 reactions · 4 comments
- May 8, 2026Add LM head quantization support for ModelOpt — vllm-project/vllm4 reactions · 7 comments
- Apr 12, 2026Add support for ModelOpt MXFP8 non-gated MoE with ReLU2 — vllm-project/vllm1 reaction · 3 comments
- Apr 2, 2026[Bugfix] Fix NVFP4+MTP crash: force unquantized mtp.fc for Qwen3.5 — vllm-project/vllm1 reaction
- Feb 8, 2026[ModelOpt MXFP8] — sgl-project/sglang1 reaction · 5 comments
45 more on GitHub
- Jan 21, 2026ORT inference fails after upgrading model's opset from 20 to 21 — microsoft/onnxruntime1 reaction · 5 comments
- Nov 15, 2026Watch list: who to follow and where (Edge0 upstream, Qwen4, llama.cpp, MLX, the labs) — toshi38/Edge00 reactions
- Nov 14, 2026Bump nvidia-modelopt from 0.47.0 to 0.48.0 — marek-burza/shrubbery0 reactions
- Nov 11, 2026build(deps-dev): bump nvidia-modelopt from 0.47.0 to 0.48.0 — verl-project/verl0 reactions · 4 comments
- Nov 10, 2026Prepare ModelOpt catalog onboarding: original six skills plus QAD — NVIDIA/skills0 reactions
- Nov 8, 2026build(deps-dev): bump nvidia-modelopt from 0.47.0 to 0.48.0 — ysq111333/verl_jev0 reactions
- Nov 5, 2026python3Packages.nvidia-modelopt: 0.47.0 -> 0.48.0 — NixOS/nixpkgs0 reactions · 1 comment
- Nov 2, 2026claude-code-marketplaces.github.nvidia.model-optimizer: 0.47.0 → 0.48.0 — sudosubin/agents.nix0 reactions
- Nov 2, 2026codex-plugins.github.nvidia.model-optimizer: 0.47.0 → 0.48.0 — sudosubin/agents.nix0 reactions
- Nov 2, 2026claude-code-plugins.github.nvidia.model-optimizer: 0.47.0 → 0.48.0 — sudosubin/agents.nix0 reactions
- Nov 2, 2026build(deps): Update nvidia-modelopt requirement from ~=0.47.0 to ~=0.48.0 — Jonah-May-OSS/wyoming-whisper-trt0 reactions · 2 comments
- Nov 2, 2026codex-marketplaces.github.nvidia.model-optimizer: 0.47.0 → 0.48.0 — sudosubin/agents.nix0 reactions
- Oct 28, 2026claude-code-plugins.github.nvidia.model-optimizer: init at 0.47.0 — sudosubin/agents.nix0 reactions
- Oct 28, 2026claude-code-marketplaces.github.nvidia.model-optimizer: init at 0.47.0 — sudosubin/agents.nix0 reactions
- Oct 28, 2026[ONNX GraphSurgeon] Export fails for a single initializer larger than 2 GiB before external-data saving — NVIDIA/TensorRT0 reactions · 2 comments
- Oct 28, 2026[Bug] make_graph serializes an individual >2 GiB initializer before external-data saving — onnx/onnx0 reactions
- Oct 26, 2026build(deps-dev): bump nvidia-modelopt from 0.44.0 to 0.47.0 — WncFht/verl-opd0 reactions
- Oct 22, 2026chore(deps-dev): Bump nvidia-modelopt from 0.44.0 to 0.47.0 — yiqiu0215/verl-main0 reactions
- Oct 14, 2026Bump nvidia-modelopt from 0.44.0 to 0.47.0 — zery00568/verlxx0 reactions
- Oct 8, 2026Add native packed-cache attention tests — yingguo-trt/Model-Optimizer0 reactions
- Oct 8, 2026Add inline-media CLI HTTP tests — yeyu-nvidia/Model-Optimizer0 reactions
- Oct 8, 2026Add FP8 zero-scale and export tests — raashish1601/Model-Optimizer0 reactions
- Oct 8, 2026Add native CUDA coverage for MXFP4 packing — raashish1601/Model-Optimizer0 reactions
- Oct 8, 2026Add NVFP4 offloaded export tests and collector typing — niko4244/Model-Optimizer0 reactions
- Oct 4, 2026codex-plugins.github.nvidia.model-optimizer: init at 0.47.0 — sudosubin/agents.nix0 reactions
- Oct 4, 2026codex-marketplaces.github.nvidia.model-optimizer: init at 0.47.0 — sudosubin/agents.nix0 reactions
- Oct 2, 2026build(deps-dev): bump nvidia-modelopt from 0.44.0 to 0.47.0 — ydwhhh/gspo0 reactions
- Sep 30, 2026build(deps-dev): bump nvidia-modelopt from 0.44.0 to 0.47.0 — verl-project/verl0 reactions · 1 comment
- Sep 29, 2026Admin AI: Install skills https://github.com/mattpocock/skills https://github.com/ — mehrshadevilb-cell/ArtistYar-Website0 reactions
- Sep 29, 2026Admin AI: Install skills https://github.com/mattpocock/skills https://github.com/ — mehrshadevilb-cell/ArtistYar-Website0 reactions
- Sep 27, 2026🔥 GitHub Trending AI 早报 · 2026-09-27 · Top 6 项目 — happydog-intj/ai-xiaohongshu-daily0 reactions
- Sep 27, 2026🔥 GitHub Trending AI 早报 · 2026-09-27 · Top 6 项目 — Mystery406/ai-xiaohongshu-daily0 reactions
- Sep 27, 2026Github trending [NVIDIA/Model-Optimizer] 一站式模型量化优化加速 — web1992/github-trending0 reactions
- Sep 26, 2026🔥 GitHub Trending AI 早报 · 2026-09-27 · Top 6 项目 — Mystery406/ai-xiaohongshu-daily0 reactions
- Sep 26, 2026🔥 GitHub Trending AI 早报 · 2026-09-27 · Top 6 项目 — happydog-intj/ai-xiaohongshu-daily0 reactions
- Sep 25, 2026Github trending [NVIDIA/Model-Optimizer] 模型推理加速优化库 — web1992/github-trending0 reactions
- Sep 19, 2026Bump nvidia-modelopt from 0.46.0 to 0.46.1 — marek-burza/shrubbery0 reactions
- Sep 17, 2026Add Anima 2B support to Model Optimizer for calibrated PTQ and future NVFP4 QAT/QAD — ruwwww/Model-Optimizer0 reactions
- Sep 16, 2026build(deps-dev): bump nvidia-modelopt from 0.44.0 to 0.46.1 — verl-project/verl0 reactions · 1 comment
- Sep 15, 2026Bump nvidia-modelopt from 0.44.0 to 0.46.1 — HuipengHuang/opd-rl0 reactions
- Sep 9, 2026build(deps-dev): bump nvidia-modelopt from 0.44.0 to 0.46.0 — HuipengHuang/verl-baseline0 reactions
- Sep 9, 2026[None][docs] retarget ModelOpt examples llm_ptq paths to hf_ptq — NVIDIA/TensorRT-LLM0 reactions · 1 comment
- Sep 4, 2026DeepSeek-V4-Pro-0813-NVFP4 — Bibbleq/frontier-model-tracker0 reactions · 1 comment
- Aug 28, 2026[Doc] Broken links in READMEs referencing files removed by legacy TensorRT backend cleanup — NVIDIA/TensorRT-LLM0 reactions · 1 comment
- Aug 19, 2026Fix ModelOpt integration for the >=0.44 list quant_cfg format — huggingface/diffusers0 reactions
On Bluesky · 4
- Sep 25, 2026📦 NVIDIA / Model-Optimizer ⭐ 3,886 (+22) 🗒 Python A unified library of SOTA model optimization techniques like quantization,… — @github-trending.bsky.social5 likes and reposts
- Sep 27, 2026to optimize inference speed. #devopsish — @chrisshort.net1 likes and repost
- Sep 26, 2026今日のGitHubトレンド NVIDIA/Model-Optimizer NVIDIA Model Optimizerは、量子化やプルーニング、蒸留などの手法を用いてモデルを高速化するためのライブラリです。 Hugging… — @dailygithubtrends.bsky.social1 likes and repost
- Sep 25, 20265. NVIDIA Model-Optimizer Library for Deployment LINK 6. World Action Agent for Robot Manipulation via VLMs LINK — @hackobar.bsky.social0 likes and reposts · 1 reply
Used with
Projects with it also use spec and research.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
SentrySkillspec
Produce a verified spec — problem, desired outcome, and acceptance criteria — before building. Use when given a GitHub/Linear issue link, when starting from a fuzzy in-conversation idea, or asked to "figure out what to do" / "scope this" / "what should I build" — whenever the *what* isn't yet pinned down. Grills the ambiguities, checks the premise against the code, and produces acceptance criteria you sign off on — then hands to design-first.
Also in 3 projects that use this
NVIDIASkillresearch
Before designing a feature, research how comparable libraries solve the problem, check best practices, known pitfalls, and upstream changes. Use when starting a new feature, investigating an unfamiliar area, or the user asks to research something.
Also in 3 projects that use this
Other versions31 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 31
- NVIDIA/TensorRT-Model-Optimizer★ 5.6ksame name
- H-mmer/pentest-agents★ 815same name
- elitan/frost★ 324same name
- k1p1l0/claude-telegram-supercharged★ 144same name
- block/builderbot★ 112same name
- no-session/pstack★ 96same name
- joelhooks/joelclaw★ 65same name
- rainday/smart-blog-skills★ 35same name
- freemty/labmate★ 25same name
- spotify/ads-agentic-tools★ 20same name
21 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository33 other files it gives its agents.
- AGENTS.mdAGENTS.md
- tools/debugger/CLAUDE.mdCLAUDE.md
- tools/launcher/CLAUDE.mdCLAUDE.md
- accessing-mlflowSkill
- benchmark-model-kernelsSkill
- commonSkill
- compare-resultsSkill
- day0-releaseSkill
- debugSkill
- deploymentSkill
- evaluationSkill
- launching-evalsSkill
- modelopt-accessing-mlflowSkill
- modelopt-benchmark-model-kernelsSkill
- modelopt-compare-resultsSkill
- modelopt-day0-releaseSkill
- modelopt-debugSkill
- modelopt-deploymentSkill
- modelopt-evaluationSkill
- modelopt-launching-evalsSkill
- modelopt-monitorSkill
- modelopt-ptqSkill
- modelopt-qadSkill
- modelopt-quant-recipe-searchSkill
- modelopt-release-cherry-pickSkill
- modelopt-run-agentxSkill
- modelopt-speculative-decodingSkill
- ptqSkill
- qadSkill
- quant-recipe-searchSkill
- release-cherry-pickSkill
- run-agentxSkill
- speculative-decodingSkill
The file
We split this skill into the rules it gives an agent
1 rule. 0 are common, 0 are distinctive, and 1 is very specific to this project.
0
Common
Most of the 27 edited versions we read keep these.
0
Distinctive
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 6
Read and follow modelopt-monitor, resolving its relative references from the canonical modelopt-monitor/ directory.
“Common” is measured: kept by at least 6 in 10 of the 27 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Read and follow modelopt-monitor, resolving its
relative references from the canonical modelopt-monitor/ directory.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.