Add Model
guoqingbao/xinfer
Adapt and port new LLM model architectures to this xinfer project.
vLLM Ascend plugin for LLM inference serving on Huawei Ascend NPU.
Install Vllm Ascend
$ npx skills add ascend-ai-coding/awesome-ascend-skills --skill vllm-ascend -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install ascend-ai-coding/awesome-ascend-skills vllm-ascend --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/ascend-ai-coding/awesome-ascend-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/inference/vllm-ascend .claude/skills/vllm-ascend && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "vllm-ascend" agent skill from https://github.com/ascend-ai-coding/awesome-ascend-skills/tree/main/skills/inference/vllm-ascend into .claude/skills/vllm-ascend/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vllm-ascend", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/ascend-ai-coding/awesome-ascend-skills/tree/main/skills/inference/vllm-ascendType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add ascend-ai-coding/awesome-ascend-skills --skill vllm-ascend -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install ascend-ai-coding/awesome-ascend-skills vllm-ascend --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ascend-ai-coding/awesome-ascend-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/inference/vllm-ascend .agents/skills/vllm-ascend && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "vllm-ascend" agent skill from https://github.com/ascend-ai-coding/awesome-ascend-skills/tree/main/skills/inference/vllm-ascend into .agents/skills/vllm-ascend/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vllm-ascend", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add ascend-ai-coding/awesome-ascend-skills --skill vllm-ascend -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install ascend-ai-coding/awesome-ascend-skills vllm-ascend --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ascend-ai-coding/awesome-ascend-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/inference/vllm-ascend .cursor/skills/vllm-ascend && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "vllm-ascend" agent skill from https://github.com/ascend-ai-coding/awesome-ascend-skills/tree/main/skills/inference/vllm-ascend into .cursor/skills/vllm-ascend/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vllm-ascend", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/ascend-ai-coding/awesome-ascend-skills.git --path skills/inference/vllm-ascend--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add ascend-ai-coding/awesome-ascend-skills --skill vllm-ascend -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install ascend-ai-coding/awesome-ascend-skills vllm-ascend --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ascend-ai-coding/awesome-ascend-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/inference/vllm-ascend .gemini/skills/vllm-ascend && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "vllm-ascend" agent skill from https://github.com/ascend-ai-coding/awesome-ascend-skills/tree/main/skills/inference/vllm-ascend into .gemini/skills/vllm-ascend/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vllm-ascend", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install ascend-ai-coding/awesome-ascend-skills vllm-ascendInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add ascend-ai-coding/awesome-ascend-skills --skill vllm-ascend -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/ascend-ai-coding/awesome-ascend-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/inference/vllm-ascend .github/skills/vllm-ascend && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "vllm-ascend" agent skill from https://github.com/ascend-ai-coding/awesome-ascend-skills/tree/main/skills/inference/vllm-ascend into .github/skills/vllm-ascend/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vllm-ascend", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add ascend-ai-coding/awesome-ascend-skills --skill vllm-ascend -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install ascend-ai-coding/awesome-ascend-skills vllm-ascend --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ascend-ai-coding/awesome-ascend-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/inference/vllm-ascend .opencode/skills/vllm-ascend && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "vllm-ascend" agent skill from https://github.com/ascend-ai-coding/awesome-ascend-skills/tree/main/skills/inference/vllm-ascend into .opencode/skills/vllm-ascend/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vllm-ascend", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
vllm-ascend- GitHub stars
- 174
- Token cost
- ~2.7k tokens
- SKILL.md length
- 270 words
- Files
- 10 (incl. scripts, references)
- Skills in repo
- 70
- Repo updated
- First seen
- Licence
- None found
At a glance
vLLM Ascend plugin for LLM inference serving on Huawei Ascend NPU.
- Offline batch inference
- SKILL.md covers Quick Start, Installation, Deployment and Quantization, plus 6 more sections
- Runs Shell and Python scripts from its folder; calls python, docker and pip; reaches github.com
- API server deployment
What it does
Vllm Ascend is an agent skill from ascend-ai-coding/awesome-ascend-skills. vLLM Ascend plugin for LLM inference serving on Huawei Ascend NPU. Use for offline batch inference, API server deployment, quantization inference (with msmodelslim quantized models), tensor/pipeline parallelism for distributed serving, and OpenAI-compatible API endpoints. Supports Qwen, DeepSeek, GLM, LLaMA models with Ascend-optimized kernels.
Its SKILL.md is about 2.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 11 other files, including scripts and reference files (for example `references/deployment.md`, `references/distributed.md` and `references/installation.md`).
It sits in AI & LLM Engineering, covering LLM inference and serving. It works with vLLM, OpenAI, DeepSeek and Qwen. The repository describes itself as: A comprehensive knowledge base for Huawei Ascend NPU development, structured as distributed Agent Skills. https://ascend-ai-coding.github.io/awesome-ascend-skills/.
When your agent uses it
- Offline batch inference
- API server deployment
- Quantization inference (with msmodelslim quantized models)
- Tensor/pipeline parallelism for distributed serving
Example prompts
- “/vllm-ascend”
Requirements
- Python 3
- A Bash shell
- Docker
What it can do on your machine
Read from SKILL.md and the folder at commit 62a4ecb. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 3 files in scripts/ (Shell and Python), which the agent can run.
Shell commands in SKILL.md call:
pythondockerpipgitcurl
From the folder's file list and the shell code blocks in SKILL.md.
Network
Hosts in commands or code, which the agent is likely to contact:
github.com
Also links to:
docs.vllm.aihiascend.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Vllm Ascend loads about 2.7k tokens when it runs, and up to ~16k if it reads all its reference files. Until then it costs about 90 tokens; SKILL.md has 270 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
Without a licence we can't republish the file, so here is its outline and opening line. It has 270 words (~2,710 tokens).
“vLLM-Ascend is a plugin for vLLM that enables efficient LLM inference on Huawei Ascend AI processors. It provides Ascend-optimized kernels, quantization support, and distributed inference capabilities.”
- name
- vllm-ascend
- keywords
- vllm, vllm-ascend, inference, llm serving, 推理服务, 大模型部署, tensor parallelism, 张量并行, distributed inference, 分布式推理, ascend npu, quantization, 量化推理, openai api…
Files
SKILL.md and 9 other files (scripts, references) in skills/inference/vllm-ascend of ascend-ai-coding/awesome-ascend-skills.
- SKILL.md
- references/deployment.md
- references/distributed.md
- references/installation.md
- references/performance.md
- references/quantization.md
- references/troubleshooting.md
- scripts/benchmark.py
- scripts/check_env.sh
- scripts/deploy_service.sh
Open the folder on GitHubat commit 62a4ecb
Compare with similar skills
Vllm Ascend next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Vllm Ascend this skillascend-ai-coding/awesome-ascend-skills | 174 | — | ~2.7k | Automated safety check: Pass | None | |
| Add Modelguoqingbao/xinfer | 334 | — | ~4.2k | Automated safety check: Notes | MIT | |
| Visionxiincs/claude-code-vision-skill | 170 | — | ~1.2k | Automated safety check: Pass | MIT | |
| vLLM Model ServingOrchestra-Research/AI-Research-SKILLs | 13k | 5 repos | ~2.3k | Automated safety check: Pass | MIT | |
| Serving LLMs On Instinctamd/skills | 406 | — | ~4k | Automated safety check: Notes | MIT | |
| LLM Pipeline Profiler AnalysisBBuf/AI-Infra-Auto-Driven-SKILLS | 925 | — | ~3.9k | Automated safety check: Pass | None |
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Categories
Questions about Vllm Ascend
What does Vllm Ascend do?
vLLM Ascend plugin for LLM inference serving on Huawei Ascend NPU. Vllm Ascend is an agent skill from ascend-ai-coding/awesome-ascend-skills. vLLM Ascend plugin for LLM inference serving on Huawei Ascend NPU.
When should I use Vllm Ascend?
Vllm Ascend fits situations like: offline batch inference; API server deployment; quantization inference (with msmodelslim quantized models); tensor/pipeline parallelism for distributed serving.
How do I install Vllm Ascend in Claude Code?
Run `npx skills add ascend-ai-coding/awesome-ascend-skills --skill vllm-ascend -a claude-code`. Or copy the skill folder (skills/inference/vllm-ascend in ascend-ai-coding/awesome-ascend-skills) into .claude/skills/vllm-ascend in your project. Claude Code loads it when a task matches its description.
How do I install Vllm Ascend in Codex?
Run `npx skills add ascend-ai-coding/awesome-ascend-skills --skill vllm-ascend -a codex`. Or copy the skill folder (skills/inference/vllm-ascend in ascend-ai-coding/awesome-ascend-skills) into .agents/skills/vllm-ascend in your project. Codex loads it when a task matches its description.
Can I use Vllm Ascend in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ascend-ai-coding/awesome-ascend-skills --skill vllm-ascend -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/vllm-ascend, .gemini/skills/vllm-ascend, .github/skills/vllm-ascend and .opencode/skills/vllm-ascend in your project.
What does Vllm Ascend need to run?
Going by SKILL.md and its folder, Vllm Ascend needs a shell and Python for the scripts in its folder and the command-line tools its instructions call (python, docker, pip, git and curl). Our summary lists: Python 3; A Bash shell; Docker.
Does Vllm Ascend access the network?
SKILL.md names 3 domains. In commands or code: github.com; the agent is likely to contact it when it follows the instructions. As links in the text: docs.vllm.ai and hiascend.com. This is read from the text; nothing was executed.
Is Vllm Ascend safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Vllm Ascend use?
No licence was found for Vllm Ascend or its repository. Without one, default copyright applies: ask the author before reusing or redistributing it.
How many tokens does Vllm Ascend use?
About 2.7k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 14k tokens, read only when the agent opens those files.
What are the alternatives to Vllm Ascend?
Skills that share tags, products or a category with Vllm Ascend: Add Model (guoqingbao/xinfer, 334 stars), Vision (xiincs/claude-code-vision-skill, 170 stars), vLLM Model Serving (Orchestra-Research/AI-Research-SKILLs, 13k stars) and Serving LLMs On Instinct (amd/skills, 406 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Vllm Ascend?
ascend-ai-coding (a GitHub organization) maintains it in ascend-ai-coding/awesome-ascend-skills, which has 174 GitHub stars. The repository holds 70 skills in this directory. The repository was last updated on October 9, 2026.
Source: ascend-ai-coding/awesome-ascend-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.