# Vllm Model Tutorial > > - **Type:** Skill - **Install:** `agentstack add skill-shen-shanshan-vllm-dev-skills-vllm-model-tutorial` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [shen-shanshan](https://agentstack.voostack.com/s/shen-shanshan) - **Installs:** 0 - **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills) - **Latest version:** 0.1.0 - **License:** Apache-2.0 - **Upstream author:** [shen-shanshan](https://github.com/shen-shanshan) - **Source:** https://github.com/shen-shanshan/vllm-dev-skills/tree/master/skills/vllm-model-tutorial - **Website:** https://zhuanlan.zhihu.com/p/2031696581678866733 ## Install ```sh agentstack add skill-shen-shanshan-vllm-dev-skills-vllm-model-tutorial ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # vLLM Model Tutorial Generator Generate a comprehensive model technical tutorial document for a given model supported by vLLM. ## Workflow ### Step 1: Identify the Model Extract the model name from the user's request. Normalize common aliases: - "Qwen3-VL" / "Qwen3VL" → Qwen3-VL - "Qwen2.5-VL" → Qwen2.5-VL - "DeepSeek-V3" / "DSv3" → DeepSeek-V3 - "InternVL3" / "InternVL 3" → InternVL3 - "Llama 4" → Llama 4 - "GPT-OSS" → GPT-OSS If the model name is ambiguous, ask the user to clarify. ### Step 2: Research the Model Gather information from multiple sources. This is the most critical step — thorough research determines document quality. **2a. Find Technical Reports and Papers** Search for the model's official technical report, paper, or blog post: - Use WebSearch: `"{model_name} technical report arxiv"` or `"{model_name} paper"` - Use WebFetch to read the paper/report and extract architecture details, innovations, benchmarks - For model series: also find reports for predecessor models to build the evolution timeline **2b. Gather Model Family Information** Build the model family comparison context: - Search for the full model series evolution (e.g., Qwen-VL → Qwen2-VL → Qwen2.5-VL → Qwen3-VL) - For each variant: collect parameter counts, release dates, key innovations, performance benchmarks - Find HuggingFace and ModelScope links for each variant (search `huggingface.co/{model_id}`) - Collect technical report / paper links for each variant **2c. Analyze Model Architecture** Extract detailed architecture information from papers, docs, and the LLM Architecture Gallery: - Overall architecture design (encoder-decoder, decoder-only, cross-attention) - Key components: attention mechanism (MHA/GQA/MQA/MLA), FFN type (dense/MoE), normalization, activation - For VLMs: ViT architecture, visual token projection, multimodal fusion strategy - Context length, vocabulary size, hidden dimensions, layer counts - Special tokens, chat template, generation config - Reference: `https://sebastianraschka.com/llm-architecture-gallery/` for comparative context **2d. Explore vLLM Source Code** Find the model's implementation in vllm-project/vllm: - Use `gh` CLI to find model files: `gh search code "repo:vllm-project/vllm {model_keyword}" --path vllm/model_executor/models` - For VLMs, also search multimodal processing: `gh search code "repo:vllm-project/vllm {model_keyword}" --path vllm/multimodal` - Read key implementation files to understand: - Model registration and configuration - Core class hierarchy (which vLLM base classes are extended) - Input processing pipeline (text + multimodal) - Forward pass implementation details - vLLM-specific optimizations applied - See [references/model-research-guide.md](references/model-research-guide.md) for detailed code analysis methodology **2e. Cross-Reference Documentation** - Check vLLM docs: `https://docs.vllm.ai/en/latest/models/supported_models/` - Search for known issues: `gh search issues "repo:vllm-project/vllm {model_keyword}" --label bug` ### Step 3: Generate the Tutorial Document Read [references/style-guide.md](references/style-guide.md) for complete document structure and formatting conventions. Key requirements: - Write in **Chinese (简体中文)**, keeping English for technical terms - Follow the multi-part structure defined in the style guide - Include rich visual elements (see style guide for Mermaid diagram patterns) - Include technical principle deep-dives for relevant mechanisms (MoE, MLA, GQA, ViT, etc.) - Include document header with version info and date - Include a "文档概述" section with target audience and reading guide ### Step 4: Save Output Save the generated markdown file to `outputs/` directory relative to this skill's location: - File path: `outputs/{model_name_snake_case}.md` (e.g., `outputs/qwen3_vl.md`, `outputs/deepseek_v3.md`) - Create the `outputs/` directory if it doesn't exist - Use snake_case for file names (lowercase, underscores) The skill directory is the same directory as this SKILL.md file. ## Model Identification Heuristics When the user's request is ambiguous, use these heuristics to determine what to include: - If the model name contains "VL" or "Vision" → it's a VLM, include Part 5 (ViT computation) - If the model is known MoE (DeepSeek-V3, Mixtral, Qwen3-MoE) → emphasize MoE in Part 2 - If the model has MLA (DeepSeek-V3 series) → include MLA deep-dive in Part 2 - If the model is dense + GQA (Llama 4, Qwen3 dense) → emphasize GQA analysis - If the model generates images/video → check for DiT architecture and include if relevant ## Quality Checklist Before saving the document, verify: - [ ] Document has 4+ Mermaid diagrams (architecture, flow, sequence, class) - [ ] Document has 3+ comparison/reference tables - [ ] Document includes actual code snippets from vLLM source with file path annotations - [ ] Document follows Chinese writing convention with English technical terms - [ ] All sections have substantive content (no placeholder text) - [ ] Document header includes version and date metadata - [ ] Model series comparison table includes: model name, params, release date, key innovations, paper link, HF/ModelScope link - [ ] Technical principle deep-dives present for relevant mechanisms - [ ] Code location index table in appendix maps components to file paths - [ ] For VLM models: Part 5 (ViT) is present and complete; For non-VLM: Part 5 is omitted ## References - [references/style-guide.md](references/style-guide.md) — Document structure, formatting conventions, and output patterns - [references/model-research-guide.md](references/model-research-guide.md) — Detailed research methodology for code analysis - External: [LLM Architecture Gallery](https://sebastianraschka.com/llm-architecture-gallery/) — Comparative model architecture reference ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [shen-shanshan](https://github.com/shen-shanshan) - **Source:** [shen-shanshan/vllm-dev-skills](https://github.com/shen-shanshan/vllm-dev-skills) - **License:** Apache-2.0 - **Homepage:** https://zhuanlan.zhihu.com/p/2031696581678866733 Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-shen-shanshan-vllm-dev-skills-vllm-model-tutorial - Seller: https://agentstack.voostack.com/s/shen-shanshan - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.