Tiny LLM
Tiny-llm is a course that teaches systems engineers how to serve LLM inference on Apple Silicon by building a tiny vLLM and Qwen.
- •When you need to deploy LLM models on Apple devices
- •When you want to optimize LLM serving for low-latency and efficiency
- •When you're a systems engineer looking to learn about LLM inference on Apple Silicon
python example.py --model tiny_vllm --input "What is the meaning of life?"
Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.
Expect 20–40 minutes in a terminal — or let your AI agent drive it.
mkdir -p ~/.claude/skills/tiny-llm && curl -fsSL https://workflowstacks.com/api/skills/tiny-llm/claude-skill -o ~/.claude/skills/tiny-llm/SKILL.mdOpens the app with this repo with the prompt ready to go — no copy-paste needed.
Tiny LLM is a large Python project (~23k lines across 145 code files, plus 2 test files). It is a full software project: use it through its install path rather than reading it end to end. Last commit this month, Apache-2.0 license, has a test suite.
- 1README.mdStart here — what it does and how to install it
- 2AGENTS.mdThe instructions the AI actually follows
- 3main.pyWhere the program starts running
- 4pyproject.tomlDependencies and the commands it exposes
- 5src/tiny_llm_ref/__init__.pyInside src/ — the main logic begins here
Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.
Expect 20–40 minutes in a terminal — or let your AI agent drive it.
mkdir -p ~/.claude/skills/tiny-llm && curl -fsSL https://workflowstacks.com/api/skills/tiny-llm/claude-skill -o ~/.claude/skills/tiny-llm/SKILL.mdOpens the app with this repo with the prompt ready to go — no copy-paste needed.
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