{
 "schema_version": 1,
 "generated": "2026-10-31",
 "id": "google-ai-edge-litert-lm--try-an-on-device-llm-from-the-terminal",
 "repo": "google-ai-edge/LiteRT-LM",
 "repo_url": "https://github.com/google-ai-edge/LiteRT-LM",
 "repo_slug": "google-ai-edge-litert-lm",
 "title": "Run Gemma on your own machine from the terminal",
 "page_title": "How to run Gemma on your own machine from the terminal with LiteRT LM",
 "summary": "Install the LiteRT LM CLI with uv and chat with a Gemma model without writing any code.",
 "who": "A developer curious whether an on-device model is good enough for their app before committing to it.",
 "problem": "Testing on-device models usually means setting up a mobile project before you see a single answer.",
 "outcome": "A local model answering prompts in your terminal, downloaded straight from Hugging Face.",
 "steps": [
  "Install the CLI with `uv tool install litert-lm`",
  "Run litert-lm run with --from-huggingface-repo pointing at a model repo such as google/gemma-3n-E2B-it-litert-lm, the model name and a --prompt",
  "Try a larger model like gemma-4-E4B-it with --backend=gpu and --speculative-decoding=on if your hardware supports it",
  "Use `litert-lm describe` to inspect a model's metadata, capabilities and runtime requirements before loading it",
  "Read the LiteRT LM CLI Guide for advanced usage"
 ],
 "agent_prompt": "Install google-ai-edge/LiteRT-LM with uv, run the gemma-3n-E2B-it-int4 model from the README with a test prompt, then try the Gemma 4 E4B example with the GPU backend. Report speed and whether each worked on my machine.",
 "agents": [
  "claude-code",
  "codex",
  "cursor"
 ],
 "difficulty": "Beginner",
 "time": "15 minutes",
 "basis": "readme",
 "caveat": "Model downloads are large, and GPU or NPU acceleration depends on your hardware and drivers.",
 "category": "Software and DevOps",
 "tags": [
  "Software and DevOps"
 ],
 "lang": "c++",
 "requirements": [
  "uv to install the CLI with uv tool install",
  "Linux, macOS, Windows or Raspberry Pi for the CLI; Android, iOS or the web through the language APIs",
  "A LiteRT-LM model, for example from the litert-community Hugging Face repos"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [],
 "best": true,
 "score": 1.0,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/google-ai-edge-litert-lm/try-an-on-device-llm-from-the-terminal/",
 "md": "https://dedupedai.com/use-cases/google-ai-edge-litert-lm/try-an-on-device-llm-from-the-terminal/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/google-ai-edge-litert-lm--try-an-on-device-llm-from-the-terminal.json",
 "api_version": "v1"
}