{
 "schema_version": 1,
 "generated": "2026-10-31",
 "id": "ml-explore-mlx-lm--quantize-and-publish-a-model-for-mlx",
 "repo": "ml-explore/mlx-lm",
 "repo_url": "https://github.com/ml-explore/mlx-lm",
 "repo_slug": "ml-explore-mlx-lm",
 "title": "Quantize a model and publish it for MLX",
 "page_title": "How to quantize a model and publish it for MLX with MLX LM",
 "summary": "Convert a Hugging Face model to a 4-bit MLX model and optionally upload it to the Hub.",
 "who": "A developer who wants a smaller, faster version of a model that has no MLX build yet.",
 "problem": "Full precision models are too large to run comfortably on most Macs.",
 "outcome": "A quantized MLX model saved locally in mlx_model, and optionally uploaded to your Hub repo.",
 "steps": [
  "Install with `pip install mlx-lm`",
  "Quantize from the command line with `mlx_lm.convert --model mistralai/Mistral-7B-Instruct-v0.3 -q`",
  "Add --upload-repo with your target repo name to publish the result to Hugging Face",
  "Or do it in Python with convert(repo, quantize=True, upload_repo=upload_repo)",
  "Run the converted model with mlx_lm.generate --model pointing at it"
 ],
 "agent_prompt": "With ml-explore/mlx-lm, quantize <model id> to 4-bit using mlx_lm.convert -q, test it with mlx_lm.generate, and upload it to <my HF repo> with --upload-repo.",
 "agents": [
  "claude-code",
  "codex",
  "cursor",
  "gemini-cli"
 ],
 "difficulty": "Intermediate",
 "time": "30 minutes",
 "basis": "readme",
 "caveat": "This card is written from the ml-explore/mlx-lm README, not from a test run by Deduped. Before you start, make sure you have a Mac with Apple silicon. Check the repo for newer instructions before you rely on these steps.",
 "category": "Software and DevOps",
 "tags": [
  "Software and DevOps"
 ],
 "lang": "python",
 "requirements": [
  "A Mac with Apple silicon",
  "Python with pip or conda",
  "macOS 15 or later to speed up models that are large relative to RAM",
  "Optional: a Hugging Face account to upload converted models"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [
  {
   "tag": "credentials",
   "notice": "Uploading uses your Hugging Face credentials and publishes the model; check the original model's license allows redistribution."
  }
 ],
 "best": false,
 "score": 0.9,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/ml-explore-mlx-lm/quantize-and-publish-a-model-for-mlx/",
 "md": "https://dedupedai.com/use-cases/ml-explore-mlx-lm/quantize-and-publish-a-model-for-mlx/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/ml-explore-mlx-lm--quantize-and-publish-a-model-for-mlx.json",
 "api_version": "v1"
}