{
 "schema_version": 1,
 "generated": "2026-10-20",
 "id": "whitecircle-halo--quantize-checkpoint-nvfp4",
 "repo": "whitecircle/halo",
 "repo_url": "https://github.com/whitecircle/halo",
 "repo_slug": "whitecircle-halo",
 "title": "Quantize a trained checkpoint to NVFP4",
 "page_title": "How to quantize a trained checkpoint to NVFP4 with Halo",
 "summary": "Convert a saved checkpoint to a low-precision NVFP4 format with one Halo run command.",
 "who": "An engineer preparing a trained model for cheaper inference who has a checkpoint directory from a Halo run.",
 "problem": "Low-precision formats need a conversion step that is easy to get wrong when done by hand.",
 "outcome": "A quantized NVFP4 checkpoint written to a new output directory.",
 "steps": [
  "Start the Halo image with your checkpoint directory mounted (inferred: standard docker run with a volume).",
  "List the available tools: `halo run --list`.",
  "Run the quantization: `halo run quantize-to-lowp --input_dir <checkpoint_dir> --output_dir <quantized_dir> --format nvfp4`. (inferred, check the README)",
  "Load the output with the standard loader for your inference server to confirm it is readable."
 ],
 "agent_prompt": "In the whitecircle/halo repo, run `halo run quantize-to-lowp` with --input_dir <checkpoint_dir> and --output_dir <quantized_dir> and --format nvfp4 inside the Halo image. Expected output: a quantized checkpoint in the output directory. Verify that the input and output paths are different directories before running.",
 "agents": [
  "any"
 ],
 "difficulty": "Intermediate",
 "time": "1 hour",
 "basis": "readme",
 "caveat": "The README gives the NVFP4 flag but does not state which inference engines accept the quantized output, so test it in your serving stack first.",
 "category": "Software and DevOps",
 "tags": [
  "Software and DevOps"
 ],
 "lang": "python",
 "requirements": [
  "Docker installed on a host with an NVIDIA GPU (Hopper H100/H200 or Blackwell B200/B300 images are published)",
  "Network access to public.ecr.aws to pull the Halo image anonymously",
  "A Hugging Face model or the example configs in the examples/ folder",
  "Enough GPU memory for the chosen model and method (the README shows single-GPU LoRA and 8-GPU runs)"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [],
 "best": false,
 "score": 0.7,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/whitecircle-halo/quantize-checkpoint-nvfp4/",
 "md": "https://dedupedai.com/use-cases/whitecircle-halo/quantize-checkpoint-nvfp4/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/whitecircle-halo--quantize-checkpoint-nvfp4.json",
 "api_version": "v1"
}