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How to run a quantized NeoHorse-1 on your own hardware with Neo Horse

Download a GGUF or MLX build of NeoHorse-1 that uses less memory than BF16 weights.

Who it is forA developer or hobbyist with a laptop or modest GPU who wants to try NeoHorse-1 without a large server.

The problemFull BF16 weights take more disk and memory than a personal machine can spare.

The outcomeA smaller NeoHorse-1 checkpoint running on local hardware, with the quantization level chosen to fit memory limits.

What you need first

  • A machine with enough GPU memory for the 4B or 9B BF16 weights, or a quantized GGUF or MLX build for smaller hardware
  • Python with pip available
  • Disk space for the downloaded checkpoint from Hugging Face or ModelScope

Steps

  1. Open the NeoHorse-1-4B-GGUF or NeoHorse-1-9B-GGUF page on Hugging Face.
  2. Choose the 8-bit, 5-bit, or 4-bit file if you need less space, or the 16-bit BF16 file for full precision.
  3. On Apple silicon, use the MLX versions from the NeoHorse-1 MLX collection on Hugging Face.
  4. Follow the deployment instructions on that model card for the runtime you choose.
  5. Send one short prompt and confirm the reply is coherent before running longer tasks.

Prompt for your agent

In the TokenRhythm/NeoHorse repo, find the GGUF build of NeoHorse-1-4B on Hugging Face at <model-page> and pick the 4-bit quantized file. Follow that model card's deployment instructions and return the model's reply to the prompt <test-prompt>. Verify that the file size of the chosen build is smaller than the 16-bit BF16 build.

Caveat. The README names GGUF and MLX builds but does not say which runtime to use with them, so check each model card before choosing one.

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