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How to use the server as a text classifier endpoint with Halogen Flash Server

Send labeling and classification requests to the local model through the classify endpoint.

Who it is forAn operations or data analyst who needs to sort incoming text into categories without sending it to an outside service.

The problemSorting large batches of text by hand is slow, and the text may be too sensitive to send to a hosted classifier.

The outcomeEach input text comes back with a category label produced by the local model.

What you need first

  • An AMD Strix Halo machine (the README measures on a Ryzen AI Max+ 395 with 128 GB)
  • Podman installed, with access to /dev/kfd and /dev/dri
  • Access to the BIOS to set the UMA frame buffer carve-out to its minimum
  • Disk space for the model weights, which the container downloads on first run
  • Network access to ghcr.io and the model download source

Steps

  1. Start the server with the Quickstart run command, keeping the model on the local machine.
  2. Read docs/classifier.md to learn the request shape for classification.
  3. Read docs/CLASSIFY.md for the classify endpoint reference.
  4. Send a small batch of sample texts and check the labels against a few examples you already know.
  5. Scale the batch once the labels match your expectations.

Prompt for your agent

Using the repo peonist-ai/halogen-flash-server, send the texts in <input file> to the classify endpoint as described in docs/CLASSIFY.md. Expected output: one label per input text. Verify the labels on ten known examples before running the full batch.

Caveat. The README points to docs/CLASSIFY.md for the endpoint, and this summary does not restate its request format, so check that page before building on it.

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