# neuron.example.toml — example configuration
#
# Copy to /etc/neuron/neuron.toml and adjust for your environment.
#
# Environment variable overrides use NEURON_ prefix with __ separators:
#   NEURON_PORT=13131

port = 13131

# -- Harnesses ---------------------------------------------------------------
# Each [[harnesses]] entry enables an inference engine. Currently only
# "candle" is supported — it runs in-process and uses huggingface/candle
# for inference on local CUDA devices (or CPU when CUDA is unavailable).

[[harnesses]]
name = "candle"

# -- Candle harness settings -------------------------------------------------
# Optional tuning for the candle harness.

[harness.candle]
# HuggingFace cache directory for model weights.
#
# Resolution order (first hit wins):
#   1. `hf_cache` here in this file.
#   2. `HF_HUB_CACHE` env var — same convention as the Python
#      `huggingface_hub` library, so an existing cache directory shared
#      with other tooling can be reused without per-tool config.
#   3. `HF_HOME` env var (cache appended as `$HF_HOME/hub`).
#   4. hf-hub's default (`~/.cache/huggingface/hub`).
#
# For per-host overrides (e.g. one neuron has an SSD with prefetched
# weights), prefer a systemd drop-in over editing this file:
#   /etc/systemd/system/neuron.service.d/local.conf:
#     [Service]
#     Environment=HF_HUB_CACHE=/archive/hf-cache
# hf_cache = "/var/lib/neuron/hf-cache"

# -- Default models ----------------------------------------------------------
# Models listed here are loaded automatically when the neuron service
# activates. Loading is sequential — a slow or failing entry doesn't
# block the rest of the fleet, but it does push out the time before
# neuron starts serving HTTP, so keep the list short. Operators can
# load additional models on demand via POST /models/load.
#
# Make sure data/neuron.service's TimeoutStartSec is generous enough to
# cover the slowest entry's first-time download + materialisation.

# [[default_models]]
# model_id = "Qwen/Qwen3-0.6B-GGUF"
# harness = "candle"
# quant = "Q4_K_M"
# devices = [0]