feat(neuron): wire candle harness load/unload via GGUF

Stage 2 of the candle-native pivot. Fleshes out CandleHarness with a
LoadedModel registry keyed by model_id, hf-hub-backed GGUF download,
and Qwen3 quantized weight construction via candle-transformers'
quantized_qwen3 module. unload_model drops the entry; Drop on the
candle ModelWeights frees device memory.

Device selection prefers CUDA (gated behind the new `cuda` feature),
falling back to CPU when CUDA is unavailable so default builds work
on non-GPU hosts. The candle CUDA toolchain isn't pulled in unless
`--features cuda` is passed, keeping CI green on CPU runners.

Config gains a [harness.candle] block with an optional hf_cache path.
HarnessRegistry::from_configs now takes HarnessSettings so per-harness
config flows through.

A gated tests/candle_lifecycle.rs exercises real load → list → unload
→ list-empty when run with `--features cuda-integration` against a
host with HF network access. The default-feature test in tests/api.rs
covers the wrong-harness rejection path without needing the network.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

neuron.example.toml

@@ -8,9 +8,17 @@
 port = 13131
 
 # -- Harnesses ---------------------------------------------------------------
-# Each [[harnesses]] entry declares an inference engine. Currently only
+# 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.
+# 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. When unset, hf-hub's
+# default (~/.cache/huggingface) is used.
+# hf_cache = "/var/lib/neuron/hf-cache"