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>

crates/neuron/src/main.rs

@@ -51,7 +51,7 @@ async fn main() -> Result<()> {
     // Build harness registry from config. In-process harnesses (candle)
     // need to know neuron's own bind URL so they can return it from
     // inference_endpoint.
-    let registry = HarnessRegistry::from_configs(&cfg.harnesses, &bind_url);
+    let registry = HarnessRegistry::from_configs(&cfg.harnesses, &bind_url, &cfg.harness);
     discovery_result.harnesses = registry.names();
 
     let health_cache = Arc::new(health::HealthCache::new());