training: restructure as vLLM plugin package

- Convert to installable package with entry points for vLLM auto-discovery
- Add checkpoint_sync.py: Python replacement for Rust checkpoint binary
  - Block-level diffing of safetensors files (4KB blocks)
  - vLLM→HF weight name conversion built-in
  - Scheduled 10min after training jobs (batched)
- API change: /train now takes raw token IDs (context_ids + continuation_ids)
  - No tokenizer on training side, client owns tokenization
- Remove superseded code: standalone scripts, Rust binary, tokenizer helpers

Install: pip install -e ./training
Then vLLM auto-loads via entry point.

Co-Authored-By: Proof of Concept <poc@bcachefs.org>

training/apollo_plugin/__init__.py

@@ -0,0 +1,17 @@
+"""Apollo training plugin for vLLM.
+
+Enables continuous fine-tuning alongside live inference by:
+1. Exporting CUDA IPC handles for weight sharing
+2. Providing a training worker daemon (/train endpoint)
+3. Block-level checkpoint sync to safetensors files
+
+Install: pip install -e /path/to/training
+Then vLLM auto-loads via entry point.
+"""
+
+from .export_hook import _patch_model_runner
+
+
+def register():
+    """Called by vLLM's plugin loader on startup."""
+    _patch_model_runner()