model_backend.py was using Python-style exclusive end (layers[start:end])
while all callers (CLI, tests, QUICKSTART) use inclusive 0-based indexing.
Result: 24-layer model with shard_end=23 ran only 23 layers and never
set is_tail=True, so decode_tail() was never called and responses were empty.
- is_tail: == total_layers → >= total_layers - 1
- _run_layers: layers[start:end] → layers[start:end+1]
- Validation: > total_layers → >= total_layers (was also wrong)
Inference confirmed: Qwen2.5-0.5B-Instruct now returns real LLM output.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Layer count is now fetched from the curated catalog (zero network calls
for known models) or via AutoConfig.from_pretrained() (~1 KB config.json
only) when model_id is given without --shard-start/--shard-end.
- model_catalog: add detect_num_layers(), two small Qwen models at top
- startup: _detect_num_layers() helper; shard range auto-derived
- wizard: show detected layer count for custom HF repos
- tests: 3 new tests for auto-shard; fix catalog-order assumptions
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- `meshnet-node` with no args runs interactive setup wizard on first run,
then starts directly on subsequent runs using saved config
- Wizard auto-detects all GPUs/VRAM, shows curated model list with per-quant
VRAM requirements, marks models that exceed available VRAM as incompatible,
offers HuggingFace Hub browse as escape hatch
- Persistent config saved to ~/.config/meshnet/config.json (0o600)
- Live rich dashboard (tokens/sec EMA, VRAM, requests, peers, uptime) with
automatic plain-text fallback when stdout is not a TTY (WSL2/SSH/CI)
- All wizard values overridable via CLI flags; --reset-config re-runs wizard
- `meshnet-node models` lists curated models; `--browse` fetches HF Hub top-20
- `meshnet-node config` prints saved config
- `meshnet-node start ...` preserved for backward compatibility
- 19 new tests; 97 passed, 1 skipped (no regressions)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Tracker: add GET /v1/tracker-nodes/<model> returning nodes registered
with tracker_mode=true whose shard_start matches the model's first layer
- Node: StubNodeServer and TorchNodeServer accept tracker_mode/tracker_url;
when tracker_mode=True (or auto-detected via shard_start==0 for Torch),
/v1/chat/completions is served alongside /forward
- TorchNodeServer: full pipeline implementation — encode_prompt → route
selection via tracker → binary forward through remaining hops → decode
- Gateway: _handle_chat_completions checks _get_tracker_nodes() first and
proxies round-robin to tracker-nodes; falls back to existing direct
pipeline when none found (preserves all US-005 backward compat)
- CLI: --tracker-mode and --tracker-url flags added to meshnet-node start
- Test: two stub tracker-nodes + two mid-shard nodes for gpt2; 10 requests;
round-robin 5/5 split verified; all OpenAI-format responses validated
- All 78 tests pass
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
