docs: add ADRs and user stories for real model inference stack (US-011–014)

ADR-0008: binary activation wire format — raw bfloat16 over HTTP, zstd compression,
128-token chunked prefill; replaces base64 JSON (~33% overhead removed).

ADR-0009: coverage-first shard assignment and tracker-as-first-layer-node —
any node serving layers[0..k] becomes the inference entry point for that model;
bin-packing fills all coverage gaps before adding redundancy; tracker issues
LOAD_SHARD/DROP_SHARD rebalance directives; nodes declare VRAM + quantization.

US-011: binary wire format migration
US-012: real PyTorch layer execution (transformers + bitsandbytes, test on GPT-2)
US-013: coverage-first tracker bin-packing with VRAM-aware shard assignment
US-014: tracker-as-node (tracker node serves first layers + handles client requests)

CONTEXT.md: Tracker Node, Coverage Map, Rebalance Directive terms added.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

.scratch/distributed-inference-network/issues/13-coverage-first-shard-assignment.md

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+# US-013 — Coverage-first tracker shard assignment
+
+Upgrade the tracker's route selection to a coverage-first, speed-weighted bin-packing algorithm. The tracker maintains a live coverage map per model, issues rebalance directives when gaps appear, and assigns shard ranges to nodes based on declared VRAM, quantization, and benchmark throughput.
+
+## Context
+
+Per ADR-0009:
+- **Coverage gap** = any layer range in a model with zero serving nodes → model is unroutable
+- **Coverage-first**: fill all gaps before adding redundancy to covered ranges
+- **Speed-weighted bin-packing**: fill fastest nodes to their VRAM limit first; cascade to next-fastest
+- **Continuous rebalancing**: triggered every 30s and on every node join/leave event
+- **Rebalance directive**: `LOAD_SHARD` or `DROP_SHARD` instruction sent from tracker to node
+
+Node registration now includes capability data:
+```json
+{
+  "wallet": "...",
+  "endpoint": "http://10.0.0.5:7000",
+  "vram_bytes": 25769803776,
+  "ram_bytes": 137438953472,
+  "quantizations": ["nf4", "int8", "bfloat16"],
+  "benchmark_tokens_per_sec": 12.4,
+  "benchmark_model": "openai-community/gpt2"
+}
+```
+
+Model preset metadata (stored in tracker config) includes `bytes_per_layer` at each quantization level and `total_layers`. The tracker can compute max assignable layers for any node.
+
+## Acceptance Criteria
+
+- Tracker accepts `vram_bytes`, `ram_bytes`, `quantizations`, `benchmark_tokens_per_sec` in the node registration payload
+- `GET /v1/coverage/<model_preset>` returns the coverage map: list of `{start_layer, end_layer, node_count}` for each assigned range
+- A model is only routable when all layer ranges have `node_count >= 1`
+- When a new node registers, the tracker assigns it to the highest-priority uncovered range (or expands the most-loaded range if fully covered)
+- When a node disconnects (heartbeat timeout), the tracker marks affected ranges as reduced-coverage and, if any reach 0, issues `LOAD_SHARD` directives to idle nodes within 30 seconds
+- The shard assignment respects VRAM: `assigned_layers <= floor((vram_bytes * 0.8) / bytes_per_layer_at_quant)`
+- Speed weighting: given two idle nodes both capable of covering a gap, the tracker assigns the wider sub-range to the faster node
+- An integration test: start tracker with a model preset, register three nodes with different VRAM, assert the coverage map shows 100% coverage and the largest VRAM node received the widest shard range
+- A second integration test: kill the node covering the middle layer range; assert tracker issues a `LOAD_SHARD` directive to an idle node and coverage recovers
+- `python -m pytest` passes
+- Commit only this story's changes
+
+## Implementation Notes
+
+- Coverage map data structure: `dict[str, list[tuple[int, int, int]]]` keyed by model preset
+- Bin-packing is run in-process on the tracker (not a separate service); it's triggered by registration/heartbeat events and a 30s periodic timer
+- Rebalance directives are delivered as responses to node heartbeat POSTs (node polls tracker every 10s anyway) — no new push channel needed
+- `bytes_per_layer` is read from a `model_presets.json` config file; add GPT-2 (12 layers, ~30MB/layer bfloat16) as the test preset
+- The current tracker `_select_route` function is extended, not replaced — backward compat with stub nodes that don't send VRAM data (default to 8GB / bfloat16 if omitted)