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neuron-tai/.scratch/distributed-gguf-runtime/evidence/DGR-010/BLOCKED.md
2026-07-15 23:42:58 +03:00

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# DGR-010 — Blocked handoff
Status: blocked
Date: 2026-07-15
## Blocker
I verified the local workspace and mounted-drive model storage, but there is no
certified dense-Llama artifact available on this machine to run the required
real-model two-process acceptance.
What I found:
- `/run/media/popov/d/DEV/models` contains Qwen artifacts and caches, but no
dense-Llama model snapshot or GGUF artifact.
- `/run/media/popov/d/DEV/llamacpp/llama.cpp/models` contains only vocab GGUFs,
not a certified dense-Llama model.
- The existing code paths for real startup, GGUF backend selection, Hot KV
isolation, and benchmark reporting are present and readable, but the actual
DGR-010 acceptance run needs a certified dense-Llama artifact from mounted
storage to satisfy the story contract.
## Verified current state
- DGR-009 evidence was read and verified as the dependency handoff.
- `packages/node/meshnet_node/startup.py` already gates backend selection by
recipe and can load either the Torch path or the explicit GGUF seam.
- `packages/node/meshnet_node/hot_kv_state.py`, `boundary_adapter.py`, and
`gguf_ownership.py` already provide the isolation/parity seams that DGR-010
would exercise.
- The repo has no existing `evidence/DGR-010/README.md` yet, which is expected
because the story has not been completed.
## Commands run
```bash
sed -n '1,260p' .scratch/distributed-gguf-runtime/RALPH-CONTEXT.md
sed -n '1,260p' .scratch/distributed-gguf-runtime/issues/10-pass-local-real-model-two-process-acceptance.md
sed -n '1,260p' .scratch/distributed-gguf-runtime/evidence/DGR-009/README.md
git status --short
find /run/media/popov/d/DEV -type f \( -name '*.gguf' -o -name '*.safetensors' -o -name 'config.json' \) | rg -i 'llama|tinyllama|meta-llama|hf-internal-testing|qwen'
```
## Next step to unblock
Provide or mount a certified dense-Llama artifact on the configured mounted
drive storage, then rerun the DGR-010 acceptance path with
`MESHNET_ENABLE_REAL_INFERENCE_TESTS=1`.
## Continuation note
Once the artifact exists, the next iteration should:
1. Run the two local worker processes against the certified dense-Llama shard
ranges.
2. Capture parity, concurrency, memory, and failure metrics.
3. Write `evidence/DGR-010/README.md` with the real results and then update the
issue status.