neuron-tai/.scratch/distributed-gguf-runtime/evidence/DGR-010/BLOCKED.md

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

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.