Four fixes from automated code review:
1. score.py: pre-compute embeddings per content string to cut API
calls from O(P*T) to O(P+T). Was making 2*P*T HTTP round-trips
to Ollama embeddings per memory_extraction sample; now P+T.
Significant speed-up on cloud endpoints where each embed is a
network call.
2. metrics.py: bump VRAMPoller default interval from 100ms to 500ms.
nvidia-smi subprocess every 100ms was creating measurable jitter
on the timing numbers. 500ms still captures peak (inference VRAM
is steady-state during a request) with much less overhead.
3. orchestrator.py: use argparse.BooleanOptionalAction for
--continue-on-error so --no-continue-on-error actually disables
the default. The previous combination of store_true and
default=True made the flag a permanent True.
4. summarize.py: drop the buggy first attempt at the reasoning
filter (had operator-precedence issue with `or` and `and`).
Keep only the clean prefix-based filter that was already on
the next line shadowing it.
Skeleton for benchmarking ≤4B-parameter Ollama models on MemPalace
classification and extraction tasks. Outputs per-(model, task, mode)
metrics: accuracy, latency (TTFT, TPS, e2e p50/p95), VRAM (resident
and peak).
Layout under benchmarks/model_eval/:
- candidates.yaml: 16 models across 3 tiers, with family/size/variant
metadata pulled from ollama.com/library/<family>/tags
- metrics.py: timing extraction from Ollama response, VRAMPoller for
peak-memory tracking via nvidia-smi, embedding-similarity scoring,
host-info introspection, percentile aggregation
- runner.py: runs one (model, task, mode) triple, dispatches per-task
prompt building and scoring
- orchestrator.py: iterates candidates × tasks, writes CSV
- tasks/{calibration,room_classification,entity_extraction,
memory_extraction}/{prompts.py,score.py}: per-task prompt builders
and scorers
Uses mempalace.llm_client.get_provider directly so the benchmark runs
the same code path as production. strip_thinking_tokens lives locally
in metrics.py for now; will switch to mempalace.local_model once that
module lands on develop.
Datasets land in a follow-up commit.
Igor Lins e Silva