blakeslee-research/nucleic-purpose-classifier

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Purpose classifier

This directory is the reproducible data and training pipeline for docs/PURPOSE_CLASSIFIER.md. The current slice covers work item 2 and the first part of work item 3: deterministic curation/splitting, a frozen v1 eval set, MiniLM fine-tuning, temperature calibration, shared confidence thresholds, and the frozen-set accuracy, recall, hard-slice, calibration, and latency report.

Data contract

The canonical generated sources are listed in data/generation-manifest.json. round2-NN.jsonl files are retained generation batches and intentionally duplicate purpose-prompts-round2.jsonl; they are provenance, not additional training input.

prepare_data.py:

validates the strict generated-record schema;
removes exact and high-overlap word-trigram duplicates;
fails for review if a high-overlap pair has conflicting labels;
keeps shipped fixtures completely outside source data;
holds every vague-eval record out of training;
optionally applies a completed, versioned human-review ledger before splitting;
stratifies by primary purpose, slice, and primary language; and
verifies that the deterministic test partition still matches the versioned data/frozen-test-v1.jsonl.

The frozen test set is the synthetic JSONL plus the 87 classifiable records in Tests/NucleicCoreTests/Fixtures/purpose-prompts.json. The fixture file's five general records are excluded because general is deliberately not a model label. The exact membership and hashes are locked in data/dataset-v1-manifest.json.

Prepare

From the repository root:

python3 ml/purpose-classifier/validate-data.py
python3 ml/purpose-classifier/prepare_data.py
python3 -m unittest discover -s ml/purpose-classifier/tests -p 'test_*.py'

For a newly generated raw 200-record batch, enable batch-shape checks explicitly with validate-data.py path/to/batch.jsonl --batch-size 200 --expected-total 200.

The generated train/validation copies land under .artifacts/dataset-v1/ and are gitignored. A source, curation, seed, or split-policy change that moves the frozen test set fails closed. After reviewing such a change, intentionally version it with:

python3 ml/purpose-classifier/prepare_data.py --refresh-frozen-test

Train purpose-lite

Use a dedicated virtual environment. The base model is pinned to a specific sentence-transformers/all-MiniLM-L6-v2 commit: a 6-layer, 384-dimensional encoder. The training collator always pads/truncates to 128 tokens so the later ONNX/Core ML export can expose a fixed 1 x 128 runtime shape. Long prompts preserve both ends as [CLS] + 63 head tokens + [SEP] + 62 tail tokens + [SEP]; this keeps the ask when it follows a pasted log or stack trace while retaining enough leading context to interpret it.

python3 -m venv ml/purpose-classifier/.venv
ml/purpose-classifier/.venv/bin/pip install -r ml/purpose-classifier/requirements.txt
ml/purpose-classifier/.venv/bin/python ml/purpose-classifier/train.py

The default requirements use PyTorch's CPU-only wheel on Linux, avoiding an accidental multi-gigabyte CUDA install in CI and development containers. For NVIDIA, AMD, or Intel accelerator training, install the platform's torch==2.13.0 build using PyTorch's platform selector, then install requirements-base.txt.

Training writes a local checkpoint, calibration.json, and metrics.json under outputs/purpose-lite-v1/. It selects checkpoints and fits temperature on label-scorable validation records. When deriving nested HIGH/MEDIUM/LOW cutoffs, every vague-eval record counts as an abstention miss even if its synthetic label happens to match. The incoming checkpoint is scored and retained as epoch zero, so a continuation run cannot silently replace it with a regression. Validation early stopping defaults to two epochs without an improvement greater than 0.05 points.

Continuation training accepts a local checkpoint. --boundary-weight is an opt-in, validation-selected loss weight for the measured weakest slice; it does not add held-out fixtures to training:

ml/purpose-classifier/.venv/bin/python ml/purpose-classifier/train.py \
  --model ml/purpose-classifier/outputs/purpose-lite-v1/model \
  --epochs 3 --learning-rate 3e-6 --warmup-ratio 0 \
  --boundary-weight 2 \
  --output-dir ml/purpose-classifier/outputs/purpose-lite-v1-boundary-tune \
  --overwrite-output

For QAT, --quantization-aware replaces the model's linear and embedding forwards with straight-through fake quantization matching the shipping QDQ graph: per-tensor uint8 embeddings, per-channel symmetric int8 linear weights, and per-tensor uint8 activations. Parameter names remain unchanged, so the selected checkpoint reopens as an ordinary Transformers model and uses the same export.py path. Keep the incoming checkpoint as epoch zero and select QAT only on validation. Training logs progress every 50 batches by default (--progress-steps 0 disables it), so a long CPU run remains observable:

ml/purpose-classifier/.venv/bin/python ml/purpose-classifier/train.py \
  --model ml/purpose-classifier/outputs/purpose-lite-v1-boundary-tune/model \
  --epochs 2 --learning-rate 1e-6 --warmup-ratio 0 \
  --early-stopping-patience 1 --boundary-weight 2 --quantization-aware \
  --output-dir ml/purpose-classifier/outputs/purpose-lite-v1-qat1 \
  --overwrite-output

On dataset v1, that validation-selected run produced a 23,148,500-byte int8 graph at 94.88% frozen accuracy (889/937), 94.46% scored-hard accuracy, and 98.19% scorable PyTorch↔ONNX agreement. It was the pre-distillation quantized candidate and remained two correct predictions below the 95% gate. A subsequent validation-selected 5e-7 epoch improved int8 validation accuracy from 93.31% to 93.71% but regressed frozen accuracy to 94.34%; it is rejected. Do not continue optimizer-only QAT sweeps on this split. The next model iteration should incorporate reviewed boundary data and be selected on a revised validation/frozen dataset version.

To target only the remaining float→int8 decision drift, cache the float teacher in a separate inference process and use its logits for QAT distillation. Keeping teacher and student models out of the same process avoids doubling peak resident memory:

ml/purpose-classifier/.venv/bin/python ml/purpose-classifier/cache_teacher.py \
  --model ml/purpose-classifier/outputs/purpose-lite-v1-boundary-tune/model \
  --output ml/purpose-classifier/outputs/purpose-lite-v1-boundary-teacher.pt \
  --overwrite-output
ml/purpose-classifier/.venv/bin/python ml/purpose-classifier/train.py \
  --model ml/purpose-classifier/outputs/purpose-lite-v1-boundary-tune/model \
  --distillation-cache \
    ml/purpose-classifier/outputs/purpose-lite-v1-boundary-teacher.pt \
  --distillation-weight 0.9 --distillation-temperature 2 \
  --distillation-selection-weight 0.5 --quantization-aware \
  --epochs 2 --learning-rate 1e-6 --warmup-ratio 0 --boundary-weight 1 \
  --output-dir ml/purpose-classifier/outputs/purpose-lite-v1-distilled-qat \
  --overwrite-output

The cache binds each logit row to normalized prompt hash plus expected label. Training fails closed if either split changes. Selection combines label accuracy with float-teacher agreement, retains the incoming checkpoint as epoch zero, and logs label/distillation loss separately. A 64-record wiring run exercised cache loading, shuffled row alignment, backpropagation, selection, and ordinary checkpoint reload. The current shared CPU runtime then showed severe post-batch throttling, so no full candidate result is claimed from that canary.

Native Apple Silicon training with MLX

Use the MLX backend when training on Apple Silicon. It implements the same six-layer BERT classifier, fixed head-tail tokenization, export-matched QAT graph, cached-teacher distillation, validation selection, and early stopping with native MLX arrays. Fake quantization is decomposed into Metal-supported round, clip, and straight-through-gradient operations, avoiding PyTorch's unsupported MPS fake-quant operator. Selected weights are written back with the original Hugging Face parameter names, so the existing PyTorch export.py and eval.py paths remain unchanged.

Install the additional pinned dependency into the macOS virtual environment:

ml/purpose-classifier/venv/bin/python -m pip install \
  -r ml/purpose-classifier/requirements-mlx.txt

Before the first full run on a new MLX or Transformers version, run the fail-closed parity check. It requires exact fake-quant primitives, float-logit parity, matching QAT predictions with bounded backend drift, healthy QAT gradients, and an exact Hugging Face → MLX → Hugging Face weight round trip:

ml/purpose-classifier/venv/bin/python ml/purpose-classifier/verify_mlx.py \
  --model ml/purpose-classifier/outputs/purpose-lite-v1-boundary-tune/model

Then run the distilled QAT candidate natively on Metal:

ml/purpose-classifier/venv/bin/python -u ml/purpose-classifier/train_mlx.py \
  --model ml/purpose-classifier/outputs/purpose-lite-v1-boundary-tune/model \
  --distillation-cache \
    ml/purpose-classifier/outputs/purpose-lite-v1-boundary-teacher.pt \
  --distillation-weight 0.9 --distillation-temperature 2 \
  --distillation-selection-weight 0.5 --quantization-aware \
  --epochs 4 --early-stopping-patience 1 \
  --learning-rate 1e-6 --warmup-ratio 0 --boundary-weight 1 \
  --progress-steps 1 \
  --output-dir ml/purpose-classifier/outputs/purpose-lite-v1-distilled-qat-mlx-4e \
  --overwrite-output

The full Metal run stopped after epoch three and selected epoch two at 94.75% fake-quant validation accuracy. Its 23,148,500-byte int8-QDQ export scores 95.20% frozen (892/937), 95.19% macro recall, 94.17% scored-hard accuracy, and 98.08% scorable PyTorch↔ONNX agreement. Every purpose recall is above 91%, and the vague-abstention and routing-tier-drift gates pass. This is the current accuracy-qualified shipping candidate; latency and energy/residency still require measurement on the target Apple and Windows accelerator runtimes.

Convert and validate Core ML

Core ML Tools no longer maintains the legacy ONNX converter, so the Apple artifact is converted directly from the selected Hugging Face checkpoint. convert_coreml.py uses a fixed-shape export-only BERT forward to avoid dynamic Transformers masking helpers, checks that forward against Transformers before conversion, writes an ML Program package, and records hashes for every package file.

Install the pinned converter in the macOS environment and create the package:

ml/purpose-classifier/venv/bin/python -m pip install \
  -r ml/purpose-classifier/requirements-coreml.txt
ml/purpose-classifier/venv/bin/python ml/purpose-classifier/convert_coreml.py \
  --model-dir \
    ml/purpose-classifier/outputs/purpose-lite-v1-distilled-qat-mlx-4e/model \
  --output \
    ml/purpose-classifier/outputs/purpose-lite-v1-distilled-qat-mlx-4e/coreml/purpose-lite-v1-fp16.mlpackage \
  --overwrite-output

The direct float16 package is the conversion baseline, not the accepted Apple artifact. On the first physical Apple-Silicon run it scored 94.98% (890/937), two correct decisions behind the accepted ONNX graph, with 97.97% scorable label agreement. Calibrate a Core ML-native W8A8 candidate with the same deterministic 256-record sample and QDQ policy as the ONNX exporter:

ml/purpose-classifier/venv/bin/python ml/purpose-classifier/quantize_coreml.py \
  --model \
    ml/purpose-classifier/outputs/purpose-lite-v1-distilled-qat-mlx-4e/coreml/purpose-lite-v1-fp16.mlpackage \
  --model-dir \
    ml/purpose-classifier/outputs/purpose-lite-v1-distilled-qat-mlx-4e/model \
  --output \
    ml/purpose-classifier/outputs/purpose-lite-v1-distilled-qat-mlx-4e/coreml/purpose-lite-v1-w8a8.mlpackage \
  --overwrite-output

Activation calibration is grouped to keep temporary Core ML packages bounded and prints progress while it runs. The candidate uses per-tensor asymmetric uint8 activations, per-channel symmetric int8 linear weights, and per-tensor asymmetric uint8 embedding weights. It fails the command if the resulting package exceeds 25 MiB.

Run the frozen gate with CPU+Neural Engine placement and compare labels directly with the accepted int8 ONNX artifact. Gated Core ML evaluation fails closed without --compare-onnx, and requires at least 99.5% scorable label agreement:

ml/purpose-classifier/venv/bin/python ml/purpose-classifier/eval.py \
  --model-dir \
    ml/purpose-classifier/outputs/purpose-lite-v1-distilled-qat-mlx-4e/model \
  --calibration \
    ml/purpose-classifier/outputs/purpose-lite-v1-distilled-qat-mlx-4e/calibration.json \
  --coreml-model \
    ml/purpose-classifier/outputs/purpose-lite-v1-distilled-qat-mlx-4e/coreml/purpose-lite-v1-w8a8.mlpackage \
  --coreml-compute-units cpu-and-ne \
  --compare-onnx \
    ml/purpose-classifier/outputs/purpose-lite-v1-distilled-qat-mlx-4e/export/purpose-lite-v1-int8-qdq.onnx \
  --report \
    ml/purpose-classifier/outputs/purpose-lite-v1-distilled-qat-mlx-4e/coreml/ane-frozen-eval.json

Record the compute plan separately; this reports both operation-count and estimated-cost ANE shares. Repeat evaluation with --coreml-compute-units cpu-only --no-gate before the energy comparison in ENERGY_AND_RESIDENCY.md:

ml/purpose-classifier/venv/bin/python ml/purpose-classifier/inspect_coreml.py \
  --model \
    ml/purpose-classifier/outputs/purpose-lite-v1-distilled-qat-mlx-4e/coreml/purpose-lite-v1-w8a8.mlpackage \
  --compute-units cpu-and-ne \
  --report \
    ml/purpose-classifier/outputs/purpose-lite-v1-distilled-qat-mlx-4e/coreml/ane-compute-plan.json

For a wiring smoke test, use a small deterministic prefix:

ml/purpose-classifier/.venv/bin/python ml/purpose-classifier/train.py \
  --epochs 1 --max-train-records 64 --max-validation-records 64 \
  --output-dir ml/purpose-classifier/outputs/smoke --overwrite-output

Evaluate

ml/purpose-classifier/.venv/bin/python ml/purpose-classifier/eval.py

The command returns failure unless label-scorable frozen accuracy is at least 95%, every purpose recall is at least 85%, at least 90% of the deliberately context-free vague-eval slice resolves LOW, every misroute stays within one routing cost tier, and measured batch-one p95 is at most 20 ms. Use --no-gate only for diagnostic runs.

Export and score ONNX

ml/purpose-classifier/.venv/bin/python ml/purpose-classifier/export.py
ml/purpose-classifier/.venv/bin/python ml/purpose-classifier/eval.py \
  --onnx-model ml/purpose-classifier/outputs/purpose-lite-v1/export/purpose-lite-v1-int8-qdq.onnx \
  --report ml/purpose-classifier/outputs/purpose-lite-v1/export/int8-frozen-eval.json

export.py emits fixed-shape opset-17 fp16 and int8-QDQ graphs, a tokenizer/ normalization contract, golden tokenizations, shared calibration config, graph checks, artifact hashes, and a size report. Its default 256-record quantization calibration sample is deterministic and stratified by purpose, slice, and primary language; the export report records the seed, distribution, and prompt hashes. The int8 graph is the ≤25 MiB shipping candidate; the fp16 graph remains the accelerator-oriented conversion input.

When scoring ONNX, add --compare-pytorch to measure artifact drift against --model-dir. The report then includes overall, label-scorable, and per-slice label agreement plus every correct→incorrect, incorrect→correct, and changed-wrong-label transition:

ml/purpose-classifier/.venv/bin/python ml/purpose-classifier/eval.py \
  --onnx-model ml/purpose-classifier/outputs/purpose-lite-v1/export/purpose-lite-v1-int8-qdq.onnx \
  --compare-pytorch --no-gate

Audit curation

Run the semantic embedding duplicate audit. It also emits the deterministic, purpose/slice/language-stratified 10% human label-and-difficulty review CSV:

ml/purpose-classifier/.venv/bin/python ml/purpose-classifier/audit_data.py

The semantic pass uses the same commit-pinned MiniLM encoder and fixed 128-token input as purpose-lite. Similarity only proposes review candidates; it never edits source data or the frozen split automatically. The 18 word-trigram exclusions and the human-review completion rule are recorded in data/curation-review-v1.json; the semantic report is versioned as data/semantic-audit-v1.json.

Optional human review

The dataset owner accepted the curated generated labels and difficulty metadata as-is on 2026-07-31, so the blank 1,219-row review sample is not a training or rollout blocker. It remains available as an optional future audit. Check its progress without running the embedding audit again:

ml/purpose-classifier/.venv/bin/python ml/purpose-classifier/review_data.py

Mark each row accept, relabel, or reject. accept and reject leave the four reviewed* fields blank; reject requires notes. For relabel, blank reviewed fields retain their generated value, <none> clears a secondary purpose, and notes are required. If a secondary purpose is added or removed, set reviewedSlice consistently (mixed when a secondary is present). The validator rejects stale generated columns, missing or duplicate sample rows, invalid label combinations, and partially completed rows.

When every row has a human decision, write the versionable ledger:

ml/purpose-classifier/.venv/bin/python ml/purpose-classifier/review_data.py --finalize

Build an isolated candidate split first:

ml/purpose-classifier/.venv/bin/python ml/purpose-classifier/prepare_data.py \
  --human-review ml/purpose-classifier/data/human-review-v1.json \
  --output-dir ml/purpose-classifier/.artifacts/reviewed-candidate \
  --frozen-test ml/purpose-classifier/.artifacts/reviewed-frozen-candidate.jsonl \
  --manifest ml/purpose-classifier/.artifacts/reviewed-manifest-candidate.json \
  --refresh-frozen-test

Inspect the ledger, decision summary, candidate manifest, and split diff. Only then rerun the same command with the three candidate-path overrides removed to intentionally replace the versioned frozen dataset and manifest.

--regenerate recreates a blank CSV in the current schema and is only appropriate before review begins.

The one-time, hardware-bound energy and accelerator-residency procedure is in ENERGY_AND_RESIDENCY.md.

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