Train and evaluate Dohnuts

Use Python 3.12 and a working PyTorch ROCm installation on the RX 7900 XTX. The recorded training environment is preserved in data/manifests/environment-freeze.txt. Follow the PDM setup first; the package sources and lock file select the ROCm wheels.

pdm install --check --prod -G train -G agent
pdm run python scripts/run_experiment.py

The Qwen3.5 adapter limits PyTorch’s caching allocator to 80% of visible VRAM, leaving headroom for the desktop and GPU driver on a shared workstation.

The script downloads the pinned base and public datasets when absent, converts the complete mixture, isolates related examples and identical images across splits, and freezes token eligibility. It trains seed 42 for 3,600 optimizer updates, resuming saved optimizer and sampling state after interruption.

Development accuracy selects the checkpoint within this run. LoRA is merged, three temperatures are fitted on the calibration partition, and the complete held-out partition is evaluated. Test scores never choose a model. The workflow exports the selected checkpoint directly after calibration and evaluation, then completes acceptance, quality diagnostics and benchmarks. Variation across seeds is unmeasured.

The script then runs checkpoint/Bub acceptance, quality diagnostics, local Laya/Laya Vision references, JevBench’s frozen public tasks, and the complete frozen Laya chart protocol. API benchmarks measure latency and resource use. Every failed stage stops execution. Completed stages are recorded; rerunning the command resumes the same recipe. A different recipe or dataset requires a separate experiment directory. The current method warms up for 72 updates, follows cosine decay through update 2,400, and uses the 10% learning-rate floor through update 3,600. This is the schedule used by the selected Dohnuts 0.1.0 export.

--steps changes an experiment’s total budget. Extending a completed run retains optimizer and random state, sampling position, and the decay horizon. Development selection includes its existing best checkpoint, followed by calibration, evaluation, and acceptance. Checkpoint steps are experiment coordinates, not additional product versions.

The RLCD controls are --sigma and --ce-weight, defaulting to 0.3 and 1.0; --steps sets the total update budget. --data, --model, and --output identify local assets and output locations. They do not select architectures or change the fixed method. Different backbone support uses the adapter interface.

Base initialization and checkpoint initialization use the same 26-group mixture and training workflow. To initialize from an exported checkpoint:

pdm run python scripts/run_experiment.py --initialize-from runs/v1/checkpoint --output runs/domain

The checkpoint supplies the starting parameters and a fresh optimizer/schedule. The runner automatically resumes a saved update in its output directory, restoring optimizer and sampling state. Both modes use the same fused kernels, differentiable prefix sharing, frozen-image cache, RLCD objective, development selection and calibration. There is no separate incremental training program.

Outputs

The default data directory is data/processed/v1; outputs go to runs/v1:

Artifact	Contents
checkpoint/	Selected LoRA/head weights, base revision, temperatures, selection metadata, checksum
seed-42/resources.jsonl	Timestamped GPU memory, power, utilization, temperature and process RSS
seed-42/metrics.jsonl	Loss, reward, development accuracy, learning rate, and GPU memory
seed-42/test-predictions.jsonl	Stable example IDs, targets, raw logits
seed-42/evaluation.json	Raw/calibrated per-task, decision-type and candidate-count metrics
acceptance/report.json	Actual checkpoint reload, decision interface, and Bub SDK acceptance
quality-audit/	Candidate rotations, image ablations, and batch-size sensitivity
benchmarks/	End-to-end timing samples and resource measurements
laya-chart/	Frozen application/language suites, calibration diagnostics and parallel decision timing
jevbench/	JevBench public-task predictions, raw evidence, and same-ID reference comparisons
metrics/	CSV tables, comparison JSON, and a self-contained results report
figures/	Separately rendered comparison figures, chart data, and source checksums
recipe.json	Fixed settings, exposed RLCD values, and source hashes

from dohnuts.predictor import Predictor

model = Predictor.from_checkpoint("runs/v1/checkpoint")

Render comparison figures from the saved results using the plotting instructions.

The compact checkpoint requires its pinned base model, which defaults to .cache/models/Qwen3.5-0.8B. Raw data, cached base weights, and generated results are local artifacts rather than package contents.