0.4.0

[0.4.0] - 2026-10-24

Added

• Portable training: brainforge train check prefights hardware, model
  compatibility (architecture, chat template, pre-quantized and remote-code
  rejection, gated-model HF_TOKEN guidance) and a memory estimate without
  downloading model weights; --json and --strict modes for CI. Device
  auto-detection covers CUDA/ROCm, Intel XPU, Apple MPS and CPU; TPU/NPU
  fail fast with a --device cpu escape. training.device,
  training.quantization and training.backend (new auto defaults)
  resolve a per-run plan recorded in train_summary.json and eval.json.
  make train-check, make install-rocm and make install-xpu targets.
  Docs: new docs/reference/hardware.md; README, tutorial, training,
  configuration, CLI and architecture pages updated. CUDA/ROCm/XPU paths are
  implemented and unit-tested but not hardware-validated.
• huggingface-hub>=0.26 is now a direct dependency (preflight model
  metadata checks; previously available only transitively).
• Graceful failure: PipelineDef.fallback_examples (default false) turns a
  rejected case into an abstention training record (assistant target is a
  canonical insufficient_information verdict plus the gate reasons) instead
  of dropping it. Metadata carries fallback and fallback_reason; dataset
  validation accepts those records without a passing quality gate. The
  rejected-case audit trail is unchanged. ADR-0016.
• train task-eval treats abstention as a third outcome class and reports
  abstain_rate and abstain_recall (of the records the judge could not
  resolve, how many the model refused too).
• Critic escalation: PipelineDef.retry_with_critic (default null) names a
  critic role that runs, followed by a second judge pass, when the first pass
  is rejected. The critic never sees the first judge verdict. Records from a
  retry carry critic_retry and first_pass_reasons in metadata.

Changed

• Training and evaluation no longer hard-require CUDA: the device plan
  resolves automatically and qlora degrades to plain LoRA where 4-bit is
  unavailable. train evaluate and train task-eval default to
  --quantization auto; train run accepts --device and
  --quantization overrides.
• Rewrite the README for evaluators: dataset-first quickstart, verified
  mechanics, explicit limits, and intent-based docs routing. The docs hub and
  first-model tutorial use the same commands, vocabulary, and qualifiers.
• AirLLM evaluated and not adopted (ADR-0017): layer streaming helps models
  that do not fit VRAM, but its custom training API is incompatible with the
  PEFT/TRL, KD, merge and GGUF contracts, and throughput rules out local
  dataset generation.
• Provider failures are isolated per case: the case is recorded with a
  provider_error reason and the batch continues instead of aborting. Run
  summaries gain fallback and errors counters.
• run_pipeline writes the dataset and the KD sidecar once, atomically, at
  the end of the run (temp file plus rename). A crash plus rerun can no
  longer duplicate lines, and a rerun after a config or prompt tweak no
  longer keeps stale records. The dataset file always exists after a run,
  empty when nothing is accepted.
• New docs/reference/references.md maps each research paper and standard to
  what it informs (judge independence, contamination, QLoRA, KD, RAG,
  graceful failure). Stale links fixed: ADR-0008 pointed at the retired
  GitLab host, README advertised a make docs-serve target that does not
  exist.

Fixed

• Force pillow>=13.0.0 to clear its known CVEs despite fastembed's <13
  cap, and bump certifi to 2026.10.19.
• Bump filelock to 4.2.4 in the lock, clearing CVE-2026-112907,
  CVE-2026-112913 and CVE-2026-112940 (fixed upstream in 4.2.1).
• Audit the uv lock with pip-audit --no-deps --disable-pip so pip's
  resolver cannot reject the Pillow override.