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D1A 0.2.0

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@github-actions github-actions released this 02 Oct 14:04
· 615 commits to main since this release
v0.2.0
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Learn about vigilant mode.

The first D1A release: Kev's decision model moved onto Gemma 4, with trained checkpoints, an Apple Silicon backend, agent tools, model routing, and photos and voice.

What's new

  • Gemma 4 is the default base. D1A trains and serves on Gemma 4 E2B and E4B. The Qwen bases Kev was built for still work.
  • Better models. D1A-E4B v0.2 (hybrid) adds Kev's later training stages, Japanese decisions and agent routing on top of v0.1: Japanese 0.664 → 0.835, English on new sources 0.680 → 0.704, English on trained sources unchanged (0.848), agent-factory routing 0.933.
  • Fast on a Mac. On Apple Silicon D1A runs on MLX with 8-bit exports that are checked against the PyTorch reference, and Gemma 4's per-layer embeddings are read from disk per request: E2B uses 3.0 GB instead of 4.2 GB.
  • Use it from agents. Ready-made question sets (intake, judge, tier, gate, route) and an MCP server turn each decision into one tool call, and D1A can say whether a prompt needs a small, medium or large model.
  • Use it in your own code. from d1a import D1A loads a checkpoint once and answers questions in-process, with no server.
  • Photos and voice notes. d1a.media answers the same typed questions about an image or a short audio clip, using Gemma 4's own vision and audio encoders (no captioning or speech-to-text). It loads its model on the first request and frees it when idle.

Added

  • MLX backend for Gemma 4 (sliding-window and KV-shared layers), quantized MLX exports (scripts/export_mlx.py) and golden-vector parity checks (scripts/golden_vectors.py) (#39).
  • Gemma 4 per-layer embeddings read from the weight files per request instead of held in memory, with bit-identical results: 1.3 GB less for the 8-bit E2B export, 1.5 GB less for E4B (D1A_PLE_FLASH=0 to decline) (#90).
  • d1a.D1A: load a checkpoint once and answer questions in-process (#47).
  • Agent-factory presets and an MCP server (d1a.mcp_server, extra mcp): d1a_intake, d1a_judge, d1a_tier, d1a_gate, d1a_route, d1a_decide (#52).
  • d1a.media: System One questions about a photo or a voice clip (POST /v1/systemone/media, extra media) (#83), loaded on demand and dropped after --idle-unload seconds (#85).
  • d1a.serve: --device with a kernel probe and a startup self-check of the readout (#43); recent batch latency in /v1/models (#48); a warning when the checkpoint is not calibrated, and calibrated in /v1/models (#54).
  • Training: resume points for LoRA runs (#46); --extra_suites to train on several frozen suites in one run (#67).
  • d1a/backbone.py: what differs between model families in one place, for the torch and MLX paths (#70, #77).
  • scripts/kev_share.py: how much of D1A is still Kev's code, by area (#76).
  • Reports: model routing v0.1 findings, English and Japanese (#56); Apple's Core AI and Foundation Models (macOS and iOS 27) compared with D1A (#86).
  • README: GIFs of the eleven playground demos, and animated architecture diagrams generated by docs/arch/make_svgs.py (#44, #83, #84).
  • CI: Python 3.12 and 3.13, MLX tests on Apple Silicon, a lint pass, a package build and install check, and this release workflow.

Changed

  • The package, modules and settings are D1A's: kev → d1a, KEV_* → D1A_* environment variables, model name d1a-latest (kev-latest and jev-latest still answer) (#1).
  • The default training base is Gemma 4 E2B, and backend="auto" picks MLX for Gemma 4 on Apple Silicon (#1, #39).
  • Model routing sends a prompt to the small tier only at p(small) ≥ 0.7, so close calls go up a tier (#55).
  • The frozen suites are pinned at kev-suites cc4bac8, which adds the hard-v1 and documents-v1 training partitions (#68).

Fixed

  • A proxy in front of d1a.serve (the playground's) could reuse a connection the server had just closed (ECONNRESET): the server now keeps idle connections for 75 s (#53).
  • A GPU whose driver or PyTorch build cannot run a kernel no longer crashes the server on its first request: the device is probed at startup and the server falls back to the CPU (#43).
  • A non-finite loss or gradient skips its batch instead of ending the run; three in a row still end it, because then the weights themselves are broken (#46).

Removed

  • Kev's research tooling, deployments and records that D1A does not use: 26 files, about 5,000 lines (#69).

Models tested with this release

Checkpoint Hugging Face tag
D1A-E4B v0.2 (hybrid), PyTorch JohnP1/d1a-e4b@v0.2-hybrid
D1A-E4B v0.2 (hybrid), MLX 8-bit JohnP1/d1a-e4b-mlx-q8@v0.2-hybrid
D1A-E2B v0.2.1 (2 epochs, calibrated), PyTorch JohnP1/d1a-e2b@v0.2.1-2epoch-calibrated
D1A-E2B v0.2, MLX 8-bit JohnP1/d1a-e2b-mlx-q8

Upgrade notes (from Kev)

  • Rename imports and commands: python -m kev.serve → python -m d1a.serve, from kev... → from d1a..., and KEV_* environment variables → D1A_*.
  • On Apple Silicon the server now uses MLX for Gemma 4 by default; set D1A_BACKEND=torch for PyTorch MPS.
  • Optional features are extras: serve (HTTP server), mlx (Apple Silicon), media (photos and voice), mcp (agent tools).
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