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JohnP1
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Model Details
Model Provider
JohnP1
Model Tree
google/gemma-4-E2B-it
Input Modalities
Output Modalities
Supported Functionality
971342c0): D1A-E2B-it
v0.6's adapter, retrained for one epoch of its all-skills mix on that exact trunk (jonpol01/d1a#341).base_ct in d1a_config.json names that checkpoint. d1a rebuilds the trunk from it when it loads this adapter, every
int4 weight written as scale × integer, so the adapter runs on the weights it was trained on. Exact in fp32 (D1A_DTYPE=fp32),
rounded in bf16.Needs d1a with jonpol01/d1a#346 and #348, and on a Mac's GPU (MPS) #354:
bash
pip install "d1a[serve,media] @ git+https://github.com/jonpol01/d1a"D1A_MERGE=0 python -m d1a.serving.serve --run JohnP1/d1a-e2b-it-qat --port 8009Decisions and chat come from that one server: D1A_MERGE=0 keeps the adapter separate, so chat switches it off and talks as
Gemma 4 E2B-it QAT itself. Photos, voice and video come from the media server, which loads the full model with its encoders
(the QAT checkpoint's own, the vision projection QAT retrained included):
bash
python -m d1a.serving.media --run JohnP1/d1a-e2b-it-qat --port 8010On an Apple M1 Max, PyTorch bf16 (MPS), this repo's files as published:
billing 0.65 (the MLX build: 0.67).JohnP1/d1a-e2b-it@v0.6 where those matter.<bos>: trained with id 2 at the start of every record; d1a adds it.Apache-2.0. Base model: Gemma 4 by Google (Apache-2.0). Code: github.com/jonpol01/d1a, built on Kev by Jared Palmer (Apache-2.0). Japanese decision data derived from JGLUE by Yahoo Japan Corporation and Waseda University (CC BY-SA 4.0). Pull-request data from NousResearch/hermes-agent (MIT); the labelled PR dataset itself, including the recent PRs, is private.
pip install "d1a[serve,media] @ git+https://github.com/jonpol01/d1a"D1A_MERGE=0 python -m d1a.serving.serve --run JohnP1/d1a-e2b-it-qat --port 8009python -m d1a.serving.media --run JohnP1/d1a-e2b-it-qat --port 8010