Text Classification
MLX
Safetensors
English
Japanese
gemma4
d1a
decision-model
pull-requests
apple-silicon
quantized
8-bit precision
Instructions to use JohnP1/d1a-e4b-mlx-q8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use JohnP1/d1a-e4b-mlx-q8 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download JohnP1/d1a-e4b-mlx-q8 --local-dir d1a-e4b-mlx-q8
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Commit History
7 days ago
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config.json: vision_config
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Model card: wording
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15 days ago
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Card: drop the note on where D1A is served
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19 days ago
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v0.6.1: routing temperature (use_case_temperatures.routing), same weights as v0.6 (jonpol01/d1a#264)
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20 days ago
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Card: the chosen D1A avatar
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Card: add the D1A avatar
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21 days ago
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Card: say what D1A is first, with an example
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22 days ago
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D1A-E4B v0.6
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25 days ago
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D1A-E4B v0.5 MLX 8-bit (from JohnP1/d1a-e4b@v0.5): same build as v0.4, only the first weight shard differs
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29 days ago
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v0.4: pull-request labeling round 2, MLX 8-bit with media encoders
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30 days ago
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v0.3: add the base's vision and audio encoders (media/) for photos and voice
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v0.3: pull-request labeling (English and Japanese), MLX 8-bit
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D1A-E4B v0.2 (hybrid) MLX 8-bit, per-layer embeddings 4-bit; golden parity 7/274 flips, mean dp 0.020
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Calibration temperature 1.46 (fitted on the study's calibration split; the published head.pt had been left at 1.0)
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Card: parity numbers (3/274 changed answers)
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D1A-E4B MLX q8 (per-layer embeddings 4-bit); parity check pending
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initial commit
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