Text Classification
MLX
Safetensors
English
prism_hadamard_qwen35
system-one
decision
calibrated-probabilities
jev
bonsai
ternary
apple-silicon
lora
vision
on-device
Instructions to use dakebakery/Bonsai-JEV-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use dakebakery/Bonsai-JEV-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download dakebakery/Bonsai-JEV-mlx --local-dir Bonsai-JEV-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
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Download PACK-RUNTIME.md from dakebakery/Bonsai-JEV-mlx: direct link, hf CLI and curl.
- Browser
- Download file 969 Bytes
-
https://huggingface.co/dakebakery/Bonsai-JEV-mlx/resolve/main/PACK-RUNTIME.md
- Command line
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hf download hf://dakebakery/Bonsai-JEV-mlx/PACK-RUNTIME.md
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curl -L -o PACK-RUNTIME.md https://huggingface.co/dakebakery/Bonsai-JEV-mlx/resolve/main/PACK-RUNTIME.md
969 Bytes
| # Hadamard MLX preview | |
| Text-only affine 2-bit/group-128 weights. Load with the bundled runtime: | |
| ```python | |
| import sys | |
| sys.path.insert(0, '/path/to/pack/runtime') | |
| from artifact import load_model | |
| model, config = load_model('/path/to/pack') | |
| ``` | |
| Install `runtime/requirements.txt` on Apple Silicon. Ordinary MLX loaders do not apply the required transforms. Vision and MTP are not included. | |
| `hadamard.json` uses the version-1 Prism contract accepted by MLX Swift's PrismHadamardConfiguration. Names refer to the saved MLX tensor namespace. GDN activations are already grouped in the bundled runtime; do not permute them again. Swift full-model loading still requires model integration; layer support alone is insufficient. | |
| Reload validation checks serialization, not model quality or cross-runtime equivalence. Tokenizer validation checks vocabulary IDs and BPE merge ranks; it does not certify pre-tokenizer behavior. The chat template is copied from the source GGUF. | |