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
File size: 969 Bytes
3b57fd1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | # 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.
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