Instructions to use vimalnakrani/hy-embodied-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use vimalnakrani/hy-embodied-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir hy-embodied-mlx vimalnakrani/hy-embodied-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
| # Quantization ladder | |
| Variants produced by `hy_embodied_mlx/quantize.py` from the bf16 MLX | |
| conversion: affine quantization, group size 64, at 8/6/5/4 bits plus a | |
| 3-bit probe kept strictly experimental. Policy: the 448 decoder linears | |
| (text and `_v` paths) and the tied embedding table are quantized — 449 | |
| modules per variant; the vision tower, merger, and all norms stay bf16 | |
| (the ViT fc2 input dim, 4304, is not divisible by 64, and the vision stack | |
| is a small fraction of total bytes). Every per-module decision is written | |
| to `quantization_manifest.json` in the variant directory; nothing is | |
| skipped silently. | |
| Measured on M3 Max, 36 GB, macOS 26.5.2, mlx 0.32.0 | |
| (`oracle/smoke_quant.py`, full outputs in `docs/smoke/`). Decode rate is | |
| the median of 5 runs of 64 greedy tokens after a text prefill; image | |
| prefill is the 333-token synthetic-image prompt (300 vision tokens) | |
| including the ViT forward. | |
| | variant | weights | decode tok/s | image prefill (s) | | |
| |---|---|---|---| | |
| | bf16 | 7.05 GiB | 66.8 | 0.18 | | |
| | 8-bit | 4.14 GiB | 106.9 | 0.18 | | |
| | 6-bit | 3.36 GiB | 124.3 | 0.18 | | |
| | 5-bit | 2.98 GiB | 136.9 | 0.18 | | |
| | 4-bit | 2.59 GiB | 157.6 | 0.18 | | |
| | 3-bit (experimental) | 2.20 GiB | 171.2 | 0.18 | | |
| Image prefill is flat across tiers because it is dominated by the ViT, | |
| which stays bf16 everywhere. | |
| The smoke records also carry greedy token agreement against the bf16 | |
| oracle over 64 steps. That number falls quickly at lower bits (a single | |
| flipped token cascades in greedy decoding) and is not a quality metric; | |
| outputs remain coherent down to 3-bit on the smoke prompts. Quality per | |
| tier is measured by pointing accuracy in the eval harness, not by token | |
| agreement. | |