Instructions to use mlx-community/Bernini-v2-int4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/Bernini-v2-int4 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Bernini-v2-int4 mlx-community/Bernini-v2-int4
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Bernini-v2 (int4, MLX)
4-bit variant of mlx-community/Bernini-v2-bf16
— Apple-MLX conversion of ByteDance/Bernini-Diffusers-v2
(revision 399cf6a, Apache-2.0).
Quantization scope: the two Wan2.2-A14B transformer experts only (attention + FFN Linears,
4-bit, group size 64; embeddings/norms/head/modulation stay bf16). The planner plane
(mllm/, vit_decoder, planner_glue), umT5 encoder, and VAE pass through at bf16 —
weight-only int4 on diffusion transformers costs measurable quality (bf16 is the recommended
default; this variant is the memory-constrained opt-in).
See the bf16 repo's card for the full component table, conversion notes, retrained-experts
warning (not interchangeable with Bernini-R-*), usage pointers, and provenance.
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Model tree for mlx-community/Bernini-v2-int4
Base model
ByteDance/Bernini-Diffusers-v2