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
File size: 634 Bytes
e407d68 | 1 2 3 4 5 6 7 8 9 10 11 12 | {
"source": "ByteDance/Bernini-Diffusers-v2",
"source_commit": "399cf6a18a4c523b367b2b1ac25a2a61009e7df3",
"dtype": "bfloat16",
"converted": "2026-08-18",
"components": [
"experts",
"planner",
"t5+vae copied from mlx-community/Bernini-R-bf16 (bit-identical, AB-R-0097)"
],
"note": "experts RETRAINED vs Bernini-R (AB-R-0097) -> full reconvert via premap_diffusers_to_wan + mlx-video sanitize, CPU-stream, chunked evals; in-checkpoint fp32 T5 shards 26-31 never downloaded; mllm saved in HF Qwen2.5-VL layout (mllm. prefix stripped); connector/mask_tokens keys verbatim upstream in planner_glue.safetensors"
} |