Instructions to use iamEvanYT/K2-Horizon-MoVA-36B-A4B-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use iamEvanYT/K2-Horizon-MoVA-36B-A4B-MLX-4bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir K2-Horizon-MoVA-36B-A4B-MLX-4bit iamEvanYT/K2-Horizon-MoVA-36B-A4B-MLX-4bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
K2-Horizon-MoVA-36B-A4B-MLX-4bit
4-bit affine (group size 64) MLX conversion of
IFM/K2-Horizon-MoVA-36B-A4B
for Apple Silicon via mlx-lm.
The K2 MoVA architecture has no stock mlx-lm implementation, so this repo
ships a custom k2_horizon.py, referenced from config.json (model_file).
MoE/MoVA router matrices are kept in BF16; all large projections are 4-bit.
Measured on a 64 GB M4 Pro Mac mini: ~43 tok/s single-stream decode, 18.9 GB peak RSS, perplexity 2.1-8.6 on generic prose/code.
from mlx_lm import load, stream_generate
model, tokenizer = load("iamEvanYT/K2-Horizon-MoVA-36B-A4B-MLX-4bit")
for chunk in stream_generate(model, tokenizer, "Hello.", max_tokens=64):
print(chunk.text, end="", flush=True)
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Model size
37B params
Tensor type
BF16
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U32 ·
Hardware compatibility
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Base model
IFM/K2-Horizon-MoVA-36B-A4B