Instructions to use AbstractFramework/flux.2-klein-base-4b-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use AbstractFramework/flux.2-klein-base-4b-4bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir flux.2-klein-base-4b-4bit AbstractFramework/flux.2-klein-base-4b-4bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
flux.2-klein-base-4b-4bit
This repository contains MLX-Gen saved weights for black-forest-labs/FLUX.2-klein-base-4B. The checkpoint is designed for local Apple Silicon inference with mlx-gen.
It uses the mflux/MLX saved-weight layout and MLX quantization tensors. It is not a Diffusers or Transformers from_pretrained() checkpoint.
Source Model
Original model: black-forest-labs/FLUX.2-klein-base-4B.
License and Access
This quantized derivative follows the Apache 2.0 license of the source model.
Quantization
This is an MLX 4-bit checkpoint. Model-specific quantization predicates may keep unsupported layers unquantized or at a different precision. See the MLX-Gen quantization docs for compatibility notes.
Compatibility
Requires mlx-gen >= 0.18.2.
Generated with mlx-gen 0.18.2.
Use the mlxgen command and Python import path for new MLX-Gen projects.
Usage
python -m pip install -U mlx-gen
mlxgen download --model AbstractFramework/flux.2-klein-base-4b-4bit
mlxgen generate \
--model AbstractFramework/flux.2-klein-base-4b-4bit \
--prompt "Your prompt here" \
--steps 20 \
--seed 42 \
--output image.png
Attribution
MLX-Gen is based on mflux by Filip Strand and the original mflux contributors. This model card is generated by MLX-Gen so derived checkpoints keep that attribution visible.
Quantized and contributed by @lpalbou.
4-bit
Model tree for AbstractFramework/flux.2-klein-base-4b-4bit
Base model
black-forest-labs/FLUX.2-klein-base-4B