Instructions to use cs2764/GLM-5-abliterated-dq3-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use cs2764/GLM-5-abliterated-dq3-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir GLM-5-abliterated-dq3-mlx cs2764/GLM-5-abliterated-dq3-mlx
- Transformers
How to use cs2764/GLM-5-abliterated-dq3-mlx with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("cs2764/GLM-5-abliterated-dq3-mlx", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
GLM-5-abliterated_dq3
This model is a DQ3 quantized version of the original model [GLM-5-abliterated](Local Model).
It was quantized locally using the mlx_lm library.
Quantization Methodology (DQ3)
This model was quantized using the dynamic DQ3 (3-bit / 4-bit / 8-bit mixed) approach, inspired by the methodology described in the mlx-community/Kimi-K2.5-mlx-DQ3_K_M-q8 repository.
The weights are mixed based on MLX layers:
- Expert layers (switch_mlp / mlp) are quantized to 3-bit.
- The first 5 layers are kept at higher quality (5-bit).
- Every 5th layer is medium quality (4-bit).
- All other layers (e.g. attention, normalization) remain at 8-bit to serve as the "8-bit brain".
- Downloads last month
- 59
Model size
744B params
Tensor type
BF16
路
U32 路
F32 路
Hardware compatibility
Log In to add your hardware
Quantized
Inference Providers NEW
This model isn't deployed by any Inference Provider. 馃檵 Ask for provider support