Instructions to use Wwayu/GLM-4.5-Iceblink-106B-A12B-mlx-4Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Wwayu/GLM-4.5-Iceblink-106B-A12B-mlx-4Bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download Wwayu/GLM-4.5-Iceblink-106B-A12B-mlx-4Bit --local-dir GLM-4.5-Iceblink-106B-A12B-mlx-4Bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
File size: 1,068 Bytes
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license: mit
datasets:
- zerofata/Instruct-Anime
- zerofata/Roleplay-Anime-Characters
- zerofata/Instruct-Anime-CreativeWriting
- zerofata/Summaries-Anime-FandomPages
base_model: zerofata/GLM-4.5-Iceblink-106B-A12B
tags:
- mlx
---
# Wwayu/GLM-4.5-Iceblink-106B-A12B-mlx-4Bit
The Model [Wwayu/GLM-4.5-Iceblink-106B-A12B-mlx-4Bit](https://huggingface.co/Wwayu/GLM-4.5-Iceblink-106B-A12B-mlx-4Bit) was converted to MLX format from [zerofata/GLM-4.5-Iceblink-106B-A12B](https://huggingface.co/zerofata/GLM-4.5-Iceblink-106B-A12B) using mlx-lm version **0.26.4**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("Wwayu/GLM-4.5-Iceblink-106B-A12B-mlx-4Bit")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
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