---
language:
- en
library_name: mlx
tags:
- glm
- MOE
- pruning
- compression
- mlx
license: mit
name: cerebras/GLM-4.7-Flash-REAP-23B-A3B
description: 'This model was obtained by uniformly pruning 25% of experts in GLM-4.7-Flash
  using the REAP method.

  '
readme: 'https://huggingface.co/cerebras/GLM-4.7-Flash-REAP-23B-A3B/main/README.md

  '
license_link: https://huggingface.co/zai-org/GLM-4.7-Flash/blob/main/LICENSE
pipeline_tag: text-generation
base_model: cerebras/GLM-4.7-Flash-REAP-23B-A3B
---

# warshanks/GLM-4.7-Flash-REAP-23B-A3B-8bit

This model [warshanks/GLM-4.7-Flash-REAP-23B-A3B-8bit](https://huggingface.co/warshanks/GLM-4.7-Flash-REAP-23B-A3B-8bit) was
converted to MLX format from [cerebras/GLM-4.7-Flash-REAP-23B-A3B](https://huggingface.co/cerebras/GLM-4.7-Flash-REAP-23B-A3B)
using mlx-lm version **0.30.4**.

## Use with mlx

```bash
pip install mlx-lm
```

```python
from mlx_lm import load, generate

model, tokenizer = load("warshanks/GLM-4.7-Flash-REAP-23B-A3B-8bit")

prompt = "hello"

if tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True, return_dict=False,
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)
```