---
language:
- en
library_name: mlx
tags:
- glm
- MOE
- pruning
- compression
- mlx
license: mit
name: cerebras/Kimi-Linear-REAP-35B-A3B-Instruct
description: 'This model was obtained by uniformly pruning 30% of experts in Kimi-Linear-48B-A3B-Instruct
  using the REAP method.

  '
readme: 'https://huggingface.co/cerebras/Kimi-Linear-REAP-35B-A3B-Instruct/main/README.md

  '
pipeline_tag: text-generation
base_model: cerebras/Kimi-Linear-REAP-35B-A3B-Instruct
---

# NexVeridian/Kimi-Linear-REAP-35B-A3B-Instruct-4bit

This model [NexVeridian/Kimi-Linear-REAP-35B-A3B-Instruct-4bit](https://huggingface.co/NexVeridian/Kimi-Linear-REAP-35B-A3B-Instruct-4bit) was
converted to MLX format from [cerebras/Kimi-Linear-REAP-35B-A3B-Instruct](https://huggingface.co/cerebras/Kimi-Linear-REAP-35B-A3B-Instruct)
using mlx-lm version **0.28.4**.

## Use with mlx

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

```python
from mlx_lm import load, generate

model, tokenizer = load("NexVeridian/Kimi-Linear-REAP-35B-A3B-Instruct-4bit")

prompt = "hello"

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

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