Instructions to use abnormalmapstudio/Qwen3-Omni-30B-A3B-Instruct-mxfp4-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use abnormalmapstudio/Qwen3-Omni-30B-A3B-Instruct-mxfp4-mlx with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("abnormalmapstudio/Qwen3-Omni-30B-A3B-Instruct-mxfp4-mlx") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use abnormalmapstudio/Qwen3-Omni-30B-A3B-Instruct-mxfp4-mlx with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "abnormalmapstudio/Qwen3-Omni-30B-A3B-Instruct-mxfp4-mlx" --prompt "Once upon a time"
File size: 1,005 Bytes
957a3a1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | ---
license: other
license_name: apache-2.0
language:
- en
tags:
- multimodal
- mlx
library_name: mlx
pipeline_tag: text-generation
base_model: Qwen/Qwen3-Omni-30B-A3B-Instruct
---
# abnormalmapstudio/Qwen3-Omni-30B-A3B-Instruct-mxfp4-mlx
This model [abnormalmapstudio/Qwen3-Omni-30B-A3B-Instruct-mxfp4-mlx](https://huggingface.co/abnormalmapstudio/Qwen3-Omni-30B-A3B-Instruct-mxfp4-mlx) was
converted to MLX format from [Qwen/Qwen3-Omni-30B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Instruct)
using mlx-lm version **0.28.1**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("abnormalmapstudio/Qwen3-Omni-30B-A3B-Instruct-mxfp4-mlx")
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)
```
|