Instructions to use TerminatorPower/Qwen3.5-4B-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use TerminatorPower/Qwen3.5-4B-MLX-4bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Qwen3.5-4B-MLX-4bit TerminatorPower/Qwen3.5-4B-MLX-4bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
| base_model: Qwen/Qwen3.5-4B | |
| library_name: mlx | |
| tags: | |
| - mlx | |
| - qwen3.5 | |
| - vision-language-model | |
| - quantized | |
| - 4bit | |
| license: apache-2.0 | |
| <p align="center"> | |
| <a href="https://apps.apple.com/tr/app/vanta-local-ai-llm-chat/id6758898098"> | |
| <img src="banner.png" alt="Vanta — Local AI LLM Chat" width="100%" /> | |
| </a> | |
| </p> | |
| <h1 align="center">Qwen3.5-4B-MLX-4bit</h1> | |
| <p align="center"> | |
| A verbatim mirror of | |
| <a href="https://huggingface.co/mlx-community/Qwen3.5-4B-MLX-4bit">mlx-community/Qwen3.5-4B-MLX-4bit</a>, | |
| kept here so the <b>Vanta</b> iOS app always has a stable place to download it from. | |
| </p> | |
| ## Run it on your iPhone with Vanta | |
| This is one of the built-in one-tap downloads in **Vanta — Local AI LLM Chat**, a | |
| local-first AI chat app for iPhone and iPad. Vanta runs models like this one fully | |
| on-device with Apple's MLX framework — no account and no cloud, your chats stay on | |
| your device. Because it's a vision-capable model, you can also chat about images. | |
| **[Download Vanta on the App Store →](https://apps.apple.com/tr/app/vanta-local-ai-llm-chat/id6758898098)** | |
| --- | |
| > **This is a copy.** Every file in this repository is an exact copy of | |
| > [`mlx-community/Qwen3.5-4B-MLX-4bit`](https://huggingface.co/mlx-community/Qwen3.5-4B-MLX-4bit). | |
| > We cloned it so that **Vanta Client always has a reliable, always-available source** | |
| > to download this model from, independent of any upstream changes. All credit for the | |
| > model weights and the MLX conversion goes to | |
| > [mlx-community](https://huggingface.co/mlx-community) and the original authors. | |
| --- | |
| ## Model Details | |
| - **Original Model:** [Qwen/Qwen3.5-4B](https://huggingface.co/Qwen/Qwen3.5-4B) | |
| - **Quantization:** 4-bit (5.347 bits per weight) | |
| - **Group Size:** 64 | |
| - **Format:** MLX SafeTensors | |
| - **Framework:** [mlx-vlm](https://github.com/Blaizzy/mlx-vlm) | |
| - **Disk Size:** ~2.9G | |
| ## Conversion Details | |
| This model was converted using `mlx-vlm` from the | |
| [`pc/fix-qwen35-predicate`](https://github.com/Blaizzy/mlx-vlm/tree/pc/fix-qwen35-predicate) | |
| branch, which includes fixes for Qwen3.5 model support (proper handling of MoE gate | |
| layers, `shared_expert_gate`, and `A_log` casting). | |
| **Conversion command:** | |
| ```bash | |
| python3 -m mlx_vlm convert \ | |
| --hf-path "Qwen/Qwen3.5-4B" \ | |
| --mlx-path "./Qwen3.5-4B-MLX-4bit" \ | |
| -q --q-bits 4 --q-group-size 64 | |
| ``` | |
| ## Related Models | |
| - **bf16 (full precision):** [mlx-community/Qwen3.5-4B-MLX-bf16](https://huggingface.co/mlx-community/Qwen3.5-4B-MLX-bf16) | |
| - **8-bit quantized:** [mlx-community/Qwen3.5-4B-MLX-8bit](https://huggingface.co/mlx-community/Qwen3.5-4B-MLX-8bit) | |
| - **Original:** [Qwen/Qwen3.5-4B](https://huggingface.co/Qwen/Qwen3.5-4B) | |
| ## Usage | |
| ```python | |
| from mlx_vlm import load, generate | |
| model, processor = load("TerminatorPower/Qwen3.5-4B-MLX-4bit") | |
| output = generate( | |
| model, | |
| processor, | |
| prompt="Describe this image.", | |
| image="path/to/image.jpg", | |
| max_tokens=512 | |
| ) | |
| print(output) | |
| ``` | |
| **CLI:** | |
| ```bash | |
| python3 -m mlx_vlm.generate \ | |
| --model TerminatorPower/Qwen3.5-4B-MLX-4bit \ | |
| --image path/to/image.jpg \ | |
| --prompt "Describe this image." | |
| ``` | |
| ## License | |
| This model inherits the [Apache 2.0 license](https://huggingface.co/Qwen/Qwen3.5-4B) | |
| from the original Qwen model. The mirror does not add any restrictions. | |