---
language: en
library_name: mlx
base_model_relation: quantized
pipeline_tag: image-text-to-text
tags:
- mlx
- text-generation-inference
- qwen3_5
- mlx-vlm
- uncensored
- abliterated
- apple-silicon
- 4-bit
- 8-bit
license: apache-2.0
base_model:
- huihui-ai/Huihui-Qwen3.8-27B-abliterated
---

# **Qwen3.8-27B-abliterated-MLX**

> Qwen3.8-27B-abliterated is an uncensored version of Qwen/Qwen3.8-27B created by huihui-ai through abliteration — a crude, proof-of-concept activation-editing technique that removes refusal behavior directly from model weights without relying on TransformerLens. In its latest update, only layers 18 through 51 are ablated (previously the first 15 layers were also retained unablated, and remain so), leaving the earliest layers, MTP, and visual components unmodified in an effort to preserve more of the base model's original performance; a prior revision (d42ca89) is available for those who want the earlier ablation range. The model is usable directly via Hugging Face Transformers (with enable_thinking togglable at inference) or through Ollama via huihui_ai/Qwen3.8-abliterated, and ships with explicit usage warnings: it has undergone no rigorous safety optimization, may produce sensitive or inappropriate content, is recommended only for research or controlled testing rather than public-facing production use, and places full legal and ethical responsibility for outputs on the user, with huihui.ai disclaiming liability. It's released under the Apache 2.0 license.

## Model Variants & Storage Footprint

> [!NOTE]
> View the tree here: [huggingface-tree.hf.space](https://strangertoolshf-huggingface-tree.hf.space/#models/prithivMLmods/Qwen3.8-27B-abliterated-MLX/main)

```plaintext
prithivMLmods/Qwen3.8-27B-abliterated-MLX (main)  [~93.83 GB Total]
├── 4bit/                                        [~15.03 GB]
│   ├── model shards: 3 safetensors (~15.00 GB)
│   └── metadata & configs (~26.7 MB)
│
├── 8bit/                                        [~27.53 GB]
│   ├── model shards: 6 safetensors (~27.50 GB)
│   └── metadata & configs (~26.7 MB)
│
└── [BF16 Base Files]                            [~51.33 GB]
    ├── model shards: 11 safetensors (~51.30 GB)
    └── metadata & configs (~25.5 MB)
```

## Use with mlx

```bash
pip install -U mlx-vlm
```

### BF16 Variant (Base Model)

Because the BF16 weights and configurations are located in the repository root, reference the model repository directly:

```bash
python -m mlx_vlm generate \
  --model prithivMLmods/Qwen3.8-27B-abliterated-MLX \
  --max-tokens 100 \
  --temperature 0.0 \
  --prompt "Describe this image." \
  --image <path_to_image>
```

### 8-bit Variant

Load the 8-bit quantized weights from the `8bit` subfolder:

#### CLI (Command Line)

```bash
python -m mlx_vlm generate \
  --model prithivMLmods/Qwen3.8-27B-abliterated-MLX/8bit \
  --max-tokens 100 \
  --temperature 0.0 \
  --prompt "Describe this image." \
  --image <path_to_image>
```

#### Python API

```python
from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config

# Load model from the 8bit subfolder
model_path = "prithivMLmods/Qwen3.8-27B-abliterated-MLX"
model, processor = load(model_path, subfolder="8bit")
config = load_config(model_path, subfolder="8bit")

image = ["<path_to_image>"]
prompt = "Describe this image."
formatted_prompt = apply_chat_template(processor, config, prompt, num_images=len(image))

output = generate(model, processor, formatted_prompt, image=image, max_tokens=100, temperature=0.0)
print(output.text)
```

### 4-bit Variant

Load the 4-bit quantized weights from the `4bit` subfolder:

#### CLI (Command Line)

```bash
python -m mlx_vlm generate \
  --model prithivMLmods/Qwen3.8-27B-abliterated-MLX/4bit \
  --max-tokens 100 \
  --temperature 0.0 \
  --prompt "Describe this image." \
  --image <path_to_image>
```

#### Python API

```python
from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config

# Load model from the 4bit subfolder
model_path = "prithivMLmods/Qwen3.8-27B-abliterated-MLX"
model, processor = load(model_path, subfolder="4bit")
config = load_config(model_path, subfolder="4bit")

image = ["<path_to_image>"]
prompt = "Describe this image."
formatted_prompt = apply_chat_template(processor, config, prompt, num_images=len(image))

output = generate(model, processor, formatted_prompt, image=image, max_tokens=100, temperature=0.0)
print(output.text)
```

## License and Attribution

This model is based on and/or incorporates the following open-source projects and models:

* **Qwen3.8-27B (Base):** [https://huggingface.co/Qwen/Qwen3.8-27B](https://huggingface.co/Qwen/Qwen3.8-27B)
* **Huihui-Qwen3.8-27B-abliterated:** [https://huggingface.co/huihui-ai/Huihui-Qwen3.8-27B-abliterated](https://huggingface.co/huihui-ai/Huihui-Qwen3.8-27B-abliterated)
* **mlx-vlm:** [https://github.com/Blaizzy/mlx-vlm](https://github.com/Blaizzy/mlx-vlm)
* **MLX:** [https://github.com/ml-explore/mlx](https://github.com/ml-explore/mlx)

This model is released under the [Apache License 2.0](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md).