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base_model: Goekdeniz-Guelmez/Qwen3-4B-Sky-High-Hermes-gabliterated
tags:
- uncensored
- gabliteration
- mlx
datasets:
- mlabonne/harmless_alpaca
- mlabonne/harmful_behaviors
library_name: mlx
arxiv: '2512.18901'
pipeline_tag: text-generation
model-index:
- name: ZeroXClem_Qwen3-4B-Sky-High-Hermes-gabliterated
results:
- task:
type: text-generation
dataset:
name: Harmless Alpaca
type: harmless_alpaca
metrics:
- type: pass@1
value: 0.0992
name: KL Divergence
- task:
type: text-generation
dataset:
name: Harmful Behaviors
type: harmful_behaviors
metrics:
- type: pass@1
value: 0.02
name: Refusal Rate
---
# mlx-community/Qwen3-4B-Sky-High-Hermes-gabliterated-4bit
This model [mlx-community/Qwen3-4B-Sky-High-Hermes-gabliterated-4bit](https://huggingface.co/mlx-community/Qwen3-4B-Sky-High-Hermes-gabliterated-4bit) was
converted to MLX format from [Goekdeniz-Guelmez/Qwen3-4B-Sky-High-Hermes-gabliterated](https://huggingface.co/Goekdeniz-Guelmez/Qwen3-4B-Sky-High-Hermes-gabliterated)
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("mlx-community/Qwen3-4B-Sky-High-Hermes-gabliterated-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, return_dict=False,
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
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