MLX
Safetensors
English
qwen2
obliteratus
abliteration
uncensored
obliterate
mlx-my-repo
8-bit precision
Instructions to use usermma/NEXUS-Coder-Abliterated-mlx-8Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use usermma/NEXUS-Coder-Abliterated-mlx-8Bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir NEXUS-Coder-Abliterated-mlx-8Bit usermma/NEXUS-Coder-Abliterated-mlx-8Bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
| language: en | |
| tags: | |
| - obliteratus | |
| - abliteration | |
| - uncensored | |
| - obliterate | |
| - mlx | |
| - mlx-my-repo | |
| base_model: usermma/NEXUS-Coder-Abliterated | |
| # usermma/NEXUS-Coder-Abliterated-mlx-8Bit | |
| The Model [usermma/NEXUS-Coder-Abliterated-mlx-8Bit](https://huggingface.co/usermma/NEXUS-Coder-Abliterated-mlx-8Bit) was converted to MLX format from [usermma/NEXUS-Coder-Abliterated](https://huggingface.co/usermma/NEXUS-Coder-Abliterated) using mlx-lm version **0.31.2**. | |
| ## Use with mlx | |
| ```bash | |
| pip install mlx-lm | |
| ``` | |
| ```python | |
| from mlx_lm import load, generate | |
| model, tokenizer = load("usermma/NEXUS-Coder-Abliterated-mlx-8Bit") | |
| prompt="hello" | |
| if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None: | |
| messages = [{"role": "user", "content": prompt}] | |
| prompt = tokenizer.apply_chat_template( | |
| messages, tokenize=False, add_generation_prompt=True | |
| ) | |
| response = generate(model, tokenizer, prompt=prompt, verbose=True) | |
| ``` | |