Instructions to use Neo39982/Behemoth-R1-123B-v2-mlx-4Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Neo39982/Behemoth-R1-123B-v2-mlx-4Bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Behemoth-R1-123B-v2-mlx-4Bit Neo39982/Behemoth-R1-123B-v2-mlx-4Bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
| base_model: TheDrummer/Behemoth-R1-123B-v2 | |
| tags: | |
| - mlx | |
| # Neo39982/Behemoth-R1-123B-v2-mlx-4Bit | |
| The Model [Neo39982/Behemoth-R1-123B-v2-mlx-4Bit](https://huggingface.co/Neo39982/Behemoth-R1-123B-v2-mlx-4Bit) was converted to MLX format from [TheDrummer/Behemoth-R1-123B-v2](https://huggingface.co/TheDrummer/Behemoth-R1-123B-v2) 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("Neo39982/Behemoth-R1-123B-v2-mlx-4Bit") | |
| 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) | |
| ``` | |