Instructions to use mrtoots/TheDrummer-Behemoth-R1-123B-v2-MLX-8Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mrtoots/TheDrummer-Behemoth-R1-123B-v2-MLX-8Bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir TheDrummer-Behemoth-R1-123B-v2-MLX-8Bit mrtoots/TheDrummer-Behemoth-R1-123B-v2-MLX-8Bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
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Download README.md from mrtoots/TheDrummer-Behemoth-R1-123B-v2-MLX-8Bit: direct link, hf CLI and curl.
- Browser
- Download file 1.12 kB
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https://huggingface.co/mrtoots/TheDrummer-Behemoth-R1-123B-v2-MLX-8Bit/resolve/main/README.md
- Command line
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hf download hf://mrtoots/TheDrummer-Behemoth-R1-123B-v2-MLX-8Bit/README.md
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curl -L -o README.md https://huggingface.co/mrtoots/TheDrummer-Behemoth-R1-123B-v2-MLX-8Bit/resolve/main/README.md
1.12 kB
| base_model: TheDrummer/Behemoth-R1-123B-v2 | |
| tags: | |
| - mlx | |
| # huggingtoots/TheDrummer-Behemoth-R1-123B-v2-MLX-8Bit | |
| The Model [huggingtoots/TheDrummer-Behemoth-R1-123B-v2-MLX-8Bit](https://huggingface.co/huggingtoots/TheDrummer-Behemoth-R1-123B-v2-MLX-8Bit) was converted to MLX format from [TheDrummer/Behemoth-R1-123B-v2](https://huggingface.co/TheDrummer/Behemoth-R1-123B-v2) using mlx-lm version **0.26.4**. | |
| ➡️ <span style="color:#800080">If you want a free consulting session, </span>[fill out this form](https://forms.gle/xM9gw1urhypC4bWS6) <span style="color:#800080">to get in touch!</span> 🤗 | |
| ## Use with mlx | |
| ```bash | |
| pip install mlx-lm | |
| ``` | |
| ```python | |
| from mlx_lm import load, generate | |
| model, tokenizer = load("huggingtoots/Behemoth-R1-123B-v2-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) | |
| ``` | |