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
-
https://huggingface.co/mrtoots/TheDrummer-Behemoth-R1-123B-v2-MLX-8Bit/resolve/main/README.md
- Command line
-
hf download hf://mrtoots/TheDrummer-Behemoth-R1-123B-v2-MLX-8Bit/README.md
-
curl -L -o README.md https://huggingface.co/mrtoots/TheDrummer-Behemoth-R1-123B-v2-MLX-8Bit/resolve/main/README.md
1.12 kB
metadata
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 was converted to MLX format from TheDrummer/Behemoth-R1-123B-v2 using mlx-lm version 0.26.4.
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Use with mlx
pip install mlx-lm
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)