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
- Atomic Chat
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Download README.md from Neo39982/Behemoth-R1-123B-v2-mlx-4Bit: direct link, hf CLI and curl.
- Browser
- Download file 870 Bytes
-
https://huggingface.co/Neo39982/Behemoth-R1-123B-v2-mlx-4Bit/resolve/main/README.md
- Command line
-
hf download hf://Neo39982/Behemoth-R1-123B-v2-mlx-4Bit/README.md
-
curl -L -o README.md https://huggingface.co/Neo39982/Behemoth-R1-123B-v2-mlx-4Bit/resolve/main/README.md
870 Bytes
metadata
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 was converted to MLX format from TheDrummer/Behemoth-R1-123B-v2 using mlx-lm version 0.31.2.
Use with mlx
pip install mlx-lm
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)