Instructions to use atarayosd/RakutenAI-2.0-mini-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use atarayosd/RakutenAI-2.0-mini-instruct with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir RakutenAI-2.0-mini-instruct atarayosd/RakutenAI-2.0-mini-instruct
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
atarayosd/RakutenAI-2.0-mini-instruct
The Model atarayosd/RakutenAI-2.0-mini-instruct was converted to MLX format from Rakuten/RakutenAI-2.0-mini-instruct using mlx-lm version 0.21.1.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("atarayosd/RakutenAI-2.0-mini-instruct")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
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Model size
2B params
Tensor type
F16
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