Text Classification
MLX
Safetensors
English
qwen3_5_text
mlx-vlm
decision-model
calibrated
system-one
Instructions to use nativ-community/decider-2b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use nativ-community/decider-2b with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir decider-2b nativ-community/decider-2b
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
decider-2b for mlx-vlm
Mapika/decider-2b v11 (revision 533964da), with its weight names changed from model.language_model.* to model.* so the checkpoint loads through the standard mlx-vlm loader. Tensor values and dtypes are unchanged (bf16), and the config, tokenizer and decider_config.json are copied as-is.
decider-2b is a calibrated decision model by Mapika, fine-tuned from Qwen/Qwen3.5-2B-Base. It returns a probability distribution over the options of each typed question in one forward pass. See the original model card for training, evaluation and limitations.
from mlx_vlm import load
from mlx_vlm.models.decider2 import Decider2
model, processor = load("nativ-community/decider-2b")
decider = Decider2(model, processor)
result = decider.predict(
{"ticket": "Please refund my duplicate charge"},
{
"department": {
"type": "choice",
"instructions": "Which team should handle this ticket?",
"criteria": {"billing": None, "technical": None, "sales": None},
},
"refund": {"type": "noul", "instructions": "Does the customer request a refund?"},
},
)
print(result["answers"])
On the same inputs, this checkpoint gives exactly the same predict output as the original.
License: Apache 2.0, inherited from the original model.
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Model size
2B params
Tensor type
BF16
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Hardware compatibility
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