---
library_name: mlx
license: other
license_name: lfm1.0
license_link: LICENSE
pipeline_tag: image-text-to-text
tags:
- liquid
- lfm3
- edge
- decision
- classification
- calibration
- system-one
- multimodal
- vision
- mlx
language:
- en
- ar
- zh
- fr
- de
- it
- ja
- ko
- pt
- es
- vi
- th
- id
- hi
- ru
- pl
base_model: LiquidAI/d1-2B
base_model_relation: quantized
---
# d1-2B-MLX-4bit
4-bit MLX version of [d1-2B](https://huggingface.co/LiquidAI/d1-2B) for Apple Silicon. The weights take 1.35 GB.
d1-2B is a decision model for text, pictures, and video. It answers named, typed questions over a state with calibrated probabilities in one forward pass, without generating tokens.
The language model uses 4-bit affine quantization with groups of 64. The vision tower, projector and norms stay in FP16. Against the original checkpoint run in float32, it changed 3 of 52 test answers and moved probabilities by up to 0.25. Prefer [FP16](https://huggingface.co/LiquidAI/d1-2B-MLX-fp16) or [8-bit](https://huggingface.co/LiquidAI/d1-2B-MLX-8bit) where calibration matters.
Find architecture and usage details in the [original model card](https://huggingface.co/LiquidAI/d1-2B).
## Use with MLX-VLM
Support for d1 in [MLX-VLM](https://github.com/Blaizzy/mlx-vlm) is in progress. We will update this card when an MLX-VLM release supports it.
```python
from mlx_vlm import load
from mlx_vlm.utils import load_image
model, processor = load("LiquidAI/d1-2B-MLX-4bit")
# Text: several named questions over one state, answered in one pass
ticket = "I was charged twice and my parcel never arrived. Please refund one charge."
out = model.system_one(ticket, {
"refund": {"type": "noul", "instructions": "Is the customer asking for a refund?"},
"team": {"type": "choice", "instructions": "Which team should handle this?",
"criteria": {"billing": "Charges, refunds, invoices",
"shipping": "Delivery and parcels",
"technical": "App or site faults"}},
"topics": {"type": "choice", "multiple": True,
"instructions": "Which topics does the message raise?",
"criteria": {"billing": "Payments and charges",
"delivery": "Shipping and parcels",
"login": "Signing in to the account"}},
"urgency": {"type": "score", "instructions": "How urgent is this ticket?",
"criteria": ["low", "medium", "high"]},
})
a = out["answers"]
print(round(a["refund"]["noul"], 2), a["team"]["choice"], a["topics"]["choices"],
a["urgency"]["legend"][str(int(a["urgency"]["score"]))])
# 1.0 billing ['billing', 'delivery'] medium
print(out["usage"])
# {'input_tokens': 189, 'output_tokens': 0}
# Picture: the photo is the whole state
image = load_image("http://images.cocodataset.org/val2017/000000039769.jpg")
out = model.system_one(None, {
"cats": {"type": "number", "instructions": "How many cats are there?", "range": [0, 10], "step": 1},
"remote": {"type": "box", "instructions": "Where is the remote control on the left?"},
}, media=[image])
a = out["answers"]
print(a["cats"]["number"], {k: round(v, 2) for k, v in a["remote"]["box"].items()})
# 2.0 {'top': 0.16, 'bottom': 0.24, 'left': 0.06, 'right': 0.28}
```
`model.system_one` returns `{"answers": {name: answer}, "usage": {...}}`, and `model.system_one_batch` takes several requests. The original model card describes every question type and answer field.
From the command line, the answers print as JSON:
```bash
python -m mlx_vlm decide --model LiquidAI/d1-2B-MLX-4bit \
--state "I was charged twice this month. Please refund one of the charges." \
--questions '{"refund": {"type": "noul", "instructions": "Is the customer asking for a refund?"}}'
```
More options in the [MLX guide](https://docs.liquid.ai/deployment/on-device/mlx).
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