| --- |
| 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 |
| --- |
| |
| <div align="center"> |
| <img |
| src="https://huggingface.co/LiquidAI/LFM3-1.7B/resolve/main/assets/liquid-ai-banner.webp" |
| alt="Liquid AI" |
| style="width: 100%; max-width: 100%; height: auto; display: block; margin: 0;" |
| /> |
| <div align="center" style="display: flex; justify-content: center; align-items: center; gap: 1.25em; margin: 8px 0 1.7rem; padding: 0; line-height: 1.3;"> |
| <a href="https://console.liquid.ai/dashboard" style="display: inline-flex; align-items: center; gap: 6px;"><img src="https://huggingface.co/LiquidAI/LFM3-1.7B/resolve/main/assets/icons/api.svg" width="16" height="16" alt="" aria-hidden="true" style="display: block; flex: 0 0 16px; width: 16px; height: 16px; margin: 0;" /><strong>API</strong></a> |
| <a href="https://playground.liquid.ai/" style="display: inline-flex; align-items: center; gap: 6px;"><img src="https://huggingface.co/LiquidAI/LFM3-1.7B/resolve/main/assets/icons/try.svg" width="16" height="16" alt="" aria-hidden="true" style="display: block; flex: 0 0 16px; width: 16px; height: 16px; margin: 0;" /><strong>Try</strong></a> |
| <a href="https://docs.liquid.ai/lfm/getting-started/welcome" style="display: inline-flex; align-items: center; gap: 6px;"><img src="https://huggingface.co/LiquidAI/LFM3-1.7B/resolve/main/assets/icons/docs.svg" width="16" height="16" alt="" aria-hidden="true" style="display: block; flex: 0 0 16px; width: 16px; height: 16px; margin: 0;" /><strong>Docs</strong></a> |
| <a href="https://www.liquid.ai/blog/lfm3" style="display: inline-flex; align-items: center; gap: 6px;"><img src="https://huggingface.co/LiquidAI/LFM3-1.7B/resolve/main/assets/icons/blog.svg" width="16" height="16" alt="" aria-hidden="true" style="display: block; flex: 0 0 16px; width: 16px; height: 16px; margin: 0;" /><strong>Blog</strong></a> |
| </div> |
| </div> |
| |
| # 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 |
| <hr style="margin: -0.7em 0 1.2em 0; border: 0; border-top: 1px solid rgba(127, 127, 127, 0.3);" /> |
|
|
| 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). |
|
|
