d1-3B
Liquid AI d1-3B is a compact multimodal decision model built on LFM2.5-VL-3B. It produces calibrated choices for bounded visual and text decisions with a 32K-token context; it is not a general chat or coding model and should be validated on a labelled task-specific set.
Deployment and license note
Decision models return bounded choices, scores, or yes/no probabilities instead of long-form text. Public benchmark results are useful for screening candidates, but production thresholds should be calibrated on a labelled set from your own routing, guardrail, or action-selection workflow.
Task Fit
Bounded routing, classification, scoring, guardrails, and action selection with structured probabilities.
Not marked for code agent in the current library.
Not marked for code in the current library.
Not marked for chat in the current library.
Not marked for rag in the current library.
Image or visual understanding, not necessarily image generation.
Not marked for image generation in the current library.
Not marked for video generation in the current library.
Not marked for voice in the current library.
Source Confidence
Variants and Quant Artifacts
Choose the artifact first; hardware fit follows from RAM, VRAM, format, and runtime.
| Quant | Format | Quality | Min RAM | Reco RAM | Runtime | Action |
|---|---|---|---|---|---|---|
| Q4_K_M | safetensors | balanced | 4GB | 8GB | transformers, mlx | Plan with this |
Recommended Hardware
Lowest estimated 5-year cost that can run this model.
Enough unified/system memory with a balanced 5-year cost.
Highest local performance signal among compatible hardware.
Benchmarks
No benchmark data is available for this model yet.
Source and Review
Execution evidence
Run d1-3B with a documented recipe
Recipes connect hardware, a model artifact, tools, settings, verification, and a reportable result.
No verified recipe is linked to this record yet.
Compatibility estimates remain available in the planner. A recipe appears here only after its exact stack and verification protocol are documented.