---
license: other
license_name: lfm1.0
license_link: LICENSE
base_model:
- LiquidAI/d1-omni-600M
- LiquidAI/d1-3B
library_name: sima-lmm
pipeline_tag: image-text-to-text
tags:
- sima.ai
- modalix
- mla
- edge
- decision-model
- liquid
- lfm2.5
---

# LiquidAI's d1 decision models, compiled for the SiMa.ai MLSoC Modalix

[Open d1](https://huggingface.co/blog/LiquidAI/open-d1) by Liquid AI is two "System-1"
decision models: each answers a typed question (one of several options, a score on a scale,
yes or no) about a text or about a picture in one forward pass, generating nothing. These are
those two checkpoints compiled for the Modalix MLA. They are not Liquid AI's files and Liquid
AI has not endorsed them.

> **Licence: the [LFM Open License v1.0](LICENSE), not Apache-2.0.** It is Liquid AI's, it
> applies to these files as derivative works of the checkpoints, and a copy is in every
> folder. In short: free for research and non-commercial use, and for commercial use by an
> organisation with less than 10 million US dollars of annual revenue; commercial use above
> that needs a licence from Liquid AI. Read the licence itself before relying on this summary.

| folder | checkpoint | precision | one pass of the trunk, by tokens | a picture | same decision as PyTorch | size |
|---|---|---|---|---|---|---|
| `d1` | [d1-omni-600M](https://huggingface.co/LiquidAI/d1-omni-600M) | A_BF16_W_INT8 | 128: 10 ms, 512: 33 ms | 48 ms | 26 of 27 reference decisions (int8 weights) | 1.56 GB |
| `d1-3b` | [d1-3B](https://huggingface.co/LiquidAI/d1-3B) | A_BF16_W_INT8 | 128: 92 ms, 384: 228 ms, 512: 310 ms | 189 ms | 27 of 27 reference decisions (int8 weights) | 12.78 GB |

Measured on a Modalix DevKit, inside the runtime. A pass holds a picture and several
questions about it: with `d1-3b` three questions about a new picture take about 0.5 s in all
(the picture once, then one pass of the 384-token chain), and with `d1`, whose trunk reads
the picture again with each question, about 0.2 s. "Same
decision" is against the fp32 PyTorch checkpoint on 27 reference questions, 14 of them about
pictures.

## What was changed

The weights are Liquid AI's, **modified**: rounded to int8 (activations are bfloat16,
`A_BF16_W_INT8`) and compiled into graphs for the MLA with SiMa.ai's Model Compiler, by
this repository's author. Nothing was retrained. So the answers are an approximation of the checkpoints':
on the reference questions the largest difference in a probability was 0.15 for `d1` where
the decision was the same, and 0.07 for `d1-3b`.

Left out of the port: a large picture is read as one picture of at most 512 x 512 pixels
(256 positions) where Liquid AI's code cuts it into tiles; d1-omni's audio encoder is not
compiled; a state longer than the longest graph is cut at its end.

## What is in a folder

| file | what it is |
|---|---|
| `d1_s<N>_l<LL>n<K>_stage1_mla.elf` | K layers of the trunk from layer LL, for rows of up to N tokens: one graph a length in `d1`, a chain of eight in `d1-3b` |
| `d1_vision_n1024_stage1_mla.elf`, `d1_projector_m256_stage1_mla.elf` | the vision tower, for a picture of up to 1024 patches, and its projector |
| `token_embeddings.bf16` | the embedding table, looked up on the CPU (and read against for d1-3B's logits) |
| `position_embedding.f32`, `type_embeddings.f32` | the tower's position table, and d1-omni's question types |
| `tokenizer.json` | the checkpoint's tokenizer, unchanged |
| `d1_config.json` | token ids, temperatures, and which graphs there are, in order |
| `LICENSE` | the LFM Open License v1.0, unchanged |

## Using them

The files run on a Modalix board with the runtime and web app from
[neat-decision-studio](https://github.com/dotimothy/neat-decision-studio), whose Models page
downloads a model from here onto the board and loads it on the MLA; the list it reads is in
[dotimothy/sima-laya](https://huggingface.co/dotimothy/sima-laya), with the other decision models. By hand:

```bash
hf download dotimothy/sima-d1 --include "d1/*" --local-dir .
./laya run d1 --state "We were billed twice for March." \
    --question '{"type": "noul", "instructions": "Is this a billing problem?"}'
```

They are not usable with PyTorch or ONNX Runtime; for that, use Liquid AI's checkpoints,
[LiquidAI/d1-omni-600M](https://huggingface.co/LiquidAI/d1-omni-600M) and
[LiquidAI/d1-3B](https://huggingface.co/LiquidAI/d1-3B).

## Attribution

The models are Liquid AI's (Liquid AI, Inc.), released under the LFM Open License v1.0. The
compilation, the runtime and this page are not Liquid AI's.