| --- |
| license: apache-2.0 |
| base_model: |
| - convaiinnovations/laya |
| library_name: sima-lmm |
| tags: |
| - sima.ai |
| - modalix |
| - mla |
| - edge |
| - decision-model |
| - modernbert |
| --- |
| |
| # Laya decision models, compiled for the SiMa.ai MLSoC Modalix |
|
|
| [Laya](https://github.com/NandhaKishorM/laya) by Convai Innovations is a "System-1" decision |
| model: an encoder (ModernBERT-large, or mmBERT-base for the multilingual one) with a decision |
| head that answers a typed question about a piece of text in one forward pass. It picks one of |
| several options, scores on a scale, or says yes or no. |
|
|
| These are those checkpoints compiled for the Modalix MLA. Every graph is a single MLA stage, |
| with no layers on the CPU, and a decision takes about 19 ms at 128 tokens (8 ms for the |
| multilingual model). |
|
|
| | folder | model | precision | latency per decision, by tokens | same decision as PyTorch | size | |
| |---|---|---|---|---|---| |
| | `general` | Laya | BF16 | 128: 19.5 ms, 256: 35.2 ms, 512: 87.5 ms | 100 / 100 | 3.27 GB | |
| | `general-int8` | Laya INT8 | A_BF16_W_INT8 | 128: 14 ms | 98 / 100 | 0.67 GB | |
| | `typed-decisions` | Laya typed-decisions | BF16 | 128: 19.4 ms, 512: 87.5 ms, 1024: 282 ms | 99 / 100 | 4.35 GB | |
| | `multilingual` | Laya multilingual | BF16 | 128: 8.1 ms, 256: 15.3 ms, 512: 33.9 ms, 1024: 116.5 ms | 100 / 100 | 2.61 GB | |
| | `dino` | Laya-dino | BF16 | 64: 18 ms | 30 / 30 game states | 0.95 GB | |
| |
| Latency is one decision on a Modalix DevKit, measured inside the runtime. "Same decision" is |
| against the fp32 PyTorch model on 100 decisions with the 128-token graph. Precision `BF16` is |
| weights and activations in bfloat16; `A_BF16_W_INT8` keeps bfloat16 activations and stores |
| the weights as int8. |
|
|
| [CLM v0.1 8B](https://huggingface.co/Contrastive-LM/CLM-v0.1-8B), a second kind of decision |
| model on Qwen3-8B, is compiled the same way and published at |
| [dotimothy/sima-clm](https://huggingface.co/dotimothy/sima-clm). |
|
|
| LiquidAI's d1 models, which also read pictures, are published at |
| [dotimothy/sima-d1](https://huggingface.co/dotimothy/sima-d1), apart from these because their |
| licence is another (the LFM Open License v1.0). |
|
|
| ## What is in a folder |
|
|
| | file | what it is | |
| |---|---| |
| | `laya_s<N>_stage1_mla.elf` | the compiled graph for sequences of up to N tokens | |
| | `token_embeddings.bf16` | the embedding table, looked up on the CPU | |
| | `act_tail.f32` | the last layer of the act head, run on the CPU | |
| | `tokenizer.json` | the checkpoint's tokenizer | |
| | `laya_config.json` | token ids, calibration temperatures, and which graphs there are | |
|
|
| `models.json` lists every folder with file sizes and SHA-256 sums, here and in the |
| repositories named above: the web app reads it to know what it can download. |
|
|
| ## 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): its Models page downloads a model from |
| this repository onto the board and loads it on the MLA. By hand: |
|
|
| ```bash |
| hf download dotimothy/sima-laya --include "general/*" --local-dir . |
| ./laya run general --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 the original checkpoints at |
| [convaiinnovations/laya](https://huggingface.co/convaiinnovations/laya). |
|
|
| ## License and attribution |
|
|
| Apache-2.0. The models are Convai Innovations' Laya checkpoints |
| (https://github.com/NandhaKishorM/laya, Apache-2.0), compiled without retraining. The |
| exception is `dino`, whose decision head was fine-tuned on top of the English checkpoint for |
| a browser game. Zero-shot quality is the checkpoints': the port reproduces PyTorch's answers, |
| including its wrong ones. |
|
|
