---
license: apache-2.0
base_model:
- Phocinae/Phocinae-Largha-150M-v1
library_name: onnx
tags:
- ollaya
- onnx
- decision-model
- system-one
pipeline_tag: text-classification
---

# largha for Ollaya

[Ollaya](https://github.com/ollaya-dev/ollaya) package of **[Phocinae/Phocinae-Largha-150M-v1](https://huggingface.co/Phocinae/Phocinae-Largha-150M-v1)** by Phocinae.
Ollaya runs open decision models locally, the way Ollama runs LLMs: typed questions in,
calibrated answers out, behind a TypeSafe-compatible API.

```sh
ollaya run largha
```

## What is in this repository

This repository holds only the files Ollaya derives, with no weights. Each graph is an ONNX export of the
original model whose weights **reference the authors' own weight files by byte offset**,
so `ollaya pull` downloads the weights from the upstream repositories, unmodified and pinned to a
commit, and verifies their sha256.

| Tag | Upstream | Files |
|---|---|---|
| `largha:150m` | [Phocinae/Phocinae-Largha-150M-v1@5300e85](https://huggingface.co/Phocinae/Phocinae-Largha-150M-v1/tree/5300e85a7d991f4f0cdb68dc25813f76902752d4) | `150m/model-fp32.onnx`, `150m/model-fp16.onnx`, `150m/decision.json`, `150m/calibration.json` |

Each tag has an fp32 graph (CPU) and an fp16 graph (GPU). Each tag also has `decision.json` (sequence layout, special tokens) and
`calibration.json` (temperatures).

## Parity

Ollaya's runtime matches laya's own loader (0.3.7, which renders TypeSafe questions as the 0.3.20 Largha was trained with) on the v1.1 checkpoint in fp32: 2,383 questions, identical token ids and markers, the same decision on every question, probabilities within 3.5e-6 on the CPU, CUDA and the Apple GPU (MLX). The fp16 graph agrees on 99.87% of decisions (3 near-ties).

## License

Same as the upstream model (Apache-2.0). Ollaya itself is Apache-2.0.