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license: apache-2.0
base_model:
- bespokelabs/Bespoke-Nimble-9B-v2
- Qwen/Qwen3.5-9B
library_name: onnx
tags:
- ollaya
- onnx
- decision-model
- system-one
pipeline_tag: text-classification
---
# nimble for Ollaya
[Ollaya](https://github.com/ollaya-dev/ollaya) package of **[bespokelabs/Bespoke-Nimble-9B-v2](https://huggingface.co/bespokelabs/Bespoke-Nimble-9B-v2)** and **[Qwen/Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B)** by Bespoke Labs (adapter) and the Qwen team (base model).
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 nimble
```
## 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 |
|---|---|---|
| `nimble:9b` | [bespokelabs/Bespoke-Nimble-9B-v2@4b8c04d](https://huggingface.co/bespokelabs/Bespoke-Nimble-9B-v2/tree/4b8c04d1ac2cea3e41e5e3c4d2130bcead2c0abe), [Qwen/Qwen3.5-9B@c202236](https://huggingface.co/Qwen/Qwen3.5-9B/tree/c202236235762e1c871ad0ccb60c8ee5ba337b9a) | `9b/model-fp32.onnx`, `9b/decision.json`, `9b/calibration.json` |
Each tag has an fp32 graph, used on CPU and GPU. Each tag also has `decision.json` (sequence layout, special tokens) and
`calibration.json` (temperatures).
## Parity
Ollaya's Rust runtime matches the author's reference code (serving_schema.prepare_prompts and inference.candidate_logits, PyTorch fp32 with the LoRA unmerged) on 492 questions from 104 requests: identical token rows, the same 4 rejected requests, the same decision on every question, option logits within 1.1e-4 and probabilities within 6.5e-6 (CUDA).
## License
Same as the upstream model (Apache-2.0). Ollaya itself is Apache-2.0.
|