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decima is an open-weight model for text classification from Ollaya, released under Apache License 2.0. Its published files total 13.5 MB.
Ollaya package of amyrmahdy/decima-small and amyrmahdy/decima-base and amyrmahdy/decima-agent by A. M. Madani (amyrmahdy).
By Ollaya, published under apache-2.0, revision 1b48d25e927e.
Ollaya package of amyrmahdy/decima-small and amyrmahdy/decima-base and amyrmahdy/decima-agent by A. M. Madani (amyrmahdy). Ollaya runs open decision models locally, the way Ollama runs LLMs: typed questions in, calibrated answers out, behind a TypeSafe-compatible API. 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. Each tag has an fp32 graph, used on CPU and GPU. Each tag also has decision.json (sequence layout, special…
Ollaya package of amyrmahdy/decima-small and amyrmahdy/decima-base and amyrmahdy/decima-agent by A. M. Madani (amyrmahdy). Ollaya runs open decision models locally, the way Ollama runs LLMs: typed questions in, calibrated answers out, behind a TypeSafe-compatible API.
ollaya run decima
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 |
|---|---|---|
decima:small |
amyrmahdy/decima-small@2e7f4d0 | small/model-fp32.onnx, small/decision.json, small/calibration.json |
decima:base |
amyrmahdy/decima-base@2468005 | base/model-fp32.onnx, base/decision.json, base/calibration.json |
decima:agent |
amyrmahdy/decima-agent@5c14a46 | agent/model-fp32.onnx, agent/decision.json, agent/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).
Ollaya's Rust runtime matches the author's own code (decima/model.py, render.py and systemone.py, fp32) on 581 questions from 122 requests for each of small and base, and 591 questions from 132 requests for agent 2.2, ten of them too long for the option window, on CPU and CUDA (RTX 4090 and RTX 5090): identical token rows and truncation, the same 18 rejected requests, the same decision on every question, scores within 2.2e-5 and probabilities within 5.4e-6.
Same as the upstream model (Apache-2.0). Ollaya itself is Apache-2.0.
11 files, 13.5 MB in total. The weights are 3 files totalling 13.5 MB in onnx.
| File | Type | Size | SHA-256 |
|---|---|---|---|
| agent/model-fp32.onnx | Weights | 5.1 MB | 4e0f79b79f28 |
| base/model-fp32.onnx | Weights | 5.1 MB | 14bda1306d8e |
| small/model-fp32.onnx | Weights | 3.3 MB | f83c59768e53 |
| agent/calibration.json | Configuration | 291 B | — |
| agent/decision.json | Configuration | 2.7 KB | — |
| base/calibration.json | Configuration | 291 B | — |
| base/decision.json | Configuration | 2.6 KB | — |
| small/calibration.json | Configuration | 291 B | — |
| small/decision.json | Configuration | 2.7 KB | — |
| README.md | Documentation | 2.5 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
Released by Ollaya through its official repository on Hugging Face. Read the license.
| Precision | Weights in memory |
|---|---|
| As published | 13.5 MB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Yes. decima is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.
This is a RoBERTa-base model trained on ~124M tweets from January 2018 to December 2021, and finetuned for sentiment analysis with the TweetEval benchmark. The original Twitter-based RoBERTa model can be found here and the original reference paper is TweetEval. This model is suitable for English. 0 -> Negative; 1 -> Neutral; 2 -> Positive This sentiment analysis model has been integrated into TweetNLP. You can access the demo here.
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https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2 with ONNX weights to be compatible with Transformers.js. If you haven't already, you can install the Transformers.js JavaScript library from NPM using: Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named onnx).
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