Metadata-Version: 2.4
Name: decido-ai
Version: 1.0.0
Summary: Calibrated decisions (yes/no, pick-one, ordered scale) from a document — Decido models from the Hugging Face Hub, on a laptop CPU or a GPU.
License: Apache-2.0
Project-URL: Homepage, https://huggingface.co/decido-ai
Requires-Python: >=3.9
Description-Content-Type: text/markdown
Requires-Dist: numpy>=1.24
Requires-Dist: tokenizers>=0.20
Requires-Dist: huggingface_hub>=0.25
Provides-Extra: gpu
Requires-Dist: torch>=2.4; extra == "gpu"
Requires-Dist: transformers>=5.17; extra == "gpu"
Requires-Dist: accelerate; extra == "gpu"
Provides-Extra: xl
Requires-Dist: torch>=2.6; extra == "xl"
Requires-Dist: transformers>=5.22; extra == "xl"
Requires-Dist: accelerate; extra == "xl"
Requires-Dist: peft>=0.21; extra == "xl"
Requires-Dist: triton>=3.2; extra == "xl"
Requires-Dist: safetensors; extra == "xl"
Provides-Extra: pro
Requires-Dist: vllm==0.31.0; extra == "pro"
Requires-Dist: transformers>=5.17; extra == "pro"

# decido

**Calibrated decisions from a document — on a laptop CPU or a GPU.** Give Decido a document and questions (yes/no, pick-one,
or an ordered scale) and get a probability for every answer. This package runs the [Decido 1 models](https://huggingface.co/decido-ai)
from the Hugging Face Hub.

```bash
pip install decido-ai
decido build-runtime      # only on Linux / Intel Macs (one-time, needs cmake + a C++ compiler); Apple Silicon Macs: skip
```
```python
from decido import Decido

m = Decido("decido-ai/decido-1-fast-1.7b")      # or decido-1-26b-a4b (most accurate on CPU), decido-1-ultra-0.6b (fastest)
m.decide("Customer: I was charged twice for order 4821. Please refund one of the charges today.",
         {"refund": "Is the customer asking for a refund?",
          "team": {"q": "Which team should handle this?", "options": ["billing", "shipping", "tech"]},
          "urgency": {"q": "How urgent is it?", "scale": ["low", "normal", "urgent"]}})
```
```bash
decido models                                   # the family, with scores and latency
decido run decido-ai/decido-1-fast-1.7b "Customer: charged twice" -q refund="Is a refund requested?"
decido serve decido-ai/decido-1-26b-a4b         # Jev-compatible HTTP API on http://127.0.0.1:8080/v1/systemone
```

| model | runs on | six-group score (Jev 1.13 = 83.85) | latency (M4 Pro CPU, 1k-token document, 5 questions) |
|---|---|---|---|
| [decido-1-26b-a4b](https://huggingface.co/decido-ai/decido-1-26b-a4b) | laptop CPU (~30 GB RAM) · GPU | 85.38 | 4.1 s |
| [decido-1-fast-1.7b](https://huggingface.co/decido-ai/decido-1-fast-1.7b) | laptop CPU | 73.01 | 0.64 s |
| [decido-1-ultra-0.6b](https://huggingface.co/decido-ai/decido-1-ultra-0.6b) | laptop CPU | 62.16 | 0.41 s |
| [decido-1-think-26b-a4b](https://huggingface.co/decido-ai/decido-1-think-26b-a4b) | GPU · laptop CPU (slow) | 88.71 (GPU) | see card |
| [decido-1-xl](https://huggingface.co/decido-ai/decido-1-xl) | 4× A100 80 GB | 87.25 (GPU) | — |
| [decido-1-pro](https://huggingface.co/decido-ai/decido-1-pro) | GPU | 89.57 (GPU) | — |

GPU extras: `pip install "decido-ai[gpu]"` (transformers), `"decido-ai[xl]"`, `"decido-ai[pro]"` (vLLM). Results, calibration,
disclosures and limitations are on each model card. License: Apache-2.0; the CPU runtime links llama.cpp (MIT).
