Instructions to use NullStateV1/nullstate-intelligence with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NullStateV1/nullstate-intelligence with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("NullStateV1/nullstate-intelligence", dtype="auto") - Notebooks
- Google Colab
- Kaggle
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license: mit
language: en
library_name: transformers
tags:
- nullstate
- agent-intelligence
- payment-infrastructure
- x402
- ap2
- agent-economy
---
# NullState Intelligence
NullState's AI model for agent task intelligence, scoring, and settlement orchestration.
## Overview
NullState uses a dual-model architecture:
- **Phi-3-mini-4k-instruct** (local, Microsoft) - 20% of queries
- **Google Gemini 2.0 Flash** (cloud) - 80% of queries
- Keyword fallback - ~2% when both unavailable
## Usage
```python
from nullstate_intelligence import NullStateModel
model = NullStateModel()
result = model.score_task("Calculate 15% APR on 1000 USDC for 30 days")
```
## Training Data
Model training data is collected from live gateway telemetry at `greensol.me/nullstate`. Each interaction is labeled with:
- Task type classification
- Response quality score (1-5)
- Revenue stream attribution
- Latency metrics
## Protocol Support
The model understands and generates:
- x402 payment challenges
- AP2 mandate structures
- MCP JSON-RPC tool calls
- KYA challenge/response pairs
## Deployment
Deployed as part of the NullState gateway stack. Dual-model with graceful degradation.
[Full documentation →](https://greensol.me/nullstate/docs/architecture)
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