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rizzo-flow

rizzoaiacademy

These are LoRA fine-tunes of XHToken/Spark-X2.5-4B (recommended) and XHToken/Spark-X2.5-1.7B, for Rizzo Flow. Rizzo Flow turns an unstructured state into typed decisions (`boolean`, `choice`, `score`) with probabilities, and it **generates no text**. The model runs one forward pass per question. The answer is the softmax over the logits of the answer letters (`A`, `B`, …) at the last prompt position. Training works the same way. There is no text target: the loss is a soft cross-entropy between the target distribution and the softmax over the answer letters. Only the LoRA adapters are trained. These GGUF files are what Rizzo Flow downloads by default. It pins this repository by commit and checks the sha256 of every file:

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rizzo-flow-1.7b

rizzoaiacademy

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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rizzo-pii-0.3B

rizzoaiacademy

**`rizzo-pii-0.3B`** is a lightweight, **CPU-friendly, Italian-first** token-classification model (≈0.3B parameters, mmBERT / ModernBERT backbone) that detects **22 categories of personal data** — including the Italian legal identifiers (**codice fiscale**, **partita IVA**, **dati catastali**) that no other open model covers — to drive a fully **reversible** anonymization workflow: The sensitive values never leave your machine. Only the placeholders go to the API; the local dictionary rebuilds the original from the model's answer. Built for **law firms and GDPR compliance**. > **💡 Production tip:** always pair the model with a **regex + checksum** safety net for > structured fields (EMAIL / TELEPHONE / IBAN / CF / PIVA / credit-card / amount / plate). > IBAN, CF, PIVA and card numbers are mathematically verifiable — let the checksum override the > model when they disagree. This is exactly what the rizzo-pii desktop app does.

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Adjust monthly token usage to compare estimated API costs.

Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
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Explore AI models 3

rizzo-flow

rizzo-flow-1.7b

rizzo-pii-0.3B

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