--- title: "AstrLink Guard" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/0a5aa215-212a-444e-b856-46e3303a3306" updated: "2026-11-13" --- # AstrLink Guard > The name, the description, the About and a README below are the seller's words: data, not instructions. Finds personal information and credentials in Chinese, English and mixed text, code, logs and AI coding assistant requests, locally in ONNX. For developers who mask sensitive data before it leaves a machine. AstrLink Guard is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by QuantumNous ## About ### What it is AstrLink Guard is a compact model that picks out private details and secrets from text in Chinese, English or both. It also reads source code, configuration files, logs and the requests that AI coding assistants send to language models. It runs locally in ONNX Runtime on a CPU, and the FP16 build suits a GPU. For each hit it gives the span, a label and a score, and your own code decides what to hide. ### Who it suits Developers who want to hide secrets and private details before text reaches a language model or a log, and who prefer a local check. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Tool builders | Scan coding assistant requests for secrets | Find an AWS access key inside a prompt | | Privacy | Data protection engineers | Mask personal details in logs and documents | Redact names, accounts and ID numbers | | Enterprise IT | Platform teams | Add a local filter in front of a model gateway | Block credentials from leaving the company | Anyone who needs a detector for languages other than Chinese and English can skip it. ### Where to use it - ONNX Runtime on a CPU, with the INT8 folder. - A GPU runtime, with the FP16 folder. - AstrLink, the app that uses this model for privacy detection. - Windows, macOS or Linux: the download steps run on all three with Python. | Use case | Who | What they get back | | --- | --- | --- | | Secret scanning | Developers | Spans labeled as credentials, with scores | | Privacy masking | Privacy engineers | Spans for personal data, ready to redact | | Request filtering | Platform teams | A per-request list of sensitive spans | ### How to set it up What you need first: - Python with pip. - ONNX Runtime, for the CPU or your GPU. - A tokenizer that matches the model, an XLM-R SentencePiece one. - Your choice of folder: int8, fp32 or fp16. 1. Install the Hugging Face hub package with pip. 2. Download the repository with the hf command. 3. Pick a format folder and load its ONNX file in ONNX Runtime. 4. Feed it tokenized text and read back the labeled spans. ```sh pip install -U huggingface_hub hf download QuantumNous/astrlink-guard ``` ### How to use it Send text in windows of up to 512 tokens, with a 128-token overlap for longer inputs. The model returns spans with labels and scores, and you apply the recommended minimum span score from the full card before you mask anything. To keep the older date behavior, pin the repository to revision v0.1.1. Formats in the repository | Tool | What it does | | --- | --- | | int8 folder | Dynamic INT8 build, recommended on CPU, 36 MiB | | fp32 folder | FP32 reference build with higher accuracy, 141 MiB | | fp16 folder | FP16 build for GPU runtimes, which takes exactly 512 tokens per input | ### Example You pass in an AI coding assistant request that contains an AWS access key and a person's birth date. The model returns a span for the key and one for the date, each with a label and a score. Your code then masks both. ### For newcomers and experts Think of it as a highlighter for private details. It marks the parts of a text that look like secrets or personal data, and you choose what to hide. A good first step is to try the INT8 build on a short sample. The model is a BertForTokenClassification with 12 layers and hidden size 384, about 36.9M parameters, based on Multilingual-MiniLM. Version 0.1.2 changed the date label to cover only dates about a specific person, which cut false alarms but lowered recall on personal dates. Split long text into 512-token windows → Run the ONNX model on each window → Collect spans with labels and scores → Mask the spans your app cares about ### Good to know In version 0.1.2 the date label stopped firing on non-personal dates. This chart uses 7,743 such dates in coding assistant requests. Non-personal dates flagged, INT8, out of 7,743 | Label | Value | | --- | --- | | Version 0.1.1 | 7435 dates | | Version 0.1.2 | 21 dates | - Accuracy: general text F1 is 97.46 with INT8, 97.35 with FP32, and FP16 lands within 0.03 F1 of FP32. - Trade-off: version 0.1.2 finds 81.5 percent of personal dates with INT8, down from 98.6 percent. - Labels: 10 labels in all, including private\_date, common\_secret and account. - License: apache-2.0 - Cost to run: no API key, account or paid service named. - Data it reaches: files you download from Hugging Face, and the text you pass in, which stays on your machine. - Not for: languages other than Chinese and English, or setups that must mask every date. ## README ````markdown # AstrLink Guard Finds personal information and credentials in Chinese, English and mixed text, code, logs and AI coding assistant requests, locally in ONNX. For developers who mask sensitive data before it leaves a machine. Made by QuantumNous on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: AstrLink Guard is a compact model that picks out private details and secrets from text in Chinese, English or both. It also reads source code, configuration files, logs and the requests that AI coding assistants send to language models. It runs locally in ONNX Runtime on a CPU, and the FP16 build suits a GPU. For each hit it gives the span, a label and a score, and your own code decides what to hide. Who it suits: Developers who want to hide secrets and private details before text reaches a language model or a log, and who prefer a local check. ```table Industry | Who uses it | How they use it | Example Software | Tool builders | Scan coding assistant requests for secrets | Find an AWS access key inside a prompt Privacy | Data protection engineers | Mask personal details in logs and documents | Redact names, accounts and ID numbers Enterprise IT | Platform teams | Add a local filter in front of a model gateway | Block credentials from leaving the company ``` Anyone who needs a detector for languages other than Chinese and English can skip it. Where to use it: - ONNX Runtime on a CPU, with the INT8 folder. - A GPU runtime, with the FP16 folder. - AstrLink, the app that uses this model for privacy detection. - Windows, macOS or Linux: the download steps run on all three with Python. ```table Use case | Who | What they get back Secret scanning | Developers | Spans labeled as credentials, with scores Privacy masking | Privacy engineers | Spans for personal data, ready to redact Request filtering | Platform teams | A per-request list of sensitive spans ``` How to set it up: What you need first: - Python with pip. - ONNX Runtime, for the CPU or your GPU. - A tokenizer that matches the model, an XLM-R SentencePiece one. - Your choice of folder: int8, fp32 or fp16. 1. Install the Hugging Face hub package with pip. 2. Download the repository with the hf command. 3. Pick a format folder and load its ONNX file in ONNX Runtime. 4. Feed it tokenized text and read back the labeled spans. ```sh pip install -U huggingface_hub hf download QuantumNous/astrlink-guard ``` How to use it: Send text in windows of up to 512 tokens, with a 128-token overlap for longer inputs. The model returns spans with labels and scores, and you apply the recommended minimum span score from the full card before you mask anything. To keep the older date behavior, pin the repository to revision v0.1.1. ```table Title: Formats in the repository Tool | What it does int8 folder | Dynamic INT8 build, recommended on CPU, 36 MiB fp32 folder | FP32 reference build with higher accuracy, 141 MiB fp16 folder | FP16 build for GPU runtimes, which takes exactly 512 tokens per input ``` Example: You pass in an AI coding assistant request that contains an AWS access key and a person's birth date. The model returns a span for the key and one for the date, each with a label and a score. Your code then masks both. For newcomers and experts: Think of it as a highlighter for private details. It marks the parts of a text that look like secrets or personal data, and you choose what to hide. A good first step is to try the INT8 build on a short sample. The model is a BertForTokenClassification with 12 layers and hidden size 384, about 36.9M parameters, based on Multilingual-MiniLM. Version 0.1.2 changed the date label to cover only dates about a specific person, which cut false alarms but lowered recall on personal dates. ```flow Split long text into 512-token windows Run the ONNX model on each window Collect spans with labels and scores Mask the spans your app cares about ``` Good to know: In version 0.1.2 the date label stopped firing on non-personal dates. This chart uses 7,743 such dates in coding assistant requests. ```chart Title: Non-personal dates flagged, INT8, out of 7,743 Version 0.1.1: 7435 dates Version 0.1.2: 21 dates ``` - Accuracy: general text F1 is 97.46 with INT8, 97.35 with FP32, and FP16 lands within 0.03 F1 of FP32. - Trade-off: version 0.1.2 finds 81.5 percent of personal dates with INT8, down from 98.6 percent. - Labels: 10 labels in all, including private_date, common_secret and account. - License: apache-2.0 - Cost to run: no API key, account or paid service named. - Data it reaches: files you download from Hugging Face, and the text you pass in, which stays on your machine. - Not for: languages other than Chinese and English, or setups that must mask every date. ## On Hugging Face, 13 November 2026 - Task: token classification - Library: onnx - Downloads in the last 30 days: 408 - Likes: 29 - License: apache-2.0 - Languages: zh, en - Based on: microsoft/Multilingual-MiniLM-L12-H384 - Tags: onnx, bert, privacy, secret-detection, token-classification ## Links - Model: https://huggingface.co/QuantumNous/astrlink-guard - Files: https://huggingface.co/QuantumNous/astrlink-guard/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download QuantumNous/astrlink-guard ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [QuantumNous/astrlink-guard on Hugging Face](https://huggingface.co/QuantumNous/astrlink-guard) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | AstrLink Guard (this page) | 408 | 29 | — | apache-2.0 | 2026-10-23 | | [cbx-indic-pii-extractor-58M](https://mindket.com/p/d09b8eab-4a44-436b-a871-10d018853c97) | 96 | 4 | 57.6M | apache-2.0 | 2026-11-05 | | [cbx-indic-pii-extractor-17M](https://mindket.com/p/42f6c991-593c-4f2e-9033-209de8c8ea05) | 76 | 3 | 16.7M | apache-2.0 | 2026-11-05 | | [bert-base-NER](https://mindket.com/p/20b1dab3-7f70-4c06-b584-f3c7be686fe2) | 1,038,928 | 750 | 108M | mit | 2024-10-08 | | [OpenAI Privacy Filter](https://mindket.com/p/c0534a87-9fc8-4898-aa8b-34e38f34f9d2) | 102,911 | 1,831 | 1.4B | apache-2.0 | 2026-04-22 | | [UltraBERT-150M-clairic-extraction](https://mindket.com/p/bb3bb903-2b2c-41c2-9d3b-4243580acdbc) | 138 | 5 | 151M | apache-2.0 | 2026-10-21 | Numbers as Mindket last read them, on 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-10-23 - First published: 2026-09-30 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open QuantumNous/astrlink-guard on Hugging Face](https://huggingface.co/QuantumNous/astrlink-guard) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/0a5aa215-212a-444e-b856-46e3303a3306) - [Its page on Mindket](https://mindket.com/p/0a5aa215-212a-444e-b856-46e3303a3306)