HaS Text Model (Q8_0)

HaS (Hide and Seek) is an on-device privacy model providing a complete pipeline from entity recognition to anonymization and restoration.

📦 0.6B parameters, Q8_0 quantization, 639 MB
🔒 Data never leaves device — local inference, no network required
🌍 8 languages natively supported: Chinese, English, Portuguese, French, Spanish, German, Korean, Japanese
⚡ Apple M4 benchmark: prefill 1,600–2,800 tok/s, generation 96–120 tok/s

1. Core Capabilities

Traditional anonymization (regex, Presidio, etc.) only does pattern matching. HaS is an on-device Agentic privacy pipeline — a set of composable atomic capabilities that solve multi-turn consistency, reversible restoration, and post-anonymization data usability.

Capability	Description
3-Level Semantic Tags	Instead of `[REDACTED]`, produces tags like `<Amount[1].ContractAmount.NumberSymbol>` — LLMs understand "this is a contract amount", preserving data usability
Coreference Resolution	"CloudGenius Inc.", "CloudGenius", "云创智能" → all unified as `<Organization[1].Company.Name>`. Different forms, same ID
Multi-turn Consistency	Carries historical mapping dictionaries for incremental anonymization. Entity IDs stay consistent across turns. Same mechanism supports recursive chunking for long documents
Reversible Restoration	Anonymized text can be processed by cloud LLMs (translation, rewriting, etc.), then Seek restores the tags back to original values
Open-set Entity Types	Trained on ~70,000 entity types. Users can freely specify any type name without being limited to predefined categories
Public/Private Distinction	"Industrial and Commercial Bank of China" preserved, "Li Hong 138-xxxx" anonymized — only redacts what should be redacted

2. Six Atomic Capabilities

#	Capability	Description
1	NER	Recognize named entities of specified types
2	Hide_with	Anonymize using an existing mapping dictionary (maintains cross-text consistency)
3	Hide_without	First-time anonymization (no mapping, model generates tags autonomously)
4	Pair	Extract mapping relationships from original and anonymized text pairs
5	Split	Split composite tags into atomic single-entity mappings
6	Seek	Restore tagged text using a mapping dictionary

3. Structured Semantic Tags & Coreference Resolution

3-Level Semantic Tags

Tags use a <EntityType[ID].Category.Attribute> three-level structure:

<Address[1].City.CityName>             ← identifies this as a city name
<Address[2].StreetAddress.FullAddress>  ← identifies this as a detailed address
<Amount[1].ContractAmount.NumberSymbol> ← identifies this as a contract amount
<Phone[1].Mobile.FullNumber>           ← identifies this as a mobile number

Comparison with traditional approaches:

Traditional	HaS 3-Level Tag
`[ADDRESS]`	`<Address[1].City.CityName>`
`[ADDRESS]`	`<Address[2].StreetAddress.FullAddress>`
`[MONEY]`	`<Amount[1].ContractAmount.NumberSymbol>`

Coreference Resolution

The same entity often appears in multiple forms. HaS automatically recognizes they refer to the same object and unifies them under one ID:

Original forms               Unified tag
───────────────────           ───────────────────────
CloudGenius Inc.          →   <Organization[1].Company.Name>
CloudGenius               →   <Organization[1].Company.Name>
云创智能                   →   <Organization[1].Company.Name>
CG                        →   <Organization[1].Company.Name>

This ensures anonymized text remains logically coherent — LLMs seeing multiple <Organization[1]> know it's the same company. Critical for multi-turn conversations and long document chunking: entity IDs remain globally consistent across turns and chunks.

4. Quick Start

Recommended deployment with llama.cpp:

llama-server -m has_text_model.gguf -ngl 999 -c 8192 -np 1 -fa on -ctk q8_0 -ctv q8_0

Listens on http://127.0.0.1:8080/v1 by default
OpenAI Chat Completions compatible API
~1.56 GB total memory with recommended settings

5. Usage Scenarios

The 6 atomic capabilities can be composed into various privacy pipelines:

Scenario	Description	Capabilities Used
Redacted Sharing	Auto-anonymize files, emails, code before sending; retain mapping for restoration	Hide → Pair
Privacy Scanning	Scan files/directories, list all sensitive entities, assess exposure risk	NER
Privacy Knowledge Base	Anonymize documents before ingestion; restore query results via mapping	Hide → Pair (write), Seek (read)
Log Redaction	Batch-anonymize ops logs before handing to support teams	Hide → Pair
Secure Cloud Chat	Anonymize text before sending to cloud LLM; restore LLM responses	NER → Hide → Pair → Seek
AI Memory Privacy	Store Agent long-term memory in anonymized form; restore on demand	Hide → Pair (store), Seek (recall)

6. Prompt Templates

⚠️ Templates must match character-for-character — the model was trained on these exact templates. Any deviation may degrade output quality.

NER

Recognize the following entity types in the text.
Specified types:{types_json_array}
<text>{text}</text>

Hide_with (with mapping)

Turn 1: Same as NER template

Turn 2:

Replace the above-mentioned entity types in the text according to the existing mapping pairs:{mapping_json}

Hide_without (without mapping)

Turn 1: Same as NER template

Turn 2 (fixed text, no variables):

Replace the above-mentioned entity types in the text.

Pair

<original>{original_text}</original>
<anonymized>{anonymized_text}</anonymized>
Extract the mapping from anonymized entities to original entities.

Split

Split each composite anonymized key into atomic keys.
Composite mapping:
{composite_mapping_json_array}

Seek

The mapping from anonymized entities to original entities:
{mapping_json}
Restore the original text based on the above mapping:
{text_with_tags}

7. Speed Benchmarks

Test platform: Apple M4, Q8_0 model, llama-server recommended settings

HaS ships with a CLI tool has-text that orchestrates model capabilities with programmatic tools into ready-to-use commands (scan, hide, seek). The following are end-to-end CLI times:

scan = Model-NER

hide = Model-NER → Model-Hide → Tool-Pair → Tool-Mapping Merge (with self-check; Model-Split called for composite tags)

seek = Tool-Language Detection → Tool-Seek (string replacement) or Model-Seek (cross-language) → self-check

Scenario	Text Length	Entity Types	Scan Only	Scan+Anonymize	Restore
Email redaction	~130 chars	5	0.7s	1.7s	0.09s
Medical record	~230 chars	8	1.4s	3.3s	0.09s
Business contract	~280 chars	10	1.9s	4.3s	0.10s
Full agreement	~900 chars	10	4.0s	11.5s	0.10s
Contract → translated to English → restore	~280 chars	10	—	—	2.8s
Chat → processed by cloud LLM → restore	~240 chars	7	—	—	2.2s
Ops log redaction	~760 chars	8	1.7s	6.7s	0.08s

Same-language restoration uses string replacement (constant time); cross-language automatically switches to model inference

8K context per chunk. With recursive chunking, can process documents of hundreds of thousands of tokens

8. Quantization Versions

Version	Quantization	File Size	Runtime Memory	Notes
Q8_0	8.50 BPW	639 MB	~1.56 GB	Recommended, best output quality
Q4_K_M	5.24 BPW	397 MB	~1.29 GB	Faster inference, lower memory, for resource-constrained environments

中文版

HaS Text Model (Q8_0)

HaS（Hide and Seek） 是一个端侧部署的隐私模型，提供从实体识别到脱敏还原的完整管线。

📦 0.6B 参数，Q8_0 量化，639 MB
🔒 数据不出设备，本地推理，无需联网
🌍 8 语言原生支持：中、英、葡、法、西、德、韩、日
⚡ Apple M4 实测：prefill 1,600–2,800 tok/s，生成 96–120 tok/s

一、核心能力

传统脱敏方案（正则、Presidio 等）只做模式匹配。HaS 的定位是端侧 Agentic 隐私管线——用一组可组合的原子能力解决多轮一致、可逆还原和脱敏后数据可用性的问题。

能力	说明
三级语义标签	脱敏后不是 `[REDACTED]`，而是 `<金额[1].合同金额.数字符号>` 这样携带语义的标签——LLM 一看就知道"这是一笔合同金额"，保持脱敏后数据可用性
指代消解	"云创智能有限公司"、"云创智能"、"CloudGenius"→ 全部归为 `<组织[1].企业.名称>`。不同写法，同一编号
多轮一致	携带历史映射字典做增量脱敏，跨轮次实体编号一致。同一机制支持递归分块处理超长文档
可逆还原	脱敏后的文本可先交给云端 LLM 处理（翻译、改写等），Seek 能对处理后文本中的标签进行还原
开集指定	训练覆盖约 7 万种实体类型，用户可自由指定任意类型名称，不受预定义类别限制
公私区分	"中国工商银行"保留，"李红 138-xxxx"脱敏——只脱该脱的，不过度脱敏

二、6 个原子能力

#	能力	说明
1	NER	识别指定类型的命名实体
2	Hide_with	使用已有映射字典脱敏（保持跨文本一致）
3	Hide_without	首次脱敏（无映射，模型自主生成标签）
4	Pair	从原文和脱敏文本对中提取映射关系
5	Split	拆分复合标签为原子单实体映射
6	Seek	根据映射字典还原含标签的文本

三、结构化语义标签与指代消解

三级语义标签

脱敏后的标签采用 <实体类型[编号].分类.属性> 三级结构：

<地址[1].城市.市名>          ← 知道这是一个城市名
<地址[2].街道门牌.完整地址>   ← 知道这是一个详细地址
<金额[1].合同金额.数字符号>   ← 知道这是一笔合同金额，不只是普通数字
<电话[1].手机号.完整号码>     ← 知道这是手机号，不是座机或传真

对比传统脱敏方案：

传统方案	HaS 三级标签
`[ADDRESS]`	`<地址[1].城市.市名>`
`[ADDRESS]`	`<地址[2].街道门牌.完整地址>`
`[MONEY]`	`<金额[1].合同金额.数字符号>`

同样是地址，三级标签区分了"城市名"和"详细地址"；同样是金额，标签告诉你这是"合同金额"。脱敏后的文本对 LLM 仍然可理解、可推理。

指代消解

同一实体在文本中往往以多种形式出现。HaS 会自动识别它们指向同一对象，统一归为同一编号：

原文中的写法              脱敏后统一为
───────────────────       ───────────────────────
云创智能科技有限公司   →   <组织[1].企业.名称>
云创智能              →   <组织[1].企业.名称>
CloudGenius           →   <组织[1].企业.名称>
云创                  →   <组织[1].企业.名称>

这确保了脱敏后的文本逻辑自洽——LLM 看到多处 <组织[1]> 就知道是同一家公司，而不会误以为是不同实体。在多轮对话和长文档分块中尤为关键：跨轮次、跨分块的实体编号全局一致。

四、快速开始

推荐使用 llama.cpp 推理框架：

llama-server -m has_text_model.gguf -ngl 999 -c 8192 -np 1 -fa on -ctk q8_0 -ctv q8_0

默认监听 http://127.0.0.1:8080/v1
API 兼容 OpenAI Chat Completions 格式
推荐配置下总内存约 1.56 GB

五、使用场景

6 个原子能力可以组合成多种隐私管线：

场景	说明	使用能力
脱敏分享	文件、邮件、代码在外发前自动脱敏，保留映射表可随时还原	Hide → Pair
全量隐私扫描	扫描文件或目录，列出所有敏感实体，评估泄露风险	NER
隐私知识库	文档先脱敏再入库，查询结果通过映射表还原原文	Hide → Pair（写入）、Seek（读取）
日志脱敏	运维日志在交给支持团队前批量脱敏	Hide → Pair
安全云端对话	脱敏后文本发给云端 LLM 处理，LLM 返回结果再还原	NER → Hide → Pair → Seek
AI 记忆隐私	Agent 的长期记忆以脱敏形式存储，使用时按需还原	Hide → Pair（存储）、Seek（召回）

上述场景仅为典型示例。6 个原子能力可自由组合，适配任意隐私需求。

六、提示词模板

⚠️ 模板必须逐字符精确匹配，模型基于这些模板训练。任何偏差（空格、换行、标点）都可能降低输出质量。

NER

Recognize the following entity types in the text.
Specified types:{types_json_array}
<text>{text}</text>

{types_json_array}：JSON 数组，紧接 Specified types: 无空格。如 ["组织","地址","人名"]
{text}：用户原始文本

输出：JSON 对象，key 为实体类型，value 为识别到的实体数组。

Hide_with（带映射脱敏）

第 1 轮：与 NER 模板相同

第 2 轮：

Replace the above-mentioned entity types in the text according to the existing mapping pairs:{mapping_json}

{mapping_json}：已有映射字典，类型 Record<string, string[]>

输出：脱敏后文本。

Hide_without（无映射脱敏）

第 1 轮：与 NER 模板相同

第 2 轮（固定文本，无变量）：

Replace the above-mentioned entity types in the text.

输出：脱敏后文本，标签由模型自主生成。

Pair（提取映射）

<original>{original_text}</original>
<anonymized>{anonymized_text}</anonymized>
Extract the mapping from anonymized entities to original entities.

输出：Record<string, string[]> 映射 JSON。

Split（拆分复合标签）

Split each composite anonymized key into atomic keys.
Composite mapping:
{composite_mapping_json_array}

输出：拆分后的原子映射。

Seek（还原）

The mapping from anonymized entities to original entities:
{mapping_json}
Restore the original text based on the above mapping:
{text_with_tags}

输出：还原后的原文。

七、速度评估

测试平台：Apple M4，Q8_0 模型，llama-server 推荐配置

HaS 配套了 CLI 工具 has-text，将模型的原子能力与程序化工具编排为开箱即用的命令（scan、hide、seek）。以下为 CLI 端到端耗时，每个命令内部组合了模型调用与工具调用：

scan = Model-NER

hide = Model-NER → Model-Hide → Tool-Pair → Tool-Mapping Merge（含自检；遇复合标签时额外调用 Model-Split）

seek = Tool-Language Detection → Tool-Seek（字符串替换）或 Model-Seek（跨语言时）→ 自检

场景	原文长度	实体类型	仅扫描	扫描+脱敏	还原
邮件脱敏	~130 字	5 类	0.7s	1.7s	0.09s
病历脱敏	~230 字	8 类	1.4s	3.3s	0.09s
合同脱敏	~280 字	10 类	1.9s	4.3s	0.10s
协议书脱敏	~900 字	10 类	4.0s	11.5s	0.10s
合同脱敏后翻译为英文再还原	~280 字	10 类	—	—	2.8s
聊天记录脱敏后经云端 LLM 处理再还原	~240 字	7 类	—	—	2.2s
运维日志脱敏	~760 字	8 类	1.7s	6.7s	0.08s