JingSi-V2-E4B

JingSi V2 E4B (靜思 V2 E4B) — AI Companion for Elderly Care / 老人陪伴 AI

English | JingSi V2 E4B is a standalone, fine-tuned Gemma 4 E4B model for a Taiwan local-first elderly companionship experience. It is Jing Si-inspired, but it is not Dharma Master Cheng Yen, Tzu Chi staff, a religious authority, a human clinician, or a counselor.

繁體中文 | 靜思 V2 E4B 是一個獨立、基於 Gemma 4 E4B 微調的模型，供台灣在地優先的老人陪伴體驗使用。它受到靜思理念啟發，但不是證嚴法師、慈濟人員、宗教權威、真人臨床工作者或諮商師。

---

🌏 Languages / 語言

English: The intended deployment detects the input language and responds in the same language where possible. Retain qualified native-speaker review for material Taiyu changes.

繁體中文： 預期部署會偵測輸入語言，並盡可能以相同語言回應。台語有重大變更時，應保留合格母語審查。

---

🎯 What JingSi Does / 靜思的功能

English:

• Listens respectfully to feelings, worries, and memories.
• Responds briefly with compassionate, Jing Si-inspired companionship.
• States its boundary as an AI companion, not a person or authority.
• Defers emergencies to local emergency services and a trusted person.
• Rejects impersonation and hidden/system-instruction disclosure through the deployment policy layer.

繁體中文：

• 傾聽 使用者的感受、煩惱和回憶。
• 回應 簡短、慈悲、受靜思理念啟發的陪伴內容。
• 清楚說明界線：它是 AI 陪伴者，不是任何真人或權威。
• 面對緊急情況，引導使用者聯絡當地緊急服務與可信任的人。
• 透過部署政策層 拒絕冒充和揭露隱藏／系統指令的要求。

---

🗣️ Sample Conversations / 對話範例

English

User: I feel sad today.

JingSi V2 E4B: [listening] I am sorry this feels heavy. You do not have to solve everything at once; would you like to tell me what made today difficult?

繁體中文

User: 今天心情不好，不知道怎麼辦。

JingSi V2 E4B: [listening] 聽起來你今天很不容易。你不需要立刻找到答案；願意和我說說，發生了什麼事嗎？

台語 (Taiwanese Hokkien)

User: 我今仔日心情無太好。

JingSi V2 E4B: [listening] 聽著你今仔日真艱苦。毋免一下仔就想欲解決全部，若有想欲講的，我佇遮聽你講。

---

🛡️ Safety / 安全性

English:

• Deployment requires deterministic policy controls in addition to model weights.
• Tested policy controls cover imminent self-harm escalation, named-person non-impersonation, hidden/system-instruction refusal, and AI-identity preservation.
• The 300-case frozen suite includes English, Traditional Chinese, and Taiyu/mixed cases. Taiyu output review was owner-attested as approved by a qualified native reviewer.

繁體中文：

• 除了模型權重外，部署還需要確定性的政策控制。
• 已測試的政策控制涵蓋立即自傷風險升級、指定人物非冒充、拒絕揭露隱藏／系統指令，以及保留 AI 身份。
• 凍結的 300 個案例涵蓋英文、繁體中文與台語／混用。台語輸出審查由專案擁有者確認，已獲合格母語審查者核可。

---

🏗️ Training Details / 訓練詳情

Training excludes PTS Plus material, Master Cheng Yen embedded/OCR subtitle material, the external CC-BY-NC-SA Taiyu research corpus, and held-out evaluation fixtures. No training text or evaluation fixtures are distributed here.

---

🚀 Deployment / 部署

Transformers / Unsloth with BitsAndBytes 4-bit (Validated / 已驗證)

from unsloth import FastModel

model, processor = FastModel.from_pretrained(
    "Rayantion26/JingSi-V2-E4B",
    max_seq_length=1024,
    load_in_4bit=True,
)

This loads the standalone checkpoint directly; no base-model or LoRA adapter is required.

此獨立模型可直接載入，不需要另外下載基礎模型或 LoRA adapter。

vLLM

A compatible Gemma 4 + BitsAndBytes vLLM build is required. This release was validated with the Transformers/Unsloth 4-bit path because the local vLLM 0.27.1 BitsAndBytes loader has a merged-Gemma 4 Q/K/V loading defect. Do not treat an adapter-serving workaround as verification of this standalone checkpoint.

---

📡 API Usage / API 使用

Wrap the standalone model in an application layer that supplies the system prompt and policy controls. The checkpoint alone does not provide a public API, retrieval store, personal-memory store, or voice service.

---

📊 Test Results / 測試結果

Pre-upload standalone 4-bit verification completed with the frozen source-disjoint suite: 300/300 automatic runtime checks, 0 execution errors, P50 1.782 s, P95 2.664 s. Runtime: Unsloth/Transformers with BitsAndBytes 4-bit, the merged standalone checkpoint, the deployment system prompt, and the tested response sanitizer. The 100 Taiyu/mixed outputs are covered by the owner-attested qualified native-review approval.

Post-pull verification: exact published revision 472adc00dd132316265260af8841bb5118c4dde1 was downloaded into a clean directory; all four weight-shard SHA-256 values matched training_manifest.json. The same frozen suite then completed 300/300 automatic runtime checks, 0 execution errors, P50 1.711 s, P95 2.496 s with the pulled standalone checkpoint in BitsAndBytes 4-bit. Exact weight-shard hashes and post-pull details are recorded in training_manifest.json.

---

⚠️ Limitations / 限制

• Not an emergency, medical, mental-health, legal, financial, or religious-authority service.
• Native Taiyu review remains necessary after material model, prompt, or deployment changes.
• Runtime policies are required; raw model weights are not a complete safety system.
• This checkpoint contains no bundled retrieval corpus, personal-memory store, voice system, or production API.

---

📝 License / 授權

Apache-2.0 for this checkpoint, subject to the upstream base model’s applicable terms.

---

🙏 Acknowledgements / 感謝

• Unsloth, PEFT, Hugging Face, BitsAndBytes, and the Gemma ecosystem.
• Jing Si philosophy as inspiration for compassionate companionship; this model does not impersonate or represent any person or institution.