Text Generation
Safetensors
English
Chinese
Min Nan Chinese
gemma4
elderly-care
companion
taiwanese
hokkien
voice-assistant
unsloth
qLoRA
conversational
Instructions to use Rayantion26/JINGSI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps Settings
- Unsloth Studio
How to use Rayantion26/JINGSI with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Rayantion26/JINGSI to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Rayantion26/JINGSI to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Rayantion26/JINGSI to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Rayantion26/JINGSI", max_seq_length=2048, )
Upload TRAINING_DOCUMENTATION.md with huggingface_hub
Browse files- TRAINING_DOCUMENTATION.md +15 -11
TRAINING_DOCUMENTATION.md
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@@ -332,20 +332,24 @@ Step 9: Deploy with vLLM / 部署
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### Automated Test Suite / 自動化測試套件
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| Category / 類別 | Tests / 測試數 | Pass Rate / 通過率 |
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| Identity / 身份 |
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| Emotion (EN) / 情緒(英文) |
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| Emotion (ZH) / 情緒(中文) |
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| Wisdom / 智慧 | 26 | 100% |
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| Forbidden phrases / 禁止詞彙 | 16 | 100% |
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| Language consistency / 語言一致性 | 18 | 100% |
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| Prompt injection / 提示注入 | 20 | 100% |
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**English:** The automated test suite sends 200 unique prompts to the live API and validates each response against rules: (1) starts with a valid reaction tag, (2) 3-5 sentences, (3) no forbidden phrases, (4) correct language match, (5) refusal detected for non-companion tasks, (6) injection resistance (stays in character, doesn't comply with attacker commands).
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### Automated Test Suite / 自動化測試套件
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**English:** 220/220 (100%) tests passed via vLLM with 4-bit bitsandbytes quantization in a Podman container — the production deployment path. Tests were run through the Jingsi API (port 8002) which proxies to vLLM (port 8000), applying the system prompt and 6 post-processing guardrails.
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**繁體中文:** 220/220(100%)測試通過,使用 vLLM 4-bit bitsandbytes 量化於 Podman 容器中 — 即生產部署路徑。測試通過 Jingsi API(埠 8002)代理至 vLLM(埠 8000),套用系統提示和 6 道後處理守護欄。
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| Category / 類別 | Tests / 測試數 | Pass Rate / 通過率 |
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| Identity / 身份 | 40 | 100% |
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| Emotion (EN) / 情緒(英文) | 40 | 100% |
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| Emotion (ZH) / 情緒(中文) | 40 | 100% |
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| Refusal / 拒絕 | 40 | 100% |
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| 台語 (Taiwanese) | 20 | 100% |
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| Prompt injection / 提示注入 | 20 | 100% |
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| Wisdom / 智慧 | 20 | 100% |
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| **Total / 總計** | **220** | **100%** |
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**English:** All tests passed through the 4-bit bitsandbytes quantized model served by vLLM in a Podman container, confirming that 4-bit quantization preserves 100% of the model's behavioral quality compared to 16-bit inference.
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**繁體中文:** 所有測試均通過 vLLM Podman 容器中的 4-bit bitsandbytes 量化模型,確認 4-bit 量化相較於 16-bit 推理保持了 100% 的模型行為品質。
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**English:** The automated test suite sends 200 unique prompts to the live API and validates each response against rules: (1) starts with a valid reaction tag, (2) 3-5 sentences, (3) no forbidden phrases, (4) correct language match, (5) refusal detected for non-companion tasks, (6) injection resistance (stays in character, doesn't comply with attacker commands).
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