Text Generation
PEFT
Safetensors
Chinese
English
lora
tool-selection
tool-call
guardrail
chinese
traditional-chinese
fine-tuned
qwen2
conversational
Instructions to use GOSHUNCLE/tool_call_validator_zh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use GOSHUNCLE/tool_call_validator_zh with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-3B-Instruct") model = PeftModel.from_pretrained(base_model, "GOSHUNCLE/tool_call_validator_zh") - Notebooks
- Google Colab
- Kaggle
Upload README.md
Browse files
README.md
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- qwen2
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# tool_call_validator_zh
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> LoRA fine-tune of Qwen2.5-3B-Instruct
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> Traditional Chinese tool-call validator (guardrail) — LoRA fine-tune of Qwen2.5-3B-Instruct
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## 中文說明
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本模型是針對 **Tool Call Validation** 場景微調的繁體中文模型。基於 [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) 用 LoRA 訓練,能夠:
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1. 讀取使用者請求(user prompt)與多個候選工具的 description
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2. 透過語意比對選出最適合的工具,或在無合適工具時拒絕匹配
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- qwen2
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# tool_call_validator_zh
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> 中文 (繁體) Tool Call 驗證 / Guardrail 模型 · LoRA fine-tune of Qwen2.5-3B-Instruct
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> Traditional Chinese tool-call validator (guardrail) — LoRA fine-tune of Qwen2.5-3B-Instruct
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---
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## 中文說明
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本模型是針對 **Tool Call Validation / Guardrail** 場景微調的繁體中文模型。基於 [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) 用 LoRA 訓練,能夠:
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1. 讀取使用者請求(user prompt)與多個候選工具的 description
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2. 透過語意比對選出最適合的工具,或在無合適工具時拒絕匹配
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