Model Card for Model lianghsun/Llama-3.2-Taiwan-Legal-3B-Instruct

基於 meta-llama/Llama-3.2-3B-Instruct 模型，透過中華民國台灣法律條文及判決書等相關資料集進行微調。

Model Details

Model Description

基於 meta-llama/Llama-3.2-3B-Instruct 模型，此微調過程使用了來自中華民國台灣的法律條文與相關判決書資料集，以提升模型在法律領域的專業知識與應用能力。這些資料集涵蓋了法律條文的結構、判決書的格式，以及法庭上常見的法律語言與術語，使模型能夠更準確地理解和處理與台灣法律體系相關的問題。經過這些微調，模型將能夠更好地為法律專業人士提供幫助，並在台灣法制框架內提供更精準的回應與建議。

• Developed by: Huang Liang Hsun
• Model type: LlamaForCausalLM
• Language(s) (NLP): 主要處理繁體中文（zh-tw），針對中華民國台灣的法律用語與判決書進行微調。
• License: llama3.2
• Finetuned from model: meta-llama/Llama-3.2-3B-Instruct

Model Sources [optional]

• Repository: lianghsun/Llama-3.2-Taiwan-Legal-3B-Instruct
• Demo [optional]: (WIP)

Uses

Direct Use

[More Information Needed]

Downstream Use [optional]

[More Information Needed]

Out-of-Scope Use

[More Information Needed]

Bias, Risks, and Limitations

模型在生成法律條文和判決書內容時，可能會生成虛構或不存在的法條或判決書內容，這是模型的內在限制之一。使用者在參考這些資料時，應謹慎檢查生成的內容，並避免將模型輸出視為法律依據。建議在實際應用中，將模型生成的結果與可靠的法律見解和來源進行比對，確保準確性、合法性和適用性。

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

Training Details

Training Data

• lianghsun/tw-legal-nlp
• lianghsun/tw-structured-law-article
• [lianghsun/tw-legal-synthetic-qa]
• [lianghsun/tw-law-article-qa]
• [lianghsun/tw-judgment-qa]
• [lianghsun/tw-bar-examination-2020-chat]
• [lianghsun/tw-law-exam-choice]
• [lianghsun/tw-emergency-medicine-bench]

Training Procedure

Preprocessing

無。基本上我們並沒有針對 meta-llama/Llama-3.2-3B-Instruct 做任何的預訓練或更改其模型架構；Tokenizer 也是採用原生所提供的。

Training Hyperparameters

• Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

[More Information Needed]

Metrics

[More Information Needed]

Results

[More Information Needed]

Summary

WIP

Model Examination [optional]

[More Information Needed]

Environmental Impact

• Hardware Type: 8 x NVIDIA A100 40GB
• Hours used: 6.03 hours
• Cloud Provider: Google Cloud Platform
• Compute Region: us-central1-c
• Carbon Emitted: 0.86 kgCO$_2$eq

Technical Specifications [optional]

Model Architecture and Objective

[More Information Needed]

Compute Infrastructure

[More Information Needed]

Hardware

• 8 x NVIDIA A100 40GB

Software

• hiyouga/LLaMA-Factory

Citation

無。

Glossary [optional]

[More Information Needed]

More Information [optional]

[More Information Needed]

Model Card Authors [optional]

Huang Liang Hsun

Model Card Contact

Huang Liang Hsun

Framework versions

• PEFT 0.12.0