README.md

Quantization made by Richard Erkhov.

[Github](https://github.com/RichardErkhov)

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gemma2-2b-fraud - GGUF
- Model creator: https://huggingface.co/jslin09/
- Original model: https://huggingface.co/jslin09/gemma2-2b-fraud/


| Name | Quant method | Size |
| ---- | ---- | ---- |
| [gemma2-2b-fraud.Q2_K.gguf](https://huggingface.co/RichardErkhov/jslin09_-_gemma2-2b-fraud-gguf/blob/main/gemma2-2b-fraud.Q2_K.gguf) | Q2_K | 1.15GB |
| [gemma2-2b-fraud.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/jslin09_-_gemma2-2b-fraud-gguf/blob/main/gemma2-2b-fraud.Q3_K_S.gguf) | Q3_K_S | 1.27GB |
| [gemma2-2b-fraud.Q3_K.gguf](https://huggingface.co/RichardErkhov/jslin09_-_gemma2-2b-fraud-gguf/blob/main/gemma2-2b-fraud.Q3_K.gguf) | Q3_K | 1.36GB |
| [gemma2-2b-fraud.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/jslin09_-_gemma2-2b-fraud-gguf/blob/main/gemma2-2b-fraud.Q3_K_M.gguf) | Q3_K_M | 1.36GB |
| [gemma2-2b-fraud.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/jslin09_-_gemma2-2b-fraud-gguf/blob/main/gemma2-2b-fraud.Q3_K_L.gguf) | Q3_K_L | 1.44GB |
| [gemma2-2b-fraud.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/jslin09_-_gemma2-2b-fraud-gguf/blob/main/gemma2-2b-fraud.IQ4_XS.gguf) | IQ4_XS | 1.47GB |
| [gemma2-2b-fraud.Q4_0.gguf](https://huggingface.co/RichardErkhov/jslin09_-_gemma2-2b-fraud-gguf/blob/main/gemma2-2b-fraud.Q4_0.gguf) | Q4_0 | 1.52GB |
| [gemma2-2b-fraud.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/jslin09_-_gemma2-2b-fraud-gguf/blob/main/gemma2-2b-fraud.IQ4_NL.gguf) | IQ4_NL | 1.53GB |
| [gemma2-2b-fraud.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/jslin09_-_gemma2-2b-fraud-gguf/blob/main/gemma2-2b-fraud.Q4_K_S.gguf) | Q4_K_S | 1.53GB |
| [gemma2-2b-fraud.Q4_K.gguf](https://huggingface.co/RichardErkhov/jslin09_-_gemma2-2b-fraud-gguf/blob/main/gemma2-2b-fraud.Q4_K.gguf) | Q4_K | 1.59GB |
| [gemma2-2b-fraud.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/jslin09_-_gemma2-2b-fraud-gguf/blob/main/gemma2-2b-fraud.Q4_K_M.gguf) | Q4_K_M | 1.59GB |
| [gemma2-2b-fraud.Q4_1.gguf](https://huggingface.co/RichardErkhov/jslin09_-_gemma2-2b-fraud-gguf/blob/main/gemma2-2b-fraud.Q4_1.gguf) | Q4_1 | 1.64GB |
| [gemma2-2b-fraud.Q5_0.gguf](https://huggingface.co/RichardErkhov/jslin09_-_gemma2-2b-fraud-gguf/blob/main/gemma2-2b-fraud.Q5_0.gguf) | Q5_0 | 1.75GB |
| [gemma2-2b-fraud.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/jslin09_-_gemma2-2b-fraud-gguf/blob/main/gemma2-2b-fraud.Q5_K_S.gguf) | Q5_K_S | 1.75GB |
| [gemma2-2b-fraud.Q5_K.gguf](https://huggingface.co/RichardErkhov/jslin09_-_gemma2-2b-fraud-gguf/blob/main/gemma2-2b-fraud.Q5_K.gguf) | Q5_K | 1.79GB |
| [gemma2-2b-fraud.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/jslin09_-_gemma2-2b-fraud-gguf/blob/main/gemma2-2b-fraud.Q5_K_M.gguf) | Q5_K_M | 1.79GB |
| [gemma2-2b-fraud.Q5_1.gguf](https://huggingface.co/RichardErkhov/jslin09_-_gemma2-2b-fraud-gguf/blob/main/gemma2-2b-fraud.Q5_1.gguf) | Q5_1 | 1.87GB |
| [gemma2-2b-fraud.Q6_K.gguf](https://huggingface.co/RichardErkhov/jslin09_-_gemma2-2b-fraud-gguf/blob/main/gemma2-2b-fraud.Q6_K.gguf) | Q6_K | 2.0GB |
| [gemma2-2b-fraud.Q8_0.gguf](https://huggingface.co/RichardErkhov/jslin09_-_gemma2-2b-fraud-gguf/blob/main/gemma2-2b-fraud.Q8_0.gguf) | Q8_0 | 2.59GB |




Original model description:
---
license: gemma
datasets:
- jslin09/Fraud_Case_Verdicts
language:
- zh
base_model:
- google/gemma-2-2b
pipeline_tag: text-generation
text-generation:
  parameters:
    max_length: 400
    max_new_tokens: 400
    do_sample: true
    temperature: 0.75
    top_k: 50
    top_p: 0.9
tags:
- legal
widget:
- text: 王大明意圖為自己不法所有，基於竊盜之犯意，
  example_title: 生成竊盜罪之犯罪事實
- text: 騙人布意圖為自己不法所有，基於詐欺取財之犯意，
  example_title: 生成詐欺罪之犯罪事實
- text: 梅友乾明知其無資力支付酒店消費，亦無付款意願，竟意圖為自己不法之所有，
  example_title: 生成吃霸王餐之詐欺犯罪事實
- text: 闕很大明知金融帳戶之存摺、提款卡及密碼係供自己使用之重要理財工具，
  example_title: 生成賣帳戶幫助詐欺犯罪事實
- text: 通訊王明知近來盛行以虛設、租賃、借用或買賣行動電話人頭門號之方式，供詐騙集團作為詐欺他人交付財物等不法用途，
  example_title: 生成賣電話SIM卡之幫助詐欺犯罪事實
- text: 趙甲王基於行使偽造特種文書及詐欺取財之犯意，
  example_title: 偽造特種文書(契約、車牌等)詐財
library_name: transformers
---
# 判決書「犯罪事實」欄草稿自動生成
本模型是以司法院公開之「詐欺」案件判決書做成之資料集，基於 [Google Gemma2:2b](https://huggingface.co/google/gemma-2-2b) 模型進行微調訓練，可以自動生成詐欺及竊盜案件之犯罪事實段落之草稿。資料集之資料範圍從100年1月1日至110年12月31日，所蒐集到的原始資料共有 74823 篇（判決以及裁定），我們只取判決書的「犯罪事實」欄位內容，並把這原始的資料分成三份，用於訓練的資料集有59858篇，約佔原始資料的80%，剩下的20%，則是各分配10%給驗證集（7482篇），10%給測試集（7483篇）。在本網頁進行測試時，請在模型載入完畢並生成第一小句後，持續按下Compute按鈕，就能持續生成文字。或是輸入自己想要測試的資料到文字框中進行測試。或是可以到[這裡](https://huggingface.co/spaces/jslin09/legal_document_drafting)有更完整的使用體驗。

# 使用範例
如果要在自己的程式中調用本模型，可以參考下列的 Python 程式碼，藉由呼叫 API 的方式來生成刑事判決書「犯罪事實」欄的內容。
<details>
  <summary> 點擊後展開 </summary>
<pre>
  <code>
import requests, json
from time import sleep
from tqdm.auto import tqdm, trange

API_URL = "https://api-inference.huggingface.co/models/jslin09/gemma2-2b-fraud"
API_TOKEN = 'XXXXXXXXXXXXXXX' # 調用模型的 API token
headers = {"Authorization": f"Bearer {API_TOKEN}"} 

def query(payload):
    response = requests.post(API_URL, headers=headers, json=payload)
    return json.loads(response.content.decode("utf-8"))

prompt = "森上梅前明知其無資力支付酒店消費，亦無付款意願，竟意圖為自己不法之所有，"
query_dict = {
	"inputs": prompt,
}
text_len = 300
t = trange(text_len, desc= '生成例稿', leave=True)
for i in t:
    response = query(query_dict)
    try:
        response_text = response[0]['generated_text']
        query_dict["inputs"] = response_text
        t.set_description(f"{i}: {response[0]['generated_text']}")
        t.refresh()
    except KeyError:
        sleep(30) # 如果伺服器太忙無回應，等30秒後再試。
        pass
print(response[0]['generated_text'])
</code>
</pre>
</details>

或是，你要使用 transformers 套件來實作你的程式，將本模型下載至你本地端的電腦中執行，可以參考下列程式碼:
<details>
  <summary> 點擊後展開 </summary>
<pre>
  <code>
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jslin09/gemma2-2b-fraud")
model = AutoModelForCausalLM.from_pretrained("jslin09/gemma2-2b-fraud")

</code>
</pre>
</details>

# 致謝
微調本模型所需要的算力，是由[評律網](https://www.pingluweb.com.tw/)提供 NVIDIA H100。特此致謝。

# 引文訊息

```
@misc{lin2024legal,
      title={Legal Documents Drafting with Fine-Tuned Pre-Trained Large Language Model}, 
      author={Chun-Hsien Lin and Pu-Jen Cheng},
      year={2024},
      eprint={2406.04202},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
      url = {https://arxiv.org/abs/2406.04202}
}
```