🚀 QuantFactory/TableLLM-13b-GGUF
这是一个专为处理表格数据操作任务而设计的强大量化模型,能根据不同场景生成代码解决方案或直接文本答案,有效满足实际办公场景需求。
🚀 快速开始
本项目是使用 llama.cpp 创建的 RUCKBReasoning/TableLLM-13b 的量化版本。
✨ 主要特性
模型基础信息
属性 |
详情 |
基础模型 |
TableLLM - 13b |
库名称 |
transformers |
任务标签 |
表格问答 |
许可证 |
llama2 |
数据集 |
RUCKBReasoning/TableLLM - SFT |
语言 |
英文 |
标签 |
表格、问答、代码 |
模型功能特性
TableLLM 系列包含 TableLLM - 7B 和 TableLLM - 13B 两个不同规模的模型,它们基于 CodeLlama - 7b - Instruct - hf 和 CodeLlama - 13b - Instruct - hf 进行微调。该模型能根据不同场景生成代码解决方案或直接文本答案来处理表格数据操作任务:
- 代码生成:用于处理嵌入电子表格的表格数据,常涉及表格的插入、删除、更新、查询、合并和绘图操作。
- 文本生成:用于处理嵌入文档的表格数据,常涉及短表格的查询操作。
评估结果
在多个基准测试中对 TableLLM 的代码解决方案生成能力和文本答案生成能力进行了评估,结果如下:
模型 |
WikiTQ |
TAT - QA |
FeTaQA |
OTTQA |
WikiSQL |
Spider |
自建基准测试 |
平均得分 |
TaPEX |
38.5 |
– |
– |
– |
83.9 |
15.0 |
/ |
45.8 |
TaPas |
31.5 |
– |
– |
74.2 |
23.1 |
/ |
|
42.92 |
TableLlama |
24.0 |
22.2 |
20.5 |
6.4 |
43.7 |
9.0 |
/ |
20.7 |
GPT3.5 |
58.5 |
72.1 |
71.2 |
60.8 |
81.7 |
67.4 |
77.1 |
69.8 |
GPT4 |
74.1 |
77.1 |
78.4 |
69.5 |
84.0 |
69.5 |
77.8 |
75.8 |
Llama2 - Chat (13B) |
48.8 |
49.6 |
67.7 |
61.5 |
– |
– |
– |
56.9 |
CodeLlama (13B) |
43.4 |
47.2 |
57.2 |
49.7 |
38.3 |
21.9 |
47.6 |
43.6 |
Deepseek - Coder (33B) |
6.5 |
11.0 |
7.1 |
7.4 |
72.5 |
58.4 |
73.9 |
33.8 |
StructGPT (GPT3.5) |
52.5 |
27.5 |
11.8 |
14.0 |
67.8 |
84.8 |
/ |
48.9 |
Binder (GPT3.5) |
61.6 |
12.8 |
6.8 |
5.1 |
78.6 |
52.6 |
/ |
42.5 |
DATER (GPT3.5) |
53.4 |
28.4 |
18.3 |
13.0 |
58.2 |
26.5 |
/ |
37.0 |
TableLLM - 7B (Ours) |
58.8 |
66.9 |
72.6 |
63.1 |
86.6 |
82.6 |
78.8 |
72.8 |
TableLLM - 13B (Ours) |
62.4 |
68.2 |
74.5 |
62.5 |
90.7 |
83.4 |
80.8 |
74.7 |
💻 使用示例
基础用法
代码解决方案
单表插入、删除、更新、查询和绘图操作的提示模板:
[INST]Below are the first few lines of a CSV file. You need to write a Python program to solve the provided question.
Header and first few lines of CSV file:
{csv_data}
Question: {question}[/INST]
两表合并操作的提示模板:
[INST]Below are the first few lines two CSV file. You need to write a Python program to solve the provided question.
Header and first few lines of CSV file 1:
{csv_data1}
Header and first few lines of CSV file 2:
{csv_data2}
Question: {question}[/INST]
csv_data
字段填充你提供的表格文件的前几行,示例如下:
Sex,Length,Diameter,Height,Whole weight,Shucked weight,Viscera weight,Shell weight,Rings
M,0.455,0.365,0.095,0.514,0.2245,0.101,0.15,15
M,0.35,0.265,0.09,0.2255,0.0995,0.0485,0.07,7
F,0.53,0.42,0.135,0.677,0.2565,0.1415,0.21,9
M,0.44,0.365,0.125,0.516,0.2155,0.114,0.155,10
I,0.33,0.255,0.08,0.205,0.0895,0.0395,0.055,7
文本答案
短表直接文本答案生成的提示模板:
[INST]Offer a thorough and accurate solution that directly addresses the Question outlined in the [Question].
### [Table Text]
{table_descriptions}
### [Table]
{table_in_csv}
### [Question]
{question}
### [Solution][INST/]
高级用法
如需了解更多关于如何使用 TableLLM 的详细信息,请参考我们的 GitHub 页面:https://github.com/TableLLM/TableLLM
📚 详细文档
关于 TableLLM 的更多详细信息,可参考原模型论文:TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios
📄 许可证
本项目使用 llama2 许可证。