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ai-modelscope
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README.md
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README.md
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license: Apache License 2.0
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#model-type:
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##如 gpt、phi、llama、chatglm、baichuan 等
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#- gpt
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#domain:
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##如 nlp、cv、audio、multi-modal
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#- nlp
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#language:
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##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa
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#- cn
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#metrics:
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##如 CIDEr、Blue、ROUGE 等
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#- CIDEr
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#tags:
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##各种自定义，包括 pretrained、fine-tuned、instruction-tuned、RL-tuned 等训练方法和其他
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#- pretrained
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#tools:
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##如 vllm、fastchat、llamacpp、AdaSeq 等
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#- vllm
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license: cc-by-sa-4.0
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library_name: transformers
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pipeline_tag: text-generation
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base_model: defog/sqlcoder-7b-2
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---
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### 当前模型的贡献者未提供更加详细的模型介绍。模型文件和权重，可浏览“模型文件”页面获取。
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#### 您可以通过如下git clone命令，或者ModelScope SDK来下载模型
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SDK下载
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```bash
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#安装ModelScope
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pip install modelscope
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# QuantFactory/sqlcoder-7b-2-GGUF
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This is quantized version of [defog/sqlcoder-7b-2](https://huggingface.co/defog/sqlcoder-7b-2) created using llama.cpp
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# Model Card for SQLCoder-7B-2
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A capable large language model for natural language to SQL generation.
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## Model Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Developed by:** [Defog, Inc](https://defog.ai)
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- **Model type:** [Text to SQL]
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- **License:** [CC-by-SA-4.0]
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- **Finetuned from model:** [CodeLlama-7B]
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### Model Sources [optional]
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- [**HuggingFace:**](https://huggingface.co/defog/sqlcoder-70b-alpha)
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- [**GitHub:**](https://github.com/defog-ai/sqlcoder)
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- [**Demo:**](https://defog.ai/sqlcoder-demo/)
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## Uses
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This model is intended to be used by non-technical users to understand data inside their SQL databases. It is meant as an analytics tool, and not as a database admin tool.
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This model has not been trained to reject malicious requests from users with write access to databases, and should only be used by users with read-only access.
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## How to Get Started with the Model
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Use the code [here](https://github.com/defog-ai/sqlcoder/blob/main/inference.py) to get started with the model.
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## Prompt
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Please use the following prompt for optimal results. Please remember to use `do_sample=False` and `num_beams=4` for optimal results.
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```
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```python
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#SDK模型下载
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from modelscope import snapshot_download
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model_dir = snapshot_download('QuantFactory/sqlcoder-7b-2-GGUF')
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```
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Git下载
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```
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#Git模型下载
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git clone https://www.modelscope.cn/QuantFactory/sqlcoder-7b-2-GGUF.git
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### Task
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Generate a SQL query to answer [QUESTION]{user_question}[/QUESTION]
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### Database Schema
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The query will run on a database with the following schema:
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{table_metadata_string_DDL_statements}
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### Answer
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Given the database schema, here is the SQL query that [QUESTION]{user_question}[/QUESTION]
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[SQL]
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```
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<p style="color: lightgrey;">如果您是本模型的贡献者，我们邀请您根据<a href="https://modelscope.cn/docs/ModelScope%E6%A8%A1%E5%9E%8B%E6%8E%A5%E5%85%A5%E6%B5%81%E7%A8%8B%E6%A6%82%E8%A7%88" style="color: lightgrey; text-decoration: underline;">模型贡献文档</a>，及时完善模型卡片内容。</p>
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## Evaluation
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This model was evaluated on [SQL-Eval](https://github.com/defog-ai/sql-eval), a PostgreSQL based evaluation framework developed by Defog for testing and alignment of model capabilities.
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You can read more about the methodology behind SQLEval [here](https://defog.ai/blog/open-sourcing-sqleval/).
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### Results
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We classified each generated question into one of 6 categories. The table displays the percentage of questions answered correctly by each model, broken down by category.
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| | date | group_by | order_by | ratio | join | where |
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| -------------- | ---- | -------- | -------- | ----- | ---- | ----- |
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| sqlcoder-70b | 96 | 91.4 | 97.1 | 85.7 | 97.1 | 91.4 |
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| sqlcoder-7b-2 | 96 | 91.4 | 94.3 | 91.4 | 94.3 | 77.1 |
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| sqlcoder-34b | 80 | 94.3 | 85.7 | 77.1 | 85.7 | 80 |
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| gpt-4 | 72 | 94.3 | 97.1 | 80 | 91.4 | 80 |
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| gpt-4-turbo | 76 | 91.4 | 91.4 | 62.8 | 88.6 | 77.1 |
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| natural-sql-7b | 56 | 88.6 | 85.7 | 60 | 88.6 | 80 |
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| sqlcoder-7b | 64 | 82.9 | 74.3 | 54.3 | 74.3 | 74.3 |
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| gpt-3.5 | 72 | 77.1 | 82.8 | 34.3 | 65.7 | 71.4 |
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| claude-2 | 52 | 71.4 | 74.3 | 57.1 | 65.7 | 62.9 |
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## Model Card Contact
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Contact us on X at [@defogdata](https://twitter.com/defogdata), or on email at [founders@defog.ai](mailto:founders@defog.ai)
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