Instructions to use nobodynosql/sql_codellama with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nobodynosql/sql_codellama with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="nobodynosql/sql_codellama")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nobodynosql/sql_codellama")
model = AutoModelForCausalLM.from_pretrained("nobodynosql/sql_codellama")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use nobodynosql/sql_codellama with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nobodynosql/sql_codellama"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nobodynosql/sql_codellama",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/nobodynosql/sql_codellama

SGLang

How to use nobodynosql/sql_codellama with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "nobodynosql/sql_codellama" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nobodynosql/sql_codellama",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "nobodynosql/sql_codellama" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nobodynosql/sql_codellama",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use nobodynosql/sql_codellama with Docker Model Runner:
```
docker model run hf.co/nobodynosql/sql_codellama
```

sql_codellama 介绍

SQL-Codellama是一个用于text2SQL的模型。

模型底座

它是基于codellama模型构建的，该模型通过使用qlora进行训练。

训练数据

训练数据包含了spider、starcode等数据集。这个模型的目标是将自然语言查询转换为SQL查询。

功能

Text to SQL( 以下简称Text2SQL)，是将自然语言文本（Text）转换成结构化查询语言SQL的过程，属于自然语言处理-语义分析（Semantic Parsing）领域中的子任务。它的目的可以简单概括为：“打破人与结构化数据之间的壁垒”，即普通用户可以通过自然语言描述完成复杂数据库的查询工作，得到想要的结果。它通过学习语法、语义和查询意图来理解用户的问题，并根据对应的数据库结构生成相应的SQL查询语句。SQL-Codellama的训练过程经过了大量的数据预处理、特征提取和模型训练，以提高其准确性和性能。它可以应用于各种领域，如数据分析、数据库查询优化等。SQL-Codellama的设计和训练过程是为了使其能够处理复杂的查询，并产生高质量的SQL查询结果。它的目标是为用户提供准确、高效的文本到SQL转换，从而帮助用户更轻松地进行数据库查询和数据分析。

 git clone https://www.modelscope.cn/tomatoModelScope/sql_codellama.git

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