Instructions to use OneGate/OG-SQL-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OneGate/OG-SQL-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="OneGate/OG-SQL-7B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("OneGate/OG-SQL-7B") model = AutoModelForCausalLM.from_pretrained("OneGate/OG-SQL-7B") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use OneGate/OG-SQL-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "OneGate/OG-SQL-7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OneGate/OG-SQL-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/OneGate/OG-SQL-7B
- SGLang
How to use OneGate/OG-SQL-7B 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 "OneGate/OG-SQL-7B" \ --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": "OneGate/OG-SQL-7B", "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 "OneGate/OG-SQL-7B" \ --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": "OneGate/OG-SQL-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use OneGate/OG-SQL-7B with Docker Model Runner:
docker model run hf.co/OneGate/OG-SQL-7B
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license: cc-by-4.0
language:
- en
tags:
- Text-to-sql
---
# OGSQL-7B

### Model Description
OGSQL-7B was fine-tuned for the task of converting natural language text into SQL queries.
- **Model type**: Transformer
- **Language(s) (NLP)**: SQL (target language for generation)
- **Finetuned from model**: codellama 7b
## Use Case
OGSQL-7B is designed to facilitate the conversion of natural language queries into structured SQL commands, aiding in database querying without the need for manual SQL knowledge.
## How to Get Started with the Model
```python
# Example code to load and use the model
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
model_name = "OGSQL-7B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
def generate_sql(query):
inputs = tokenizer.encode(query, return_tensors="pt")
outputs = model.generate(inputs)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
# Example use
query = """
using this context:
-- Create Customers Table
CREATE TABLE Customers (
customer_id INTEGER PRIMARY KEY,
name TEXT NOT NULL,
email TEXT,
join_date DATE
);
-- Create Products Table
CREATE TABLE Products (
product_id INTEGER PRIMARY KEY,
name TEXT NOT NULL,
price DECIMAL(10, 2)
);
-- Create Orders Table
CREATE TABLE Orders (
order_id INTEGER PRIMARY KEY,
customer_id INTEGER,
product_id INTEGER,
order_date DATE,
quantity INTEGER,
total_price DECIMAL(10, 2),
FOREIGN KEY (customer_id) REFERENCES Customers(customer_id),
FOREIGN KEY (product_id) REFERENCES Products(product_id)
);
show me all the orders from last month , sort by date
"""
print(generate_sql(query))
```
## alternatively you can use this notebook:
[](https://colab.research.google.com/drive/1zfuzV3R1GQflHV_va03WArb8vhwPh_2T)
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