Instructions to use OneGate/OGSQL-Mistral-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OneGate/OGSQL-Mistral-7B with Transformers:

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

pipe = pipeline("text-generation", model="OneGate/OGSQL-Mistral-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OneGate/OGSQL-Mistral-7B")
model = AutoModelForCausalLM.from_pretrained("OneGate/OGSQL-Mistral-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use OneGate/OGSQL-Mistral-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OneGate/OGSQL-Mistral-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OneGate/OGSQL-Mistral-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OneGate/OGSQL-Mistral-7B

SGLang

How to use OneGate/OGSQL-Mistral-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/OGSQL-Mistral-7B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OneGate/OGSQL-Mistral-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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/OGSQL-Mistral-7B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OneGate/OGSQL-Mistral-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use OneGate/OGSQL-Mistral-7B with Docker Model Runner:
```
docker model run hf.co/OneGate/OGSQL-Mistral-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

OGSQL-Mistral7B

Model Description

OGSQL-Mistral7B was fine-tuned for the task of converting natural language text into SQL queries.

Model type: Mixture Of Experts (MoE)
Language(s) (NLP): SQL (target language for generation)
Finetuned from model: Mistral 7B instruct

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

# Example code to load and use the model
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model_name = "OGSQL-Mistral7B"
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:

Downloads last month: 15

Safetensors

Model size

7B params

Tensor type

F16

Model tree for OneGate/OGSQL-Mistral-7B

Quantizations

2 models