Instructions to use LoudAI/Mistral-7B-Instruct-SQL-ian with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LoudAI/Mistral-7B-Instruct-SQL-ian with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LoudAI/Mistral-7B-Instruct-SQL-ian")
model = AutoModelForCausalLM.from_pretrained("LoudAI/Mistral-7B-Instruct-SQL-ian", device_map="auto")
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 Settings

vLLM

How to use LoudAI/Mistral-7B-Instruct-SQL-ian with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/LoudAI/Mistral-7B-Instruct-SQL-ian

SGLang

How to use LoudAI/Mistral-7B-Instruct-SQL-ian 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 "LoudAI/Mistral-7B-Instruct-SQL-ian" \
    --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": "LoudAI/Mistral-7B-Instruct-SQL-ian",
		"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 "LoudAI/Mistral-7B-Instruct-SQL-ian" \
        --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": "LoudAI/Mistral-7B-Instruct-SQL-ian",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LoudAI/Mistral-7B-Instruct-SQL-ian with Docker Model Runner:
```
docker model run hf.co/LoudAI/Mistral-7B-Instruct-SQL-ian
```

Mistral-7B-Instruct-SQL-ian / README.md

kubwa(LoudAI)

Update README.md

333eadc verified about 2 years ago

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	---
	license: apache-2.0
	library_name: transformers
	---

	# Mistral-7B-Instruct-SQL-ian

	## About the Model

	<!-- Provide a longer summary of what this model is. -->

	This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.3. https://huggingface.co/datasets/gretelai/synthetic_text_to_sql

	- Model Name: Mistral-7B-Instruct-SQL-ian
	- Developed by: kubwa
	- Base Model Name: mistralai/Mistral-7B-Instruct-v0.3
	- Base Model URL: [Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3)
	- Base Model Description: The Mistral-7B-Instruct-v0.3 Large Language Model (LLM) is an instruct fine-tuned version of the Mistral-7B-v0.3.
	Mistral-7B-v0.3 has the following changes compared to Mistral-7B-v0.2

	- Extended vocabulary to 32768
	- Supports v3 Tokenizer
	- Supports function calling
	- Dataset Name: gretelai/synthetic_text_to_sql
	- Dataset URL: [synthetic_text_to_sql](https://huggingface.co/datasets/gretelai/synthetic_text_to_sql)
	- Dataset Description: gretelai/synthetic_text_to_sql is a rich dataset of high quality synthetic Text-to-SQL samples, designed and generated using Gretel Navigator, and released under Apache 2.0.

	## Prompt Template

	```
	<s>
	### Instruction:
	{question}

	### Context:
	{schema}

	### Response:
	```

	## How to Use it

	```python
	from transformers import AutoTokenizer, AutoModelForCausalLM
	import torch

	model = AutoModelForCausalLM.from_pretrained("kubwa/Mistral-7B-Instruct-SQL-ian")
	tokenizer = AutoTokenizer.from_pretrained("kubwa/Mistral-7B-Instruct-SQL-ian",use_fast=False)

	text = """<s>
	### Instruction:
	What is the total volume of timber sold by each salesperson, sorted by salesperson?

	### Context:
	CREATE TABLE salesperson (salesperson_id INT, name TEXT, region TEXT); INSERT INTO salesperson (salesperson_id, name, region) VALUES (1, 'John Doe', 'North'), (2, 'Jane Smith', 'South'); CREATE TABLE timber_sales (sales_id INT, salesperson_id INT, volume REAL, sale_date DATE); INSERT INTO timber_sales (sales_id, salesperson_id, volume, sale_date) VALUES (1, 1, 120, '2021-01-01'), (2, 1, 150, '2021-02-01'), (3, 2, 180, '2021-01-01');

	### Response:
	"""

	device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
	model.to(device)

	inputs = tokenizer(text, return_tensors="pt")
	inputs = {key: value.to(device) for key, value in inputs.items()}

	outputs = model.generate(**inputs, max_new_tokens=300, pad_token_id=tokenizer.eos_token_id)

	print(tokenizer.decode(outputs[0], skip_special_tokens=True))

	```

	## Example Output

	```
	### Instruction:
	What is the total volume of timber sold by each salesperson, sorted by salesperson?

	### Context:
	CREATE TABLE salesperson (salesperson_id INT, name TEXT, region TEXT); INSERT INTO salesperson (salesperson_id, name, region) VALUES (1, 'John Doe', 'North'), (2, 'Jane Smith', 'South'); CREATE TABLE timber_sales (sales_id INT, salesperson_id INT, volume REAL, sale_date DATE); INSERT INTO timber_sales (sales_id, salesperson_id, volume, sale_date) VALUES (1, 1, 120, '2021-01-01'), (2, 1, 150, '2021-02-01'), (3, 2, 180, '2021-01-01');

	### Response:
	SELECT salesperson.name, SUM(timber_sales.volume) as total_volume FROM salesperson JOIN timber_sales ON salesperson.salesperson_id = timber_sales.salesperson_id GROUP BY salesperson.name ORDER BY total_volume DESC;
	```

	## Hardware and Software
	- Training Hardware: 4 Tesla V100-PCIE-32GB GPUs

	## License
	- Apache-2.0