Instructions to use yasserrmd/Text2SQL-1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yasserrmd/Text2SQL-1.5B with Transformers:

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

pipe = pipeline("text-generation", model="yasserrmd/Text2SQL-1.5B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("yasserrmd/Text2SQL-1.5B")
model = AutoModelForCausalLM.from_pretrained("yasserrmd/Text2SQL-1.5B")
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]:]))

Inference
Local Apps Settings

vLLM

How to use yasserrmd/Text2SQL-1.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yasserrmd/Text2SQL-1.5B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yasserrmd/Text2SQL-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/yasserrmd/Text2SQL-1.5B

SGLang

How to use yasserrmd/Text2SQL-1.5B 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 "yasserrmd/Text2SQL-1.5B" \
    --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": "yasserrmd/Text2SQL-1.5B",
		"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 "yasserrmd/Text2SQL-1.5B" \
        --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": "yasserrmd/Text2SQL-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use yasserrmd/Text2SQL-1.5B with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for yasserrmd/Text2SQL-1.5B to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for yasserrmd/Text2SQL-1.5B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for yasserrmd/Text2SQL-1.5B to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="yasserrmd/Text2SQL-1.5B",
    max_seq_length=2048,
)

Docker Model Runner
How to use yasserrmd/Text2SQL-1.5B with Docker Model Runner:
```
docker model run hf.co/yasserrmd/Text2SQL-1.5B
```

Text2SQL-1.5B / README.md

yasserrmd

Update README.md

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	---
	base_model: unsloth/qwen2.5-coder-1.5b-instruct-bnb-4bit
	tags:
	- text-generation-inference
	- transformers
	- unsloth
	- qwen2
	- trl
	- sft
	license: apache-2.0
	language:
	- en
	datasets:
	- gretelai/synthetic_text_to_sql
	---

	# Text2SQL-1.5B Model

	## Overview
	Text2SQL-1.5B is a powerful natural language to SQL model designed to convert user queries into structured SQL statements. It supports complex multi-table queries and ensures high accuracy in text-to-SQL conversion.

	## System Instruction
	To ensure consistency in model outputs, use the following system instruction:

	> **Always separate code and explanation. Return SQL code in a separate block, followed by the explanation in a separate paragraph. Use markdown triple backticks (` ```sql ` for SQL) to format the code properly. Write the SQL query first in a separate code block. Then, explain the query in plain text. Do not merge them into one response.

	For json result use the following
	> **Always separate SQL code and explanation. Return SQL queries in a JSON format containing two keys: 'query' and 'explanation'. The response should strictly follow the structure: {\"query\": \"SQL_QUERY_HERE\", \"explanation\": \"EXPLANATION_HERE\"}. The 'query' key should contain only the SQL statement, and the 'explanation' key should provide a plain-text explanation of the query. Do not merge them into one response.

	## Prompt Format
	The prompt format should include both the user query and the table structure using a `CREATE TABLE` statement. The expected message format should be:

	```json
	messages = [
	{"role": "system", "content": "Always separate code and explanation. Return SQL code in a separate block, followed by the explanation in a separate paragraph. Use markdown triple backticks (```sql for SQL) to format the code properly. Write the SQL query first in a separate code block. Then, explain the query in plain text. Do not merge them into one response. The query should always include the table structure using a CREATE TABLE statement before executing the main SQL query."},
	{"role": "user", "content": "Show the total sales for each customer who has spent more than $50,000."},
	{"role": "user", "content": "
	CREATE TABLE sales (
	id INT PRIMARY KEY,
	customer_id INT,
	total_amount DECIMAL(10,2),
	FOREIGN KEY (customer_id) REFERENCES customers(id)
	);

	CREATE TABLE customers (
	id INT PRIMARY KEY,
	name VARCHAR(255)
	);
	"}
	]
	```

	## Model Usage

	### Using the Model for Text-to-SQL Conversion
	The following code demonstrates how to use the model to convert natural language queries into SQL statements:

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

	# Load tokenizer and model
	tokenizer = AutoTokenizer.from_pretrained("yasserrmd/Text2SQL-1.5B")
	model = AutoModelForCausalLM.from_pretrained("yasserrmd/Text2SQL-1.5B")

	# Define the pipeline
	pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

	# Define system instruction
	system_instruction = "Always separate code and explanation. Return SQL code in a separate block, followed by the explanation in a separate paragraph. Use markdown triple backticks (```sql for SQL) to format the code properly. Write the SQL query first in a separate code block. Then, explain the query in plain text. Do not merge them into one response. The query should always include the table structure using a CREATE TABLE statement before executing the main SQL query."

	# Define user query
	user_query = "Show the total sales for each customer who has spent more than $50,000.
	CREATE TABLE sales (
	id INT PRIMARY KEY,
	customer_id INT,
	total_amount DECIMAL(10,2),
	FOREIGN KEY (customer_id) REFERENCES customers(id)
	);

	CREATE TABLE customers (
	id INT PRIMARY KEY,
	name VARCHAR(255)
	);
	"

	# Define messages for input
	messages = [
	{"role": "system", "content": system_instruction},
	{"role": "user", "content": user_query},
	]

	# Generate SQL output
	response = pipe(messages)


	# Print the generated SQL query
	print(response[0]['generated_text'])
	```





	# Uploaded model

	- Developed by: yasserrmd
	- License: apache-2.0
	- Finetuned from model : unsloth/qwen2.5-coder-1.5b-instruct-bnb-4bit

	This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.

	[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)