Instructions to use cycloneboy/SLM-SQL-Base-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cycloneboy/SLM-SQL-Base-1B with Transformers:

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

pipe = pipeline("text-generation", model="cycloneboy/SLM-SQL-Base-1B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cycloneboy/SLM-SQL-Base-1B")
model = AutoModelForCausalLM.from_pretrained("cycloneboy/SLM-SQL-Base-1B")
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 cycloneboy/SLM-SQL-Base-1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cycloneboy/SLM-SQL-Base-1B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cycloneboy/SLM-SQL-Base-1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cycloneboy/SLM-SQL-Base-1B

SGLang

How to use cycloneboy/SLM-SQL-Base-1B 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 "cycloneboy/SLM-SQL-Base-1B" \
    --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": "cycloneboy/SLM-SQL-Base-1B",
		"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 "cycloneboy/SLM-SQL-Base-1B" \
        --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": "cycloneboy/SLM-SQL-Base-1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use cycloneboy/SLM-SQL-Base-1B with Docker Model Runner:
```
docker model run hf.co/cycloneboy/SLM-SQL-Base-1B
```

Add model card for SLM-SQL

by nielsr HF Staff - opened Jul 31, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+46

-3

nielsr

Jul 31, 2025

This PR adds a comprehensive model card for the SLM-SQL model.
It includes:

A link to the paper: SLM-SQL: An Exploration of Small Language Models for Text-to-SQL
The appropriate pipeline_tag: text-generation, making the model discoverable on the Hugging Face Hub for Text-to-SQL tasks.
The library_name: transformers, indicating likely compatibility with the Hugging Face Transformers library.

The model card's content summarizes the model's purpose, methodology, and key findings based on the paper's abstract. It also notes that the authors plan to release the dataset, model, and code on GitHub, and includes a generic, commented-out usage example for transformers pending the official release of the code.

Add model card for SLM-SQL29002d95

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README.md

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