Instructions to use circlelee/gemma-2-2b-it-nl2sql with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use circlelee/gemma-2-2b-it-nl2sql with Transformers:

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

pipe = pipeline("text-generation", model="circlelee/gemma-2-2b-it-nl2sql")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("circlelee/gemma-2-2b-it-nl2sql")
model = AutoModelForCausalLM.from_pretrained("circlelee/gemma-2-2b-it-nl2sql", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use circlelee/gemma-2-2b-it-nl2sql with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "circlelee/gemma-2-2b-it-nl2sql"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "circlelee/gemma-2-2b-it-nl2sql",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/circlelee/gemma-2-2b-it-nl2sql

SGLang

How to use circlelee/gemma-2-2b-it-nl2sql 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 "circlelee/gemma-2-2b-it-nl2sql" \
    --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": "circlelee/gemma-2-2b-it-nl2sql",
		"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 "circlelee/gemma-2-2b-it-nl2sql" \
        --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": "circlelee/gemma-2-2b-it-nl2sql",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use circlelee/gemma-2-2b-it-nl2sql with Docker Model Runner:
```
docker model run hf.co/circlelee/gemma-2-2b-it-nl2sql
```

gemma-2-2b-it-nl2sql / README.md

circlelee

Update README.md

dfe8a9e verified about 2 years ago

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---
base_model: unsloth/gemma-2-2b-it-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- gemma2
- trl
- sft
datasets:
- Clinton/Text-to-sql-v1
---
# Uploaded  model

- **Developed by:** circlelee
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-2-2b-it-bnb-4bit

This gemma2 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)






## Model Information

Summary description and brief definition of inputs and outputs.

### Description

This model is based on Gemma2 and is fine-tuned to generate SQL from Natural Language.

### Usage

Below we share some code snippets on how to get quickly started with running the model. First, install the Transformers library with:
```sh
pip install -U transformers
...
from transformers import AutoTokenizer, AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("circlelee/gemma-2-2b-it-nl2sql")
tokenizer = AutoTokenizer.from_pretrained("circlelee/gemma-2-2b-it-nl2sql", trust_remote_code=True)
table_schemas = "CREATE TABLE person ( name VARCHAR, age INTEGER, address VARCHAR )"
user_query = "people whoes ages are older than 27 and name starts with letter 'k'"
messages = [
        {"role": "user", "content": f"""Use the below SQL tables schemas paired with instruction that describes a task. make SQL query that appropriately completes the request for the provided tables. And make SQL query according the steps.
{table_schemas}
step 1. check columns that I want.
step 2. check condition that I want.
step 3. make SQL query to get every information that I want.
{user_query}
"""}
]
formated_messages = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, return_tensors="pt")
input_ids = tokenizer(formated_messages, return_tensors="pt")
outputs = model.generate(**input_ids, max_new_tokens=64)
print(tokenizer.decode(outputs[0]))
```

	---
	base_model: unsloth/gemma-2-2b-it-bnb-4bit
	language:
	- en
	license: apache-2.0
	tags:
	- text-generation-inference
	- transformers
	- unsloth
	- gemma2
	- trl
	- sft
	datasets:
	- Clinton/Text-to-sql-v1
	---

	# Uploaded model

	- Developed by: circlelee
	- License: apache-2.0
	- Finetuned from model : unsloth/gemma-2-2b-it-bnb-4bit

	This gemma2 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)






	## Model Information

	Summary description and brief definition of inputs and outputs.

	### Description

	This model is based on Gemma2 and is fine-tuned to generate SQL from Natural Language.

	### Usage

	Below we share some code snippets on how to get quickly started with running the model. First, install the Transformers library with:
	```sh
	pip install -U transformers
	...
	from transformers import AutoTokenizer, AutoModelForCausalLM

	model = AutoModelForCausalLM.from_pretrained("circlelee/gemma-2-2b-it-nl2sql")
	tokenizer = AutoTokenizer.from_pretrained("circlelee/gemma-2-2b-it-nl2sql", trust_remote_code=True)

	table_schemas = "CREATE TABLE person ( name VARCHAR, age INTEGER, address VARCHAR )"
	user_query = "people whoes ages are older than 27 and name starts with letter 'k'"
	messages = [
	{"role": "user", "content": f"""Use the below SQL tables schemas paired with instruction that describes a task. make SQL query that appropriately completes the request for the provided tables. And make SQL query according the steps.
	{table_schemas}
	step 1. check columns that I want.
	step 2. check condition that I want.
	step 3. make SQL query to get every information that I want.

	{user_query}
	"""}
	]

	formated_messages = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, return_tensors="pt")
	input_ids = tokenizer(formated_messages, return_tensors="pt")

	outputs = model.generate(**input_ids, max_new_tokens=64)
	print(tokenizer.decode(outputs[0]))
	```