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

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metadata

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 and Huggingface's TRL library.

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:

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]))