Instructions to use adamwhite625/gemma-2-2b-finetuned-text2sql with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use adamwhite625/gemma-2-2b-finetuned-text2sql with Transformers:

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

pipe = pipeline("text-generation", model="adamwhite625/gemma-2-2b-finetuned-text2sql")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("adamwhite625/gemma-2-2b-finetuned-text2sql")
model = AutoModelForCausalLM.from_pretrained("adamwhite625/gemma-2-2b-finetuned-text2sql")
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 adamwhite625/gemma-2-2b-finetuned-text2sql with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "adamwhite625/gemma-2-2b-finetuned-text2sql"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "adamwhite625/gemma-2-2b-finetuned-text2sql",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/adamwhite625/gemma-2-2b-finetuned-text2sql

SGLang

How to use adamwhite625/gemma-2-2b-finetuned-text2sql 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 "adamwhite625/gemma-2-2b-finetuned-text2sql" \
    --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": "adamwhite625/gemma-2-2b-finetuned-text2sql",
		"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 "adamwhite625/gemma-2-2b-finetuned-text2sql" \
        --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": "adamwhite625/gemma-2-2b-finetuned-text2sql",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use adamwhite625/gemma-2-2b-finetuned-text2sql 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 adamwhite625/gemma-2-2b-finetuned-text2sql 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 adamwhite625/gemma-2-2b-finetuned-text2sql to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for adamwhite625/gemma-2-2b-finetuned-text2sql to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="adamwhite625/gemma-2-2b-finetuned-text2sql",
    max_seq_length=2048,
)

Docker Model Runner
How to use adamwhite625/gemma-2-2b-finetuned-text2sql with Docker Model Runner:
```
docker model run hf.co/adamwhite625/gemma-2-2b-finetuned-text2sql
```

Gemma-2-2B Text-to-SQL Expert

A specialized SQL generation model fine-tuned on 78k+ samples. Built with Unsloth for efficiency and speed.

Overview

This model is a fine-tuned version of Gemma-2-2B-Instruct, specialized in translating natural language questions into SQL queries. It has been trained to understand database schemas and generate precise SQL commands without conversational filler.

Model Architecture: Gemma 2 (2B Parameters)
Fine-tuning Method: QLoRA (via Unsloth)
Dataset: b-mc2/sql-create-context
Output: Raw SQL Query

Quick Start

1. Install Dependencies

pip install "unsloth[colab-new] @ git+[https://github.com/unslothai/unsloth.git](https://github.com/unslothai/unsloth.git)"
pip install --no-deps xformers "trl<0.9.0" peft accelerate bitsandbytes

2. Run Inference (Python)

from unsloth import FastLanguageModel
import torch

# 1. Load Model & Tokenizer
model_name = "adamwhite625/gemma-2-2b-finetuned-text2sql"
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = model_name,
    max_seq_length = 2048,
    dtype = None,
    load_in_4bit = True,
)
FastLanguageModel.for_inference(model)

# 2. Define Schema & Question
question = "Find the name and address of the employee who is the oldest."
schema = "CREATE TABLE employee (name VARCHAR, address VARCHAR, age INTEGER)"

# 3. Format Prompt (Strict Format)
prompt = f"""Write a SQL query to answer the following question given the database schema:
{question}
Schema:
{schema}"""

# 4. Generate SQL
inputs = tokenizer.apply_chat_template(
    [{"role": "user", "content": prompt}],
    add_generation_prompt = True,
    return_tensors = "pt",
).to("cuda")

outputs = model.generate(input_ids=inputs, max_new_tokens=128, use_cache=True)
result = tokenizer.batch_decode(outputs)

# Print only the generated SQL (extracting from the response)
print(result[0].split("<start_of_turn>model")[-1].replace("<end_of_turn>", "").strip())

Example Output

Input:

Find the name and address of the employee who is the oldest. Schema: CREATE TABLE employee (name VARCHAR, address VARCHAR, age INTEGER)

Output:

SELECT name, address FROM employee ORDER BY age DESC LIMIT 1

Downloads last month: 3

Safetensors

Model size

3B params

Tensor type

BF16

Model tree for adamwhite625/gemma-2-2b-finetuned-text2sql

Base model

unsloth/gemma-2-2b-it-bnb-4bit

Finetuned

(170)

this model