Instructions to use fahmiaziz/qwen3-1.7B-text2sql with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fahmiaziz/qwen3-1.7B-text2sql with Transformers:

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

pipe = pipeline("text-generation", model="fahmiaziz/qwen3-1.7B-text2sql")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("fahmiaziz/qwen3-1.7B-text2sql")
model = AutoModelForCausalLM.from_pretrained("fahmiaziz/qwen3-1.7B-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

vLLM

How to use fahmiaziz/qwen3-1.7B-text2sql with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fahmiaziz/qwen3-1.7B-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": "fahmiaziz/qwen3-1.7B-text2sql",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/fahmiaziz/qwen3-1.7B-text2sql

SGLang

How to use fahmiaziz/qwen3-1.7B-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 "fahmiaziz/qwen3-1.7B-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": "fahmiaziz/qwen3-1.7B-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 "fahmiaziz/qwen3-1.7B-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": "fahmiaziz/qwen3-1.7B-text2sql",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use fahmiaziz/qwen3-1.7B-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 fahmiaziz/qwen3-1.7B-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 fahmiaziz/qwen3-1.7B-text2sql to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for fahmiaziz/qwen3-1.7B-text2sql to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="fahmiaziz/qwen3-1.7B-text2sql",
    max_seq_length=2048,
)

Docker Model Runner
How to use fahmiaziz/qwen3-1.7B-text2sql with Docker Model Runner:
```
docker model run hf.co/fahmiaziz/qwen3-1.7B-text2sql
```

Fine-tuning Qwen3-1.7B for Text-to-SQL Task

This project demonstrates the fine-tuning of the Qwen3-1.7B language model using a combined and preprocessed dataset for Text-to-SQL generation. The goal is to train the model to generate SQL queries from natural language questions given database schemas.

Dataset

We used the fahmiaziz/text2sql-dataset, which merges examples from:

Wikisql
Bird
Spider
Synthetic SQL samples

Before training, the dataset was cleaned and filtered by:

Removing DDL/DML examples (INSERT, UPDATE, DELETE, etc.)
Deduplicating examples based on semantic hashing of both SQL and questions
Filtering only SELECT-style analytical queries

Training Format

Since Qwen3 models require a two-part output (<think> + final answer), and our dataset does not contain intermediate reasoning, we left the <think> section empty during fine-tuning.

Example Format:


<|im_start|>system
Given the database schema and the user question, generate the corresponding SQL query.
<|im_end|>
<|im_start|>user

\[SCHEMA]
CREATE TABLE Inclusive\_Housing (Property\_ID INT, Inclusive VARCHAR(10), Property\_Size INT);
INSERT INTO Inclusive\_Housing (Property\_ID, Inclusive, Property\_Size)
VALUES (1, 'Yes', 900), (2, 'No', 1100), (3, 'Yes', 800), (4, 'No', 1200);

\[QUESTION]
What is the average property size in inclusive housing areas?
<|im_end|>
<|im_start|>assistant
<think>

</think>
SELECT AVG(Property\_Size) FROM Inclusive\_Housing WHERE Inclusive = 'Yes';
<|im_end|>

Training Configuration

Due to hardware limitations, full model training was not possible. Instead, we applied LoRA (Low-Rank Adaptation) with the following configuration:

LoRA rank (r): 128
LoRA alpha: 256
Hardware: Kaggle T4 x2 GPUs

Training Hyperparameters

per_device_train_batch_size = 6,
gradient_accumulation_steps = 2,
warmup_steps = 5,
max_steps = 500,
num_train_epochs = 3,
learning_rate = 1e-4,
fp16 = not is_bf16_supported(),
bf16 = is_bf16_supported(),
logging_steps = 25,
optim = "adamw_8bit",
weight_decay = 0.01,
lr_scheduler_type = "linear",
seed = 3407,
output_dir = "outputs_v4",
dataset_text_field = "text",
max_seq_length = 1024,

Training Results

global_step=500,
training_loss=0.5783241882324218

Evaluation

We evaluated the model using Exact Match (EM) score on a manually selected sample of 100 examples. We get score 50%

Notes

In future iterations, we plan to:
- Add complex/long context schema
- Full Finetuning

Uploaded finetuned model

Developed by: fahmiaziz
License: apache-2.0
Finetuned from model : unsloth/Qwen3-1.7B

This qwen3 model was trained 2x faster with Unsloth and Huggingface's TRL library.

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