Instructions to use markrodrigo/Qwen-3.5-4B-Spatial-SQL-1.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use markrodrigo/Qwen-3.5-4B-Spatial-SQL-1.1 with Transformers:

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

pipe = pipeline("text-generation", model="markrodrigo/Qwen-3.5-4B-Spatial-SQL-1.1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("markrodrigo/Qwen-3.5-4B-Spatial-SQL-1.1")
model = AutoModelForCausalLM.from_pretrained("markrodrigo/Qwen-3.5-4B-Spatial-SQL-1.1", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use markrodrigo/Qwen-3.5-4B-Spatial-SQL-1.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "markrodrigo/Qwen-3.5-4B-Spatial-SQL-1.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "markrodrigo/Qwen-3.5-4B-Spatial-SQL-1.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/markrodrigo/Qwen-3.5-4B-Spatial-SQL-1.1

SGLang

How to use markrodrigo/Qwen-3.5-4B-Spatial-SQL-1.1 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 "markrodrigo/Qwen-3.5-4B-Spatial-SQL-1.1" \
    --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": "markrodrigo/Qwen-3.5-4B-Spatial-SQL-1.1",
		"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 "markrodrigo/Qwen-3.5-4B-Spatial-SQL-1.1" \
        --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": "markrodrigo/Qwen-3.5-4B-Spatial-SQL-1.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use markrodrigo/Qwen-3.5-4B-Spatial-SQL-1.1 with Docker Model Runner:
```
docker model run hf.co/markrodrigo/Qwen-3.5-4B-Spatial-SQL-1.1
```

Model Information

This model, Qwen-3.5-4B-Spatial-SQL-1.1, is a 4B, narrow use case, text to spatial SQL, lightly fine-tuned model. In general, its primary use case is the Natural Language command adaptation of particular geographic spatial functions as normally defined in pure SQL. Data input should be a combination of an English prefix in the form of a question, and a coordinate prompt injection, likely from an active mapping system application coordinate list. Output is PostGIS spatial SQL.

There are five primary geographic functions released in version 1.1.

Model developer: Mark Rodrigo

Github: https://github.com/mprodrigo/spatialsql

Model Architecture: The model is a QLoRA / Supervised Fine Tuning (SFT)

Model Input / Output Overview:

Input: Text plus coordinate prompt injection.
Output: PostGIS spatial SQL
NOTE: Inputs and outputs are in meters and or geographic decimal degrees WGS 84 coordinates.

Function	Question Input	Geo Input	SQL Execution Output
Area	Area question	Polygon	Number - Area sq meters
Centroid	Center question	Polygon	Point
Buffer	Buffer distance	Point	Polygon
Length	Length question	Line	Number - Length in meters

Function	Question Input	Geo Input 1	Geo Input 2	SQL Execution Output
Distance	Distance question	Point, Line, or Polygon	Point, Line, or Polygon	Number - Distance between geometries in meters

Example Prompting

pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, temperature=0.4, return_full_text=False,)

Build the messages
messages = [
{"role": "system", "content": "Write a PostGIS SQL statement for the following."},
{"role": "user", "content": "What is the thousand meter buffer for the following point? : 'Point(-3.78621945 40.4463195)'"},
]

Apply ChatML template
prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=False, )

Examples

AREA

Message: What is the area for the polygon? : 'Polygon ((-3.7515154 40.3855551, -3.7514972 40.3856581, -3.7507005 40.3855767, -3.7507167 40.3854722, -3.7515154 40.3855551))'

Response: SELECT ST_Area(geog) As area FROM (select 'Polygon ((-3.7515154 40.3855551, -3.7514972 40.3856581, -3.7507005 40.3855767, -3.7507167 40.3854722, -3.7515154 40.3855551))' :: geography geog) subquery;

CENTROID

Message: What is the centroid for the polygon? : 'Polygon ((-3.6934636 40.4808785, -3.6933352 40.4811486, -3.6930125 40.4810598, -3.693141 40.4807897, -3.6934636 40.4808785))'

Response: SELECT ST_AsText(ST_Centroid(geog)) As centroid FROM (select 'Polygon ((-3.6934636 40.4808785, -3.6933352 40.4811486, -3.6930125 40.4810598, -3.693141 40.4807897, -3.6934636 40.4808785))' :: geography geog) subquery;

BUFFER

Message: What is the thousand meter buffer for the following point? : 'Point(-8.7522658 41.3862664)'

Response: SELECT ST_AsText(ST_Buffer(geog, 1000)) as buffer FROM (select 'Point(-8.7522658 41.3862664)' :: geography geog) subquery;

LENGTH

Message: How long is the line? : 'LINESTRING (-3.6976693 40.4263178, -3.6986082 40.4258729)'

Response: SELECT ST_Length(geog) As length FROM (select 'LINESTRING (-3.6976693 40.4263178, -3.6986082 40.4258729)' :: geography geog) subquery;

DISTANCE

Message: How far apart is the point and line? : 'Point(-109.87549823 38.60574249)' 'LineString(-109.24324628 38.76349931, -109.4821773 38.6875815)'

Response: SELECT ST_Distance(gg1, gg2) As distance FROM (select 'Point(-109.87549823 38.60574249)' :: geography gg1, 'LineString(-109.24324628 38.76349931, -109.4821773 38.6875815)' :: geography gg2) subquery;

A Few Known Question Variation Examples

AREA

What is the area for the geometry?

What is the area for this polygon?

CENTROID

What is the centroid for the geometry?

What is the center point of the polygon?

BUFFER

What is the 100 meter buffer for the following point?

Buffer the following point a thousand meters.

What is the 1000 meter buffer for the following point?

LENGTH

What is the length of the line?

How long is this line?

DISTANCE

The distance between the polygon and the line is?

What is the distance between the points?

How far apart are the two lines?

llama.cpp / Hyperparameter Recommendations For Inference

max context ~ 262,000

top k ~ 100

temp ~ .4-.5 or lower

Agent Considerations

Agents are being considered as a separate project. Agents would mostly be related to pulling the coordinates from a mapping UI, and executing the SQL from responses against a PostGIS database.

Further Reference - link this

https://postgis.net/docs/PostGIS_Special_Functions_Index.html#PostGIS_GeographyFunctions

Evaluation data

More information needed

Training data

Custom synthetic

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-4
distributed_type: multi-GPU
num_devices: 2
optimizer: Adam 8bit
lr_scheduler_type: linear

Training results

Training Loss	Step	Validation Loss
1.1283	10	0.8828
0.7981	20	0.7432
0.7351	30	0.6847
0.6901	40	0.6427
0.6298	50	0.5980
0.5857	60	0.5558
0.5421	70	0.5335

Framework versions

transformers 5.14.1
torch 2.13.0
peft 0.20.0
bitsandbytes 0.50.0
datasets 5.0.1
tokenizers 0.23.1

Downloads last month: -

Safetensors

Model size

4B params

Tensor type

BF16

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