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
```

Qwen-3.5-4B-Spatial-SQL-1.1 / README.md

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	---
	license: apache-2.0
	base_model:
	- Qwen/Qwen3.5-4B
	base_model_relation: finetune
	language:
	- en
	- zh
	pipeline_tag: text-generation
	tags:
	- code
	- spatial
	- SQL
	- GIS
	- PostGIS
	library_name: transformers
	---

	### 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.
	</br>
	Output: PostGIS spatial SQL
	</br>
	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 \|

	</br>

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

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


	### Examples


	AREA
	<p></p>

	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))'
	<p></p>

	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;
	<p></p>

	CENTROID
	<p></p>

	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))'
	<p></p>

	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
	<p></p>

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

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

	<p></p>
	LENGTH
	<p></p>

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

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

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

	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;
	<p></p>

	### A Few Known Question Variation Examples

	<p></p>
	AREA
	<p></p>
	What is the area for the geometry?
	<p></p>
	What is the area for this polygon?
	<p></p>
	CENTROID
	<p></p>
	What is the centroid for the geometry?
	<p></p>
	What is the center point of the polygon?
	<p></p>
	BUFFER
	<p></p>
	What is the 100 meter buffer for the following point?
	<p></p>
	Buffer the following point a thousand meters.
	<p></p>
	What is the 1000 meter buffer for the following point?
	<p></p>
	LENGTH
	<p></p>
	What is the length of the line?
	<p></p>
	How long is this line?
	<p></p>
	DISTANCE
	<p></p>
	The distance between the polygon and the line is?
	<p></p>
	What is the distance between the points?
	<p></p>
	How far apart are the two lines?

	### llama.cpp / Hyperparameter Recommendations For Inference
	max context ~ 262,000
	<p></p>
	top k ~ 100
	<p></p>
	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