Llama-3.1-8B-Instruct-Spatial-SQL-1.0

Model Information

This model, Llama-3.1-8B-Instruct-Spatial-SQL-1.0, is an 8B, 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 four primary geographic functions released in version 1.0.

Model developer: Mark Rodrigo

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

Docker: https://hub.docker.com/r/kartoza/postgis

Model Context Protocol (MCP): https://github.com/modelcontextprotocol/servers/tree/main/src/postgres

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.

Example Prompt / Prompt File

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

### Instruction: Write a PostGIS SQL statement for the following.

### Input:

### Response:

<|eot_id|><|start_header_id|>assistant<|end_header_id|>

Examples

AREA

### Input: 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))'

### Input: ¿Cuál es el área para el polígono? : 'Polygon ((-3.7515154 40.3855551, -3.7514972 40.3856581, -3.7507005 40.3855767, -3.7507167 40.3854722, -3.7515154 40.3855551))'

### Input: 多邊形的面積是多少？ : '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

### Input: 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))'

### Input: ¿Cuál es el centroide del polígono? : '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;

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

### Input: ¿Cuál es el buffer de mil metros para lo siguiente punto? : 'Point(-8.7522658 41.3862664)'

### Input: 以下點的千米緩衝區是多少？ : '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;

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

### Input: ¿Cuánto dura la línea? : 'LINESTRING (-3.6976693 40.4263178, -3.6986082 40.4258729)'

### Input: 隊伍有多長？ : '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;

A Few Known Question Variation Examples

llama.cpp / Hyperparameter Recommendations For Inference

max context ~ 8,000 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/manual-3.3/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: 2e-04
• train_batch_size: 10
• eval_batch_size: 3
• distributed_type: multi-GPU
• num_devices: 2
• optimizer: Adam 8bit
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Framework versions

• Transformers 4.44.0
• Pytorch 2.4.0
• peft 0.12.0
• Datasets 2.21.0
• Tokenizers 0.19.1