Instructions to use markrodrigo/Llama-3.1-8B-Instruct-Spatial-SQL-1.0-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use markrodrigo/Llama-3.1-8B-Instruct-Spatial-SQL-1.0-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="markrodrigo/Llama-3.1-8B-Instruct-Spatial-SQL-1.0-gguf",
	filename="Llama-3.1-8B-Instruct-Spatial-SQL-1.0-Q3_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use markrodrigo/Llama-3.1-8B-Instruct-Spatial-SQL-1.0-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf markrodrigo/Llama-3.1-8B-Instruct-Spatial-SQL-1.0-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf markrodrigo/Llama-3.1-8B-Instruct-Spatial-SQL-1.0-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf markrodrigo/Llama-3.1-8B-Instruct-Spatial-SQL-1.0-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf markrodrigo/Llama-3.1-8B-Instruct-Spatial-SQL-1.0-gguf:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf markrodrigo/Llama-3.1-8B-Instruct-Spatial-SQL-1.0-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf markrodrigo/Llama-3.1-8B-Instruct-Spatial-SQL-1.0-gguf:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf markrodrigo/Llama-3.1-8B-Instruct-Spatial-SQL-1.0-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf markrodrigo/Llama-3.1-8B-Instruct-Spatial-SQL-1.0-gguf:Q4_K_M

Use Docker

docker model run hf.co/markrodrigo/Llama-3.1-8B-Instruct-Spatial-SQL-1.0-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use markrodrigo/Llama-3.1-8B-Instruct-Spatial-SQL-1.0-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "markrodrigo/Llama-3.1-8B-Instruct-Spatial-SQL-1.0-gguf"
# 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/Llama-3.1-8B-Instruct-Spatial-SQL-1.0-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/markrodrigo/Llama-3.1-8B-Instruct-Spatial-SQL-1.0-gguf:Q4_K_M

Ollama
How to use markrodrigo/Llama-3.1-8B-Instruct-Spatial-SQL-1.0-gguf with Ollama:
```
ollama run hf.co/markrodrigo/Llama-3.1-8B-Instruct-Spatial-SQL-1.0-gguf:Q4_K_M
```

Unsloth Studio

How to use markrodrigo/Llama-3.1-8B-Instruct-Spatial-SQL-1.0-gguf 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 markrodrigo/Llama-3.1-8B-Instruct-Spatial-SQL-1.0-gguf 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 markrodrigo/Llama-3.1-8B-Instruct-Spatial-SQL-1.0-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for markrodrigo/Llama-3.1-8B-Instruct-Spatial-SQL-1.0-gguf to start chatting

How to use markrodrigo/Llama-3.1-8B-Instruct-Spatial-SQL-1.0-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf markrodrigo/Llama-3.1-8B-Instruct-Spatial-SQL-1.0-gguf:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "markrodrigo/Llama-3.1-8B-Instruct-Spatial-SQL-1.0-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use markrodrigo/Llama-3.1-8B-Instruct-Spatial-SQL-1.0-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf markrodrigo/Llama-3.1-8B-Instruct-Spatial-SQL-1.0-gguf:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default markrodrigo/Llama-3.1-8B-Instruct-Spatial-SQL-1.0-gguf:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use markrodrigo/Llama-3.1-8B-Instruct-Spatial-SQL-1.0-gguf with Docker Model Runner:
```
docker model run hf.co/markrodrigo/Llama-3.1-8B-Instruct-Spatial-SQL-1.0-gguf:Q4_K_M
```

Lemonade

How to use markrodrigo/Llama-3.1-8B-Instruct-Spatial-SQL-1.0-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull markrodrigo/Llama-3.1-8B-Instruct-Spatial-SQL-1.0-gguf:Q4_K_M

Run and chat with the model

lemonade run user.Llama-3.1-8B-Instruct-Spatial-SQL-1.0-gguf-Q4_K_M

List all available models

lemonade list

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

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

Example Prompt / Prompt File

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

You are a helpful assistant. You are an expert at PostGIS and Postgresql and SQL and psql.

<|eot_id|><|start_header_id|>user<|end_header_id|>

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

### Input:

{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))'

### Input: 多邊形的質心是什麼？ : '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

### 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;

LENGTH

### 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

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?

llama.cpp / Hyperparameter Recommendations For Inference

max context ~ 8,000 or lower

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/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

Training Loss	Epoch	Step	Validation Loss
0.5438	3	10	0.5247
0.4889	3	20	0.4494
0.4072	3	30	0.4051

Framework versions

Transformers 4.44.0
Pytorch 2.4.0
peft 0.12.0
Datasets 2.21.0
Tokenizers 0.19.1

Downloads last month: 67

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

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