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Model: markrodrigo/Llama-3.2-1B-Instruct-Spatial-SQL-1.0
Source: Original Platform

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README.md

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+---
+license: llama3.2
+base_model: meta-llama/Meta-Llama-3.2-1B
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- code
+- spatial
+- sql
+- GIS
+- PostGIS
+---
+
+**ENGLISH ONLY - Use 8b models for alternate languages.**
+
+### Model Information
+
+This model, Llama-3.2-1B-Instruct-Spatial-SQL-1.0, is an 1B, 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.
+</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 |
+
+### Example Prompt / Prompt File
+
+<|begin_of_text|><|start_header_id|>system<|end_header_id|>
+<p></p>
+You are a helpful assistant. You are an expert at PostGIS and Postgresql and SQL and psql.
+<p></p>
+<|eot_id|><|start_header_id|>user<|end_header_id|>
+
+\### Instruction: Write a PostGIS SQL statement for the following.
+<p></p>
+
+\### Input:
+
+<p></p>
+{input}
+
+<p></p>
+
+\### Response:
+
+<|eot_id|><|start_header_id|>assistant<|end_header_id|>
+
+
+### Examples
+
+
+AREA
+<p></p>
+
+\### 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))'
+<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>
+
+\### 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))'
+<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>
+
+\### Input: 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>
+
+\### Input: 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>
+
+### 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?
+
+
+### llama.cpp / Hyperparameter Recommendations For Inference
+max context ~ 8,000 or lower
+<p></p>
+top k ~ 100 or lower
+<p></p>
+temp ~ .4-.7 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: 3e-05
+- train_batch_size: 10
+- eval_batch_size: 3
+- distributed_type: multi-GPU
+- num_devices: 2
+- optimizer: Adam 8bit
+- lr_scheduler_type: linear
+- num_epochs: 20
+
+### Training results
+
+| Training Loss | Step | Validation Loss |
+|:-------------:|:----:|:---------------:|
+| 1.9106        |  10  |   1.9183        |
+| 1.1674        |  20  |   1.1710        |
+| 0.7446        |  30  |   0.7589        |
+| 0.6143        |  40  |   0.6220        |
+| 0.5773        |  50  |   0.5695        |
+| 0.5328        |  60  |   0.5334        |
+| 0.5146        |  70  |   0.5096        |
+| 0.4895        |  80  |   0.4925        |
+| 0.4893        |  90  |   0.4772        |
+| 0.4710        |  100 |   0.4612        |
+| 0.4572        |  110 |   0.4438        |
+| 0.4358        |  120 |   0.4233        |
+| 0.4002        |  130 |   0.4014        |
+| 0.3812        |  140 |   0.3768        |
+| 0.3461        |  150 |   0.3492        |
+| 0.3222        |  160 |   0.3202        |
+| 0.3043        |  170 |   0.2921        |
+| 0.2727        |  180 |   0.2651        |
+| 0.2378        |  190 |   0.2403        |
+| 0.2229        |  200 |   0.2184        |
+| 0.2121        |  210 |   0.1990        |
+| 0.1891        |  220 |   0.1834        |
+| 0.1867        |  230 |   0.1723        |
+| 0.1848        |  240 |   0.1654        |
+| 0.1732        |  250 |   0.1631        |
+
+
+
+
+### Framework versions
+
+- Transformers 4.45.1
+- Pytorch 2.5.0
+- peft 0.13.2
+- Datasets 3.0.1
+- Tokenizers 0.20.1

config.json

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+{
+  "_name_or_path": "meta-llama/Llama-3.2-1B-Instruct",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 16,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 32.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": true,
+  "torch_dtype": "float16",
+  "transformers_version": "4.45.2",
+  "use_cache": true,
+  "vocab_size": 128256
+}

generation_config.json

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+{
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.45.2"
+}

model.safetensors

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+size 2471645464

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|eot_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<|eot_id|>"
+}