Insect Label Parser — Setup Instructions

This tool reads raw entomology collection label text and extracts structured data (country, state, locality, date, collector, elevation, etc.) as JSON. It runs entirely on your computer — no internet connection required after the one-time setup.

Step 1 — Which file do I need?

Copy one of these files from output/gguf/ to your computer:

File	Size	Use when
`ento-label-parser-q4_k_m.gguf`	3.2 GB	Your computer has 8 GB RAM (most laptops)
`ento-label-parser-q5_k_m.gguf`	3.4 GB	Your computer has 16 GB RAM or more (slightly better quality)

Not sure how much RAM you have?

Mac: Apple menu → About This Mac → look for "Memory"
Windows: Settings → System → About → look for "Installed RAM"

The Q4 file works well for this task. Label parsing is a simple extraction job — the quality difference between Q4 and Q5 is very small.

Option A: LM Studio (recommended for most users — no terminal needed)

LM Studio is a free desktop app with a chat interface, similar to ChatGPT but running fully on your own machine.

Install

Go to lmstudio.ai and download the version for your operating system (Mac, Windows, or Linux)
Install and open it

Load the model

In LM Studio, click My Models in the left sidebar
Click "Load model from file" (or drag the .gguf file into the window)
Navigate to the ento-label-parser-q4_k_m.gguf file you copied in Step 1
Wait for the model to load (progress bar at the bottom)

Configure the system prompt

This step tells the model what it is supposed to do.

Click the Chat icon in the left sidebar
Find the System Prompt box (usually at the top of the right panel)
Paste this text exactly:

Parse this insect collection label and return a JSON object with the extracted fields. Only include fields that are present in the label.

Set Temperature to 0 in the model settings panel (this makes output deterministic — the same label always gives the same result)

Parse a label

Paste the raw label text into the chat box and press Enter. The model will return a JSON object. Example:

Input:

U.S.A., Texas: Austin, Travis Co., 15.iv.2021, J. Doe, sweeping

Output:

{
  "country": "USA",
  "state": "Texas",
  "county": "Travis",
  "verbatim_locality": "Austin",
  "verbatim_date": "15.iv.2021",
  "start_date_year": "2021",
  "start_date_month": "4",
  "start_date_day": "15",
  "verbatim_collectors": "J. Doe",
  "verbatim_method": "sweeping"
}

Option B: Ollama (for users comfortable with a terminal)

Ollama is a lightweight tool that runs models from the command line and also exposes a local API for scripting.

Requirement: Ollama version 0.20.7 or newer

Older versions do not support this model's architecture. Check your version:

ollama --version

If it shows a version older than 0.20.7, update from ollama.com.

Install

Go to ollama.com, download, and install for your operating system.

Register the model

Open a terminal, navigate to the project folder, and run:

ollama create ento-label-parser -f Modelfile

You only need to do this once.

Parse a label

ollama run ento-label-parser "U.S.A., Texas: Austin, 15.iv.2021, J. Doe"

Or pipe a text file:

cat my_label.txt | ollama run ento-label-parser

Troubleshooting

The model is very slow. This is normal on a laptop without a dedicated GPU. The Q4 file typically takes 5–30 seconds per label on a CPU. If you have an NVIDIA or AMD GPU with 4+ GB of video memory, Ollama and LM Studio will use it automatically and be much faster.

LM Studio says "not enough memory." Try the Q4 file if you were using Q5. If Q4 also fails, your computer may have less than 8 GB of RAM available — try closing other applications first.

Ollama says "unknown model architecture: gemma4". Your Ollama version is too old. Update it from ollama.com.

The output is not valid JSON. Occasionally the model will include a short thinking passage before the JSON. Copy just the { ... } portion of the output. If this happens frequently, make sure Temperature is set to 0.