Instructions to use SpeciesFileGroup/ento-model-parse with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SpeciesFileGroup/ento-model-parse with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="SpeciesFileGroup/ento-model-parse",
	filename="ento-label-parser-q4_k_m.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use SpeciesFileGroup/ento-model-parse with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf SpeciesFileGroup/ento-model-parse:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf SpeciesFileGroup/ento-model-parse:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf SpeciesFileGroup/ento-model-parse:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf SpeciesFileGroup/ento-model-parse: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 SpeciesFileGroup/ento-model-parse:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf SpeciesFileGroup/ento-model-parse: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 SpeciesFileGroup/ento-model-parse:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SpeciesFileGroup/ento-model-parse:Q4_K_M

Use Docker

docker model run hf.co/SpeciesFileGroup/ento-model-parse:Q4_K_M

LM Studio
Jan
Ollama
How to use SpeciesFileGroup/ento-model-parse with Ollama:
```
ollama run hf.co/SpeciesFileGroup/ento-model-parse:Q4_K_M
```

Unsloth Studio new

How to use SpeciesFileGroup/ento-model-parse 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 SpeciesFileGroup/ento-model-parse 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 SpeciesFileGroup/ento-model-parse to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for SpeciesFileGroup/ento-model-parse to start chatting

Pi new

How to use SpeciesFileGroup/ento-model-parse with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf SpeciesFileGroup/ento-model-parse: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": "SpeciesFileGroup/ento-model-parse:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use SpeciesFileGroup/ento-model-parse with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf SpeciesFileGroup/ento-model-parse: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 SpeciesFileGroup/ento-model-parse:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use SpeciesFileGroup/ento-model-parse with Docker Model Runner:
```
docker model run hf.co/SpeciesFileGroup/ento-model-parse:Q4_K_M
```

Lemonade

How to use SpeciesFileGroup/ento-model-parse with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SpeciesFileGroup/ento-model-parse:Q4_K_M

Run and chat with the model

lemonade run user.ento-model-parse-Q4_K_M

List all available models

lemonade list

ento-model-parse / INSTRUCTIONS.md

dmozzherin

add Go web tool

dbd6f57 28 days ago

preview code

raw

history blame contribute delete

4.33 kB

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.