Instructions to use rajofearth/inventorygemma with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rajofearth/inventorygemma with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="rajofearth/inventorygemma",
	filename="InventoryGemma-268M-BF16.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 rajofearth/inventorygemma with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf rajofearth/inventorygemma:BF16
# Run inference directly in the terminal:
llama-cli -hf rajofearth/inventorygemma:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf rajofearth/inventorygemma:BF16
# Run inference directly in the terminal:
llama-cli -hf rajofearth/inventorygemma:BF16

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 rajofearth/inventorygemma:BF16
# Run inference directly in the terminal:
./llama-cli -hf rajofearth/inventorygemma:BF16

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 rajofearth/inventorygemma:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf rajofearth/inventorygemma:BF16

Use Docker

docker model run hf.co/rajofearth/inventorygemma:BF16

LM Studio
Jan
Ollama
How to use rajofearth/inventorygemma with Ollama:
```
ollama run hf.co/rajofearth/inventorygemma:BF16
```

Unsloth Studio new

How to use rajofearth/inventorygemma 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 rajofearth/inventorygemma 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 rajofearth/inventorygemma to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for rajofearth/inventorygemma to start chatting

Docker Model Runner
How to use rajofearth/inventorygemma with Docker Model Runner:
```
docker model run hf.co/rajofearth/inventorygemma:BF16
```

Lemonade

How to use rajofearth/inventorygemma with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull rajofearth/inventorygemma:BF16

Run and chat with the model

lemonade run user.inventorygemma-BF16

List all available models

lemonade list

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This repository is publicly accessible, but you have to accept the conditions to access its files and content.

InventoryGemma

This repository contains a GGUF model file (.gguf) based on google/gemma-3-270m, tuned to act as an expert grocery inventory manager.

Disclaimer: This is my greatest failure — a highly overfitted fat pig of a model. Use at your own risk. 😅

Quickstart (run the GGUF)

Option A: `llama.cpp`

Build or install llama.cpp (see the upstream project).
Run an inference command like:

./llama-cli -m "InventoryGemma-268M-BF16.gguf" -p "Hello!"

Option B: GUI apps (LM Studio / similar)

Import/open the .gguf file in your app and start chatting.

Prompt format (expected input/output)

Provide the prompt in this shape (Markdown table works well):

You are an expert inventory manager for a grocery store.

Current inventory:

| Product | Current Stock | Average Weekly Sales |
|---|---:|---:|
| Apples | 52 | 48 |
| Oil | 379 | 44 |
| Sugar | 279 | 58 |
| Pasta | 302 | 6 |
| Cereal | 16 | 28 |
| Cheese | 47 | 2 |

Provide recommendations:
- List products that are low on stock (current stock < weekly sales) as urgent.
- Suggest restocking for products where current stock <= 3 * weekly sales.
- Recommended order quantity: enough to reach 4 weeks of stock, rounded up to nearest 10, minimum one week's sales.
- Provide a summary of total units to order.

Example output:

Urgent low stock items:
- Cereal

Recommended restocks:
- Apples: order 140 units (current: 52, weekly sales: 48)
- Cereal: order 100 units (current: 16, weekly sales: 28)

Summary: restock 2 products, total order quantity 240 units.

Notes

Runtimes differ: Some runtimes handle system prompts/templates differently; if results look off, try placing the instruction block at the start of the user prompt exactly as shown above.
Hardware: BF16 GGUF typically runs best on modern CPUs/GPUs supported by your runtime; exact performance depends on backend and quantization.

License

The repository is marked as MIT (see front matter). If the included weights have additional upstream license terms, document them here as well.

Downloads last month: -

GGUF

Model size

0.3B params

Architecture

gemma3

Hardware compatibility

16-bit

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