Instructions to use rajofearth/inventorygemma with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use rajofearth/inventorygemma with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -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 serve -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 Desktop
- 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
- Atomic Chat
metadata
license: mit
language:
- en
base_model:
- google/gemma-3-270m
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
.gguffile 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.