Model Card for FridgeBuddy (Gemma 1B - Fine-tuned)

FridgeBuddy is a fine-tuned version of gemma-3-1b-it, designed as a lightweight voice assistant for smart fridges. It answers simple cooking-related questions offline, and was trained on a small dataset of prompt-response examples to generate recipe suggestions, food pairings, and quick preparation ideas.

Model Details

Model Description

Developed by: Arnaud Vitale
Shared by: Epitech organization on Hugging Face
Model type: Decoder-only LLM (Gemma 1B, fine-tuned with LoRA)
Language(s): English (food, kitchen, cooking vocabulary)
License: Apache 2.0
Finetuned from model: unsloth/gemma-3-1b-it-unsloth-bnb-4bit

Model Sources

Repository: https://huggingface.co/Epitech/FridgeBuddy
Colab Notebook: https://colab.research.google.com/drive/1QFCiMVqswVaogeEE0a9OQS_e6pQBn2Fr?usp=sharing

Uses

Direct Use

This model can be used to:

Suggest quick recipes
Answer questions like "What can I make with tuna and rice?"
Help generate creative food combinations

Out-of-Scope Use

Not intended for use outside kitchen/culinary contexts
Should not be used for critical health or safety decisions

Bias, Risks, and Limitations

This model was trained on a very small synthetic dataset with limited diversity. As such:

It may hallucinate facts or recipes
It may fail to recognize unusual ingredient names or dietary needs

Recommendations

Use FridgeBuddy in a constrained, offline, non-critical environment (ex: IoT kitchen setup). It's a lightweight proof of concept, not a production model.

How to Get Started with the Model

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("Epitech/gemma3b-maths-children")
tokenizer = AutoTokenizer.from_pretrained("Epitech/gemma3b-maths-children")

prompt = "<|user|>\nWhat can I cook with tuna and cream?\n<|assistant|>\n"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0]))

Training Details

Training Data

The model was trained on a synthetic .jsonl dataset (fridgebuddy_dataset_en.jsonl) of 100 prompt-response pairs related to food and kitchen use cases.

Training Procedure

LoRA fine-tuning with Unsloth + PEFT
Float32 precision
3 epochs — 36 steps

Training Hyperparameters

batch_size: 2 (per device)
gradient_accumulation_steps: 4
learning_rate: 2e-5
epochs: 3
optimizer: adamw_8bit

Evaluation

The model was qualitatively evaluated using generation on unseen prompts. It responded well to basic kitchen questions, but showed expected limitations in vocabulary and instruction-following depth.

Epitech
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FridgeBuddy