You can deploy this model and start earning money today!

Epitech/
FridgeBuddy

Name: FridgeBuddy
Rating: 2.5 (9 reviews)

peft

safetensors

unsloth/gemma-3-1b-it-unsloth-bnb-4bit

adapter

Views

No views yet

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

python

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/
FridgeBuddy

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

Model Details

Model Description

Model Sources

Uses

Direct Use

Out-of-Scope Use

Bias, Risks, and Limitations

Recommendations

How to Get Started with the Model

Training Details

Training Data

Training Procedure

Training Hyperparameters

Evaluation

Technical Specifications

Model Architecture and Objective

Software

Epitech/FridgeBuddy

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

Model Details

Model Description

Model Sources

Uses

Direct Use

Out-of-Scope Use

Bias, Risks, and Limitations

Recommendations

How to Get Started with the Model

Training Details

Training Data

Training Procedure

Training Hyperparameters

Evaluation

Technical Specifications

Model Architecture and Objective

Software

Epitech/
FridgeBuddy