| --- |
| base_model: unsloth/gemma-3-1b-it-unsloth-bnb-4bit |
| library_name: peft |
| language: |
| - en |
| --- |
| |
| # 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. |
| |
| ## Technical Specifications |
| |
| ### Model Architecture and Objective |
| |
| - Base: `gemma-3-1b-it`, 1.3B parameters |
| - Finetuning: LoRA adapters via PEFT |
| |
| ### Software |
| |
| - PEFT 0.15.2 |
| - Transformers |
| - Accelerate |
| - Unsloth |
