This model is a fine-tuned version of Falcon3-7B-Instruct designed to extract nutritional components from a given food name. It returns a structured list of nutrients (proteins, fats, vitamins, minerals, etc.) based on a curated food composition dataset.

The fine-tuning was performed using QLoRA (Quantized Low‑Rank Adaptation) to achieve efficient training with limited GPU memory.

Model Details

Model Description

Developed by: Maigamhdsidy
Funded by: Academic project (I3AFD Course)
Shared by: Maigamhdsidy
Model type: Causal Language Model fine‑tuned for structured information extraction
Language(s) (NLP): English
License: Apache‑2.0
Finetuned from model: tiiuae/Falcon3-7B-Instruct

Model Sources

Repository: https://huggingface.co/maigamhdsidy/food-assistant
Demo: You can test it via the inference widget on this page.

Uses

Direct Use

You can use this model to:

Automatically extract nutrient lists from food names.
Enrich food databases or recipe applications.
Assist in dietary planning and nutritional analysis.

Downstream Use

The model can be integrated into chatbots, mobile apps, or web services that need quick nutritional information.

Out‑of‑Scope Use

The model is not a general‑purpose conversational AI.
It should not be used for medical diagnosis or clinical nutrition advice.
It may hallucinate values for foods not present in the training set.

Bias, Risks, and Limitations

Data bias: The training data comes from a specific dataset that may not cover all foods or cuisines.
Hallucination: For unknown foods, the model may generate plausible but incorrect nutrient values.
Units: The model outputs values with units (g, mg, kcal, etc.) but does not guarantee precision.
Recommendations: Always verify generated nutritional data with trusted sources before using in critical applications.

How to Get Started with the Model

python

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Base model + adapter
base_model_name = "tiiuae/Falcon3-7B-Instruct"
adapter_path = "maigamhdsidy/food-assistant"

tokenizer = AutoTokenizer.from_pretrained(adapter_path)

model = AutoModelForCausalLM.from_pretrained(
    base_model_name,
    device_map="auto",
    trust_remote_code=True
)
model = PeftModel.from_pretrained(model, adapter_path)

# Example prompt
prompt = """Given the following food name, return only its nutrient components.

Food name: Banana

Required output:
- Return only the components list."""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))