README.md

# Model Card: T5-Base Fine-Tuned for Recipe Direction Generation (FP16)
**Model Overview**
- **Model Name:** t5-base-recipe-finetuned-fp16
- **Model Type:** Sequence-to-Sequence Transformer
- **Base Model:** google/t5-base (220M parameters)
- **Quantization:** FP16 (half-precision floating-point)
- **Task:** Generate cooking directions from a list of ingredients

**Intended Use**
- This model is designed to generate step-by-step cooking directions given a list of ingredients. It’s intended for:
- Recipe creation assistance.
- Educational purposes in culinary AI research.
- Exploration of text-to-text generation in domain-specific tasks.
- Primary Users: Home cooks, recipe developers, AI researchers.


# Model Details
- **Architecture:** T5 (Text-to-Text Transfer Transformer), encoder-decoder Transformer with 12 layers, 768 hidden size, 12 attention heads.
- **Input:** Text string in the format "generate recipe directions from ingredients: <ingredient1> <ingredient2> ...".
- **Output:** Text string containing cooking directions.
- **Quantization:** Converted to FP16 for reduced memory usage (~425 MB vs. ~850 MB in FP32) and faster inference on GPU.
- **Hardware:** Fine-tuned and tested on a 12 GB NVIDIA GPU with CUDA.
# Training Data
- **Dataset:** RecipeNLG
- **Source:** Publicly available recipe dataset (downloaded as CSV)
- **Size:** 2,231,142 examples (original); subset of 178,491 used for training (10% of train split)
# Splits:
- **Train:** 178,491 examples (subset)
- **Validation:** 223,114 examples
- **Test:** 223,115 examples
- **Attributes:** ingredients (list of ingredients), directions (list of steps)
- **Preprocessing:** Converted stringified lists to text; input prefixed with "generate recipe directions from ingredients: ".

# Training Procedure
**Framework:** Hugging Face Transformers
**Hyperparameters:**
- Epochs: 2 
- Effective Batch Size: 32 (8 per device, 4 gradient accumulation steps)
- Learning Rate: 2e-5
- Optimizer: AdamW 
- Mixed Precision: FP16 (fp16=True)
- Training Time: ~12 hours estimated for subset (1 epoch); full dataset (3 epochs) estimated at ~68 hours per epoch without optimization.
- Compute: Single 12 GB GPU (NVIDIA, CUDA-enabled).
# Evaluation
- Metrics: Loss (to be filled post-training)
- Validation Loss: [TBD after training]
- Test Loss: [TBD after evaluation]
- Method: Evaluated using Trainer.evaluate() on validation and test splits.
- Qualitative: Generated directions checked for coherence with input ingredients (e.g., chicken and rice input should yield relevant steps).
# Performance
- Results: [TBD; e.g., "Validation Loss: X.XX, Test Loss: Y.YY after 1 epoch on subset"]
- Strengths: Expected to generate plausible directions for common ingredient combinations.
# Limitations:
- Limited training on subset may reduce generalization.
- Sporadic data mismatches may affect output quality.
- FP16 quantization might slightly alter precision vs. FP32.
# Usage
# Installation
```python
pip install transformers torch datasets
```
# Inference Example

```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
import torch

model_path = "./t5_recipe_finetuned_fp16"
tokenizer = T5Tokenizer.from_pretrained(model_path)
model = T5ForConditionalGeneration.from_pretrained(model_path).to("cuda").half()

ingredients = ["1 lb chicken breast", "2 cups rice", "1 onion", "2 tbsp soy sauce"]
input_text = "generate recipe directions from ingredients: " + " ".join(ingredients)
input_ids = tokenizer(input_text, return_tensors="pt", max_length=128, truncation=True).input_ids.to("cuda")

model.eval()
with torch.no_grad():
    output_ids = model.generate(input_ids, max_length=256, num_beams=4, early_stopping=True, no_repeat_ngram_size=2)
directions = tokenizer.decode(output_ids[0], skip_special_tokens=True)
print(directions)
```