---
license: mit
language:
- en
base_model: openai-community/gpt2
pipeline_tag: text-generation
tags:
- food
- recipes
- nutrition
- meal-planner
- gpt2
model_name: qrit-2
model_creator: samdak93
model_type: causal-language-model
datasets:
- custom
- samdak93/qritdataset
library_name: transformers
---

# qrit-2

## Model Details

### Model Description

- **Developed by:** samdak93  
- **Model type:** Causal Language Model  
- **Language(s):** English  
- **License:** MIT  
- **Finetuned from model:** openai-community/gpt2  

This model generates food recipes with instructions based on the user's nutritional preferences, such as "around 400 calories, high protein, low fat".

### Model Sources

- **Repository:** https://huggingface.co/samdak93/qrit-2

## Uses

### Direct Use

The model can be used to generate recipes directly via text prompts like:

> Generate a high-protein, low-fat recipe with around 400 calories.

### Out-of-Scope Use

This model is not intended for medical diagnosis, treatment planning, or diet prescriptions requiring professional approval.

## Bias, Risks, and Limitations

The model was trained on a custom dataset built by the author. It may not generalize well to all types of cuisines, dietary needs, or nutritional guidelines. It does not replace professional dietary advice.

### Recommendations

Always consult a certified nutritionist or dietitian before following specific diets, especially if you have health conditions.

## How to Get Started with the Model

```python
from transformers import pipeline

generator = pipeline("text-generation", model="samdak93/qrit-2")
prompt = "Healthy dinner recipe under 400 calories, high protein"
output = generator(prompt, max_new_tokens=200)
print(output[0]["generated_text"])
````

## Training Details

### Training Data

The model was trained on a custom dataset of food recipes with nutrition tags and instructions built by the author.

### Training Procedure

* **Platform:** Google Colab (free tier)
* **Compute:** Colab-provided GPU and RAM
* **Training regime:** fp16 mixed precision

## Evaluation

The model's output was evaluated manually for relevance, nutrition tag accuracy, and coherence of recipe instructions.

## Environmental Impact

* **Hardware Type:** Google Colab (free tier GPU)
* **Hours used:** Approx. 6 hours
* **Cloud Provider:** Google
* **Compute Region:** Unknown
* **Carbon Emitted:** Low (estimated via shared environment and short training time)

## Technical Specifications

### Model Architecture and Objective

The model is a fine-tuned version of GPT-2 (openai-community/gpt2) trained to generate nutrition-based recipes.

### Compute Infrastructure

* **Hardware:** Google Colab free GPU
* **Software:** Python, Transformers, PyTorch

## Citation

**BibTeX:**

```
@misc{qrit2,
  author = {samdak93},
  title = {qrit-2: Nutrition-based Recipe Generator},
  year = {2025},
  howpublished = {\url{https://huggingface.co/samdak93/qrit-2}},
}
```

## Model Card Contact

* **Author:** samdak93
* **Hugging Face:** [https://huggingface.co/samdak93](https://huggingface.co/samdak93)