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---
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license: mit
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language:
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- en
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base_model: openai-community/gpt2
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pipeline_tag: text-generation
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tags:
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- food
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- recipes
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- nutrition
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- meal-planner
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- gpt2
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model_name: qrit-2
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model_creator: samdak93
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model_type: causal-language-model
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datasets:
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- custom
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- samdak93/qritdataset
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library_name: transformers
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---
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# qrit-2 |
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## Model Details |
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### Model Description |
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- **Developed by:** samdak93 |
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- **Model type:** Causal Language Model |
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- **Language(s):** English |
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- **License:** MIT |
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- **Finetuned from model:** openai-community/gpt2 |
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This model generates food recipes with instructions based on the user's nutritional preferences, such as "around 400 calories, high protein, low fat". |
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### Model Sources |
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- **Repository:** https://huggingface.co/samdak93/qrit-2 |
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## Uses |
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### Direct Use |
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The model can be used to generate recipes directly via text prompts like: |
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> Generate a high-protein, low-fat recipe with around 400 calories. |
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### Out-of-Scope Use |
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This model is not intended for medical diagnosis, treatment planning, or diet prescriptions requiring professional approval. |
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## Bias, Risks, and Limitations |
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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. |
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### Recommendations |
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Always consult a certified nutritionist or dietitian before following specific diets, especially if you have health conditions. |
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## How to Get Started with the Model |
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```python |
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from transformers import pipeline |
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generator = pipeline("text-generation", model="samdak93/qrit-2") |
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prompt = "Healthy dinner recipe under 400 calories, high protein" |
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output = generator(prompt, max_new_tokens=200) |
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print(output[0]["generated_text"]) |
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```` |
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## Training Details |
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### Training Data |
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The model was trained on a custom dataset of food recipes with nutrition tags and instructions built by the author. |
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### Training Procedure |
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* **Platform:** Google Colab (free tier) |
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* **Compute:** Colab-provided GPU and RAM |
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* **Training regime:** fp16 mixed precision |
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## Evaluation |
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The model's output was evaluated manually for relevance, nutrition tag accuracy, and coherence of recipe instructions. |
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## Environmental Impact |
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* **Hardware Type:** Google Colab (free tier GPU) |
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* **Hours used:** Approx. 6 hours |
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* **Cloud Provider:** Google |
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* **Compute Region:** Unknown |
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* **Carbon Emitted:** Low (estimated via shared environment and short training time) |
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## Technical Specifications |
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### Model Architecture and Objective |
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The model is a fine-tuned version of GPT-2 (openai-community/gpt2) trained to generate nutrition-based recipes. |
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### Compute Infrastructure |
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* **Hardware:** Google Colab free GPU |
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* **Software:** Python, Transformers, PyTorch |
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## Citation |
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**BibTeX:** |
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``` |
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@misc{qrit2, |
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author = {samdak93}, |
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title = {qrit-2: Nutrition-based Recipe Generator}, |
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year = {2025}, |
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howpublished = {\url{https://huggingface.co/samdak93/qrit-2}}, |
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} |
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``` |
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## Model Card Contact |
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* **Author:** samdak93 |
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* **Hugging Face:** [https://huggingface.co/samdak93](https://huggingface.co/samdak93) |
