Create README.md

README.md

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+---
+tags:
+- text-generation
+- gpt2
+- recipes
+- natural-language-generation
+license: apache-2.0
+---
+
+# MinimalistRecipeTextGenerator
+
+## Overview
+
+This model is a fine-tuned version of the **GPT-2 (small)** language model, specifically trained to generate coherent and realistic short recipe texts. Given a prompt (e.g., "A quick chicken curry"), the model completes the text, often generating ingredient lists and basic instructions.
+
+## Model Architecture
+
+The model uses the standard **GPT-2 language modeling architecture**. 
+
+1.  **Core:** A 12-layer, 768-dimensional transformer decoder stack.
+2.  **Mechanism:** It operates based on attention mechanisms, predicting the next token in a sequence given all previous tokens.
+3.  **Training:** Fine-tuned on a dataset of simple, short recipes, enabling it to learn the structural patterns of recipes (Title -> Ingredients -> Instructions).
+4.  **Generation Parameters:** The `config.json` sets default generation parameters for high-quality output:
+    * `do_sample`: True (for creative text generation)
+    * `temperature`: 0.7 (controls randomness)
+    * `max_length`: 256 (for short, complete recipes)
+
+## Intended Use
+
+This model is intended for creative and content generation purposes:
+
+* **Creative Writing/Blogging:** Generating unique recipe ideas for food blogs or social media.
+* **Data Augmentation:** Creating synthetic, but structurally correct, recipe texts for training other culinary-focused models.
+* **Demonstration:** Serving as a basic example of fine-tuning GPT-2 on a domain-specific corpus.
+
+### How to use
+
+```python
+from transformers import pipeline
+
+generator = pipeline("text-generation", model="your_username/MinimalistRecipeTextGenerator") # Replace with actual hub path
+prompt = "Recipe for a refreshing summer salad:"
+
+output = generator(prompt, max_length=150, num_return_sequences=1, temperature=0.8)
+
+print(output[0]['generated_text'])