Create README.md

README.md

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+---
+# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
+# Doc / guide: https://huggingface.co/docs/hub/model-cards
+{}
+
+---
+
+# Model Card for dnnsdunca/ddroidlabs-GPT-2
+
+<!-- Provide a quick summary of what the model is/does. -->
+This model is based on GPT-2 and has been fine-tuned to generate text based on specific prompts. It is intended for use in generating creative writing, story generation, or any application requiring coherent and contextually relevant text output.
+
+This model card aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+The `dnnsdunca/ddroidlabs-GPT-2` model is a fine-tuned version of the GPT-2 model designed for generating high-quality text. It can be used in various applications requiring natural language generation.
+
+- **Developed by:** [Your Name or Team]
+- **Funded by [optional]:** [Funding Source]
+- **Shared by [optional]:** [Your Name or Team]
+- **Model type:** GPT-2 (Generative Pre-trained Transformer 2)
+- **Language(s) (NLP):** English
+- **License:** [License Information]
+- **Finetuned from model [optional]:** [Base model used for fine-tuning]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+- **Repository:** https://huggingface.co/Dnnsdunca/ddroidlabs-GPT-2-usage
+- **Paper [optional]:** [Link to any relevant paper]
+- **Demo [optional]:** [Link to any demo]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+This model can be used directly for generating text based on given prompts. Examples include story generation, creative writing, and dialogue generation.
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+This model can be fine-tuned further for specific tasks such as generating technical documentation, personalized content, or any other application requiring specific text generation.
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+The model should not be used for generating harmful or malicious content, including but not limited to fake news, hate speech, or any form of content intended to deceive or harm individuals.
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+The model inherits biases from the data it was trained on. Users should be aware of potential biases in the generated text and use the model responsibly.
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+Users (both direct and downstream) should be made aware of the risks, biases, and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Check if CUDA is available
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Define Model and Tokenizer
+model_name = "dnnsdunca/ddroidlabs-GPT-2"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name).to(device)
+
+# Function to generate text based on a given prompt
+def generate_text(prompt, max_length=100):
+    inputs = tokenizer(prompt, return_tensors="pt").to(device)
+    
+    with torch.no_grad():
+        outputs = model.generate(**inputs, max_length=max_length, num_return_sequences=1)
+    
+    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return generated_text
+
+# Test the System
+if __name__ == "__main__":
+    prompt = "Once upon a time"
+    generated_text = generate_text(prompt)
+    print("Generated Text:\n", generated_text)