SOP_Generator

Model Description

[Provide a brief description of your SOP (Standard Operating Procedure) Generator model. Explain what it does, its purpose, and any unique features.]

Usage

[Explain how to use the model, including any specific input formats or parameters.]

# Example code for using the model
from transformers import GPT2Tokenizer, GPTNeoForCausalLM

tokenizer = GPT2Tokenizer.from_pretrained("harshagnihotri14/SOP_Generator", )
model = GPTNeoForCausalLM.from_pretrained("harshagnihotri14/SOP_Generator", )

# Example usage
input_text ="Write an SOP for a computer science student applying to Stanford University."
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
generated_sop = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_sop)

Model Details

• Model Architecture: [GPT-neo 125M]
• Training Data: [Student SOP's]
• Input: [Explain what kind of input the model expects]
• Output: [Describe the output format]

Performance

[Provide information about the model's performance, any benchmarks, or evaluation metrics]

Limitations

[Discuss any known limitations or biases of the model]

Fine-tuning

[If applicable, provide instructions on how to fine-tune the model]

Citation

[If your model is based on published research, provide citation information]

License

This model is licensed under [specify the license, e.g., MIT, Apache 2.0, etc.]

Contact

[Provide your contact information or links to where users can ask questions or report issues]