---
license: creativeml-openrail-m
library_name: adapter-transformers
pipeline_tag: text-generation
---
# 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.]

```python
# 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]