---
# 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 Model ID

<!-- Provide a quick summary of what the model is/does. -->

This modelcard 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. -->



- **Developed by:** Mudasir692
- **Model type:** transformer
- **Language(s) (NLP):** python
- **License:** MIT
- **Finetuned from model [optional]:** Peguses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->


## Bias, Risks, and Limitations

Model might not generate coherent summary to large extent.

[More Information Needed]

### 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

import torch
from transformers import PegasusForConditionalGeneration, PegasusTokenizer

# Load the saved model and tokenizer
model_path = "peguses_chat_sum"
device = torch.device("cpu")

# Load the model and tokenizer from the saved directory
model = PegasusForConditionalGeneration.from_pretrained(model_path)
tokenizer = PegasusTokenizer.from_pretrained(model_path)

# Move the model to the correct device
model = model.to(device)


## How to Get Started with the Model
from transformers import PegasusForConditionalGeneration, PegasusTokenizer

model = PegasusForConditionalGeneration.from_pretrained("Mudasir692/peguses_chat_sum")
tokenizer = PegasusTokenizer.from_pretrained("Mudasir692/peguses_chat_sum")
input_text = """
#Person1#: Hey Alice, congratulations on your promotion!
#Person2#: Thank you so much! It means a lot to me. I’m still processing it, honestly.
#Person1#: You totally deserve it. Your hard work finally paid off. Let’s celebrate this weekend.
#Person2#: That sounds amazing. Dinner on me, okay?
#Person1#: Sure! Just let me know where and when. Oh, by the way, did you tell your family?
#Person2#: Yes, they were so excited. Mom’s already planning to bake a cake.
#Person1#: That’s wonderful! I’ll bring a gift too. It’s such a big milestone for you.
#Person2#: You’re the best. Thanks for always being so supportive.
"""
inputs = tokenizer(input_text, return_tensors="pt")
model.eval()
outputs = model.generate(**inputs, max_new_tokens=100)
generated_summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
print("generated summary", generated_summary)