---
license: mit
language:
- en
library_name: transformers
datasets:
- Cynaptics/persona-chat
---
# DialoGPT-Chat-Finetune

This is a fine-tuned version of the DialoGPT model. It has been fine-tuned on persona-based data to generate human-like conversational responses.

## Model Description
The model is based on the DialoGPT architecture and has been fine-tuned for conversational tasks, specifically targeting persona-based interactions.

## Model Details
- **Architecture:** DialoGPT-medium
- **Pretraining Data:** The original model was pretrained on a large corpus of text data.
- **Fine-tuning Data:** This model was fine-tuned on persona-based conversational data.

## Library
- Framework: PyTorch
- Model: DialoGPT-medium

## Example Usage
You can use this model via the Hugging Face `transformers` library.
To use the fine-tuned model for text generation based on a persona, follow these steps:

```python
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer

# Load the fine-tuned model and tokenizer
model_name = "hello12w/persona_chatbot"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Define the persona and prompt
prompt = prompt = f"""

Person B has the following Persona information.

Persona of Person B: My name is Sarah and I'm a 28 year old software engineer.

Persona of Person B: I love coding and developing new software applications.

Persona of Person B: In my free time, I enjoy reading sci-fi novels and playing board games.

Instruct: Person A and Person B are now having a conversation.

Following the conversation below, write a response that Person B would say based on the above Persona information.

Please carefully consider the flow and context of the conversation below, and use the Person B's Persona information appropriately to generate a response that you think is the most appropriate reply for Person B.

Persona A: Hi Sarah, I heard you're working on a cool project at work. Can you tell me more about it?

Output:

"""
input_ids = tokenizer(prompt, return_tensors="pt", truncation=True)
attention_mask = input_ids.attention_mask
input_ids = input_ids.input_ids

# Inference
with torch.no_grad():
    outputs = model.generate(
        input_ids=input_ids,
        attention_mask=attention_mask,
        max_new_tokens=200,
        do_sample=True,
        top_p=0.95,
        temperature=0.9
    )

# Decode output tokens
decoded_outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)
output = decoded_outputs[0][len(prompt):]

print(output)