---
language:
- en
license: mit
library_name: transformers
tags:
- chat
- text-generation
- persona
- phi-2
- llm
- persona-grounded
datasets:
- nazlicanto/persona-based-chat
---

## Phi 2 Persona-Chat
Phi 2 Persona-Chat is a LoRA fine-tuned version of the base [Phi 2](https://huggingface.co/microsoft/phi-2) model using the [nazlicanto/persona-based-chat](https://huggingface.co/datasets/nazlicanto/persona-based-chat) dataset. This dataset consists of over 64k conversations between *Persona A* and *Persona B*, for which a list of persona facts are provided.

The model is trained using Supervised Fine-tuning Trainer using the `reference` responses as target outputs. For the training and inference code and the full list of dependencies, you can refer to the Github [repo](https://github.com/alaradirik/finetune-phi-2).


## Running the Model

Please note that, at the moment, trust_remote_code=True is required for running the Phi 2 model. For best results, use a prompt that includes the persona facts, followed by a minimum of one conversational turn.

```
from random import randrange

import torch
from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForCausalLM


prompt = f"""
Person B has the following Persona information.

Persona of Person B: My name is David and I'm a 35 year old math teacher.
Persona of Person B: I like to hike and spend time in the nature.
Persona of Person B: I'm married with two kids.

Instruct: Person A and Person B are now having a conversation.  Following the conversation below, write a response that Person B would say base 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 are the most appropriate replying for Person B.

Persona A: Morning! I think I saw you at the parent meeting, what's your name?

Output:
"""

# load base LLM model, LoRA params and tokenizer
model = AutoModelForCausalLM.from_pretrained("nazlicanto/phi-2-persona-chat", trust_remote_code=True)
model.to("cuda")

tokenizer = AutoTokenizer.from_pretrained("nazlicanto/phi-2-persona-chat", trust_remote_code=True)

# tokenize input prompt
input_ids = tokenizer(prompt, return_tensors="pt", truncation=True).input_ids.cuda()

# inference
with torch.inference_mode():
    outputs = model.generate(
        input_ids=input_ids, 
        max_new_tokens=50, 
        do_sample=True, 
        top_p=0.1,
        temperature=0.7
    )

# decode output tokens
outputs = outputs.detach().cpu().numpy()
outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)
output = outputs[0][len(prompt):]
print(output)
```

This model is trained by [nazlicanto](https://huggingface.co/nazlicanto) and [adirik](https://huggingface.co/adirik).