Phi 2 Persona Chat

🚀 Phi 2 人物角色聊天模型

Phi 2 人物角色聊天模型是基於基礎 Phi 2 模型，使用 nazlicanto/persona-based-chat 數據集進行 LoRA 微調後的版本。該數據集包含超過 64000 段 人物 A 和 人物 B 之間的對話，並提供了一系列人物角色事實信息。

此模型使用監督微調訓練器進行訓練，以 reference 回覆作為目標輸出。關於訓練和推理代碼以及完整的依賴項列表，你可以參考 Github 倉庫。

🚀 快速開始

運行模型

請注意，目前運行 Phi 2 模型需要設置 trust_remote_code=True。為獲得最佳效果，請使用包含人物角色事實信息的提示，並至少包含一輪對話。

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)

此模型由 nazlicanto 和 adirik 訓練。

📄 許可證

本項目採用 MIT 許可證。