Danjie/Chadgpt-Llama2-7b-conversation

Chadgpt Llama2 7b conversation

Colab Example

https://colab.research.google.com/drive/1YPF7oAM0s3W93iWIqJ-kZ2NY5gQK3tZ2?usp=sharing

Install Prerequisite

!pip install peft
!pip install transformers
!pip install bitsandbytes

Login Using Huggingface Token

# You need a huggingface token that can access llama2
from huggingface_hub import notebook_login
notebook_login()

Download Model

import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" if torch.cuda.is_available() else "cpu"

peft_model_id = "danjie/Chadgpt-Llama2-7b-conversation"
config = PeftConfig.from_pretrained(peft_model_id)
model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)

# Load the Lora model
model = PeftModel.from_pretrained(model, peft_model_id)

Inference

# Run this cell to start a new conversation
conversation_history = []

def format_conversation(conversation: list[str]) -> str:
    formatted_conversation = ""
    
    # Check if the conversation has more than two turns
    if len(conversation) > 2:
        # Process all but the last two turns
        for i in range(len(conversation) - 2):
            if i % 2 == 0:
                formatted_conversation += "<Past User>" + conversation[i] + "\n"
            else:
                formatted_conversation += "<Past Assistant>" + conversation[i] + "\n"
    
    # Process the last two turns
    if len(conversation) >= 2:
        formatted_conversation += "<User>" + conversation[-2] + "\n"
        formatted_conversation += "<Assistant>" + conversation[-1]
    
    return formatted_conversation

def talk_with_llm(chat: str) -> str:
    # Encode and move tensor into cuda if applicable.
    conversation_history.append(chat)
    conversation_history.append("")
    conversation = format_conversation(conversation_history)
    
    encoded_input = tokenizer(conversation, return_tensors='pt')
    encoded_input = {k: v.to(device) for k, v in encoded_input.items()}

    output = model.generate(**encoded_input, max_new_tokens=256)
    response = tokenizer.decode(output[0], skip_special_tokens=True)
    response = response[len(conversation):]
    
    conversation_history.pop()
    conversation_history.append(response)
    return response