ChatLM

It is a chat Large Language Model finetuned with pretrained Falcon-1B model and trained on chat-bot-instructions prompts dataset. ChatLM was trained on a dataset containing normal day to day human conversations, due to limited data used in training it does not generalize well for tasks like coding, current affairs and hallucinations may occur.

ChatLM

Have a live chat with ChatLM on space https://huggingface.co/spaces/ayoolaolafenwa/ChatLM

Install Required Packages

pip install transformers
pip install accelerate
pip install einops
pip install bitsandbytes

Load Model in bfloat16

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "ayoolaolafenwa/ChatLM"

tokenizer = AutoTokenizer.from_pretrained(model_path)

model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code = True,
torch_dtype=torch.bfloat16).to("cuda")

prompt = "<user>: Give me a financial advise on investing in stocks. <chatbot>: "

tokens = tokenizer(prompt, return_tensors="pt")

token_ids = tokens.input_ids
attention_mask=tokens.attention_mask

token_ids = token_ids.to(model.device)
attention_mask=attention_mask.to(model.device)

outputs = model.generate(input_ids=token_ids, attention_mask = attention_mask,  max_length=2048,do_sample=True,
num_return_sequences=1,top_k = 10, temperature = 0.7, eos_token_id=tokenizer.eos_token_id)

output_text = tokenizer.decode(outputs[0])
output_text = output_text.replace("<|endoftext|>", "")

print(output_text)

Load Model in bfloat16 and int8

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "ayoolaolafenwa/ChatLM"

tokenizer = AutoTokenizer.from_pretrained(model_path)

model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code = True,
torch_dtype=torch.bfloat16, load_in_8bit=True)

prompt = "<user>: Give me a financial advise on investing in stocks. <chatbot>: "

tokens = tokenizer(prompt, return_tensors="pt")

token_ids = tokens.input_ids
attention_mask=tokens.attention_mask

token_ids = token_ids.to(model.device)
attention_mask=attention_mask.to(model.device)

outputs = model.generate(input_ids=token_ids, attention_mask = attention_mask,  max_length=2048,do_sample=True,
num_return_sequences=1,top_k = 10, temperature = 0.7, eos_token_id=tokenizer.eos_token_id)

output_text = tokenizer.decode(outputs[0])
output_text = output_text.replace("<|endoftext|>", "")

print(output_text)

Training procedure for Supervised Finetuning

Dataset Preparation

Chatbot Instructions prompts dataset from https://huggingface.co/datasets/alespalla/chatbot_instruction_prompts/viewer/alespalla--chatbot_instruction_prompts was processed into a supervised finetuning format for training a user prompt and a corresponding response.

Download Data

from datasets import load_dataset

dataset = load_dataset("alespalla/chatbot_instruction_prompts", split = "train")
dataset.save_to_disk('ChatBotInsP')
dataset.to_csv('CIPtrain.csv')

Code to process dataset into Supervised finetuning format

# Import pandas library
import pandas as pd

# Read the text dataset from csv file
text_data = pd.read_csv("CIPtrain.csv")

# Create empty lists for prompts and responses
prompts = []
responses = []

# Loop through the text data
for i in range(len(text_data)):
    # Get the sender, message, and timestamp of the current row
    prompt = text_data["prompt"][i]
    prompt = str(prompt)

    response = text_data["response"][i]
    response = str(response)
    
    # Add the message to the prompts list with <user> tag
    prompts.append("<user>: " + prompt)
    
    # Add the message to the responses list with <chatbot> tag
    responses.append("<chatbot>: " + response)

# Create a new dataframe with prompts and responses columns
new_data = pd.DataFrame({"prompt": prompts, "response": responses})

#alespalla/chatbot_instruction_prompts
# Write the new dataframe to a csv file
new_data.to_csv("MyData/chatbot_instruction_prompts_train.csv", index=False)

The users` prompts in the dataset are appended with the tag and the corresponding responses with the tag . Check the the modified dataset https://huggingface.co/datasets/ayoolaolafenwa/sft-data .

Training

ChatLM was supervised finetuned with pretrained Falcon 1-Billion parameters model trained on 350-Billion tokens of RefinedWeb. It was trained with a single H100 GPU for 1 epoch. It achieves Perplexity 1.738. Check the full code for supervised finetune training on its github repository https://github.com/ayoolaolafenwa/ChatLM/tree/main

ayoolaolafenwa
/

ChatLM