Fine-Tuning LLaMA for Reward Modeling

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For example, let’s say you want the model to reward responses that are both informative and engaging. You would provide a set of training data with examples of such responses, along with their corresponding scores (which could be anything from 1-5 stars). The model then learns how to assign similar scores to new, unseen responses based on its understanding of language patterns and context.

Here’s an example script that demonstrates this process:

# Import necessary libraries
from transformers import AutoTokenizer, TFBertForSequenceClassification
import re # Import regular expression library for text preprocessing
import nltk # Import nltk library for sentence tokenization

# Load the pretrained LLaMA model
tokenizer = AutoTokenizer.from_pretrained('llama-2-70b') # Load the tokenizer for the LLaMA model
model = TFBertForSequenceClassification.from_pretrained('llama-2-70b').eval() # Load the fine-tuned LLaMA model and set it to evaluation mode

# Define the training data (in this case, a list of tuples containing text and scores)
train_data = [
    ('What is the capital city of France?', 5),
    ('How many legs does an elephant have? ', 4),
    ('Can you recommend any good books to read? ', 3),
    ('Who invented the telephone? ', 2)
]

# Define a function that preprocesses and tokenizes each input text
def process_input(text):
    # Convert all punctuation marks into spaces (except for quotes, which are important for context)
    text = re.sub(r'[^\w\s"\'()]', ' ', text) # Replace all punctuation marks with spaces
    # Split the text into individual sentences and add a special token to indicate the start of each sentence
    tokens = [tokenizer.convert_tokens_to_ids(' '.join([word for word in nltk.sent_tokenize(text)]))[0] + 102 for n, text in enumerate(train_data) if n % 5 == 0] # Tokenize the text and add a special token to indicate the start of each sentence
    # Return the list of tokenized words and their corresponding scores (if any)
    return tokens, train_data[n][1]

# Define a function that takes input data and returns predicted scores based on the fine-tuned LLaMA model
def predict(input):
    # Preprocess and tokenize each input text using our helper function
    tokens, score = process_input(input)
    # Convert the list of tokenized words into a tensor that can be fed into the model
    inputs = tf.convert_to_tensor([tokens])
    # Run inference on the fine-tuned LLaMA model and get its predictions for each input text
    outputs = model(inputs, training=False)
    # Extract the predicted scores from the output tensor (which is a list of logits representing the probability of each label being true)
    preds = tf.nn.softmax(outputs[0], axis=-1)[-1]
    # Return the predicted score for this input text, rounded to two decimal places
    return round(preds * 5, 2) # Multiply the predicted scores by 5 (since the scores range from 1-5) and round to two decimal places

Fine-Tuning LLaMA for Reward Modeling is essentially a way of training a pretrained language model to give rewards based on certain criteria or instructions. By providing the model with examples and their corresponding scores, we can teach it how to assign similar scores to new, unseen responses based on its understanding of language patterns and context.

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