llama-3-reactor

This is a Re-act style model trained from meta-llama/Meta-Llama-3-8B

Dataset was parsed with:

def extract_trajectory_info(data):
    """
    Extracts the question, thoughts, actions, and observations from the trajectory field of the data.

    Parameters:
    data (dict): The data entry containing the trajectory field.

    Returns:
    dict: A dictionary containing the extracted question, thoughts, actions, and observations.
    """
    # Extracting the question
    question = data.get('question', '')

    # Extracting thoughts, actions, and observations using regex
    thoughts = re.findall(r'Thought \d+: (.+?)(?=Action|\Z)', data.get('trajectory', ''), re.DOTALL)
    actions = re.findall(r'Action \d+: (.+?)(?=Observation|\Z)', data.get('trajectory', ''), re.DOTALL)
    observations = re.findall(r'Observation \d+: (.+?)(?=Thought|\Z)', data.get('trajectory', ''), re.DOTALL)

    # Cleaning up the extracted data
    thoughts = [thought.strip() for thought in thoughts]
    actions = [action.strip() for action in actions]
    observations = [observation.strip() for observation in observations]

    return {
        'question': question,
        'thoughts': thoughts,
        'actions': actions,
        'observations': observations
    }
# Sample data
extracted_info = extract_trajectory_info(ds["train"][0])

Then remade into a new dataset with

# Predefine the instructions for the task
preamble = """Tools available:
(1) Search[entity], which searches the exact entity on Wikipedia and returns the first paragraph if it exists. If not, it will return some similar entities to search.
(2) Lookup[keyword], which returns the next sentence containing the keyword in the current passage.
(3) Finish[answer], which returns the answer and finishes the task.
"""
dataset = []
# Iterate through a specified number of examples in the training set
for i in range(len(ds['train'])):
    extracted_info = extract_trajectory_info(ds['train'][i])

    # Iterate through each thought in the extracted information
    for j in range(len(extracted_info['thoughts'])):
        out = f"{preamble}---\nQuestion: {extracted_info['question']}\n"
        prev = ""
        # Construct output for the first thought
        if j == 0:
            out += f"Thought: {extracted_info['thoughts'][0]}\n"
            out += f"Action: {extracted_info['actions'][0]}\nPAUSE\n\n\n\n"

        else:
            for k in range(1, j + 1):
                # Use appropriate indexing to avoid out-of-bounds errors
                prev += f"Thought:{extracted_info['thoughts'][j - k]}\n"
                prev += f"Action: {extracted_info['actions'][j - k]}\nPAUSE\n"

                prev += f"Observation: {extracted_info['observations'][j - k]}\n"

            out += prev  # Remove trailing space
            out += f"---\nThought: {extracted_info['thoughts'][j]}\n"
            out += f"Action: {extracted_info['actions'][j]}\nPAUSE\n\n\n\n"

        # Print the constructed output
        print(out)
        dataset.append(out)
        #print(len(out))