Fine-Tuning ChatGPT for Text Generation With W&B

{"messages": 
[{"role": "system", "content": 
"ChatGPT is an expert chatbot on machine learning and related topics."}, 
{"role": "user", "content": "What is LLMOps?"}, 
{"role": "assistant", "content": "LLMOps refers to the methodologies, practices, and tools that are designed to deploy, manage, and monitor large language models in real-world applications and production environments."}]}

{"messages": [{"role": "system", "content": "ChatGPT is an expert chatbot on machine learning and related topics."}, {"role": "user", "content": "What is LLMOps?"}, {"role": "assistant", "content": "LLMOps refers to the methodologies, practices, and tools that are designed to deploy, manage, and monitor large language models in real-world applications and production environments."}]}

{"messages": [{"role": "system", "content": "ChatGPT is an expert chatbot on machine learning and related topics."}, {"role": "user", "content": "What are the challenges of LLMOps?"}, {"role": "assistant", "content": "Some challenges include: intensive computational resource requirements, effective handling and versioning of vast model weights, real-time response expectations despite the model's size, ensuring secure deployments given potential data recall issues, managing model biases and potential ethical concerns, cost implications of continuously running large models, keeping up with rapid advancements in the field, establishing reliable feedback mechanisms, and compliance with data privacy and regulatory standards."}]}

{"messages": [{"role": "system", "content": "ChatGPT is an expert chatbot on machine learning and related topics."}, {"role": "user", "content": "What are the benefits of LLMOps?"}, {"role": "assistant", "content": "Benefits include advanced natural language understanding and generation capabilities, addressing a wide array of NLP tasks without task-specific training, improved user interactions due to human-like text processing, scalability, opportunities for domain-specific customization through fine-tuning, and reduction of manual oversight in many language tasks."}]}

{"messages": [{"role": "system", "content": "ChatGPT is an expert chatbot on machine learning and related topics."}, {"role": "user", "content": "What are the use cases for LLMOps?"}, {"role": "assistant", "content": "Use cases encompass highly advanced chatbots, automated content creation, assisting developers with code suggestions, augmented data analysis, personalized learning assistants, enhanced search functionality with natural language understanding, business intelligence through sentiment analysis or trend spotting, and real-time translation or transcription services with nuanced understanding."}]}

{"messages": [{"role": "system", "content": "ChatGPT is an expert chatbot on machine learning and related topics."}, {"role": "user", "content": "How are large language models fine-tuned?"}, {"role": "assistant", "content": "Large language models are fine-tuned using smaller, domain-specific datasets to adapt them for specific tasks or industries, ensuring more accurate and relevant responses."}]}

{"messages": [{"role": "system", "content": "ChatGPT is an expert chatbot on machine learning and related topics."}, {"role": "user", "content": "Why is model versioning important in LLMOps?"}, {"role": "assistant", "content": "Model versioning allows for tracking and managing different iterations of a model, ensuring reproducibility, and facilitating easy rollbacks to previous versions if issues arise."}]}

{"messages": [{"role": "system", "content": "ChatGPT is an expert chatbot on machine learning and related topics."}, {"role": "user", "content": "How do you handle data recall issues in large language models?"}, {"role": "assistant", "content": "Data recall issues are addressed by carefully curating and anonymizing training data, implementing strict access controls, and occasionally using techniques like differential privacy."}]}

{"messages": [{"role": "system", "content": "ChatGPT is an expert chatbot on machine learning and related topics."}, {"role": "user", "content": "How do large language models impact cloud costs?"}, {"role": "assistant", "content": "Due to their size and computational needs, large language models can significantly increase cloud infrastructure costs, especially when frequently accessed or when deployed across multiple instances."}]}

{"messages": [{"role": "system", "content": "ChatGPT is an expert chatbot on machine learning and related topics."}, {"role": "user", "content": "Are there environmental concerns with large language models?"}, {"role": "assistant", "content": "Yes, training and deploying large language models require significant computational resources, leading to increased energy consumption and carbon footprints. There are ongoing efforts to make them more efficient and eco-friendly."}]}

{"messages": [{"role": "system", "content": "ChatGPT is an expert chatbot on machine learning and related topics."}, {"role": "user", "content": "What's the significance of feedback loops in LLMOps?"}, {"role": "assistant", "content": "Feedback loops allow developers and users to report model behaviors, ensuring continuous improvement, addressing biases, and refining the model's accuracy over time."}]}

!pip install openai
!pip install wandb
!pip install git+https://github.com/wandb/wandb.git@openai-finetuning

import os
import openai
import wandb
from openai import OpenAI
from wandb.integration.openai import WandbLogger

WandbLogger.sync(project="chatgpt3.5-fine-tuning-for-Sentiment-Analysis")

openai.api_key = "Insert Your Key Here"

client = openai.Client()

training_file = client.files.create(
  file=open("/kaggle/input/largelanguagemodeltraining/LLMOpsTraining.jsonl", "rb"),
  purpose='fine-tune'
)

training_file_id = training_file.id

fine_tuning_job = client.fine_tuning.jobs.create(
  training_file=training_file_id,
  model="gpt-3.5-turbo"
)

job_id = fine_tuning_job.id

file_path = '/kaggle/input/largelanguagemodeltraining/LLMOpsTraining.jsonl'

# Process the dataset
dataset = []
with open(file_path, 'r') as file:
    for line in file:
        try:
            data_point = json.loads(line)
            dataset.append(data_point["messages"])
        except json.JSONDecodeError as e:
            print(f"Error decoding JSON: {e}")

# List to store results
results = []


# Generate responses and process them
for messages in dataset:
    system_message = next((m['content'] for m in messages if m['role'] == 'system'), None)
    user_query = next((m['content'] for m in messages if m['role'] == 'user'), None)

    # Generate completion with the fine-tuned model
    completion_ft = client.chat.completions.create(
        model="ft:gpt-3.5-turbo-0613:personal::8Lw0APbe", 
        messages=messages
    )
    response_ft = completion_ft.choices[0].message.content

    # Generate completion with the standard model
    completion_std = client.chat.completions.create(
        model="gpt-3.5-turbo",
        messages=messages
    )
    response_std = completion_std.choices[0].message.content

    # Append results to the list
    results.append({
        "system": system_message,
        "user": user_query,
        "fine_tuned_response": response_ft,
        "standard_model_response": response_std
    })

# Convert results to a DataFrame
df_results = pd.DataFrame(results)

# Log the DataFrame as a table to W&B
wandb.log({"results_table": wandb.Table(dataframe=df_results)})

wandb.finish()