Podcast
Questions and Answers
What is the primary objective of deployment for a fine-tuned model?
What is the primary objective of deployment for a fine-tuned model?
- Enhance the training algorithms used in the model
- Gather large amounts of training data
- Make your fine-tuned model accessible (correct)
- Analyze computational resources more efficiently
Which tool is NOT mentioned for hosting a model?
Which tool is NOT mentioned for hosting a model?
- AWS SageMaker
- OpenAI Deployment API
- Google Cloud Functions (correct)
- Hugging Face Spaces
What should be done to ensure data privacy during model deployment?
What should be done to ensure data privacy during model deployment?
- Use encryption for all data transfers
- Keep training data as is for better accuracy
- Share datasets with third parties for validation
- Anonymize sensitive information (correct)
What is a key consideration when deploying a model regarding compute resources?
What is a key consideration when deploying a model regarding compute resources?
What is important to do continuously after deploying the model?
What is important to do continuously after deploying the model?
What is the first step in creating a fine-tuned model with your conversations?
What is the first step in creating a fine-tuned model with your conversations?
Which tool can be used for exporting chat history?
Which tool can be used for exporting chat history?
What format should the data be converted into for model training?
What format should the data be converted into for model training?
Which of the following models provides a simple API integration for fine-tuning?
Which of the following models provides a simple API integration for fine-tuning?
What should be done to remove sensitive personal information from the data?
What should be done to remove sensitive personal information from the data?
What is the purpose of adding metadata to the conversations?
What is the purpose of adding metadata to the conversations?
What metric can be used to evaluate the model's performance?
What metric can be used to evaluate the model's performance?
Which library can be employed to split data into training, validation, and test sets?
Which library can be employed to split data into training, validation, and test sets?
Flashcards
Deployment
Deployment
Making a fine-tuned model accessible for use.
Model Hosting
Model Hosting
Making a model available on a cloud platform or local server.
Interface Creation
Interface Creation
Creating a way for users to interact with your model.
Model Iteration
Model Iteration
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Alignment Evaluation
Alignment Evaluation
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Fine-tuning LLM with your conversations
Fine-tuning LLM with your conversations
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Data Collection: Exporting conversations
Data Collection: Exporting conversations
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Data Collection: Normalizing format
Data Collection: Normalizing format
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Data Collection: Anonymizing data
Data Collection: Anonymizing data
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Data Preprocessing: Tokenization
Data Preprocessing: Tokenization
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Data Preprocessing: Adding metadata
Data Preprocessing: Adding metadata
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Data Preprocessing: Deduplication
Data Preprocessing: Deduplication
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Data Preprocessing: Formatting for training
Data Preprocessing: Formatting for training
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Study Notes
Fine-Tuning LLMs with Conversation Data
- Creating a custom AI model by fine-tuning a pre-trained large language model (LLM) using your conversation history is possible.
Data Collection
- Objective: Gathering and preparing your conversation data.
- Task: Extracting chat history from various platforms (e.g., ChatGPT, Rewind, Google Assistant).
- Tools/Resources: APIs or manual export tools (ChatGPT export, Rewind logs).
- Normalization: Converting conversations into a structured format (JSON or CSV).
- Tools/Resources: Python (e.g., pandas).
- Anonymization: Removing sensitive personal data.
- Tools/Resources: Regex methods, libraries like presidio.
Data Preprocessing
- Objective: Preparing the data for fine-tuning.
- Step: Tokenization—breaking text into model-compatible tokens.
- Tools/Resources: Hugging Face tokenizers.
- Step: Adding metadata—annotating conversations with timestamps, topics, intents.
- Tools/Resources: A schema (e.g., timestamp, topic, intent, message).
- Step: Deduplication—removing redundant conversations.
- Tools/Resources: Python set(), fuzzywuzzy for similarity.
- Step: Formatting for model training—converting data to the required format (e.g., {"prompt": ..., "completion": ...}).
- Tools/Resources: OpenAI Fine-Tuning format, Hugging Face Dataset format.
Model Selection
- Objective: Choosing the model architecture and framework.
- Framework: OpenAI GPT-3/4—simple API integration, readily available for fine-tuning.
- Framework: Hugging Face Transformers—allows for extensive control over the model and training process.
- Framework: Google’s T5 or BERT—efficient for dedicated tasks like summarization or question answering.
Fine-Tuning Process
- Objective: Training the model with the collected data.
- Step: Preparing the training dataset—splitting data into training, validation, and test sets.
- Tools/Resources: Python's scikit-learn (sklearn).
- Step: Setting up the fine-tuning pipeline—defining hyperparameters.
- Tools/Resources: Hugging Face Trainer, OpenAI fine-tuning API.
- Step: Training the model—starting the training process and monitoring.
- Tools/Resources: GPU/TPU, cloud platforms (AWS, GCP), local NVIDIA GPUs.
- Step: Evaluating—testing on unseen conversations to assess accuracy, relevance, and coherence.
- Tools/Resources: Metrics like BLEU, ROUGE, or manual analysis.
Deployment
- Objective: Making the fine-tuned model accessible.
- Step: Hosting the model—deploying on cloud or local servers.
- Tools/Resources: Hugging Face Spaces, AWS SageMaker, OpenAI Deployment API.
- Step: Creating an interface—building a user-friendly interaction tool (e.g., CLI or web-based).
- Tools/Resources: Streamlit, Flask, FastAPI.
- Step: Monitoring and iterating—continuously evaluating performance and retraining with updated data.
- Tools/Resources: Weights & Biases, or custom logging.
Key Considerations
- Data Privacy: Anonymizing sensitive data.
- Compute Resources: Cloud GPUs for large-scale training.
- Scalability: Planning model updates to accommodate more data.
- Alignment: Assessing model alignment with desired outputs.
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