Custom Fine-Tuning GPT-2 & StarCoder in PyTorch for Chatbots

Explore using the Open Assistant dataset to fine-tune GPT-2 and StarCoder 2 for general dialogue. Understand the dataset structure with top level messages and parent links across thousands of conversations.

Design dialogue templates to train fine-tuned GPT-2 and StarCoder models, using system, user, and assistant tokens to craft structured training and inference prompts.

Build a PyTorch dialog dataset from a JSON data file, tokenize prompts, add special and pad tokens, and prepare input IDs, attention masks, and shifted labels for chatbot training.

Learn to initialize the dataset for chatbot training in PyTorch by building Train.py, loading JSON data, and creating train and validation loaders with tokenization via Huggingface.

Set up a GPT-2 model for selective fine-tuning by freezing all parameters and training only target modules like C projections, C attention, and Q attention, loaded from local or HuggingFace.

Initialize the model, data loaders, and device; resize embeddings for new tokens and configure Adam with a cosine scheduler. Save losses and per-epoch models and tokenizers.

Execute the training loop by clearing memory, enabling train mode, loading batches from data loader, computing loss and logits, and updating parameters after gradient accumulation of 4 to 16 steps.

learn how to validate a fine-tuned gpt-2 and starcoder model in pytorch by evaluating on 10% of data, using gradient accumulation, and analyzing validation losses alongside training losses.

Plot the training and validation losses per epoch by averaging across gradient accumulation cycles, loading losses from losses.json, and displaying a per-epoch loss curve.

Set up model inference for chatbots by loading the trained model and tokenizer, configuring huggingface authentication, and generating dialogue with a generate.py workflow and command line options.

Master token-by-token generation with causal attention in decoder models, producing one token at a time from a prompt. Stop at the end token or max length, on cpu or gpu.

Implement real-time continuous chat by generating the assistant’s output one token at a time, using a dialogue template and the messages array for interactive questions and responses.

Learn to generate sample dialogues by hard-coding prompts, formatting dialogue templates, and running token-by-token inference to preview chatbot outputs before fine-tuning.

Fine-tune two models in PyTorch—GPT-2 large and StarCoder 2 with 3 billion parameters—train with custom tokens, test dialogue generation, and compare pre-trained versus fine-tuned performance.