Improving the Performance of Your LLM Beyond Fine Tuning

Apply text and image augmentation to boost data quantity and diversity for fine tuning. Use Python and PyTorch to craft augmented SST-2 sentiment data, including synonym replacement and back translation.

Explore data augmentation in natural language processing using Nltc and WordNet for synonym replacement, boosting text data diversity and model robustness.

Explore domain adaptation techniques to reduce data mismatch for fine tuning, leveraging multitask learning, domain adversarial training, self-training, co-training, and meta learning across source and target domains.

Learn to use Transformers for fine tuning a pre-trained Bert model on the Sst2 sentiment dataset, including tokenization, dataset loading, training with the trainer, and evaluation.

Learn to deploy sentiment analysis web apps with Huggingface spaces using a fine-tuned BERT model, choosing frameworks like Streamlit or Gradio, and configuring, deploying, and sharing spaces.

Explore magnitude pruning and other pruning techniques to reduce a fine-tuned model's size and complexity, using Python, PyTorch, and Torch pruning on a sentiment analysis task with SST-2.

Import PyTorch, torch.optim, and the pruning module to prune a model with methods like prune L1 and prune unstructured, then fine-tune for sentiment analysis using transformers and Huggingface datasets.

Learn to load and evaluate a fine-tuned sentiment analysis model using transformers and Hugging Face datasets on the sst2 dataset, measure parameter counts, and prepare for magnitude pruning with PyTorch.

Apply magnitude pruning with the PyTorch pruning module to remove lowest absolute weights, yielding a pruned model and making pruning permanent, then compare performance and size with and without pruning.

Evaluate your pruned model using Transformers and Huggingface datasets, load the Sst2 dataset, and measure parameter size while comparing performance and efficiency before and after pruning.

Distill knowledge from a large teacher model to a smaller student to boost efficiency after fine tuning, using knowledge distillation, data-free distillation, and self distillation with PyTorch.

Import PyTorch, Transformers, and Hugging Face Datasets, then load Distilbert as the student and prepare for distillation using the Sst2 dataset, with Bert as the teacher.

Load and fine-tune a Bert base uncased teacher model using Transformers and Hugging Face datasets. Tokenize and encode the Sst2 data, then train, evaluate, and report accuracy.

Load and train a Distilbert student via knowledge distillation, using the teacher's soft labels and a loss that combines cross-entropy with KL divergence.

Evaluate and prune a trained distilbert model after training using PyTorch, Transformers, and Hugging Face Datasets; measure accuracy, precision, recall, and model size, guided by knowledge distillation.

This lecture highlights January 2024 advancements in fine-tuning methods for LLMs, including quantization and selective parameter fine tuning, with practical low-cost workflows on Colab and Hugging Face.

ROPE fine tuning extends LLM context windows by modifying embeddings for llama and gpt-neo-x with math-based rules, enabling effective training on small data sets.

Explore self rewarding language models: a three-step fine tuning framework using instructional data, synthetic outputs, and self-rated rewards to boost Lama 70B performance.

Master LoRA tuning tips and tricks to improve llm performance beyond fine tuning, using Faf data sets and humor tests to identify 500-row vs 1000-row optimal data sizes.

Learn to auto train LLM models in Google Colab by preparing train.csv with text and target, configuring hyperparameters, and pushing trained models to Hugging Face, noting precision and quantization caveats.

Examine raft, a Berkeley retrieval augmented generation method that blends fine tuning with rag tuning. Use GPT-4 to produce one-page Q&A with chain-of-thought and fine-tune and rag-tune the model.

Explore GPT Auto Trainer, a simple fine tuning approach using OpenAI or anthropic keys in Google Colab, with data generation and an LM trainer workflow.

Explore how data size, from 64 to about 1 million rows, and data quality impact fine-tuning bert-like models for sentiment analysis, including data preparation and formatting across platforms.

Rag tuning relies on external retrieval, causing a needle in a haystack problem and higher costs. Fine tuning updates embeddings and internal representations for stronger, secure, cost-efficient performance.

Explore Mora fine tuning for large language models, a square-matrix, parameter-efficient method that rivals full fine tuning and Laura, with strong results in biomedical, financial, and mathematical reasoning tasks.