LM Finetuning with Huggingface on RunPod

📚 The video is about using Llama/Wizard LM Finetuning with Huggingface on RunPod to improve GPU performance.

💻 RunPod is a platform that allows users to access GPUs and run GPU-intensive tasks.

💡 The video provides step-by-step instructions on setting up a 3090 instance on RunPod and customizing it for optimal performance.

📌 The video is a tutorial on how to fine-tune the Llama/Wizard LM model using Huggingface.

🔧 The speaker provides instructions on how to set up and run the fine-tuning process.

💻 Different options for models and datasets are explained, along with parameters that can be modified.

Tokenization is the process of converting text into a format that the model can understand.

Adding a stop token to the attention mask helps the model know when to stop generating output.

Merging weights allows the model to update and learn from new data while maintaining consistent model size.

📝 This video explores the process of fine-tuning the Llama/Wizard LM model with Huggingface.

💡 Fine-tuning allows users to train a pre-created model with additional data to improve its performance.

🔧 Nvidia SMI is a useful tool for monitoring GPU usage and debugging memory issues during training.

🔑 LM Finetuning with Huggingface on RunPod: This video demonstrates how to upload and download a trained model using Huggingface and RunPod.

💡 Sequence Generation with LLM: The video explains the process of tokenization and self-attention in LLM, which forms the basis of text generation.

⚙️ Self-Attention in LLM: The self-attention mechanism helps to establish relationships and route information between words, allowing for better text generation.

The Llama/Wizard LM model uses attention mechanism to assign scores to words based on their relation to other words.

The routing of information in the model is represented by a heat map, which combines the scores of different words to create a new vector representation.

Fine-tuning the model with adapters allows for specialized tasks without losing previous knowledge and with lower memory requirements.

🧠 Using low rank matrices in fine-tuning allows for efficient storage of weights and reduces the number of parameters.

🔁 The procedure for fine-tuning with low rank matrices is similar to normal fine-tuning, but the focus is on optimizing smaller parameters instead of a larger dense matrix.

📈 Using low rank matrices can sometimes even improve performance and does not add any overhead during inference.