LM Finetuning with Huggingface on RunPod
Learn LM finetuning with Huggingface on RunPod, including customizing deployment and tokenization, and merging weights to maintain model size.
00:00:00 Learn how to set up and deploy a GPU instance on RunPod for Llama/Wizard LM Finetuning with Huggingface on PyTorch. Instructions include customizing deployment, adding disk space, and working on Jupyter Lab.
📚 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.
00:07:19 This video demonstrates the process of fine-tuning the Llama/Wizard LM model using Huggingface on RunPod. It provides instructions on installing the necessary requirements and running the script with different parameters.
📌 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.
00:14:36 Learn about tokenization and fine-tuning of the Llama/Wizard LM model using Huggingface on RunPod. Also, understand the process of merging weights to maintain model size.
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.
00:21:52 Create a concise summary of the YouTube video 'Llama/Wizard LM Finetuning with Huggingface on RunPod' without mentioning sponsorships or brand names or subscriptions.
📝 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.
00:29:07 This video provides a walkthrough of LM finetuning with Huggingface on RunPod, including uploading models and doing inference on your local machine.
🔑 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.
00:36:21 Learn about LM fine-tuning using Huggingface on RunPod, where attention is the key. Discover how adapters make model specialization easier and more efficient.
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.
00:43:36 This video explains how to use Llama/Wizard LM Finetuning with Huggingface on RunPod. It discusses the benefits of using low-rank matrices, the process of fine-tuning, and the use of QLaura for quantization.
🧠 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.
Want to deep dive into this video?
