Today, while I was eating, I came across a video (the video is attached at the end of this article). Unlike many tech channels that jump straight into discussing AI, economics, and replacing humans, this video took a more careful approach. It explained in detail how hardware specifications have influenced algorithms (or AI model architectures) over time.
Read More »Here’s a thought: Will Transformers be replaced in the future?
I’ve been intermittently reading about a fine-tuning method called Kahneman-Tversky Optimization (KTO) from various sources like HuggingFace’s official documents and other online materials. It’s similar to DPO as a way to align models with human values, but KTO’s data preparation format is much more convenient, so I’m quickly applying it to my current tasks before making time to study the detailed content in the related papers.
Read More »Note Of KTOTrainer (Kahneman-Tversky Optimization Trainer)
The following are some points in this paper:
Read More »[Paper Reading] ENTP: ENCODER-ONLY NEXT TOKEN PREDICTION
A multi-modal large language model (Multi-Modal Large Language Model) isn’t limited to text only. I know this might sound contradictory, but this is a term that has become widely accepted. What I want to document today is how to fine-tune a multi-modal model using a script.
Read More »Notes on Fine-Tuning a Multi-Modal Large Language Model Using SFTTrainer (Taking LLaVa-1.5 as an Example)
This year, due to work, I tried annotating the data myself; it was only after diving into it personally that I truly understood just how profoundly training data affects an AI model.
Read More »“Common sense, as people call it, is merely the biases learned during youth”—the training data for AI models is no different
In the process of training and fine-tuning deep neural networks, the most important and scarce resource is undoubtedly the GPU’s VRAM. Therefore, making every bit perform at its best is a critical task.
Read More »Differences in Precision Representations in Deep Learning: Float32, Float16, Float8, and BFloat16
Recently, I’ve achieved some good application results by fine-tuning Gemma-2. However, I encountered various errors when deploying it on the client’s equipment, which was quite frustrating. Currently, there isn’t a systematic troubleshooting guide online, so I’m documenting it here.
Read More »Troubleshooting Accelerated Inference of Gemma-2 on V100 GPUs Using vLLM
When applying Large Language Models (LLMs) in real-world scenarios, it’s often not just about letting the model generate text freely. We might want the model to return specific structures, such as multiple-choice questions or providing a rating. In such cases, transformers-based models can directly use the outlines tool.
Read More »Structuring Model Outputs Using the Outlines Tool
Currently, LLM services cover a wide range of fields, and Prompt Injection and Jailbreak threats to LLMs are growing by the day. A few months ago, a customer service LLM even provided incorrect information, leading to a loss of customer rights (although that wasn’t caused by a prompt attack).
Microsoft’s open-source BIPIA (Benchmarking and Defending Against Indirect Prompt Injection Attacks on Large Language Models) evaluation method, although tested six months ago without significant updates since, remains a simple and convenient testing method for the tasks I have at hand.
Read More »Evaluating LLM Defense Capabilities Using the Microsoft BIPIA Framework
Cross-lingual Modular (X-Mod) is an interesting language model architecture that modularizes the parameters for different languages as Module Units, allowing the model to use separate parameters when fine-tuning for a new language, thereby (comparatively) avoiding the problem of catastrophic forgetting.
Read More »[Paper Reading] Lifting the Curse of Multilinguality by Pre-training Modular Transformers
