Pre-trained large language models (LLMs) offer impressive capabilities like text generation, summarization, and coding out of the box. However, they aren’t universally suitable for all tasks. Sometimes, your LLM might struggle with a specific task. In such cases, one option is to fine-tune the LLM, which involves retraining the base model on new data. Although fine-tuning can be complex, costly, and not the initial solution, it’s a potent technique that organizations using LLMs should consider. Understanding the mechanics of fine-tuning, even if you’re not an expert, can guide you in making informed decisions.
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Natural Language Understanding
A free Stanford course
XCS224U
Stanford School of Engineering
This project-oriented course focuses on building efficient and reliable models for understanding human language, drawing from linguistics, natural language processing, and machine learning. It covers tasks like contextual language representation, information retrieval, and NLU model evaluation. The course involves hands-on work to build baseline models and develop original models for class-wide competitions. The second half of the course is dedicated to an individual project in natural language understanding, following best practices in the field and incorporating topics like evaluations, semantic parsing, and grounded language understanding.
https://youtube.com/playlist?list=PLoROMvodv4rOwvldxftJTmoR3kRcWkJBp&si=XsWOdyJY7KhEhDJG
ELI5: FlashAttention
The goal of this blog post is to explain flash attention in such a way that hopefully anyone who already understands attention will ask themselves:
“Why didn’t I think of this before?” followed by “It’s so easy”.
https://gordicaleksa.medium.com/eli5-flash-attention-5c44017022ad
Fine-Tuning Embedding for RAG with Synthetic Data
This repo shows you how to fine-tune an embedding model to improve RAG performance even if you don’t have labelled data (i.e. positive pairs of query/relevant documents).
We walkthrough step-by-step the process of generating a synthetic dataset with LLM, finetuning an opensource embedding model, and finally evaluating the finetuned model.
We experiment with a small scale dataset of financial PDF documents, and show that finetuning the embedding model can substantially improve retrieval performance.