Graph RAG is an Knowledge-enabled RAG approach to retrieve information from Knowledge Graph on given task. Typically, this is to build context based on entities’ SubGraph related to the task.
GraphStore backed RAG vs VectorStore RAG
As we compared how Graph RAG helps in some use cases in this tutorial, it’s shown Knowledge Graph as the unique format of information could mitigate several issues caused by the nature of the “split and embedding” RAG approach.
A modern Large Language Model (LLM) is trained using the Transformers library, which leverages the power of the Transformer network architecture. This architecture has revolutionized the field of natural language processing and is widely adopted for training LLMs. Python, a high-level programming language, is commonly used for implementing LLMs, making them more accessible and easier to comprehend compared to lower-level frameworks such as OpenXLA’s IREE or GGML. The intuitive nature of Python allows researchers and developers to focus on the logic and algorithms of the model without getting caught up in intricate implementation details.
This rentry won’t go over pre-training LLMs (training from scratch), but rather fine-tuning and low-rank adaptation (LoRA) methods. Pre-training is prohibitively expensive, and if you have the compute for it, you’re likely smart enough not to need this rentry at all.
- Hugging Face Optimum team collaborated with AutoGPTQ library for a simple API to apply GPTQ quantization on language models.
- GPTQ quantization allows open LLMs to 8, 4, 3, or 2 bits, enabling them to run on smaller hardware with minimal performance loss.
- The blog covers:
- Setting up the development environment.
- Preparing the quantization dataset.
- Loading and quantizing the model.
- Testing performance and inference speed.
- Bonus: Running inference with text generation.
- GPTQ’s purpose is explained before diving into the tutorial.
- Large Language Models (LLMs) and prompt-based heuristics are being used for off-the-shelf solutions to various NLP problems.
- LLM-based few-shot methods have shown promise but lag in Named Entity Recognition (NER) compared to other methods.
- « PromptNER » is introduced as a new algorithm for few-shot and cross-domain NER.
- PromptNER needs entity definitions and few-shot examples for a new NER task.
- PromptNER uses LLM to generate potential entities and explanations for their compatibility with entity type definitions.
- PromptNER achieves state-of-the-art performance in few-shot NER on ConLL, GENIA, and FewNERD datasets.
- It also outperforms previous methods in Cross Domain NER, setting new records on 3 out of 5 CrossNER domains with an average F1 gain of 3%.
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.
A free Stanford course
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.
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”.
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.