在日常使用大模型的时候,我们经常遇到一个问题,就是prompt提问、检索增强生成(RAG)提问和微调(Fine-Tuning)场景,当然大模型的本质都是输入-模型推理-输出,三个流程,那么这三个场景具体有什么差异性呢 今…
如下图所示为一个使用 RAG 的成功案例,通过各种手段将 Accuracy 从 Baseline 的 45% 提升到最终的 98%: 45%: 只使用简单的Cosine 相似度来检索 65%: HyDE(Hypothetical Document Embeddings),如下图所示,主要是指利用 LLM 生成一个假设答案,然后利用假设答案去检索,最终再由 LLM 生成答案。尝试后没有取得很好...
Fine-Tuning for retrieval augmented generation (RAG) with Qdrant NirantSep 4, 2023Open in GithubThe aim of this notebook is to walk through a comprehensive example of how to fine-tune OpenAI models for Retrieval Augmented Generation (RAG). We will also be integrating Qdrant and Few-Shot Lear...
比如现在火热的 RAG 方法,其中文本向量化检索就是非常核心的模块,所以今天我们就来看一篇来自 ACL 2023 Findings 的工作 —— INSTRUCTOR(Instruction-based Omnifarious Representations),这篇文章的作者们来自港大和华盛顿大学以及所属的 Meta AI 和 Allen Institute for AI 机构。 这篇文章将 Instruction Tuning 的想法...
Vector Database: Langchain - FAISS Computing Power Apple SiliconM1 TFLOPS 2.60 References: Embedding models: Hugging face Leaderboard Embeddings: Theory Embedding Visualization: Visual RepresentationAboutCreating a RAG model and Fine tuning for custom purposes Topics...
Refer to the pricing page for more information on Azure OpenAI fine-tuning costs. If you want to add out of domain knowledge to the model, you should start with retrieval augmented generation (RAG) with features like Azure OpenAI's on your data or embeddings. Often, this is a cheaper, ...
NUDGE是@SepantaZeighami等人的一篇很酷的论文,展示了你可以在几分钟内直接微调数据嵌入,而无需了解基础嵌入模型!这将带来更好的检索性能,而且你不必担心微调基础模型并重新运行数据。 微调你的嵌入模型是提高RAG/检索性能的好方法,但微调嵌入模型的问题在于速度慢,每次微调都需要重新运行所有数据的推理。 NUDGE是@Se...
RAG vs. fine-tuning vs. transfer learning Retrieval-augmented generation (RAG), fine-tuning andtransfer learningare distinct concepts that share some overarching similarities. Briefly, fine-tuning and transfer learning are strategies for applying preexisting models to new tasks, whereas RAG is a type...
1b-sentence-embeddings.md _blog.yml _events.yml _tags.yml accelerate-library.md accelerated-inference.md accelerating-pytorch.md ai-residency.md ambassadors.md asr-chunking.md autonlp-prodigy.md bert-101.md bert-cpu-scaling-part-1.md bert-cpu-scaling-part-2.md bert-inferenti...
, after pretraining a linear layer is added on top of BERT's embeddings for further classification - cf. with "BERT" section of this blog post.The output size of this layer corresponds to the number of tokens in the vocabulary, which does not depend on Wav2Vec2'...