本篇是《Rust与AI》系列的第二篇,上一篇我们主要介绍了本系列的概览和方向,定下了一个基调。本篇我们将介绍LLM的基本架构,我们会以迄今为止使用最广泛的开源模型LLaMA为例展开介绍。 LLM背景 Rust 本身是不挑 AI 模型的,但是 LLM 是当下最热的方向,我们就从它开始吧,先了解一些非常基础的背景知识。 Token LLM ...
Looking at the source documents inserted into the context window (Figure 1), none of the text segments discuss Novorossiya, resulting in this failure. Figure 1: Baseline RAG retrieved context [2] In comparison, the GraphRAG approach discovered an entity in the query, Novorossiya. This allows...
Looking at the source documents inserted into the context window (Figure 1), none of the text segments discuss Novorossiya, resulting in this failure. Figure 1: Baseline RAG retrieved context [2] In comparison, the GraphRAG approach discovered an entit...
其次，考虑到论文的数量不宜过多，可能会超过LLM的Context Window，因此限制数量大小默认为10。
from llama_index.llms import OpenRouter from llama_index.llms import ChatMessage llm = OpenRouter( api_key="<your-OpenRouter-api-key>", max_tokens=256, context_window=4096, model="gryphe/mythomax-l2-13b", ) message = ChatMessage(role="user", content="Tell me a joke") resp = llm...
(LLMs) to efficiently process overly lengthy contexts. However, recent LLMs like Gemini- 1.5 and GPT-4 show exceptional capabilities to understand long contexts directly. We conduct a comprehensive comparison between RAG and long-context (LC) LLMs, aiming to lever-age the strengths of both. ...
Memory: To remember previous instructions and answers, LLMs and chatbots like ChatGPT add this history to their context window. This buffer can be improved with summarization (e.g., using a smaller LLM), a vector store + RAG, etc. ...
Are Long-LLMs A Necessity For Long-Context Tasks? 训练和模型架构方案 Never Train from Scratch: FAIR COMPARISON OF LONGSEQUENCE MODELS REQUIRES DATA-DRIVEN PRIORS Soaring from 4K to 400K: Extending LLM's Context with Activation Beacon Never Lost in the Middle: Improving Large Language Models vi...
First, even with a context window of 10M tokens, we’d still need a way to select information to feed into the model. Second, beyond the narrow needle-in-a-haystack eval, we’ve yet to see convincing data that models can effectively reason over such a large context. Thus, without good...
No GPT‑4 tool usage: Due to our security measures, the GPT‑4 models we tested were used without any tools, such as Advanced Data Analysis and Browsing. Enabling the usage of such tools could non-trivially improve the usefulness of our models in this context. We may explore ways to ...