价格(Price):Gemma 7B、OpenChat 3.5、DeepSeek-V2、Llama 3 (8B) 上下文窗口 (Context Window):Gemini 1.5 Flash、Gemini 1.5 Pro、Claude 3 Opus、Claude 3 Sonnet 单拿出价格一栏,可以看到大模型输入/输出价格的明显变化。右侧交互框支持勾选自己想添加参与比价的其他大模型。 整个网站收录了国外主流前沿大模型,...
直接利用LLM的长文本(Long Context Window)处理能力,将所有相关文档全部输入给LLM,直接生成结果。 微调,将领域知识以参数化的形式直接嵌入LLM。 对于普通用户而言,微调模型所需要耗费的时间和经济成本都是比较高的,所以通常会在RAG和Long Context两种方案中间做选择。那么究竟哪种方案更胜一筹呢? Atai Barkai 做了一...
Gato [36] 做到了用一个生成式模型来完成多种游戏任务和vision & language 任务。由于context window s...
**价格 (Price)**:Gemma 7B、OpenChat 3.5、DeepSeek-V2、Llama 3 (8B) **上下文窗口 (Context Window)**:Gemini 1.5 Flash、Gemini 1.5 Pro、Claude 3 Opus、Claude 3 Sonnet 单拿出价格一栏,可以看到大模型输入/输出价格的明显变化。右侧交互框支持勾选自己想添加参与比价的其他大模型。 整个网站收录了国外...
14.LongRoPE: Extending LLM Context Window Beyond 2 Million Tokens 15.From Text to CQL: Bridging Natural Language and Corpus Search Engine 16.$\infty$Bench: Extending Long Context Evaluation Beyond 100K Tokens 17.Neeko: Leveraging Dynamic LoRA for Efficient Multi-Character Role-Playing Agent 18.GC...
• Context: 从代码库和企业知识库中,提取出和问题最相关内容;再结合系统环境和运行情况(如报错信息和 Log),在有限的 Context Window 中放入更高质量的信息。我们将在后文的 RAG 部分展开介绍。 类似这样的 Prompt 模版还有 RTF、Tree of Thought 等,暂时不做展开介绍。 具体来说,尽管在 Codeium 的 UI 上...
FLM-101B Configurations. The FLM-101B model is structured with a hidden state dimension of 10, 240, a layer number of 80, a context window of 2,048 tokens, 80 attention heads, and a vocabulary size of 100, 256. FLM-101B uses the AdamW optimizer [31] with β1 = 0.9 and β2 = 0....
(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. ...
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...
This not only resolves the challenge of context window expansion during retrieval but also enhances retrieval efficiency and responsiveness. Reranking 重新排序模型是优化从检索器检索到的文档集的关键。当引入额外的上下文时,语言模型经常面临性能下降的问题,重新排序可以有效地解决这个问题。核心概念包括重新排列...