BTW 词表大小此处并没有纳入讨论 zhipu 的人也用了 llama 架构而非自家的 GLM(虽然早就是这样了),标准 decoder-only 一统天下
后来又换成了Llama1 30B,没有多少提升……其实开源的一堆大模型面对这种任务时都差不多。另外,笔者也试过encoder-decoder架构的模型,比如Flan-T5-XL,发现确实比decoder only架构的模型更适合于这种任务,但也只是提高了4个点而已。 关于大模型的推理能力,我想分享几篇有趣的论文和我的实验观察。论文Large Language...
InferflowEditing configuration filespickle (safe), safetensors, gguf, llama2.cdecoder-only, encoder-decoder, encoder-only2b, 3b,3.5b, 4b, 5b, 6b, 8b✔C++ Support Matrix Pickle (Inferflow reduces the security issue of most other inference engines in loading pickle-format files). ...
Hey all! The video models are all supported in Transformers now and will be part of the v4.42 release. Feel free to check out the model checkpoints here. To get the model, update transformers by running: !pip install --upgrade git+https:...
Granite is IBM’s flagship series of LLM foundation models based on decoder-only transformer architecture. Granite language models are trained on trusted enterprise data spanning internet, academic, code, legal and finance. ReportAI in Action 2024 ...
2. 但是随着 decoder-only 的 GPT 形式的模型逐渐成为 LLM 的事实标准,如何利用 Linear Attention 的右乘特性加速单向任务成为了亟待解决的难题。为了解决这个问题,本文作者提出了利用 “分而治之” 的思想,将注意力矩阵的计算分为对角阵和非对角阵两种形式,并采用不同的方式对他们进行计算。如图 3 所示,Linear ...
Gemma and ChatGPT use a decoder transformer. Because they are decoder-only, Gemma and ChatGPT work for text-to-text LLMs but not for images and videos. Google Gemini uses both a decoder and encoder architecture. That architecture facilitates Gemini's multimodal capability, enabling it to suppor...
Granite is IBM’s flagship series of LLM foundation models based on decoder-only transformer architecture. Granite language models are trained on trusted enterprise data spanning internet, academic, code, legal and finance. Report AI in Action 2024 We surveyed 2,000 organizations about their AI in...
推理llama270b的时候 ValueError: You asked to pad the vocabulary to 32000 when the initial vocabulary size is 32001. You can only pad to a higher value. 导致推理失败 已经转换完权重 二、软件版本: -- CANN 版本 (e.g., CANN 3.0.x,5.x.x): 7.0.1 ...
图1就是我们常用的,decoder only又被称为Causal(因果),图2就是prefix-LM,GLM的原型,图3就是不太常用的T5就是这个架构 首先3种都是可以训练的,这个没啥可说的 在推理上encoder-decoder可就太不占优势了,因为它参数是前两个的两倍,得多用多少块卡啊,如果你的训练效果不能超过前两个两倍,那就都是赔的 ...