事实上 embedding 这一步占据了很大的参数量,可以看下这篇论文 TensorGPT: Efficient Compression of the Embedding Layer in LLMs based on the Tensor-Train Decomposition 2023-11-24· 广东 回复4 草木如织 Wwwwwy 对的。乘一下也知道,30k*768就快30M了 2023-12-21· 湖北 回复1 梦貘...
常见的视觉component + projection layer + LLM的范式 LanguageBind(不一样的部分) 类似ImageBind的思路,把不同模态的输入映射到同一个特征空间,以达到Bind的作用,不一样的是这里采用了Language模态来做bind 注:这个模型跟imagebind一样是预先训练好的 评价 还是传统的 VLM模型范式,pre-align其实是用LanguageBind来...
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在不同层上,BERT的效果整体要显著优于GPT GPT-2 last layer的各向异性比较严重,中间层或低层比顶层更适合做similarity任务 针对第二个问题,Instructor Embedding论文也给出了不同参数量模型的效果对比实验,如下表: 根据上表,可以发现: 相比335M的GTR_LARGE模型,参数量数十倍的4.8B的GTR-XXL模型性能并无显著上升。
GPT-2 last layer的各向异性比较严重,中间层或低层比顶层更适合做similarity任务 针对第二个问题,Instructor Embedding论文也给出了不同参数量模型的效果对比实验,如下表: 根据上表,可以发现: 相比335M的GTR_LARGE模型,参数量数十倍的4.8B的GTR-XXL模型性能并无显著上升。
This layer is the model’s final learned representation of the entire input sequence. The final embedding, however, is extracted only from the first token, which is often a special token ([CLS] in BERT) in transformer-based models. This token serves as an aggregate representation of the ...
the precisions of the pre-trained embedding-based models are consistently higher for class 1. The overall precision and recall, as well as F1 score are presented in Table 1. As seen, the pre-trained embedding-based models consistently outperform the embedding-layer-based model, albeit with a ...
After the reference documents have been embedded, you can start the RAG question and answering by visiting the URL to access the Streamlit application. AnAmazon Cognitoauthentication layer is used, so it requires creating a user account in the Amazon Cognito user pool deployed via the ...
This PR adds support for Roberta embedding models. It's mostly the same as the Bert architecture, the only thing that changes is the padding token in the Embedding layer so this PR tries to reuse B...
There is also one thing I want to implement before going back to the transformers library: support for shared weights in 8-bit quantization. While 4-bit quantization linear layer does not seem to changeself.weightparameter during forward pass, 8-bit quantization linear layer changes it dramatically...