源链接:https://github.com/huggingface/blog/blob/main/encoder-decoder.md Transformers-based Encoder-Decoder Models Transformer-based Encoder-Decoder Models !pip install transformers==4.2.1 !pip install sentencepiece==0.1.95 Thetransformer-basedencoder-decoder model was introduced by Vaswani et al. in ...
triton_max_batch_size:${MAX_BATCH_SIZE},decoupled_mode:False,max_beam_width:${MAX_BEAM_WIDTH},engine_dir:${ENGINE_PATH}/decoder,encoder_engine_dir:${ENGINE_PATH}/encoder,kv_cache_free_gpu_mem_fraction:0.8,cross_kv_cache_fraction:0.5,exclude_input_in_output:True,enable...
模型:https://huggingface.co/OpenBA 项目:https://github.com/OpenNLG/OpenBA.git 论文概述 语言大模型的发展离不开开源社区的贡献。在中文开源领域,虽有GLM,Baichuan,Moss,BatGPT之类的优秀工作,但仍存在以下空白: 主流开源大语言模型主要基于decoder-only架构或其变种,encoder-decoder架构仍待研究。 许多中文开源...
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX. - [Model card] Bert2GPT2 EncoderDecoder model (#6569) · huggingface/transformers@974bb4a
第一:各种实验表明decoder-only模型更好, Google Brain 和 HuggingFace联合发表的 What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization? 曾经在5B的参数量级下对比了两者性能。论文最主要的一个结论是decoder-only模型在没有任何tuning数据的情况下、zero-shot表现最好,而...
We will focus on the mathematical model defined by the architecture and how the model can be used in inference. Along the way, we will give some background on sequence-to-sequence models in NLP and break down the transformer-based encoder-decoder architecture into its encoder and decoder ...
右图为encoder-decoder,前k个输出token可以看到所有k个输入token,从k+1的输出token开始只能看到历史的输入token。例如y_1能看到x_1 \sim x_3输入(y_3也可以),而y_4开始只能看到x_1 \sim x_4输入 PS: 这里为了方便理解,encoder-decoder简化使用causal with prefix示意,具体详见encoder-decoder章节 ...
问EncoderDecoderModel转换解码器的分类器层EN从中可以看出，fit_transform的作用相当于transform加上fit。
If the task mainly requires understanding the input:Encoder Model Example:To determine whether a review is positive or negative, using an encoder model like BERT is sufficient. (e.g., BERT, ModernBERT). Use an If the task mainly requires generating output:Decoder Model ...
Decoder:用于生成输出序列,通过对Encoder的隐藏表示进行解码。 Transformer架构的核心特点是: 没有循环层(RNN或LSTM),而是完全基于Attention机制。 使用Multi-Head Attention机制,可以同时处理多个关键序列。 使用Position-wise Feed-Forward Networks(FFN)进行位置无关的特征学习。