(x=x, cond_BD=cond_BD_or_gss, attn_bias=None) # 处理一下token map,维度不变 logits_BlV = self.get_logits(x, cond_BD) # 从1*1到2*2到3*3的token map维度升级,在这里 f_hat, next_token_map = self.vae_quant_proxy[0].get_next_autoregressive_input(si, len(self.patch_nums), f...
Next-token prediction (NTP) over large text corpora has become the go-to paradigm to train large language models. Yet, it remains unclear how NTP influences the mapping of linguistic patterns to geometric properties of the resulting model representations. We frame training of large language models...
config.eos_token_id, pad_token_id=model.config.pad_token_id, max_new_tokens=40960, do_sample=True, top_k=2048, ) h = pos_inputs.image_size[:, 0] w = pos_inputs.image_size[:, 1] constrained_fn = processor.build_prefix_constrained_fn(h, w) logits_processor = LogitsProcessor...
最近的一些研究集中于通过精心设计的空间令牌混合器(Spatial Token Mixer, STM)来提高性能。但是,作者认为一个设计良好的通用架构可以显著提高整个骨干网络的性能,而不论配备哪种空间标记混合器。因此,本文提出了UniNeXt,一种改进的通用架构。\ 为了验证其有效性,本文采用了各种经典和现代化设计实例化了 STM。实现结果...
Token Embeddings:表示每个单词的词向量。 Segment Embeddings:表示句子所属的类别,句子 A 的标记为 0,句子 B 的标记为 1。 Position Embeddings:表示每个单词在句子中的位置。 这三种嵌入的加权和被输入到 Transformer 网络中进行处理。BERT 的最终输出是CLS标记的向量,它被用来做 NSP 的二元分类。
I'm unsure if the llama3 logits need to be within a tighter tolerance to the reference Whether it's necessary to resize the vocabulary (or if it could use a reserved token inteadAdd llama3-llava-next-8b to llava_next conversion script#31395 (comment)) ...
ConvNeXt是一个结构简单的现代CNN模型,对于每个ConvNeXt块,输入X首先由深度卷积处理,以沿空间维度传播信息。遵循MetaFormer将深度卷积抽象为负责空间信息交互的token mixer。因此,如图2所示,ConvNeXt被抽象为MetaNeXt,形式上,在MetaNeXt块中,其输入X首先被处理为:...
已安装SDK并获取鉴权Token,详情请参见 安装Blade。因为本文使用GCC 4.8,所以需要使用pre-cxx11 ABI的SDK。本文选用3.7.0版本的RPM包。说明 经过PAI... 步骤二:部署vSGX端来运行TensorFlow Serving推理服务 主要包括:Makefile:Gramine编译TensorFlow Serving tensorflow_model_server.manifest.template:Gramine配置...
LLM Embedding+XGB showed the closest performance to the XGB baseline, while Verbalized Confidence and Token Logits underperformed. DISCUSSION. These findings, consistent across multiple models and demographic groups, highlight the limitations of current LLMs in providing reliable pre-test probability ...
(conversations) texts.append(text_prompt) batch = self.processor( text=texts, images=images, padding=True, truncation=True, max_length=self.max_length, return_tensors="pt" ) labels = batch["input_ids"].clone() labels[labels == self.processor.tokenizer.pad_token_id] = -100 batch["...