also choose bge-large or bge-small as the embedding model or modify the context window size or text chunk size depending on your computing resources. Qwen 1.5 model families support a maximum of 32K context model_name = "BAAI/bge-base-en-v1.5" ) # Set the size of the text chunk for retrieval Settings.transformations = [SentenceSplitter(chunk_size=1024)] 1.15.3 现在，我们可以设置语言模型和向量模型。Qwen1.5-Chat 支持包括英语和中文 在内的多种语言对话。您可以使用 lists.append(ls1) ls1 = [ls[i]] lists.append(ls1) return lists class FAISSWrapper(FAISS): chunk_size = 250 chunk_conent = True score_threshold = 0 def similarity_search_with_score_by_vector( self, embedding:

主讲人：龙良曲 Merge or split https://blog.openai.com/generative-models/ ▪ Cat ▪ Stack ▪ Split ▪ Chunk cat ▪ Statistics about scores ▪ [class1-4, students, scores] ▪ [class5-9, students, scores] Along Along distinct dim/axis ▪ Dim=d for example stack create new dim Cat v.s. stack Split: by len Chunk: by num Thank You.

函数可以实现张量分割外，PyTorch 还提供了另一个函数 torch.chunk。他的 用法与 torch.split 非常类似，区别在于 chunk 函数的参数 chunks 指定了切割份数，而 split 函数的参数 split_size_or_sections 则是每份长度，本质上两个函数是等价的。例如，将总成 绩册张量在班级维度进行 chunk 操作，等分为 2 份，代码如下： In [11]: randn([10,35,8]) a,b = torch.chunk(x, chunks=2, dim=0) # 等分为 2 份 a.shape, b.shape Out[11]: (torch.Size([5, 35, 8]), torch.Size([5, 35, 8])) 将总成绩册张量在班级维度进行 chunk 操作，等分为 10 份，代码如下： In [12]: torch.randn([10,35,8]) result = torch.chunk(x, chunks=10, dim=0) # 等分为 10 份 len(result), result[0].shape Out[12]: (10, torch.Size([1, 35, 8])) 可以看到，torch.chunk 函数完成的功能与 torch.split 完全一样。 此外，torch

reward) self.optimizer.apply_gradients( zip(grads, controller.rnn.trainable_variables) ) The next chunk of code puts everything together and runs the search for 150 episodes. controller = Controller()

mechanisms. These ideas tackle the quadratic complexity at various levels. The simplest idea is to chunk the input sequence of length n into blocks of length b where b <<< n. The resulting score matrices

its iteration has been fixed. ‣ Fusion: Tensor and constant scalar operations, like add(t, 1), and chunk operations are now fusable. ‣ Performance improvements: dropout, 1x1 convolutions for NCHW, and weightnorm