from_pretrained("Qwen/Qwen1.5-7B-Chat") # Instead of using model.chat(), we directly use model.generate() # But you need to use tokenizer.apply_chat_template() to format your inputs as shown␣ �→below .to(device) # Directly use generate() and tokenizer.decode() to get the output. # Use `max_new_tokens` to control the maximum output length. generated_ids = model.generate( model_inputs.input_ids, max_new_tokens=512 以前，我们使用 model.chat() （有关更多详细信息，请参阅先前 Qwen 模型中的 modeling_qwen. py ）。现在，我们遵循 transformers 的实践，直接使用 model.generate() 配合 tokenizer 中的 apply_chat_template() 方法。 如果你想使用 Flash Attention 2，你可以用下面这种方式读取模型： 4 Chapter

examples. Data Augmentation is a set of techniques which leverage the original training data to generate more training examples without having to label them. We’ll familiarize ourselves with these techniques identical to the original sample. A slightly tilted cat is still a cat! The label mixing transformations generate samples based on differently labeled inputs. The target label is a composite of the inputs that 30% and a ‘hamster’ with a probability of 70%. The sample generation techniques use models to generate samples for labels. Consider a training sample for English to Spanish translation: [English: “I

range. For example, given two hyperparameters and such that is real valued in range and , RS can generate an arbitrary number of trials. For 5 trials, one possible trial set can be and for 7 trials, one their NAS model could generate variable depth child networks. Figure 7-4 shows a sketch of their search procedure. It involves a controller which samples the search space to generate candidate architectures rewards are determined by their performance on the target dataset. The controller model learns to generate better architectures as the search game progresses. Figure 7-4: An overview of Neural Architecture

in data preparation phase of machine learning.  Sequence processing: Provides functions to generate time based data from the given input data. We can use it in data preparation phase of machine learning from keras.utils import plot_model plot_model(model,to_file='image.png') This plot_model will generate an image to understand the performance of model. Keras 26 As learned earlier represent the mean of the random values to generate  stddev represent the standard deviation of the random values to generate  seed represent the values to generate random number RandomUniform Generates

a negative pair with both the inputs that are semantically dissimilar. The model is expected to generate hidden representations that are similar for positive pairs, and dissimilar for negative pairs. Figure 6-10: Contrastive learning as implemented in the SimCLR framework. The input is augmented to generate two views, and . Using the shared encoder , hidden 13 Chen, Ting, et al. "Big Self-Supervised Models 05709. representations and are generated. These are then projected down using a projection head to generate and . The model is then trained to maximize agreement between and . Negative pairs are created

architectures optimizing its various aspects. GPT-3 has captured the attention by being able to generate realistic text accompanying the given prompts. Both these models have been deployed in production accurate model (teacher) which might not be suitable for deployment. The larger model is used to generate soft labels on the training data, and the student model learns to copy both the ground-truth labels generates candidate models. These candidate models are evaluated and is used to update the state, and generate better candidate models A controller unit which is provided the possible search space (allowed

/A.ttf', '/path/B.ttf’]) data = image.generate('1234’) image.write('1234', 'out.png’) audio = AudioCaptcha(voicedir='/path/to/voices’) data = audio.generate('1234’) audio.write('1234', 'out.wav’) ImageCaptcha 实例 captcha.image.ImageCaptcha.write(‘1234’, ‘out.png’) – 生成验证码并保存 captcha.image.ImageCaptcha.generate(‘1234’) – 生成验证码图像 “Hello TensorFlow” Try it 输入与输出数据处理 输入数据处理 图像处理：RGB图 -> 灰度图 -> 规范化数据 输入数据处理

concept). Here is a quick recipe to train embedding-based models: 1. Embedding Table Generation: Generate the embeddings for the inputs using machine learning algorithms of your choice. 2. Embedding Lookup: to a single vector. The result is passed through a few dense layers and a softmax activation to generate an output tensor of size num_classes. This is similar to the Word2Vec example except the fact that specifically learn representations by showing similar and dissimilar inputs, and forcing the model to generate embeddings that have a small distance between each other for similar inputs, and a large distance

can now remove clustering variables and wrappers from our clustered model to generate the final model. We will also generate a TFLite model that we can use for inference on smartphones and other devices

• initial_epoch: 开始训练的轮次（有助于恢复之前的训练）。 返回 一个 History 对象。 异常 • RuntimeError: 如果模型从未编译。 例 def generate_arrays_from_file(path): while 1: f = open(path) for line in f: # create Numpy arrays of input line in the file x, y = process_line(line) yield (x, y) 模型 47 f.close() model.fit_generator(generate_arrays_from_file('/my_file.txt'), steps_per_epoch=1000, epochs=10) 4.2.3.9 evaluate_generator (keras.utils.Sequence) 实 例同用。 • initial_epoch: 开始训练的轮次（有助于恢复之前的训练）。 返回 一个 History 对象。 例 def generate_arrays_from_file(path): while 1: f = open(path) for line in f: # 从文件中的每一行生成输入数据和标签的 numpy 数组，