NLPLlama & Alpaca Large Model

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Series of Columns – Machine Learning [ML] Natural Language Processing [NLP] Deep Learning [DL]

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?Description? My explanation mainly includes Python, machine learning (ML), deep learning (DL), natural language processing (NLP), etc.

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1. What is Llama?

2. What can the Llama model do? ?edit

3. LORA of the Chinese Llama model

4. Realization of simple fine-tuning of Llama model

1. Fine-tuning of the Chinese Llama model

2. PEFT of the Chinese Llama model

3. Freezing of the Chinese Llama model

5. The development direction of the Chinese Llama model

In today’s era, natural language processing has become a very popular field, and Llama is one of the highly respected natural language processing frameworks. Based on the GPT-3 model, it provides rich functions and ease of use, and can help developers quickly solve problems in various natural language processing tasks.

As a natural language processing framework, Llama has several advantages:

  1. Multilingual support: Llama can support multiple languages, including English, Chinese, etc., greatly enhancing its applicability.

  2. Powerful text generation ability: Llama provides text generation tasks, which can generate natural and smooth text according to a given context or topic, with very high accuracy and reliability.

  3. Highly extensible: Llama is designed to be highly extensible and can be easily extended for new tasks or models. At the same time, it also supports running on different hardware.

  4. Ease of use: The design of Llama is very friendly, providing an easy-to-use API and graphical user interface, allowing users to enjoy efficient natural language processing capabilities.

Llama has a wide range of application scenarios, and it can be applied to text generation, sentiment analysis, text classification, named entity recognition, automatic question answering and other fields. Among them, text generation and automatic question and answer are the most well-known functions of Llama, which can be used in text creation, intelligent customer service system, automatic question and answer, etc.

Although Llama has achieved great success in the field of natural language processing, it still has some challenges and problems to be solved. For example, problems such as word segmentation and word vectors in the Chinese context need more in-depth research and solutions; at the same time, how to better combine human intelligence and machine learning to achieve more efficient and accurate natural language processing is also what Llama has been working hard to explore direction.

In short, Llama is an excellent natural language processing framework that can help developers solve various natural language processing problems. Its excellent features and wide application prospects will continue to promote the development and innovation of natural language processing technology.

One, what is Llama?

Llama is A natural language processing framework based on the GPT-3 model, which can support Chinese, but needs to use Chinese training data to fine-tune the model.

Llama provides some pre-trained language models, such as GPT-3 and T5, and you can choose one of them as the starting point for Chinese fine-tuning. At the same time, Llama also provides some fine-tuning tasks for Chinese tasks, such as text classification, named entity recognition, text generation, etc., and you can choose the appropriate fine-tuning task according to the specific task requirements.

When preparing Chinese training data, you can use public Chinese text corpora, such as People’s Daily, Wikipedia, etc. These texts need to be converted into the format required by Llama and divided into training, validation and test sets.

It should be noted that in the process of Chinese fine-tuning, the problems of Chinese word segmentation and word vectors also need to be considered. Llama provides some Chinese word segmentation tools and pre-trained Chinese word vector models (such as Chinese-BERT, RoBERTa-wwm-ext, etc.), which can be selected and used according to specific situations.

In short, Llama can support Chinese, but it needs to use Chinese training data and corresponding tools for fine-tuning and application.

Second, what can the Llama model do?

Llama is a powerful natural language processing framework, which can be applied in many fields, as follows:

  1. Text Generation: Llama provides text generation tasks that can generate natural and fluent text given a context or topic. This feature can be used in text creation, automatic content generation and other fields.

  2. Sentiment analysis: Llama can perform sentiment analysis. By analyzing the sentiment of the text, it can help companies understand customer sentiment and needs and improve customer satisfaction.

  3. Text classification: Llama can perform text classification, automatically classify text into different categories, and realize functions such as automatic classification and archiving. This function has a wide range of applications in areas such as intelligent customer service and filtering spam.

  4. Named entity recognition: Llama can perform named entity recognition, automatically recognize entities such as people, places, and organizations in the text, and realize automatic classification and management.

  5. Automatic question and answer: Llama can realize automatic question and answer function to answer questions raised by users, and can be used in intelligent customer service systems, intelligent assistants and other fields.

In addition, Llama can also be applied to fields such as public opinion analysis, machine translation, and automatic summarization. In short, Llama is a powerful natural language processing framework that can help enterprises and developers solve various natural language processing problems.

3. Lora of Chinese Llama model

The LORA (Low-resource and Robust Adaptive) of the Chinese Llama model refers to the low-resource situation, through reasonable fine-tuning strategies and data enhancement methods, so that the model can perform well and robustly on tasks in different fields.

Specifically, in LORA, the following methods can be used to improve the adaptability and robustness of the model:

  1. Zero-shot learning: In the presence of domain changes or data scarcity, zero-shot learning can be used to predict domains without labeled data, that is, no data in this domain is used during training. Common zero-shot learning methods include meta-learning, transfer learning, etc.

  2. Data enhancement: By transforming the training data with certain rules, the diversity of the training data is increased, thereby improving the generalization ability of the model. For example, the corpus can be expanded through data enhancement methods such as random replacement, deletion, insertion, etc., to increase the amount of data in the model.

  3. Adversarial training: Adversarial training refers to enhancing the robustness of the model by adding adversarial noise or attacks during the training process. For example, small-scale perturbations to the input can be used to enable the model to adapt to a wider input distribution and improve robustness.

  4. Knowledge distillation: Knowledge distillation refers to using a pre-trained larger model as a “teacher” and transferring its knowledge to a smaller model as a “student”, thereby improving the performance of the “student”. In the Chinese Llama model, you can also try to apply knowledge distillation to improve the model’s LORA performance.

It should be noted that in practical applications, it is necessary to select an appropriate method according to the characteristics of the task and the data set to improve the performance of the model. At the same time, it is necessary to balance the performance of the model and the cost of computing resources and time, and choose an appropriate strategy to implement LORA.

Fourth, Llama model simple fine-tuning realization

Llama is a natural language processing framework based on the GPT-3 model, which can be fine-tuned to adapt to different tasks. The following is a simple implementation process of Llama Chinese fine-tuning:

1. Prepare the data set: In order to perform Chinese fine-tuning, a Chinese data set needs to be prepared. Publicly available Chinese text corpora such as People’s Daily, Wikipedia, etc. can be used.

2. Install Llama: You can use the pip package manager to install Llama, run the following command:

pip install llama

3. Load the pre-trained model: Llama provides some pre-trained language models, you can use one of them as a starting point for fine-tuning. For example, the GPT-3 model can be loaded:

import llama model = llama. get_model("gpt3")

4. Define a fine-tuning task: In fine-tuning, a task needs to be defined to guide the model to learn a specific language structure and question type. For example, you can define a task that generates text:

task = llama. TextGenerationTask()

5. Prepare training data: convert the prepared Chinese text into the format required by Llama, and divide it into training set and verification set:

from llama.datasets.language_modeling import LanguageModelingDataset

train_dataset = LanguageModelingDataset(train_data)
eval_dataset = LanguageModelingDataset(eval_data)

6. Train the model: Use the prepared dataset and tasks to train the model. For example, the Adam optimizer and default hyperparameters can be used:

from llama. trainer import Trainer

trainer = Trainer(model=model, task=task, train_dataset=train_dataset, eval_dataset=eval_dataset, optimizer="adam")
trainer. train()

7. Save and load the fine-tuned model: After the training is complete, the fine-tuned model can be saved to disk for later use. For example, a model can be saved to a “model.pt” file with the following command:

model.save("model.pt")

Then, the model can be loaded with the following command:

model = llama.load_model("model.pt")

This is a simple implementation process of Llama Chinese fine-tuning. Of course, the actual fine-tuning process may require more steps and more complex algorithms.

Llama’s Chinese implementation github link GitHub – ymcui/Chinese-LLaMA-Alpaca: Chinese LLaMA & amp; Alpaca Large Language Model + Local CPU/GPU Deployment (Chinese LLaMA & amp; Alpaca LLMs)

1. Fine-tuning of the Chinese Llama model

For the fine-tuning of the Chinese Llama model, the fine-tuning process of the English language model is similar. The following is a simple fine-tuning code example, assuming that the Chinese fine-tuning dataset has been prepared:

import torch
from transformers import LlamaTokenizer, LlamaForCausalLM
from torch.utils.data import DataLoader, Dataset

tokenizer = LlamaTokenizer.from_pretrained('llama')
model = LlamaForCausalLM.from_pretrained('llama')

# Load the fine-tuning dataset
class MyDataset(Dataset):
    def __init__(self, tokenizer, data_path='train.txt', block_size=512):
        self.examples = []
        with open(data_path, 'r', encoding='utf-8') as f:
            text = f. read()
        tokenized_text = tokenizer. encode(text)
        for i in range(0, len(tokenized_text)-block_size + 1, block_size):
            self.examples.append(tokenizer.build_inputs_with_special_tokens(tokenized_text[i:i + block_size]))

    def __len__(self):
        return len(self. examples)

    def __getitem__(self, idx):
        return torch.tensor(self.examples[idx])

dataset = MyDataset(tokenizer)
dataloader = DataLoader(dataset, batch_size=2, shuffle=True)

# Define fine-tuning parameters and optimizer
epochs = 10
learning_rate = 5e-5
optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate, correct_bias=True)

# fine-tuning process
for epoch in range(epochs):
    print('current epoch:', epoch)
    for step, batch in enumerate(dataloader):
        batch = batch.to('cuda')
        loss = model(batch, labels=batch)[0]
        print(f'epoch: {epoch}, step: {step}, loss: {loss.item()}')
        loss. backward()
        optimizer. step()
        optimizer. zero_grad()
    
    # Save the fine-tuned model
    output_dir = f'./models/llama_finetuned_epoch{epoch}'
    model. save_pretrained(output_dir)
    tokenizer. save_pretrained(output_dir)

In this sample code, we loaded the pretrained Llama model and tokenizer, and prepared the Chinese fine-tuning dataset. We define a Dataset class with padding and a special token for reading and processing data, and then pass it to the DataLoader. Next, we set the training parameters and the configuration of the optimizer, and carried out the fine-tuning process, and saved the fine-tuned model through the save_pretrained method after each epoch.

It should be noted that due to the complexity of Chinese sentence patterns and grammar, larger models, more fine-tuning data, longer fine-tuning time, and higher learning rates or other hyperparameter adjustments may be required to achieve Let the fine-tuned model perform well on Chinese generation tasks.

2. PEFT of Chinese Llama model

The PEFT (Pretraining Efficiency Frontier Trade-off) of the Chinese Llama model refers to the balance between the required pre-training sample size and computing resources while maintaining a certain model quality. At present, due to the short release time of the Llama model, the research on PEFT of the Chinese Llama model is still relatively limited, and further exploration is needed.

However, in the field of English language models, there have been many similar research results. For example, the performance of the model can be improved by increasing the depth and width of the model structure (the number of neural network layers and the number of neurons per layer) and the complexity of the pre-training task, but this will also increase the computing resources required for pre-training and sample size. In practical applications, it is necessary to make a reasonable choice according to the requirements of specific tasks and the existing computing resources, time, data volume and other conditions to achieve a balance between model performance and resource utilization.

If you want to conduct similar PEFT research on the Chinese Llama model, you can try to change the hyperparameters of the model (such as hidden layer size, number of attention heads, pre-training tasks, etc.), and conduct pre-training data sets of different sizes and quality. pre-train and compare performance on the same fine-tuned dataset. During the experiment, the training effects under different PEFTs can be evaluated by calculating indicators such as training time, GPU memory usage, model size, and pre-training data set size.

3. Freezing of Chinese Llama model

Freezing of the Chinese Llama model refers to the operation of fixing some parts of the model without fine-tuning, and only fine-tuning the rest. Usually, the first few layers of the model or some layers in the pre-training process often contain some basic language knowledge or general features, which have a better migration effect for fine-tuning different tasks, so these layers can be frozen. Does not participate in subsequent fine-tuning.

In the Chinese Llama model, freezing can be achieved by setting the requires_grad attribute of some layers to False. For example, for the LlamaForCausalLM model, we can freeze the first 2 TransformerBlocks with the following code:

python
from transformers import LlamaForCausalLM

model = LlamaForCausalLM.from_pretrained('llama')

for name, param in model.named_parameters():
    if 'transformer.block' in name and int(name.split('.')[2]) < 2:
        param.requires_grad = False

In this code, we first load the pre-trained model, and then find all parameters whose name contains transformer.block and whose prefix number is less than 2 by traversing the parameter list of the model. For these parameters, we set their requires_grad attribute to False, i.e. freeze the layers.

It should be noted that in practical applications, the layer to be frozen needs to be selected according to the specific task and data set. For some tasks, it may be more effective to freeze the first few layers or the last few layers, and for some domain-specific tasks, the number of layers that need to be frozen may be different.

5. The development direction of the Chinese Llama model

Chinese Llama is one of the most widely used Chinese natural language processing pre-training models. It adopts a transformer structure similar to the GPT model and has reached the leading level in multiple Chinese NLP tasks.

At present, the development direction of Chinese Llama mainly includes the following aspects:

  1. Model effect optimization: On the premise of ensuring the operating efficiency of the model, further optimize the performance of the Chinese Llama model and achieve better performance on multiple Chinese NLP tasks.

  2. Multilingual support: In addition to Chinese, Llama can also support other languages, such as English, Spanish, German, etc., and may be further expanded to more language fields in the future.

  3. Lightweight deployment: For scenarios such as mobile terminals and embedded devices, Chinese Llama needs to further consider model compression and lightweight deployment to meet the needs of devices with low power consumption, low storage, and low computing resources.

  4. Zero-shot learning: The application of Chinese Llama in the field of zero-shot learning also has great development space and potential. In the future, more efficient zero-shot learning methods may be used to make the model better adaptable to various fields and tasks.

In short, in the future development of Chinese Llama, we will continue to explore various technologies and methods in depth to meet the needs of different scenarios and make greater contributions to the further development of the Chinese NLP field.