Understanding LLMs In Hugging Face | Generative AI with Hugging Face | Ingenium Academy

Ingenium Academy
19 Sept 202306:43

TLDRThis video from Ingenium Academy delves into large language models (LLMs) in Hugging Face, explaining their architecture based on the Transformer model. It highlights two types of Transformers: sequence-to-sequence with encoder and decoder, and causal LMs like GPT-2 with only a decoder. The video outlines the training process involving base LLMs for next-token prediction, instruction tuning for specific tasks, and alignment through reinforcement learning from human feedback. The course aims to teach how to leverage these models for various applications.

Takeaways

  • 🧠 Understanding Large Language Models (LLMs): The video emphasizes the importance of understanding the underlying architecture of LLMs, particularly the Transformer model, which forms the basis for many models used in Hugging Face.
  • 🤖 Built-in Functionality: Hugging Face offers extensive built-in functionality that automates the process of building and training LLMs, reducing the need for deep architectural knowledge for basic usage.
  • 🔍 Two Types of Transformers: The script explains two main types of Transformers - the sequence-to-sequence model with both encoder and decoder, and the causal language model which only includes the decoder.
  • 🔄 Encoder's Role: In sequence-to-sequence models, the encoder takes in text, embeds it, and processes it through a neural network to create a vectorized representation that captures semantic meaning.
  • 📤 Decoder's Role: The decoder in these models takes the encoded vectorized text and generates a probability distribution to predict the next best token, crucial for tasks like text generation.
  • 🌐 Causal Language Models: The video discusses causal LMs like GPT-2, which are trained to generate text by predicting the next token in a sequence, starting from a given prompt.
  • 🎯 Training Process: LLMs are trained using a process that involves predicting the next best token, with the model's predictions compared against known correct tokens to calculate loss and update model parameters.
  • 🔄 Three-Step Training: The script outlines a three-step training process for LLMs: base model training, instruction tuning to perform specific tasks, and alignment through reinforcement learning from human feedback.
  • 🛠️ Base LLM: The base LLM is trained on a large text corpus to predict the next token, making it adept at auto-completion but limited in functionality until further fine-tuning.
  • 📈 Instruction Tuning: After the base model is trained, it can be fine-tuned for specific tasks such as summarization, translation, or answering questions, enhancing its capabilities beyond auto-completion.
  • 🌟 Reinforcement Learning: The final step involves fine-tuning the model using human feedback to align its outputs with human values, improving the quality of its responses in various tasks.

Q & A

  • What is a large language model (LLM)?

    -A large language model (LLM) is a type of artificial neural network that is trained on a large corpus of text and is capable of understanding and generating human-like text based on the input it receives.

  • What is the underlying architecture of LLMs used in Hugging Face?

    -The underlying architecture of LLMs used in Hugging Face is based on the Transformer model, which was introduced in 2017. It consists of an encoder and decoder for sequence-to-sequence Transformers, or just a decoder for causal language models like GPT-2.

  • What is the role of the encoder in a sequence-to-sequence Transformer?

    -The encoder in a sequence-to-sequence Transformer takes in the input text, embeds it, and processes it through a neural network to produce a vectorized representation of the text, which captures the semantic meaning.

  • How does a causal language model differ from a sequence-to-sequence model?

    -A causal language model, such as GPT-2, only includes the decoder portion of the Transformer. It generates text by receiving inputs, embedding them, and outputting a probability distribution of the next best token to select.

  • What is the process for generating text with a causal language model?

    -To generate text with a causal language model, you start with a prompt, process it through the model, and then iteratively select the next best token based on the model's output until an end-of-sentence token is generated.

  • How are large language models trained?

    -Large language models are trained by predicting the next best token in a sequence. During training, the model's predictions are compared to the actual next token in the training data, and the model parameters are updated to minimize the difference.

  • What is the loss function typically used when training LLMs?

    -The loss function typically used when training LLMs is cross-entropy loss, which measures the difference between the predicted probability distribution and the actual token distribution.

  • What is a base LLM and what is it trained to do?

    -A base LLM is a large language model that has been trained on a large corpus of text to predict the next best token. It is primarily good for auto-completion tasks, where it can generate a plausible and coherent end of a sentence.

  • What is instruction tuning and how does it enhance the capabilities of a base LLM?

    -Instruction tuning is a process where a base LLM is further trained to perform specific tasks such as summarizing text, translating, answering questions, or having a conversation. This is done by fine-tuning the model with additional training data that includes instructions or context for the desired task.

  • How does reinforcement learning from human feedback align LLMs with human values?

    -Reinforcement learning from human feedback involves having humans evaluate the model's outputs, such as summaries or translations, and providing rewards. The model then learns to maximize these rewards, thereby improving its performance and aligning its outputs with human values and preferences.

Outlines

00:00

🤖 Understanding Large Language Models

This paragraph introduces the concept of large language models (LLMs) and their underlying architecture, specifically the Transformer model. It explains that while Hugging Face provides tools to automate the process of building and training LLMs, understanding the architecture can be beneficial, especially for custom model development. The Transformer architecture, introduced in 2017, is the basis for LLMs and includes two types: the sequence-to-sequence Transformer with both encoder and decoder, and the causal language model (like GPT-2) which only uses the decoder. The sequence-to-sequence model encodes input text into a vectorized form that the decoder can understand to generate a response. In contrast, the causal LM takes an input, processes it, and outputs a probability distribution of the next token, generating text one token at a time until an end-of-sentence token is produced. The training process involves adjusting the model's parameters based on the difference between predicted and actual next tokens, using cross-entropy loss.

05:00

🛠️ Training LLMs: From Base to Fine-Tuning

The second paragraph delves into the training process of LLMs, starting with a base language model (LLM) trained on a large text corpus to predict the next best token, which is useful for auto-completion tasks. To enhance the model's capabilities, instruction tuning is employed to adapt the model for specific tasks like summarization, translation, or answering questions. This involves fine-tuning the base LLM using the context and grammar it has learned. Finally, the paragraph touches on aligning models through reinforcement learning from human feedback, where human evaluations of the model's outputs are used to further refine the model's performance, ensuring its responses align with human values and expectations.

Mindmap

Keywords

💡Large Language Model (LLM)

A Large Language Model, or LLM, refers to advanced artificial intelligence systems designed to understand and generate human-like text based on vast amounts of data. In the context of the video, LLMs are built on the Transformer architecture and are capable of tasks such as text summarization, translation, and conversation. The video emphasizes the importance of understanding the underlying architecture of these models, particularly for those who wish to customize or train their own models, although Hugging Face provides functionality that automates much of this process.

💡Transformer Architecture

The Transformer architecture is a type of deep learning model introduced in 2017, which revolutionized the field of natural language processing. It is the foundation for many LLMs. The architecture consists of an encoder and a decoder for sequence-to-sequence tasks, or just a decoder for causal language models. The video explains that the encoder processes input text into a vectorized form, while the decoder generates the output sequence, such as the next word or sentence in a text.

💡Sequence-to-Sequence Transformer

A sequence-to-sequence Transformer is a specific type of Transformer model that includes both an encoder and a decoder. The encoder reads and encodes the input text, while the decoder generates the output. This type of model is used for tasks that require understanding the full context of the input, such as translation. The video uses a diagram to illustrate the structure of this model, highlighting its importance in the process of generating text.

💡Causal Language Model (LM)

A Causal Language Model, or causal LM, is a type of LLM that focuses on predicting the next item in a sequence, typically the next word or token in a sentence. Unlike sequence-to-sequence models, causal LMs do not have an encoder; they only use the decoder portion of the Transformer architecture. The video mentions GPT-2 as an example of a causal LM and explains how it generates text by outputting a probability distribution of the next best token.

💡Token

In the context of LLMs, a 'token' refers to the basic unit of text that the model processes. This can be a word or a sub-word, depending on the model's design. The video explains that causal LMs output a probability distribution over tokens, not words, which allows for more granular control over the text generation process. This is crucial for understanding how LLMs generate human-like text.

💡Autocomplete

Autocomplete is a feature often associated with LLMs, where the model predicts and completes a partially typed sentence or phrase. The video describes how base LLMs, after being trained on next-token prediction, are particularly good at autocomplete tasks. This is an example of how LLMs can be used in practical applications to assist users in text input.

💡Instruction Tuning

Instruction tuning is a process where a base LLM is further trained to perform specific tasks, such as summarizing text or answering questions, by following given instructions. The video outlines how, after a base LLM is trained for autocomplete, it can be instruction-tuned to follow more complex instructions, enhancing its capabilities beyond simple text prediction.

💡Reinforcement Learning from Human Feedback

This is an advanced training technique where an LLM's outputs are evaluated by human judges who provide feedback on the quality of the model's responses. The model then uses this feedback to improve its performance. The video describes this process as a way to align LLMs with human values and expectations, resulting in more accurate and contextually appropriate responses.

💡Base LLM

A base LLM, as mentioned in the video, is the initial version of a language model that has been trained on a large corpus of text to predict the next best token. It serves as the foundation for further training and tuning. The video explains that while a base LLM is proficient at autocomplete tasks, additional training is needed for more advanced functionalities.

💡Cross-Entropy Loss

Cross-entropy loss is a common loss function used in training LLMs, particularly in the context of predicting the next token in a sequence. The video briefly mentions it as the method for calculating the difference between the model's prediction and the actual next token during training, which is then used to update the model's parameters.

Highlights

Understanding Large Language Models (LLMs) in Hugging Face involves grasping their underlying architecture.

Hugging Face simplifies the process of building and training LLMs with built-in functionality.

LLMs are based on the Transformer architecture introduced in 2017.

Sequence-to-sequence Transformers consist of an encoder and a decoder.

The encoder processes input text into a vectorized representation.

The decoder uses the encoded representation to understand the semantic meaning of the text.

Causal language models, like GPT-2, consist only of the decoder portion.

Causal LMs are trained to output a probability distribution over tokens for text generation.

Text generation involves iteratively selecting the next best token until an end-of-sentence token is generated.

Training a causal LM involves calculating the loss using the difference between predicted and actual next tokens.

The loss function typically used is cross-entropy loss.

LLMs are initially trained as base models on large text corpora for next-token prediction.

Instruction tuning allows LLMs to follow instructions and perform tasks like summarization and translation.

Fine-tuning with reinforcement learning from human feedback aligns models with human values.

The course will cover base LLMs, instruction fine-tuning, and the three-step training process.

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