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

Ingenium Academy
19 Sept 202306:43

TLDRThis video script from Ingenium Academy's course on Hugging Face delves into the intricacies of large language models (LLMs), focusing on their architecture and functionality. It explains the Transformer model, the backbone of most LLMs, and differentiates between sequence-to-sequence and causal models like GPT-2. The script outlines the training process involving base LLMs, instruction tuning for specific tasks, and alignment through reinforcement learning from human feedback. The course aims to provide a comprehensive understanding of LLMs and their applications in text generation and processing.

Takeaways

  • 🧠 Large language models (LLMs) are based on the Transformer architecture, which was introduced in 2017.
  • 🔍 Hugging Face simplifies the process of building and training LLMs with its built-in functionalities.
  • 🤖 Understanding the architecture is helpful but not necessary for using Hugging Face's tools.
  • 🔄 There are two types of Transformers: Sequence-to-sequence (with encoder and decoder) and Causal LMs (decoder only).
  • 📄 The encoder processes input text into a vectorized representation, while the decoder generates text based on this representation.
  • 💡 Causal LMs, like GPT-2, are trained to output a probability distribution of the next best token, allowing for text generation.
  • 🎯 Training involves adjusting the model's parameters based on the difference between predicted and actual next tokens, using cross-entropy loss.
  • 🛠 Base LLMs are trained on large text corpora for next-token prediction, making them good for autocomplete tasks.
  • 📝 Instruction tuning enhances the base LLM's capabilities, allowing it to follow instructions and perform more complex tasks like summarization and translation.
  • 🏆 Fine-tuning with reinforcement learning from human feedback further aligns the model's outputs with human values and preferences.

Q & A

  • What is a large language model?

    -A large language model is a type of artificial intelligence that is trained on a large corpus of text to predict the next best token or word in a sequence. It is based on the Transformer architecture and can be used for various tasks such as text generation, translation, and summarization.

  • What is the underlying architecture of large language models?

    -Large language models are based on the Transformer architecture, which was introduced in 2017. The architecture includes two main types: the encoder-decoder (sequence-to-sequence) model and the decoder-only model (causal LM).

  • What are the two types of Transformers mentioned in the transcript?

    -The two types of Transformers mentioned are the encoder-decoder (sequence-to-sequence) Transformer and the causal LM (decoder-only) Transformer.

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

    -The encoder in a sequence-to-sequence model takes in the input text, embeds it, and processes it through a neural network to create a vectorized representation of the text, which is then passed to the decoder.

  • How does a causal language model function?

    -A causal language model functions by taking inputs, embedding them, and processing them through a neural network to output a probability distribution over tokens. It generates text by selecting the next best token based on this distribution until an end-of-sentence token is generated.

  • What is the purpose of instruction tuning in large language models?

    -Instruction tuning is used to adapt a base language model, which is primarily good for auto-completion, to perform more complex tasks such as summarizing text, translating, answering questions, and having conversations by following specific instructions.

  • What is the difference between a base LLM and an instruction-tuned model?

    -A base LLM is trained on a large corpus of text to predict the next best token and is mainly used for auto-completion. An instruction-tuned model, on the other hand, is fine-tuned on top of the base LLM to perform specific tasks like summarization, translation, or answering questions effectively.

  • How are large language models trained?

    -Large language models are trained in a process that may involve three steps: training the base LLM on next token prediction, instruction tuning to adapt the model to specific tasks, and alignment through reinforcement learning from human feedback to ensure the outputs align with human values.

  • What is reinforcement learning from human feedback?

    -Reinforcement learning from human feedback involves having humans evaluate the outputs of an instruction-tuned model, such as summaries or translations, and providing rewards. The model then learns to maximize these rewards to improve its performance and align its outputs with human preferences.

  • Why is it helpful to understand the underlying architecture of large language models?

    -Understanding the underlying architecture of large language models is helpful because it provides insight into how the models process and generate text, which can aid in building custom models, fine-tuning them for specific tasks, and improving their overall performance.

Outlines

00:00

🤖 Understanding Large Language Models

This paragraph introduces the concept of large language models (LLMs) and their underlying architecture. It emphasizes the importance of understanding the Transformer architecture, which forms the basis of LLMs. The Transformer architecture, introduced in 2017, includes two types: sequence-to-sequence (encoder-decoder) and causal language models (decoder-only). The former processes input text into a vectorized form that the decoder uses to understand semantics, while the latter focuses on predicting the next token in a sequence, useful for tasks like text generation. The paragraph also touches on the training process, which involves adjusting the model's parameters based on the difference between predicted and actual tokens, using cross-entropy loss. The goal is to move beyond simple auto-completion to more complex tasks like summarization, translation, and conversation.

05:00

🔧 Training and Fine-Tuning LLMs

The second paragraph delves into the training and fine-tuning processes of LLMs. It starts with the base LLM, which is trained on a large corpus to predict the next token, suitable for autocomplete tasks. The paragraph then discusses instruction tuning, where the base LLM is further trained to perform specific tasks like summarizing text, translating, or answering questions. Finally, it introduces the concept of aligning models through reinforcement learning from human feedback, where human evaluations of the model's outputs are used to improve its performance. This process aims to ensure that the model's outputs align with human values and expectations, leading to better summarizations, translations, and answers.

Mindmap

Keywords

💡Large Language Model (LLM)

A Large Language Model, or LLM, refers to a type of artificial neural network that is trained on a large dataset of text and is capable of understanding and generating human-like text. In the context of the video, LLMs are based on the Transformer architecture and are used for various tasks such as text generation, translation, and summarization. The video emphasizes the importance of understanding the underlying architecture of these models, especially for those who wish to build custom models.

💡Transformer Architecture

The Transformer architecture is a model architecture introduced in 2017 that has become foundational for many state-of-the-art LLMs. It is designed to handle various tasks related to natural language processing. The video explains that the architecture consists of two main parts: an encoder and a decoder for sequence-to-sequence Transformers, or just a decoder for causal language models. The script uses the Transformer architecture as a basis for explaining how LLMs process and generate text.

💡Sequence-to-Sequence Transformer

A sequence-to-sequence Transformer is a type of Transformer model that includes both an encoder and a decoder. The encoder processes the input text, while the decoder generates the output. In the video, the script describes how the encoder takes an input sentence, embeds it, and processes it through a neural network to create a vectorized representation, which the decoder then uses to generate a response.

💡Causal Language Model (LM)

A Causal Language Model, or causal LM, is a type of LLM that only includes the decoder portion of the Transformer architecture. It is trained to predict the next token in a sequence, which is useful for text generation tasks. The video script explains that causal LMs like GPT-2 are used to generate text by starting with a prompt and iteratively predicting the next best token until an end-of-sentence token is generated.

💡Token

In the context of LLMs, a 'token' refers to the basic unit of text that the model processes. Unlike words, tokens can be sub-word units that help the model handle a more diverse range of vocabulary. The video script mentions that causal LMs output a probability distribution over tokens, not words, when generating text, highlighting the model's ability to select the next best token based on the input.

💡Encoder

An encoder in the Transformer architecture is responsible for reading and embedding the input text into a vectorized form that can be understood by the model. The video script describes the encoder's role in a sequence-to-sequence model as taking in the input text, embedding it, and processing it through a neural network to create a representation that the decoder can use.

💡Decoder

A decoder in the Transformer architecture is responsible for generating the output text based on the encoded input. In the video, the script explains that in a causal LM, the decoder takes the vectorized representation from the encoder (or the previously generated tokens in the case of text generation) and outputs a probability distribution to determine the next token in the sequence.

💡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 instructions. The video script discusses how, after a base LLM is trained for autocomplete, it can be instruction-tuned to follow instructions and perform more complex tasks, making it more versatile and useful for various applications.

💡Reinforcement Learning from Human Feedback

Reinforcement Learning from Human Feedback is a method of fine-tuning LLMs by having humans evaluate and reward the model's outputs. The video script describes how, after instruction tuning, the model can be further aligned with human values by having humans provide feedback on its generated text, such as summaries or translations. This feedback is then used to train the model to improve its performance and align its outputs more closely with human preferences.

💡Base LLM

A Base LLM refers to a foundational language model that has been pre-trained on a large corpus of text to predict the next best token. The video script explains that base LLMs are primarily good for auto-completion tasks, but they can be further trained through instruction tuning and reinforcement learning to perform more complex tasks.

Highlights

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

Hugging Face simplifies building and training LLMs with built-in functionalities.

LLMs are based on the Transformer architecture introduced in 2017.

Transformers consist of two types: Sequence-to-Sequence and Causal Language Models.

Sequence-to-Sequence Transformers have both encoder and decoder portions.

The encoder processes input text into a vectorized representation.

The decoder uses this representation to understand and generate text.

Causal LMs, like GPT-2, focus on the decoder for text generation.

Causal LMs generate text by predicting the next best token.

Training involves adjusting the model based on the difference between predicted and actual tokens.

Cross-entropy loss is commonly used as the loss function in training.

Base LLMs are trained on large text corpora for next-token prediction.

Instruction tuning adapts the base LLM to perform specific tasks like summarization or translation.

Reinforcement learning with human feedback aligns models with human values.

The course will cover base LLMs, instruction fine-tuning, and reinforcement learning.

Human feedback is used to reward model responses, guiding improvement in alignment with human values.

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