LLMs Explained,
LLaMA

Meta first introduced LLaMA in February 2023. LLaMA (Large Language Model Meta AI) is a cutting-edge foundational large language model designed to assist researchers in this subfield of AI. Smaller, more performant models, such as LLaMA, allow others in the research community who lack access to large amounts of infrastructure to study these models, further democratizing access in this important, rapidly changing field. LLaMA is trained on trillions of tokens and demonstrates that it is possible to train cutting-edge models using only publicly available datasets rather than proprietary and inaccessible ones.

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An Overview of LLaMA

LLaMA, brought by Meta, is a cutting-edge foundational large language model designed to assist researchers in this subfield of AI. One of the primary goals of the LLaMA project was to develop language models that could perform well across a range of computational budgets, allowing researchers with varying resources to investigate these models.

LLaMA-13B is 10x smaller yet outperforms GPT-3

Outperforms GPT-3

LLaMA-13B outperforms GPT-3 on most benchmarks despite being ten times smaller. The LLaMA-65B also competes with the best models, Chinchilla70B and PaLM-540B.

LLaMA models were trained on 1.4 trillion tokens.

Trained on 1.4T tokens

LLaMA 65B and LLaMA 33B were trained on 1.4 trillion tokens. LLaMA 7B, the smallest model, has been trained on one trillion tokens. It's trained on repurposed data used to train other LLMs.

LLaMA was trained in 20 different languages.

Trained in 20 languages

LLaMA was trained in 20 languages using Latin or Cyrillic scripts like Spanish, French, Croatian, Hungarian, Italian, Dutch, Polish, Portuguese, Romanian, and more.

About Model

Llama, developed by Meta, is a cutting-edge language model that aims to assist researchers in artificial intelligence. It is designed to perform well across various computational budgets, enabling researchers with varying resources to investigate these models. The LLaMA project investigates the trade-offs between model size and performance, aiming to accelerate research in AI and natural language processing. LLaMA is trained on a set of language models to achieve the best possible performance, outperforming existing language models with parameters ranging from 7B to 65B. LLaMA-13B, for example, outperforms GPT-3 on most benchmarks despite being one-tenth the size. This model can be run on a single GPU, making it accessible to researchers without significant infrastructure. LLaMA's 65B-parameter model competes with the best large language models at the top of the scale, such as Chinchilla or PaLM-540B. By democratizing access to large language models, LLaMA has the potential to advance the field of natural language processing.

Research Paper

Model Repository

HuggingFace

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Training Details

Training Procedure

Our training approach is inspired by the Chinchilla scaling laws. The models are trained using the AdamW optimizer. When training a 65B-parameter model, code processes around 380 tokens/sec/GPU on a 2048 A100 GPU with 80GB of RAM.

Training data

The model was trained using the following data sources: CCNet (67%), C4 (15%), GitHub (4.5%), Wikipedia (4.5%), Books (4.5%), ArXiv (2.5%), and Stack Exchange (2%). The Wikipedia and Books domains contain data in the languages like Bulgarian, Catalan, Czech, and more.

Training dataset size

All models are trained with 4 million token batches. LLaMA-33B and LLaMA65B were trained on 1.4 trillion tokens, whereas the smaller models were trained on 1.0 trillion tokens.

Training time and resources

Training over the 1.4T token dataset is calculated to take approximately 21 days. The paper mentions that training must have taken approximately five months between December. 2022 and Feb. 2023.

Model Types

On most benchmarks, LLaMA-13B outperforms GPT-3 (175B), and LLaMA-65B competes with the best models, Chinchilla70B and PaLM-540B. The table below shows the different variants' model sizes, architectures, and optimization hyper-parameters.

params	dimension	n heads	n layers	learning rate	batch size	n tokens
7B	4096	32	32	3.0e −4	4M	1.0T
13B	5120	40	40	3.0e −4	4M	1.0T
33B	6656	52	60	1.5e −4	4M	1.4T
65B	8192	64	80	1.5e −4	4M	1.4T

Business Applications

LLaMA shows the optimal results in tasks like Question Answering, Natural language understanding, and understanding capabilities. These have multiple business applications. Some examples are listed below:

Task	Use Cases	Applications
Common sense reasoning	Chatbots, personal assistants, customer service	Virtual assistants, chatbots
Closed-book question answering	Education, legal, finance, customer service	Customer support, legal research, education
Mathematical reasoning	Education, finance, research	Scientific research, finance applications
Code generation	Software development, automation, data science	Data analysis, machine learning, robotics
Massive multitask language understanding	Natural language understanding, sentiment analysis, recommendation systems	Search engines, recommendation systems, virtual assistants
Reasoning about physical events and interactions	Robotics, autonomous systems, virtual assistants	Autonomous systems, robotics, virtual assistants
Reasoning about social interactions and dynamics	Social media analysis, sentiment analysis, customer service	Social media analytics, customer support, sentiment analysis

Model Features

LLaMA is an auto-regressive language model, based on the transformer architecture. The model comes in different sizes: 7B, 13B, 33B, and 65B parameters. LLaMA leverages various improvements that were subsequently proposed and used in different models such as PaLM.

Pre-normalization

LLaMa normalizes the input of each transformer sub-layer to improve the training stability instead of normalizing the output. The model use the RMSNorm normalizing function.

SwiGLU activation function

The model replaces the ReLU non-linearity by the SwiGLU activation function of the SwiGLU activation to improve the performance. The model uses a dimension of 2/3 4d instead of 4d, as in PaLM.

Causal multi-head attention operator

The model implemented a causal multi-head attention operator to reduce memory usage and computation. This is achieved by not storing the attention weights and not computing the key/query scores that are masked due to the causal nature of the language modeling task.

Rotary Embeddings

The model removes the absolute positional embeddings and instead uses rotary positional embeddings (RoPE) at each layer of the network. Rotary Position Embedding, or RoPE, is a type of position embedding which encodes absolute positional information with rotation matrix and naturally incorporates explicit relative position dependency in self-attention formulation.

Model Tasks

LLAMA is a powerful language model that has been trained on a diverse set of high-quality datasets, including NaturalQuestions, TriviaQA, and HellaSwag. LLAMA's training on these datasets enables it to perform various natural language processing tasks accurately and efficiently.

Common sense reasoning

This refers to the ability to reason and make inferences about the world based on prior knowledge and common sense. For instance, given a sentence such as "John put the milk in the fridge," a common sense reasoning model could infer that the milk is cold and will stay fresh longer.

Closed-book question answering

This task involves answering questions based on a given text without external knowledge sources. The LLaMA model can be trained to understand natural language text and answer questions without relying on external knowledge sources or context.

Mathematical reasoning

The LLaMA model can also perform mathematical reasoning tasks such as solving equations and performing arithmetic operations. For instance, it could be trained to understand and solve word problems such as "If John has 5 apples and he gives 2 to Jane, how many apples does John have left?"

Code generation

This task involves generating code based on natural language instructions. The LLaMA model can be trained to understand natural language instructions and generate code to perform the desired task.

Massive multitask language understanding

This refers to the ability of the LLaMA model to perform multiple natural language processing tasks simultaneously. For example, it can be trained to perform tasks such as question answering, summarization, and sentiment analysis simultaneously.

Reasoning about physical events and interactions

The LLaMA model can also reason about physical events and interactions. For instance, given a sentence such as "John kicked the ball, and it flew over the fence," it could infer that the ball was kicked with enough force to clear the fence.

Reasoning about social interactions and dynamics

This task involves understanding social interactions and dynamics based on natural language text. The LLaMA model can be trained to reason about social interactions such as conversations, negotiations, and conflicts and generate appropriate responses.

Primary intended uses

The primary use of LLaMA is research on large language models, including exploring potential applications such as question answering, natural language understanding or reading comprehension, understanding capabilities and limitations of current language models, developing techniques to improve those, evaluating and mitigating biases, risks, toxic and harmful content generations, hallucinations.

Fine-tuning Methods

LLaMA, a cutting-edge language model designed for multi-agent environments, can be fine-tuned to perform specific tasks. It is possible to improve the performance by finetuning models on code-specific tokens greatly.

Instruction Finetuning

Instruction finetuning is a technique used in natural language processing (NLP) to improve the performance of language models on a specific task by providing explicit guidance in the form of instructions or examples. This technique involves fine-tuning a pre-trained language model on a smaller dataset that is specific to the target task and includes explicit instructions or examples. Instruction finetuning has been used in various NLP tasks such as text classification, named entity recognition, and question answering. By providing explicit guidance, instruction finetuning can improve the accuracy and effectiveness of language models for a wide range of NLP tasks.

Sample Codes

Given below is a basic example of loading a pre-trained LLaMA model using the PyTorch framework.

import torch
from transformers import LlamaForMaskedLM, LlamaTokenizer

# Load LLaMA tokenizer
tokenizer = LlamaTokenizer.from_pretrained('meta-llama-base')

# Load pre-trained LLaMA model
model = LlamaForMaskedLM.from_pretrained('meta-llama-base')

# Move model and input data onto the GPU
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)
input_ids = torch.tensor(tokenizer.encode("Hello, how are you today?", add_special_tokens=True)).unsqueeze(0).to(device)

# Run inference on the LLaMA model
outputs = model(input_ids=input_ids)

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LLMs Explained, LLaMA