Llama 3 - 8B & 70B Deep Dive

Sam Witteveen
19 Apr 202423:53

TLDRMeta AI has released two models from the Llama 3 series, an 8 billion parameter model and a 70 billion parameter model, with a 405 billion parameter model on the horizon. The 8 billion parameter model is already outperforming the largest Llama 2 models, indicating significant progress. These models are available in base and instruction-tuned formats, with text-only inputs at the moment, but hints suggest a multimodal version may be released in the future. Both models have a context length of 8K and have been trained on over 15 trillion tokens, nearly double the amount of previous models. The Llama 3 models are intended for commercial and research use in English, with some non-English tokens included. The license for these models includes restrictions on improving other large language models using Llama 3's output and requires any fine-tuned models to be named with 'Llama 3' prefix. Benchmarks show the 8 billion parameter model surpassing Mistral 7B and recent Gemma instruction-tuned models, particularly in GSM A Marks. The 70 billion parameter model is competitive with proprietary models like Gemini Pro 1.5 and Claude 3. The upcoming 405 billion parameter model is expected to be a significant leap, with current tests showing results close to GPT-4. Users can access and experiment with the Llama 3 models through platforms like Hugging Face and Hugging Chat, and the models can be deployed on various cloud providers for private instances.

Takeaways

  • 🚀 Meta has released two Llama 3 models: an 8 billion parameter model and a 70 billion parameter model, with a 405 billion parameter model on the horizon.
  • 📊 The 8 billion parameter model is reported to outperform the largest Llama 2 models, indicating a significant advancement in AI capabilities.
  • 📈 Both models have a context length of 8K tokens, which is relatively short compared to other models with lengths of 32k and beyond.
  • 📝 The models have been trained on over 15 trillion tokens, nearly double the amount of tokens other models have been trained on, according to public disclosures.
  • 🔍 The training data includes non-English tokens, which may improve the model's performance on multilingual tasks.
  • 🤖 Llama 3 models are available in both base (pre-trained) and instruction-tuned formats, catering to different user needs.
  • 📜 The license for Llama 3 restricts the use of the models to not improve or create datasets for other large language models, which is a departure from open-source practices.
  • 🏢 Commercial use of Llama 3 is allowed as long as the license terms are not violated, including a clause requiring the name 'Llama 3' in any fine-tuned model names.
  • 🔗 Llama 3 has been made available on multiple cloud providers, facilitating easier access for a broader range of users.
  • 📈 Benchmarks show that Llama 3 models are competitive with other leading models like Mistral 7B and Gemma, and in some cases, outperform them.
  • 🔍 The upcoming 405 billion parameter model is showing results close to GPT-4, suggesting that an open-weights model could soon rival the performance of proprietary models.

Q & A

  • What are the two Llama 3 models released by Meta AI?

    -Meta AI has released an 8 billion parameter model and a 70 billion parameter model of Llama 3.

  • What is the significance of the 8 billion parameter model outperforming the 70 billion Llama 2 models?

    -The 8 billion parameter model outperforming the 70 billion Llama 2 models indicates a significant improvement in efficiency and performance, showcasing a leap forward in AI model development.

  • In what format are the released Llama 3 models available?

    -The Llama 3 models are available in both the base model format, also known as pre-trained format, and the instruction-tuned format.

  • What is the context length for both the 8 billion and the 70 billion Llama 3 models?

    -The context length for both the 8 billion and the 70 billion Llama 3 models is 8K tokens.

  • How many tokens were used to train the Llama 3 models?

    -The Llama 3 models were trained with over 15 trillion tokens.

  • What is the intended use for the Llama 3 models?

    -The intended use for the Llama 3 models is for commercial and research purposes, primarily in English.

  • What are the restrictions mentioned in the license for using Llama 3 models?

    -The license restricts the use of Llama 3 materials or any output to improve any other large language model excluding Llama 3, and it also requires that any fine-tuned model include the name 'Llama 3' at the beginning of the AI model name.

  • What is the status of the 405 billion parameter Llama 3 model?

    -The 405 billion parameter Llama 3 model is still in training, with a recent checkpoint showing results close to GPT-4.

  • How can users access and use the Llama 3 models?

    -Users can access and use the Llama 3 models through platforms like Hugging Face, where they can download the models and use them for various tasks.

  • What is the difference between the instruction-tuned format and the base model format of Llama 3?

    -The instruction-tuned format is designed for users who want to use the models for specific tasks, while the base model or pre-trained format is for those who want to fine-tune the model for their purposes.

  • How does the Llama 3 model handle multilingual inputs?

    -While the current Llama 3 models are primarily text-only and in English, about 5% of the tokens trained on are non-English, which may improve its performance on non-English inputs compared to other models.

  • What are some of the key features of the Llama 3 models that make them stand out from previous models?

    -Llama 3 models stand out due to their large scale, being trained on an unprecedented amount of tokens, their competitive performance in benchmarks, and the potential for future multilingual and multimodal versions.

Outlines

00:00

🚀 Introduction to Meta's Llama 3 Models Release

Meta has released two Llama 3 models, an 8 billion parameter model and a 70 billion parameter model, with a 405 billion parameter model expected in the future. The video discusses these models, their benchmarks, new licensing terms, and upcoming developments. The 8 billion parameter model is noted to outperform the largest Llama 2 model, indicating significant progress. The models are available in base and instruction-tuned formats, with text-only inputs, hinting at a potential future multimodal release. The context length for both models is 8K, and they have been trained on over 15 trillion tokens, nearly double the amount of previous models. The models are intended for commercial and research use in English, with some non-English tokens included.

05:00

🤖 Llama 3's Training and Benchmarks

The video covers the extensive training of Llama 3 with 24,000 GPUs and compares its benchmarks to other models like MistrAL 7B and Gemma. Llama 3's 8 billion parameter model shows higher marks, particularly in the GSM category, suggesting superior performance in task-oriented scenarios. The 70 billion parameter model is competitive with proprietary models like Gemini Pro 1.5 and Claude 3. The benchmarks also include an evaluation set of 800 prompts covering 12 key uses, where Llama 3 outperforms various models including GPT 3.5 and previous Llama versions. The discussion references Chinchilla's optimal scaling laws and suggests that Llama 3's training on 15 trillion tokens indicates potential for even higher token counts in future models.

10:01

📜 Llama 3 Licensing Conditions and Limitations

The video outlines the licensing conditions for Llama 3, which include restrictions for entities with over 700 million monthly active users and clauses prohibiting the use of Llama 3 materials to improve other large language models. The license requires any fine-tuned models to be named with 'Llama 3' prefix. It also details prohibited uses, allowing commercial use as long as other terms are not violated. There's mention of a 405 billion parameter model still in training, with early tests indicating performance nearing GPT-4.

15:03

🔧 Setting Up and Running Llama 3 Models

The video demonstrates how to access and run Llama 3 models using platforms like Hugging Face, LM Studio, and Hugging Chat. It covers deploying the model on cloud providers and using APIs. The speaker provides a notebook example for running the model, discussing the use of text generation pipelines, Chain of Thought prompts, and system prompts. The notebook includes examples of using the model for various tasks, highlighting its performance in reasoning, role-playing, and function calling.

20:04

📊 Llama 3 Model Performance and Future Prospects

The video concludes with an assessment of Llama 3's performance, noting it as a solid model, but not significantly better than recent models like Gemma 1.1. It suggests that fine-tuning could yield improved versions of Llama 3. The video also mentions the tokenizer's interesting aspects, which will be discussed in a future video, and briefly touches on the model's performance on multilingual tasks. The speaker invites viewers to share their observations and questions in the comments and looks forward to examining fine-tuned versions of Llama 3.

Mindmap

Keywords

💡Llama 3 models

Llama 3 models refer to the latest generation of AI language models developed by Meta AI. The video discusses two specific models released: an 8 billion parameter model and a 70 billion parameter model. These models are significant as they represent advancements in AI technology, with the smaller 8 billion parameter model outperforming the largest model from the previous Llama 2 series, indicating a substantial improvement in efficiency and capability.

💡Benchmarks

Benchmarks are standard tests or comparisons used to evaluate the performance of the Llama 3 models against other existing models like Mistral 7B and Gemma instruction-tuned model. The script mentions that the 8 billion parameter model of Llama 3 shows higher performance in benchmarks, particularly in the GSM a Marks, which is a measure of model accuracy, suggesting that it could perform better in certain tasks.

💡Instruction Tuned Format

Instruction Tuned Format is a type of model format where the AI has been specifically trained, or 'tuned,' to follow instructions provided in the input text. This format is particularly useful for users who want to use the model for specific tasks without needing to fine-tune the model themselves. The video indicates that this format is what most people will use and contrasts it with the base or pre-trained model format.

💡Context Length

Context Length refers to the amount of text that a language model can take into account when generating a response. The script mentions that both the 8 billion and 70 billion models have a context length of 8K tokens, which is relatively short compared to other models that can handle lengths of 32k tokens or more. This characteristic is important as it affects the model's ability to process and remember information.

💡Multimodal

Multimodal refers to systems that can process and understand multiple types of data or inputs, such as text, images, and sound. The video suggests that Meta AI may release a multimodal version of the Llama 3 model in the future, which would be capable of processing not just text but also images and potentially other types of data.

💡Commercial and Research Use

Commercial and Research Use denotes the intended purpose for which the Llama 3 models are licensed. The script specifies that the models are for use in commercial applications and research, primarily in English, which implies that they are designed to support businesses and academic endeavors in utilizing advanced AI language models for various tasks and studies.

💡Token

A Token in the context of language models refers to the individual elements that make up the text that the model processes, such as words or subwords. The video mentions that the models were trained on over 15 trillion tokens, which is an enormous dataset that contributes to the models' learning and performance capabilities.

💡Fine-tuning

Fine-tuning is the process of further training a pre-trained model on a specific task or dataset to improve its performance for that particular task. The video discusses the possibility of fine-tuning the Llama 3 models and the existence of scripts to facilitate this process. It is suggested that fine-tuned versions of the models might have a longer context length.

💡Hugging Face

Hugging Face is a company that provides a platform for developers to train, fine-tune, and deploy AI models. In the video, it is mentioned as a place where users can access and experiment with the Llama 3 models. Hugging Face allows users to utilize the models through its interfaces like Hugging Chat or by deploying the models to various cloud services.

💡Quantized Version

A Quantized Version of a model refers to a model that has undergone quantization, a process that reduces the precision of the model's mathematical operations to use fewer computational resources. This makes the model faster and more efficient to run, at the potential cost of some accuracy. The video mentions the possibility of running a quantized version of the Llama 3 models.

💡API

API stands for Application Programming Interface, which is a set of rules and protocols that allows different software applications to communicate with each other. The video discusses the availability of an API for the Llama 3 models, which would enable developers to integrate the models into their applications and services.

Highlights

Meta has released two Llama 3 models: an 8 billion parameter model and a 70 billion parameter model.

A 405 billion parameter model is expected to be released in the future.

The 8 billion parameter model outperforms the largest Llama 2 models.

The models are available in base and instruction-tuned formats.

The models currently only support text input, hinting at a potential future multimodal release.

Both models have a context length of 8K, which is shorter compared to other models.

The models have been trained on over 15 trillion tokens, nearly double the amount of previous models.

The training data set has been in use since March 2023 for the first model and December 2023 for the larger model.

The models are intended for commercial and research use, primarily in English, but with some non-English tokens included.

Llama 3 has been trained on four times more code compared to its predecessors.

The model was trained using 24,000 GPUs, indicating a significant computational resource investment.

Benchmarks show the 8 billion parameter model performing significantly higher in certain tasks compared to Mistral 7B and Gemma.

The 70 billion parameter model is competitive with proprietary models like Gemini Pro 1.5 and Claude 3.

Llama 3 models have been tested on an evaluation set of 800 different prompts covering 12 key uses.

The license for Llama 3 restricts its use to not improve any other large language models or fine-tune Llama 3 for other models.

Commercial use of the models is allowed as long as the license terms are not broken.

The 405 billion parameter model is still in training and showing results comparable to GPT-4.

Llama 3 can be accessed and run through various platforms like Hugging Face, LM Studio, and others.

The model demonstrates strong performance in tasks like role-playing, creative writing, and code generation.

Llama 3's tokenizer will be discussed in an upcoming video, hinting at potential changes and improvements.

Multilingual capabilities of the current version of Llama 3 did not perform as expected, but future iterations may improve on this.