Llama 3 - 8B & 70B Deep Dive

Meta AI has released two models of Llama 3, an 8 billion parameter model and a 70 billion parameter model, with a 405 billion parameter model expected soon. The 8 billion parameter model is noted to outperform the largest Llama 2 models, indicating significant progress. These models are available in both base and instruction-tuned formats, with text-only inputs but hints at a potential multimodal release in the future. Trained on over 15 trillion tokens, they have shown competitive benchmarks against models like Mistral 7B and Gemma. The license for Llama 3 restricts its use to not improve other large language models and requires any fine-tuned models to be named with 'Llama 3' prefix. Despite these restrictions, Llama 3 can be used commercially and has been made available on various cloud platforms. The video also discusses the potential of the upcoming 405 billion parameter model, which is currently training and showing results close to GPT-4. The speaker provides a guide on how to set up and experiment with Llama 3 using platforms like Hugging Face and discusses its performance on various tasks, suggesting it may be particularly good for function calling and further fine-tuning.

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 expected in the future.
• 📈 The 8 billion parameter model is reported to outperform the largest Llama 2 models, indicating significant progress in AI capabilities.
• 📚 Both models have a context length of 8K tokens, which is relatively short compared to other models with lengths of 32k tokens and beyond.
• 🔢 The models were trained on over 15 trillion tokens, nearly double the amount of tokens of other publicly known models.
• 🌐 The models have been trained with an intention for commercial and research use primarily in English, but with some non-English tokens included.
• 📝 There is a mention of potential future releases including multilingual models and possibly a new code-focused Llama model.
• 🤖 The models are available in both base (pre-trained) and instruction-tuned formats, with the latter being more user-friendly for various tasks.
• ⛔ The license for Llama 3 restricts its use to not improve or create datasets for other large language models, which is a departure from open-source practices.
• 📉 Llama 3's benchmarks show competitive performance, especially in tasks like reasoning and summarization, against other models like MISTAL and Gemma.
• 🔍 The upcoming 405 billion parameter model is hinted to be nearing the performance of GPT-4, based on early test results.
• 📋 Users can access and experiment with Llama 3 through platforms like Hugging Face, allowing for easy deployment and interaction with the models.

Q & A

Which two Llama 3 models have been 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 significance is that the smallest model in the Llama 3 release is outperforming the largest model from the previous release, indicating a substantial improvement in efficiency and performance.

What are the two formats in which the released Llama 3 models are available?

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

What does the context length of 8K for the Llama 3 models imply?

The context length of 8K implies that the models can process up to 8,000 tokens at a time, which is relatively short compared to other models that handle longer contexts.

How many tokens have the Llama 3 models been trained on, and what does this suggest about their training data?

The Llama 3 models have been trained on over 15 trillion tokens, which is the largest publicly declared amount for any model and suggests an extensive and diverse training dataset.

What is the intended use for the Llama 3 models as mentioned in the script?

The intended use for the Llama 3 models is for commercial and research purposes, primarily in English, although a portion of the training data was non-English.

What are the restrictions regarding the use of Llama 3 models for improving other language models?

The license conditions prohibit using Llama 3 materials or any output from the Llama 3 models to improve any other large language model, excluding Llama 3 itself or its fine-tuned versions.

How does the performance of the 8 billion parameter Llama 3 model compare to other models like MISTAL 7B and Gemma instruction-tuned model?

The 8 billion parameter Llama 3 model shows significantly higher performance, particularly in the GSM a Marks, which are about double those of MISTAL instruct and Gemma.

What is the current 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 that are close to those of GPT-4, suggesting it may be on par with GPT-4 once fully trained.

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, use them in applications like Hugging Chat, or deploy their own instances on cloud providers.

What are some of the key features of the Llama 3 models that have been improved from the previous Llama 2 models?

Key improvements include better performance in benchmarks, larger amounts of training data, training on more code, and the potential for multilingual capabilities due to the inclusion of non-English tokens in the training data.

What are the limitations of the Llama 3 models in terms of input and output?

Currently, the Llama 3 models are text-only for inputs and generating text tokens for outputs, with hints that a multimodal version capable of processing images and other modalities may be released in the future.

Outlines

00:00

🚀 Introduction to Meta's Llama 3 Models

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 video discusses the benchmarks, new licensing terms, and future developments for the Llama 3 series. The smallest model is noted to outperform the largest from the previous release, indicating significant progress. The models are available in base and instruction-tuned formats, with text-only inputs at the moment, hinting at a potential multimodal release in the future. The models have a context length of 8K and have been trained on over 15 trillion tokens, nearly double that of previous models. The intended use is for commercial and research purposes in English, with some non-English tokens included.

05:00

🤖 Llama 3's Training and Benchmarks

The video explores 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 particularly high scores in the GSM category, suggesting superior performance in task-oriented capabilities. The 70 billion parameter model also performs competitively against proprietary models. The discussion includes the potential for a multilingual model and the impressive training data scale, which is seven times that of Llama 2 and four times more code. The benchmarks indicate that Llama 3 models are highly competitive and show the potential to rival or exceed the performance of other leading models.

10:01

📜 Llama 3 Licensing and Limitations

The video outlines the licensing terms for Llama 3, which include restrictions on improving other large language models using Llama 3's materials and a requirement to name any fine-tuned models with 'Llama 3' prefix. It also mentions the prohibition on using the model for certain applications, such as health or legal services, without careful consideration of the license. The license allows for commercial use as long as the terms are not violated. The video also discusses the ongoing training of the 405 billion parameter model, which is showing results comparable to GPT-4, indicating a potential release of a highly competitive open-weight model in the near future.

15:03

💻 Setting Up and Running Llama 3 Models

The video provides a guide on how to access and run the Llama 3 models using platforms like Hugging Face, LM Studio, and others. It details the process of downloading the model, the availability of different versions, and the ease of use with platforms that have incorporated Llama 3. The video also demonstrates how to deploy the model on various cloud services and test out different models through APIs. Additionally, it includes a notebook example for running the model, highlighting the use of the text generation pipeline, the importance of system prompts for tailored responses, and the model's performance on various tasks such as 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 that while it is a strong model, it may not be significantly better than recent models like Gemma. It emphasizes the potential for improved fine-tuning of the base model and the anticipation of seeing how community fine-tuned versions perform. The video also mentions the upcoming discussion on the Llama 3 tokenizer and its implications. The host invites viewers to share their observations and experiences with the model and to look out for further videos on the topic.

Keywords

Llama 3 models

Llama 3 models refer to the latest iteration of AI language models developed by Meta AI. The video discusses two specific models: an 8 billion parameter model and a 70 billion parameter model. These models are significant as they represent advancements over the previous Llama 2 models, with the smallest of the released models outperforming the largest from the previous series. This showcases a notable progression in AI language model capabilities.

Benchmarks

Benchmarks are standard tests or comparisons used to evaluate the performance of AI models. In the context of the video, benchmarks are used to assess how the Llama 3 models compare with other models like the Mistral 7B and the Gemma instruction-tuned model. The benchmarks indicate that the Llama 3 models have made substantial improvements, particularly in generating responses that are more aligned with the expected outcomes.

Instruction Tuning

Instruction Tuning is a technique used to refine AI language models by providing them with specific instructions or prompts to improve their performance on certain tasks. The video mentions that the Llama 3 models are available in both a base model format and an instruction-tuned format, with the latter being more suitable for general use in tasks as it has been optimized through instruction tuning.

Context Length

Context Length refers to the amount of text that an AI language model can process at one time. The video notes that both the 8 billion and the 70 billion Llama 3 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 parameter is crucial as it affects the model's ability to understand and generate responses based on longer pieces of text.

Multimodal

Multimodal refers to systems that can process and analyze data from multiple different types of inputs, such as text, images, and sound. The video hints at the potential future release of a multimodal version of the Llama models, which would be capable of incorporating visual data along with text, expanding the scope of applications for these AI models.

Commercial and Research Use

The intended use of the Llama 3 models, as discussed in the video, is for commercial and research purposes. This indicates that the models can be employed in a wide range of professional and academic settings to drive innovation and solve complex problems. However, the video also touches on the limitations and restrictions set by the license agreement governing the use of these models.

Token

In the context of AI language models, a token represents a single unit of meaning, such as a word or a punctuation mark. The video highlights that the Llama 3 models have been trained on over 15 trillion tokens, which is an enormous dataset that significantly contributes to the models' learning and performance capabilities.

Cloud Providers

Cloud Providers are companies that offer data storage, access to servers, and various other services over the internet. The video mentions that the Llama 3 models are being made available on multiple cloud platforms, including AWS, GCP, and others. This widespread availability enables users to access and utilize the models from various providers, facilitating broader adoption.

Benchmarking

Benchmarking in the video refers to the process of comparing the Llama 3 models' performance against other models in the market. The video discusses a custom benchmark set created by Meta AI, which includes 800 different prompts covering 12 key uses. This comprehensive evaluation helps to establish the effectiveness and versatility of the Llama 3 models across a range of applications.

Open Weights

Open Weights is a term used to describe models where the learned parameters (weights) are publicly available but may not be open source in the sense that they come with certain usage restrictions. The video points out that while the Llama 3 models are available for use, they are not open source due to specific limitations in the license agreement, which restricts their use for improving other models.

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 weights to use fewer bits, making the model more efficient for deployment on specific hardware. The video discusses the possibility of running a quantized version of the Llama 3 models, which would allow for faster and more resource-friendly operations, especially on cloud platforms.

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 near future.

The 8 billion parameter model is reported to outperform the largest Llama 2 models.

The models are available in base model format and instruction-tuned format.

The models currently support text-only inputs, hinting at a potential multimodal release in the future.

Both models have a context length of 8K, which is shorter compared to other models with lengths up to 32K and beyond.

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

The 8 billion parameter model shows higher performance in benchmarks compared to Mistral 7B and Gemma instruction-tuned models.

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

Meta AI has worked with multiple cloud providers to make Llama 3 available on various platforms.

The license for Llama 3 restricts its use to not improve any other large language model excluding Llama 3.

If fine-tuning Llama 3, the name 'Llama 3' must be included at the beginning of the AI model name.

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

Llama 3 can be accessed and run through platforms like Hugging Face and AMA.

The model has been trained with techniques like curriculum learning to achieve high performance.

Llama 3's tokenizer will be discussed in an upcoming video, hinting at changes from previous models.

The current version of Llama 3 did not perform as well in multilingual tasks as expected.

The video will cover various prompts and use cases demonstrating Llama 3's capabilities.

Llama 3 is considered a good model, but not significantly better than recent models like Gemma 1.1.

The upcoming fine-tuned versions of Llama 3 are anticipated to potentially perform better.