Llama 3 - 8B & 70B Deep Dive
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
🚀 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.
🤖 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.
📜 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.
🔧 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.
📊 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
💡Benchmarks
💡Instruction Tuned Format
💡Context Length
💡Multimodal
💡Commercial and Research Use
💡Token
💡Fine-tuning
💡Hugging Face
💡Quantized Version
💡API
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.
