Notes on the 92-page Paper Released with Meta's Super Large Model Llama 3.1
The super-large model Llama 3.1 is a milestone in the open-source large model community. As a leader, Meta's project involved over 500 participants/contributors (the authors of this paper are listed alphabetically in the appendix, similar to how the Central Committee members' names are displayed by stroke order). This original text is full of implementation details:
meta Llama 3.1 paper
AIGC MV using Suno and keling （just for fun & cheering opensource milestone）
Notes：
- Llama 3.1 doesn't use sparse techniques, it's not a multi-expert system like model 4, but a dense model.
- 405B parameters, 15.6T tokens: The number of tokens is 40 times the number of parameters. Large-scale top models now emphasize data growth far exceeding parameter growth. Is this 15T tokens of data open source? (No, because even if they were willing to open source it, they wouldn't dare, as it could lead to countless data infringement lawsuits)
- Emphasizes three major levers for super-large foundation models: data, scale, and managing complexity.
- Compared to the previous generation system Llama 2, computational power has increased 50 times (using 3.8 × 10^25 FLOPs).
- Complexity management: (1) Choosing a standard dense Transformer architecture instead of a mixture of experts model to maximize training stability. (2) Adopting a relatively simple post-training procedure: Supervised Fine-Tuning (SFT), Rejection Sampling (RS), and Direct Preference Optimization (DPO). In other words, algorithm design and implementation tend towards simplification. Not using sparse techniques and multi-expert systems is for stability (but training challenges are greater, though they're not afraid). Using simpler, easier-to-implement DPO in the post-training phase instead of reinforcement learning is also for stability, as reinforcement learning has always been difficult to handle.
- Benchmark tests cover: general, code, math, reasoning, tool use, long context, and multilingual. All performances are SOTA (state-of-the-art international level).
- MMLU (Massive Multitask Language Understanding): 405B model achieves 87.3% (5-shot), 88.6% (0-shot, CoT).
- Code generation (HumanEval): 405B model reaches 89.0%, close to GPT-4.
- Math problems (GSM8K): 405B model achieves 96.8%, slightly higher than GPT-4.
- Long context tasks: Excellent performance on some tasks, such as 95.2% on QuALITY.
- Multilingual tasks (MGSM): 405B model reaches 91.6%, on par with top models. The 405B model is comparable or close to GPT-4 and Claude 3.5 Sonnet on many tasks. In short, open-source has caught up with closed-source.
- Pre-training started with an 8k window, expanded to a 128k window in the later stages of pre-training (continued training).
- After the foundation model pre-training was completed, multiple iterations of alignment "post-training" were performed. Including: (1) Aligning the model through human feedback, including multiple rounds of Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO); (2) Integrating new capabilities, such as tool use; (3) Enhancing coding and reasoning abilities (specialized optimization); (4) Safety alignment.
- Multimodal expansion (in progress, not yet released): Image, video, and speech capabilities. Including (1) Multimodal encoder pre-training: Image encoder trained on a large number of image-text pairs, aligning visual content and natural language in a unified space; (2) Speech self-training? (3) Experiments on video-text data alignment based on images.
- Language model as the core, other modalities are added later (whether added to pre-training and/or post-training). When expanding to multimodal, the language model parameters remain unchanged, adapting to multimodality, allowing multimodal alignment in the same semantic space, closer to the language model. In other words, Llama follows a modular, step-by-step approach to gradually expand to multimodality. This is not the mainstream approach (mainly referring to Open AI and Google, at least in theory) advocating for "unified multimodal native data joint pre-training". The overall impression of Llama's algorithmic strategies is seeking stability rather than innovation or unification. It tends towards practicality, not caring about leading in algorithms. For example, the integration of speech first involves speech self-training (because speech is actually very similar to text, both being language systems), then alignment between speech and text (including Automatic Speech Recognition ASR and Text-to-Speech TTS). Integrating step by step into the cross-modal large model, this approach isn't cutting-edge in terms of advancement, but it's steady progress, beneficial for engineering development, integration, and iteration. It's unclear when they will be able to release multimodal capabilities online.
- Data collection and cleaning work is very complex, but the Llama team is meticulous, which is also the data guarantee for its quality to catch up with SOTA. To recap: (1) De-duplication: URL-level de-duplication; Document-level de-duplication using MinHash algorithm; Row-level de-duplication: removing rows appearing more than 6 times every 30M documents. (2) Filtering: Removing low-quality documents, outliers, and excessively repetitive documents, using repetitive n-gram coverage to remove repetitive content (such as logs or error messages); using "dirty word" counts to filter adult websites not covered by blacklists; using token distribution KL divergence to filter documents with too many abnormal tokens. (3) Controlling data quality: Using fasttext classifier to identify text that might be cited by Wikipedia; using a Roberta-based classifier trained on Llama 2's predictions; using DistilRoberta to generate document quality scores. Also, fasttext language classifier can identify 176 languages; specially filtering two types of information: adult content and personal identity/privacy information. Special fine processing for code and math web pages.
- Data proportions: For example, downsampling over-represented data categories on the web (such as art and entertainment); data mixing ratios determined by a series of small model experiments, final data mix summary: About 50% of tokens correspond to general knowledge; 25% of tokens involve math and reasoning; 17% of tokens are code; 8% of tokens are multilingual content.
- Model architecture: Apart from empirical detail adjustments, the basic architecture of the dense model remains unchanged, so it's data and scaling that create top models. 405B model specific parameters: 126 layers; token representation dimension 16,384; 128 attention heads; model size of 405B determined according to scaling law, about the computational optimal size under 3.8 × 10^25 FLOPs training budget.
- Vocabulary: Using a vocabulary of 128K tokens. Combines 100K tokens from the tiktoken3 tokenizer and 28K additional multilingual tokens to better support non-English languages.
- Computing resources, including GPU clusters of tens of thousands of cards, massive storage, and high-speed networks, represent huge resource investments. Specific data as follows: Computing resources:
- Used up to 16,000 H100 GPUs (a very powerful graphics processor).
- Each GPU has 80GB of high-bandwidth memory, with a power of 700W.
- These GPUs are installed on servers designed by Meta itself, with 8 GPUs and 2 CPUs per server. Storage system:
- Uses a distributed file system called Tectonic.
- Provides 240PB (1PB=1000TB) of storage space, distributed across 7,500 servers.
- Can process 2TB of continuous data per second, with a peak of 7TB/second.
- A major challenge is handling the large amount of burst writes generated when processing model checkpoints (the process of saving model states).
- Three-step pre-training process: a) Initial pre-training; b) Long context continued pre-training; c) Annealing with high-quality data sources Key pre-training strategies:
- Gradually increase batch size and sequence length to balance stability and efficiency.
- Dynamically adjust data mixing to specifically enhance certain capabilities.
- Increase context length in stages to avoid early computational overhead.
- Use annealing and high-quality data in the late stages of training to fine-tune model performance.
[LLM Summary]
Llama 3: Meta's Open-Source Large Language Model Breakthrough**
1. Introduction and Overview
Meta has introduced Llama 3, a series of foundation language models designed to support various tasks including multilingual processing, programming, reasoning, and tool use. This model series includes versions with 8B, 70B, and 405B parameters, with the largest 405B parameter model adopting a dense Transformer architecture and supporting context windows of up to 128K tokens. The development of Llama 3 highlights three key factors: data quality and scale, computational scale, and complexity management.
2. Model Architecture and Pre-training Strategy
2.1 Model Architecture
Llama 3 retains the standard dense Transformer architecture rather than adopting a mixture of experts model. This choice aims to maximize training stability, reflecting Meta's emphasis on simplifying design to manage complexity. Key architectural improvements include:
- Using Grouped-Query Attention (GQA) mechanism, with 8 key-value heads per attention layer.
- Introducing attention masks to prevent self-attention between different documents in the same sequence.
- Expanding the vocabulary to 128K tokens, combining 100K tokens from the tiktoken3 tokenizer and 28K additional multilingual tokens.
- Increasing the RoPE base frequency hyperparameter to 500,000 to support longer contexts.
2.2 Pre-training Data Processing
Llama 3's pre-training data processing is extremely rigorous, including:
- Multi-level deduplication: URL-level, document-level (using MinHash algorithm), and row-level deduplication.
- Heuristic filtering: Removing low-quality documents, outliers, and excessively repetitive content.
- Model-based quality filtering: Using fasttext and Roberta-based classifiers for quality assessment.
- Special content processing: Developing specialized processing pipelines for code and mathematical content.
- Multilingual data processing: Using fasttext base language identification model, supporting 176 languages.
- Safety and privacy protection: Filtering website data containing personally identifiable information (PII) and unsafe content.
2.3 Pre-training Strategy
The pre-training process is divided into three main stages:
1. Initial pre-training: Conducted on about 15T multilingual tokens, far exceeding Llama 2's 1.8T tokens.
2. Long context pre-training: Gradually expanding from initial 8K tokens to 128K tokens context window.
3. Annealing phase: Fine-tuning with high-quality data in the final stage, using Polyak averaging to generate the final model.
Data mixing ratios are carefully designed:
- 50% general knowledge
- 25% mathematics and reasoning
- 17% code
- 8% multilingual content
3. Training Infrastructure and Challenges
3.1 Computational Resources
- Using up to 16K H100 GPUs, each equipped with 80GB HBM3 memory.
- Adopting a 4D parallel strategy: tensor parallelism, pipeline parallelism, context parallelism, and data parallelism.
3.2 Storage System
- Using the Tectonic distributed file system, providing 240PB of storage space.
- Supporting 2TB/s sustained throughput, with peak capacity of 7TB/s.
3.3 Network Optimization
- Developing the NCCLX communication library to improve network efficiency.
- Designing specific network topologies and load balancing strategies.
3.4 Training Challenges
- Experiencing 466 job interruptions during the 54-day training period, 419 of which were unexpected.
- Developing automated systems and specialized tools to handle hardware failures and network issues.
4. Post-training and Alignment
Llama 3 adopts a multi-round iterative post-training process, including:
1. Supervised Fine-Tuning (SFT)
2. Direct Preference Optimization (DPO)
3. Reward model training: Using human feedback data
4. Safety alignment: Implementing multiple rounds of safety measures
This process not only improves the model's instruction-following capabilities but also enhances safety and specific abilities (such as coding and reasoning).
5. Multimodal Expansion
Although not officially released yet, Llama 3 demonstrates promising multimodal capabilities:
- Image recognition: Training independent image encoders, integrated with the language model through adapters.
- Video understanding: Adding video adapters based on image adapters.
- Speech processing: Independently training speech encoders, then aligning with the language model.
This modular approach allows flexible addition of new modalities while maintaining core language capabilities.
6. Performance Evaluation
Llama 3 performs excellently in multiple benchmark tests:
- MMLU (5-shot): 87.3%
- HumanEval (code generation): 89.0%
- GSM8K (math problems): 96.8%
- Long context tasks (like QuALITY): 95.2%
- MGSM (multilingual tasks): 91.6%
These results indicate that Llama 3 405B is comparable or close to GPT-4 and Claude 3.5 Sonnet on multiple tasks, particularly excelling in document understanding and long context tasks.
7. Safety Considerations
Meta highly prioritizes safety in the development of Llama 3:
- Implementing strict safety measures in both pre-training and post-training stages.
- Developing the Llama Guard system-level safety solution.
- Conducting extensive red team testing and risk assessments.
8. Open Source Impact and Future Directions
Meta's decision to publicly release the entire Llama 3 series, including the 405B parameter version, may have far-reaching impacts on the AI research community:
- Promoting open, responsible AI development.
- Accelerating AGI research progress.
- Providing researchers with opportunities to examine and improve large-scale language models.
Future development directions may include:
- Further improving multimodal integration.
- Expanding context length.
- Continuously enhancing data quality and model scale.
9. Conclusion
The development of Llama 3 demonstrates Meta's deep experience and forward-thinking in large-scale AI systems. By focusing on three key levers - data quality, computational scale, and complexity management - Llama 3 has reached or approached the current state-of-the-art level on several key benchmarks. Its open-source release may drive a wave of innovation across the entire AI field, paving the way for responsible AGI development.
Llama 3: Meta's AI Chef's Latest "Divine Delicacy"
Attention, all tech enthusiasts! The Michelin three-star AI chef Meta has just unveiled a new dish! This divine delicacy named "Llama 3" is not only spicy enough but will elevate your taste buds to new heights!
1. The Chef's Secret Weapon
Imagine Llama 3 as a super nanny who speaks 8 languages, writes code, does math, and can be your personal assistant. She can handle a kindergarten full of rambunctious kids (8B version), manage a mid-sized company (70B version), or even govern a small country (405B version)! This 405B big sister can remember 128,000 "gossips" (oh no, I mean context) simultaneously, essentially a walking encyclopedia + supercomputer!
2. Ingredient Selection: Only the Freshest!
Llama 3's chefs are masters at picking ingredients:
- They "fished" 15 trillion words from the internet, nearly 10 times more than the previous generation!
- Half of these words are everyday life seasonings, a quarter are math problems and brain teasers, nearly a fifth are programmer spells, and the rest are dialects learned from world travels.
- They even invented a super weed remover, filtering out all the online garbage, repetitive, and unhealthy stuff.
3. Cooking Process: Three-Step Stir-Fry Method
Step 1: "Slow Simmer" - Start with a regular stove (8K context) to cook it halfway. Step 2: "High Heat Stir-Fry" - Switch to a super stove (gradually increasing to 128K context), reducing the sauce to be thick and fragrant. Step 3: "Low Heat Finish" - Finally, a gentle simmer with the best ingredients, the legendary "annealing" (even the chefs don't know why it's called that), bringing the flavor to its peak!
4. Kitchen Equipment: Top-of-the-Line Luxury Version
- 16,000 super high-power induction cookers (H100 GPUs) firing simultaneously!
- A refrigerator that could fit half the Pacific Ocean (240PB storage)!
- A proprietary ingredient prep system faster than 5G (NCCLX communication library)!
Imagine all these stoves firing at once, making the kitchen feel like a sauna. But our chefs persevered through the heat, changing chef uniforms 466 times in 54 days to whip up this dish!
5. Training Method: Both Cute and Well-Mannered
Being a good cook isn't enough; you've got to have manners too! So our chefs began a long "training" process:
- First came a round of "gentle education" (supervised fine-tuning)
- Then the "carrot and stick" tactic (direct preference optimization)
- Finally, they invited moral role models (safety alignment) for guidance
After all this fuss, Llama 3 not only cooks well but also knows how to please people, program, do math, and mind her manners - a true decathlon champion!
6. Special Side Dishes: Showcasing Multiple Talents
Don't think Llama 3 can only cook; she's a multi-talented "goddess":
- Storytelling from images? Piece of cake!
- Writing movie reviews? No problem!
- Recognizing songs and even singing a bit? The karaoke queen!
Although these "talents" are still in practice, they already show the potential of Li Bai's "from black hair to snow white in a day"!
7. A True Powerhouse: Dazzling Test Scores
Llama 3 participated in a series of "Top Chef Competitions," with eye-popping scores:
- College Entrance Exam (MMLU): 87.3 points (out of 100)
- Programmer Interview (HumanEval): 89 points (out of 100)
- Math Olympiad (GSM8K): 96.8 points (out of 100)
- Long Novel Reading Comprehension (QuALITY): 95.2 points (out of 100)
Bring this report card home, and even a "Tiger Mom" would be grinning from ear to ear!
8. Safety First: AI's "Security Captain"
Meta's chefs know well the principle of "don't leave guns and ammo lying around." They've assigned Llama 3 a 24/7 bodyguard team (Llama Guard) to prevent her from accidentally saying or doing the wrong thing. They even arrange occasional "moral exams" to ensure she doesn't turn into a "Terminator."
9. Open Source Feast: Everyone Can Be a Master Chef!
The most impressive part is that Meta decided to make the recipe for this "divine delicacy" completely public! It's like a Michelin three-star restaurant putting their signature dish's recipe online. Now anyone who wants to can whip it up at home! This move not only shocked other master chefs but also made countless food lovers cheer with joy!
10. Future Outlook: Reaching New Heights
Meta's chefs aren't resting on their laurels; they're already pondering the next "divine delicacy":
- Maybe a dancing Llama 4?
- Or a painting Llama 5?
- Who knows, one day we might see a Llama 6 composing symphonies!
In short, the AI world's "Michelin" journey has only just begun!
Epilogue
The birth of Llama 3 not only elevates Meta's status in the AI world but also brings a fresh breeze to the entire AI research community. This bowl of "Llama soup" is not only delicious but also brings unlimited imagination to everyone. What will the future of AI be like? Let's wait and see what flavor the next "divine delicacy" will be!
