Llama 3.1 - Multilingual, Long Context, and More!
Llama 3.1, including the massive 405B model, has arrived! This exciting new release from Meta brings a plethora of impressive upgrades and features. In this detailed overview, we’ll explore everything you need to know about Llama 3.1 and its extraordinary capabilities, highlighting why it’s a significant advancement in the field of AI and natural language processing.
Llama 3.1 Overview
Llama 3.1 is available in three distinct sizes: 8B, 70B, and 405B. Each size supports multilingual capabilities in eight languages and boasts an impressive context length of 128k tokens. This latest iteration of the Llama series not only meets but often exceeds the performance benchmarks set by GPT-4 across a wide range of text processing tasks.
Key Features and Improvements:
- Model Sizes: Llama 3.1 is available in 8B, 70B, and 405B versions, each offered as Instruct and Base models to cater to various needs.
- Context Length: All models support a context length of 128k tokens, making them highly efficient for handling extensive texts and long contexts.
- Multilingual Support: The models can operate in eight languages, including but not limited to English, German, and French, enhancing their global usability.
- Training Data: Llama 3.1 models have been trained on a staggering 15 trillion tokens and fine-tuned on 25 million human and synthetic samples, ensuring high-quality and diverse output.
- License: The commercial-friendly license allows the use of model outputs to improve other large language models (LLMs), fostering innovation.
- Quantization: The models are available in FP8, AWQ, and GPTQ formats for efficient inference, enabling deployment across various hardware setups.
- Performance: Llama 3.1 matches and frequently surpasses GPT-4 on numerous benchmarks, demonstrating its superior capabilities.
- Enhanced Capabilities: Significant improvements in coding and instruction following, along with robust support for tool use and function calling.
- Availability: The models are accessible via the Hugging Face Inference API and HuggingChat, with 1-click deployments on platforms like Hugging Face, Amazon SageMaker, and Google Cloud.
Detailed Overview
Llama 3.1 represents a major advancement, offering a range of models tailored for diverse applications. These models are designed to be efficient for deployment on consumer GPUs while also supporting large-scale, AI-native applications. The three main sizes (8B, 70B, and 405B) address different needs, with both base and instruction-tuned variants available for each size.
New Models:
- Meta-Llama-3.1-8B: The base model designed for efficient deployment across a variety of environments.
- Meta-Llama-3.1-8B-Instruct: Fine-tuned specifically for instruction following, enhancing its capability to handle guided tasks.
- Meta-Llama-3.1-70B: Suitable for large-scale, AI-native applications that require extensive processing power.
- Meta-Llama-3.1-70B-Instruct: Enhanced to manage complex instructions, making it ideal for advanced use cases.
- Meta-Llama-3.1-405B: A premier model designed for synthetic data generation and other advanced applications.
- Meta-Llama-3.1-405B-Instruct: The top-tier model for high-stakes, instruction-heavy tasks, offering unparalleled performance and reliability.
Additionally, Meta has introduced two innovative models: Llama Guard 3 and Prompt Guard. Llama Guard 3 classifies LLM inputs and responses to detect unsafe content, while Prompt Guard is designed to detect and prevent prompt injections and jailbreaks, ensuring a safer and more secure AI interaction.
Performance and Efficiency:
The Llama 3.1 models have undergone extensive training using a vast number of GPU hours, emphasizing both efficiency and scalability. The availability of quantized versions in FP8, AWQ, and GPTQ formats ensures that these models can be deployed efficiently in various environments, from consumer-grade hardware to large-scale data centers.
Memory Requirements:
Running Llama 3.1 requires substantial hardware resources, especially for the larger models. Below is a breakdown of the memory requirements for inference and training:
Inference Memory Requirements:
For inference, the memory requirements depend on the model size and the precision of the weights. Here’s a table showing the approximate memory needed for different configurations:
| Model Size | FP16 | FP8 | INT4 |
|---|---|---|---|
| 8B | 16GB | 8GB | 4GB |
| 70B | 140GB | 70GB | 35GB |
| 405B | 810GB | 405GB | 203GB |
Note: The above-quoted numbers indicate the GPU VRAM required just to load the model checkpoint. They don’t include torch reserved space for kernels or CUDA graphs.
For instance, a node equipped with 8 H100 GPUs, each with approximately 640GB of VRAM, would require running the 405B model in a multi-node configuration or using a lower precision such as FP8. The latter is generally the preferred method.
KV Cache Memory Requirements
It’s important to note that using lower precision formats like INT4 might lead to some loss in accuracy, but this trade-off can substantially cut down memory usage and boost inference speed. Besides accommodating the model weights, you’ll also need to allocate memory for the KV Cache. This cache holds the keys and values for all tokens within the model’s context to avoid recomputing them when generating new tokens. This becomes particularly crucial given the model’s extensive context length. In FP16 precision, the memory requirements for the KV cache are:
The memory requirements for the KV cache, which holds keys and values for all tokens in the model’s context, are detailed below. These requirements vary based on the model size and the number of tokens.
| Model Size | 1k tokens | 16k tokens | 128k tokens |
|---|---|---|---|
| 8B | 0.125 GB | 1.95 GB | 15.62 GB |
| 70B | 0.313 GB | 4.88 GB | 39.06 GB |
| 405B | 0.984 GB | 15.38 GB | 123.05 GB |
Training Memory Requirements
The table below provides a detailed overview of the approximate memory requirements for training Llama 3.1 models. It categorizes the memory needs based on different model sizes and token contexts, which are critical for optimizing training processes.
This information is essential for planning and resource allocation, as it helps in understanding the memory footprint associated with various contexts (ranging from 1,000 tokens to 128,000 tokens) and different model scales. These estimates are crucial for managing hardware resources efficiently during training.
| Model Size | Full Fine-tuning | LoRA | Q-LoRA |
|---|---|---|---|
| 8B | 60GB | 16GB | 6GB |
| 70B | 300GB | 160GB | 48GB |
| 405B | 3.25TB | 950GB | 250GB |
These requirements highlight the need for robust hardware to fully leverage the capabilities of Llama 3.1, particularly for training and deploying the larger models.
Evaluation:
Llama 3.1 models have been rigorously evaluated across various benchmarks, demonstrating significant improvements over previous versions. They exhibit competitive performance against other leading models like GPT-4, showcasing their advanced capabilities and efficiency.
Llama 3.1 Evaluation
Note: We are currently evaluating Llama 3.1 individually on the new Open LLM Leaderboard 2 and will update this section later today. Below is an excerpt from the official evaluation from Meta.
| Category | Benchmark | # Shots | Metric | Llama 3 8B | Llama 3.1 8B | Llama 3 70B | Llama 3.1 70B | Llama 3.1 405B |
|---|---|---|---|---|---|---|---|---|
| General | MMLU | 5 | macro_avg/acc_char | 66.7 | 66.7 | 79.5 | 79.3 | 85.2 |
| MMLU PRO (CoT) | 5 | macro_avg/acc_char | 36.2 | 37.1 | 55.0 | 53.8 | 61.6 | |
| AGIEval English | 3-5 | average/acc_char | 47.1 | 47.8 | 63.0 | 64.6 | 71.6 | |
| CommonSenseQA | 7 | acc_char | 72.6 | 75.0 | 83.8 | 84.1 | 85.8 | |
| Winogrande | 5 | acc_char | - | 60.5 | - | 83.3 | 86.7 | |
| BIG-Bench Hard (CoT) | 3 | average/em | 61.1 | 64.2 | 81.3 | 81.6 | 85.9 | |
| ARC-Challenge | 25 | acc_char | 79.4 | 79.7 | 93.1 | 92.9 | 96.1 | |
| Knowledge reasoning | TriviaQA-Wiki | 5 | em | 78.5 | 77.6 | 89.7 | 89.8 | 91.8 |
| SQuAD | 1 | em | 76.4 | 77.0 | 85.6 | 81.8 | 89.3 | |
| Reading comprehension | QuAC (F1) | 1 | f1 | 44.4 | 44.9 | 51.1 | 51.1 | 53.6 |
| BoolQ | 0 | acc_char | 75.7 | 75.0 | 79.0 | 79.4 | 80.0 | |
| DROP (F1) | 3 | f1 | 58.4 | 59.5 | 79.7 | 79.6 | 84.8 |
Training Data
Overview
Llama 3.1 was pretrained on approximately 15 trillion tokens of data sourced from publicly available resources. For fine-tuning, the model utilized publicly available instruction datasets in addition to over 25 million synthetically generated examples. This extensive dataset helps in enhancing the model’s performance and versatility across different tasks.
Data Freshness
The pretraining data for Llama 3.1 has a cutoff date of December 2023. This ensures that the model’s knowledge base is relatively up-to-date with recent developments and information up to that point.
Benchmark Scores
Base Pretrained Models
The following table presents the performance of Llama 3.1 models on various standard automatic benchmarks. The evaluations were conducted using our internal evaluations library.
| Category | Benchmark | # Shots | Metric | Llama 3 8B | Llama 3.1 8B | Llama 3 70B | Llama 3.1 70B | Llama 3.1 405B |
|---|---|---|---|---|---|---|---|---|
| General | MMLU | 5 | macro_avg/acc_char | 66.7 | 66.7 | 79.5 | 79.3 | 85.2 |
| MMLU-Pro (CoT) | 5 | macro_avg/acc_char | 36.2 | 37.1 | 55.0 | 53.8 | 61.6 | |
| AGIEval English | 3-5 | average/acc_char | 47.1 | 47.8 | 63.0 | 64.6 | 71.6 | |
| CommonSenseQA | 7 | acc_char | 72.6 | 75.0 | 83.8 | 84.1 | 85.8 | |
| Winogrande | 5 | acc_char | - | 60.5 | - | 83.3 | 86.7 | |
| BIG-Bench Hard (CoT) | 3 | average/em | 61.1 | 64.2 | 81.3 | 81.6 | 85.9 | |
| ARC-Challenge | 25 | acc_char | 79.4 | 79.7 | 93.1 | 92.9 | 96.1 | |
| Knowledge Reasoning | TriviaQA-Wiki | 5 | em | 78.5 | 77.6 | 89.7 | 89.8 | 91.8 |
| SQuAD | 1 | em | 76.4 | 77.0 | 85.6 | 81.8 | 89.3 | |
| QuAC (F1) | 1 | f1 | 44.4 | 44.9 | 51.1 | 51.1 | 53.6 | |
| BoolQ | 0 | acc_char | 75.7 | 75.0 | 79.0 | 79.4 | 80.0 | |
| DROP (F1) | 3 | f1 | 58.4 | 59.5 | 79.7 | 79.6 | 84.8 |
Instruction Tuned Models
The table below shows the performance of Llama 3.1 instruction-tuned models across various benchmarks:
| Category | Benchmark | # Shots | Metric | Llama 3 8B Instruct | Llama 3.1 8B Instruct | Llama 3 70B Instruct | Llama 3.1 70B Instruct | Llama 3.1 405B Instruct |
|---|---|---|---|---|---|---|---|---|
| General | MMLU | 5 | macro_avg/acc | 68.5 | 69.4 | 82.0 | 83.6 | 87.3 |
| MMLU (CoT) | 0 | macro_avg/acc | 65.3 | 73.0 | 80.9 | 86.0 | 88.6 | |
| MMLU-Pro (CoT) | 5 | micro_avg/acc_char | 45.5 | 48.3 | 63.4 | 66.4 | 73.3 | |
| IFEval | - | - | 76.8 | 80.4 | 82.9 | 87.5 | 88.6 | |
| Reasoning | ARC-C | 0 | acc | 82.4 | 83.4 | 94.4 | 94.8 | 96.9 |
| GPQA | 0 | em | 34.6 | 30.4 | 39.5 | 41.7 | 50.7 | |
| Code | HumanEval | 0 | pass@1 | 60.4 | 72.6 | 81.7 | 80.5 | 89.0 |
| MBPP ++ base version | 0 | pass@1 | 70.6 | 72.8 | 82.5 | 86.0 | 88.6 | |
| Multipl-E HumanEval | 0 | pass@1 | - | 50.8 | - | 65.5 | 75.2 | |
| Multipl-E MBPP | 0 | pass@1 | - | 52.4 | - | 62.0 | 65.7 | |
| Math | GSM-8K (CoT) | 8 | em_maj1@1 | 80.6 | 84.5 | 93.0 | 95.1 | 96.8 |
| MATH (CoT) | 0 | final_em | 29.1 | 51.9 | 51.0 | 68.0 | 73.8 | |
| Tool Use | API-Bank | 0 | acc | 48.3 | 82.6 | 85.1 | 90.0 | 92.0 |
| BFCL | 0 | acc | 60.3 | 76.1 | 83.0 | 84.8 | 88.5 | |
| Gorilla Benchmark API Bench | 0 | acc | 1.7 | 8.2 | 14.7 | 29.7 | 35.3 | |
| Nexus (0-shot) | 0 | macro_avg/acc | 18.1 | 38.5 | 47.8 | 56.7 | 58.7 | |
| Multilingual | Multilingual MGSM (CoT) | 0 | em | - | 68.9 | - | 86.9 | 91.6 |
Multilingual Benchmarks
The table below details the performance of Llama 3.1 models across various languages:
| Category | Benchmark | Language | Llama 3.1 8B | Llama 3.1 70B | Llama 3.1 405B |
|---|---|---|---|---|---|
| General | MMLU (5-shot, macro_avg/acc) | Portuguese | 62.12 | 80.13 | 84.95 |
| Spanish | 62.45 | 80.05 | 85.08 | ||
| Italian | 61.63 | 80.4 | 85.04 | ||
| German | 60.59 | 79.27 | 84.36 | ||
| French | 62.34 | 79.82 | 84.66 | ||
| Hindi | 50.88 | 74.52 | 80.31 | ||
| Thai | 50.32 | 72.95 | 78.21 |
Hugging Face Integration:
Llama 3.1 models are seamlessly integrated with the Hugging Face ecosystem, including the Transformers library and TGI. This integration ensures that users can easily deploy and fine-tune these models. Additionally, they are available on HuggingChat for immediate use, providing a user-friendly interface for interacting with the models.
Quantization:
In collaboration with Hugging Face, Meta has provided quantized versions of the Llama 3.1 models. This effort makes the models more accessible and efficient for deployment, reducing the computational resources required without compromising performance.
Getting Started
To use Llama 3.1 with Hugging Face Transformers, ensure you have the latest version installed. Here’s how to get started:
pip install "transformers>=4.43" --upgradeBelow is an example code snippet to use Llama 3.1 for text generation:
from transformers import pipelineimport torch
model_id = "meta-llama/Meta-Llama-3.1-8B-Instruct"pipe = pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device="cuda",)
messages = [ {"role": "user", "content": "Who are you? Please, answer in pirate-speak."},]outputs = pipe( messages, max_new_tokens=256, do_sample=False,)assistant_response = outputs[0]["generated_text"][-1]["content"]print(assistant_response)# Arrrr, me hearty! Yer lookin' fer a bit o' information about meself, eh? Alright then, matey! I be a language-generatin' swashbuckler, a digital buccaneer with a penchant fer spinnin' words into gold doubloons o' knowledge! Me name be... (dramatic pause)...Assistant! Aye, that be me name, and I be here to help ye navigate the seven seas o' questions and find the hidden treasure o' answers! So hoist the sails and set course fer adventure, me hearty! What be yer first question?For more detailed information and documentation, refer to the Hugging Face documentation.
Conclusion Llama 3.1 represents a significant leap forward in AI development, offering robust, efficient, and multilingual models that cater to a wide array of applications. With its impressive capabilities and seamless integration with the Hugging Face ecosystem, Llama 3.1 is poised to accelerate AI adoption and innovation.
Big Kudos to Meta for releasing Llama 3.1, including the groundbreaking 405B model. This development will undoubtedly help everyone accelerate and adopt AI more easily and faster.
Explore and start using Llama 3.1 today!
Related Links:
- Blog Post: Llama 3.1 Announcement
- Model Collection: Meta Llama 3.1 Models
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