Llama 3.1 405B Instruct FP8

Table of Contents

Model Overview

The Meta Llama 3.1 model is a collection of multilingual large language models (LLMs) developed by Meta. It’s designed to handle various natural language processing tasks, especially in multilingual dialogue use cases.

What makes it special?

• It’s optimized for multilingual dialogue use cases and outperforms many open-source and closed chat models on common industry benchmarks.
• It’s available in three sizes: 8B, 70B, and 405B parameters.
• It supports multiple languages, including English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.

Tasks and rank

Text Generation 28th

Natural Language Understanding 27th

Language Modelling 32nd

Dialogue Systems 15th

Chatbot 20th

Learn More About the Ranking System

Capabilities

Capable of generating both text and code, this model outperforms many open-source chat models across common industry benchmarks.

Primary Tasks

This model is designed to perform the following primary tasks:

• Text Generation: Generate human-like text based on a given prompt or input.
• Code Generation: Generate code in various programming languages based on a given prompt or input.
• Dialogue: Engage in conversation with humans, responding to questions and statements in a helpful and informative manner.

Strengths

This model has several strengths, including:

• Multilingual Support: Support for multiple languages, making it useful for a wide range of applications.
• High-Quality Text Generation: Ability to generate high-quality text that is coherent, informative, and engaging.
• Improved Safety: Incorporation of safety mitigations to reduce the risk of generating harmful or offensive content.

Alternatives

NeMo SFT with Llama 2 10500 Fine-tuned language model

Bert Base Uncased 1831 Uncased BERT model

Roberta Base 399 Masked language model

BLOOMChat 176B V1 365 Multilingual chat model

SUS Chat 34B 121 Bilingual chat model

Performance

This model showcases remarkable performance in various tasks, including multilingual dialogue, instruction tuning, and knowledge reasoning.

Speed

• Fast Inference: The optimized transformer architecture and Grouped-Query Attention (GQA) enable fast inference, making it suitable for real-time applications.
• Scalability: The model’s ability to handle large inputs (up to 128k tokens) and its efficient architecture make it an excellent choice for large-scale deployments.

Accuracy

• High Accuracy: This model achieves high accuracy on various benchmarks, including MMLU, MMLU-Pro, and CommonSenseQA, outperforming many other models in its class.
• Multilingual Support: The model’s multilingual capabilities allow it to perform well on benchmarks in multiple languages, including Portuguese, Spanish, Italian, German, French, Hindi, and Thai.

Efficiency

• Low Power Consumption: The training process consumed relatively low power, with an estimated 11,390 tons CO2eq of greenhouse gas emissions, which is a significant reduction compared to other models.
• Efficient Training: The model’s training time was approximately 39.3M GPU hours, which is a relatively short time considering the model’s size and complexity.

Benchmark Results

Benchmark	Llama 3.1 8B	Llama 3.1 70B	Llama 3.1 405B
MMLU	66.7	79.5	85.2
MMLU-Pro	36.2	55.0	61.6
CommonSenseQA	72.6	83.8	85.8
Winogrande	-	83.3	86.7

Examples

Translate 'The quick brown fox jumps over the lazy dog' from English to Spanish. El rápido zorro marrón salta sobre el perro perezoso.

Write a short Python program to calculate the area of a rectangle. def rectangle_area(length, width): return length * width

Summarize the main points of the Llama 3.1 model's intended use cases. Llama 3.1 is intended for commercial and research use in multiple languages, including assistant-like chat and natural language generation tasks.

Limitations

Like all AI models, this model has its weaknesses and limitations. Let’s take a closer look at what it can and can’t do.

Limited Context Understanding

This model can process a large amount of text, but it may not always understand the context of the conversation. This can lead to responses that are not relevant or accurate.

Lack of Common Sense

While this model has been trained on a vast amount of text data, it may not always have the same level of common sense as a human. This can result in responses that are not practical or realistic.

Biased Training Data

This model was trained on a dataset that may contain biases and stereotypes. This can lead to responses that reflect these biases, which may not be desirable.

Limited Domain Knowledge

This model has been trained on a broad range of topics, but its knowledge in specific domains may be limited. This can result in responses that are not accurate or up-to-date.

Vulnerability to Adversarial Attacks

Like all AI models, this model can be vulnerable to adversarial attacks, which are designed to manipulate the model’s responses.

Limited Transparency

This model is a complex system, and its decision-making process may not be fully transparent. This can make it difficult to understand why the model is responding in a certain way.

Dependence on Data Quality

This model is only as good as the data it was trained on. If the training data is of poor quality, the model’s responses may not be accurate or reliable.

Limited Ability to Handle Sarcasm and Humor

This model may struggle to understand sarcasm and humor, which can lead to responses that are not accurate or relevant.

Limited Ability to Handle Ambiguity

This model may struggle to handle ambiguous or unclear input, which can lead to responses that are not accurate or relevant.

Format

This model is a collection of multilingual large language models (LLMs) that uses an optimized transformer architecture. This model is designed to handle text inputs and outputs, and is optimized for multilingual dialogue use cases.

Supported Data Formats

• Input: Multilingual text
• Output: Multilingual text and code

Special Requirements

• Context Length: 128k
• Token Count: 15T+
• Knowledge Cutoff: December 2023
• Supported Languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai

Handling Inputs and Outputs

To handle inputs and outputs for this model, you can use the following code examples:

• Input: text = "Hello, how are you?"
• Output: output = model.generate(text, max_length=128)

Note that the max_length parameter is set to 128, which is the maximum context length supported by this model.

Model Architecture

This model uses an optimized transformer architecture, which is a type of neural network architecture that is well-suited for natural language processing tasks. The model is trained using a combination of supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.

Grouped-Query Attention (GQA)

This model uses Grouped-Query Attention (GQA) for improved inference scalability. GQA is a technique that allows the model to process multiple queries in parallel, which can improve performance and reduce latency.