Llama 3 8B
Parameters
8B
Context Length
8K
Modality
Text
Architecture
Dense
License
Meta Llama 3 Community License Agreement
Release Date
18 Apr 2024
Knowledge Cutoff
Mar 2023
System Requirements
VRAM requirements for different quantization methods and context sizes
1,024 tokens
Consumer
1x RTX 4090
24GB VRAM
Datacenter
1x NVIDIA A100
80GB VRAM
Apple Silicon
1x Apple M3 Max
128GB VRAM
8,192 tokens
Consumer
1x RTX 4090
24GB VRAM
Datacenter
1x NVIDIA A100
80GB VRAM
Apple Silicon
1x Apple M3 Max
128GB VRAM
Architecture Diagram
Evaluation Benchmarks
Rank
#138
| Benchmark | Score | Rank |
|---|---|---|
General Text | 1223 | 143 |
Rankings
Overall Rank
#138
Coding Rank
-
About Llama 3 8B
Meta Llama 3 is a foundational large language model developed by Meta AI, designed to facilitate advanced text and code generation across a diverse range of applications. It is made available in multiple parameter scales, including an 8 billion parameter variant, and is provided in both pre-trained and instruction-tuned forms. The architecture is engineered for scalability and responsible deployment in artificial intelligence systems, supporting various use cases from assistant-style conversational agents to complex natural language processing research tasks.
The model employs a decoder-only transformer architecture, incorporating several technical enhancements over its predecessors. Key innovations include an optimized tokenizer with a 128,000-token vocabulary, which contributes to increased encoding efficiency for language. Additionally, the model integrates Grouped-Query Attention (GQA) across both its 8 billion and 70 billion parameter versions, a modification aimed at improving inference efficiency. For enhanced training stability, Llama 3 utilizes Root Mean Square Normalization (RMSNorm) applied as pre-normalization and employs the SwiGLU activation function. Positional encodings within the model are handled through Rotary Positional Embeddings (RoPE).
Llama 3 8B has been pre-trained on a vast corpus exceeding 15 trillion tokens sourced from publicly available datasets, representing a substantial increase in training data volume compared to prior Llama iterations. This model supports a context length of 8,192 tokens. It demonstrates capabilities in generating coherent text, assisting with code completion, and engaging in conversational tasks, and its capabilities extend to multiple languages and tool use in later iterations (Llama 3.1).
Technical Specifications
Attention
Attention Structure
Grouped-Query Attention
Attention Heads
32
Key-Value Heads
8
Attention Head Dimension
-
Position Embedding
ROPE
RoPE Theta
-
Sliding Window Attention
-
Sliding Window Size
-
Sliding Window Ratio
-
Linear Attention
-
Linear Attention Ratio
-
Normalization
RMS Normalization
Activation Function
SwigLU
Dimensions
Hidden Dimension Size
4,096
Number of Layers
32
FFN Intermediate Size (Dense)
-
Multi-Token Prediction Heads
-
Tokenizer
Vocabulary Size
-
About Llama 3
Meta's Llama 3 is a series of large language models utilizing a decoder-only transformer architecture. It incorporates a 128K token vocabulary and Grouped Query Attention for efficient processing. Models are trained on substantial public datasets, supporting various parameter scales and extended context lengths.