Qwen2-0.5B
Parameters
500M
Context Length
33K
Modality
Text
Architecture
Dense
License
Apache 2.0
Release Date
7 Jun 2024
Knowledge Cutoff
-
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
32,768 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
No evaluation benchmarks for Qwen2-0.5B available.
Rankings
Overall Rank
-
Coding Rank
-
About Qwen2-0.5B
The Qwen2-0.5B model represents a compact yet capable entry in the Qwen2 series of large language models, developed by the Qwen team at Alibaba. This model is engineered to deliver foundational language processing functionalities, making it suitable for deployment in environments with constrained computational resources. As a base language model, its primary purpose is to serve as a robust starting point for further specialization through post-training methodologies, such as supervised fine-tuning or reinforcement learning from human feedback. It is designed to facilitate a range of natural language processing tasks efficiently.
Technical Specifications
Attention
Attention Structure
Grouped-Query Attention
Attention Heads
16
Key-Value Heads
8
Attention Head Dimension
-
Position Embedding
ROPE
RoPE Theta
1,000,000
Sliding Window Attention
No
Sliding Window Size
131,072
Sliding Window Ratio
-
Linear Attention
-
Linear Attention Ratio
-
Normalization
RMS Normalization
Activation Function
SwigLU
Dimensions
Hidden Dimension Size
896
Number of Layers
24
FFN Intermediate Size (Dense)
4,864
Multi-Token Prediction Heads
-
Tokenizer
Vocabulary Size
151,936
About Qwen2
The Alibaba Qwen2 model family comprises large language models built upon the Transformer architecture. It includes both dense and Mixture-of-Experts (MoE) variants, designed for diverse language tasks. Technical features include Grouped Query Attention and support for extended context lengths up to 131,072 tokens, optimizing memory footprint for inference.