Qwen2-7B

Qwen2-7B is a fully functional language model with 7 billion parameters, designed to deliver high performance across a wide range of tasks. The model features 28 layers, utilizes 28 attention heads in total with 4 key-value heads, striking an optimal balance between performance and memory efficiency. Its architecture incorporates all modern enhancements, including Grouped Query Attention (GQA), Dual Chunk Attention with YARN, and optimized Rotary Positional Embedding (RoPE) mechanisms.

Trained on the same high-quality 7-trillion-token dataset as larger models in the series, Qwen2-7B delivers strong performance across diverse knowledge domains and achieves competitive results on standard benchmarks. It supports an extended context window of 128K tokens and demonstrates exceptional multilingual capabilities.

A key advantage of Qwen2-7B is its ability to run efficiently on mid-range GPUs, making advanced AI capabilities accessible to a broader range of users and organizations. The model is well-suited for application development, large document analysis, research, and educational purposes. Additionally, it serves as an excellent base model for fine-tuning on domain-specific tasks, offering a strong trade-off between capability and resource requirements.

Announce Date: 24.07.2024
Parameters: 7B
Context: 32K
Attention Type: Full Attention
VRAM requirements: 5.0 GB using 4 bits quantization
Developer: Alibaba
Transformers Version: 4.41.2
License: Apache 2.0

Use our pre-built public endpoints to test inference and explore Qwen2-7B capabilities.

Rent your own physically dedicated instance with hourly or long-term monthly billing.

We recommend deploying private instances in the following scenarios:

• maximize endpoint performance,
• enable full context for long sequences,
• ensure top-tier security for data processing in an isolated, dedicated environment,
• use custom weights, such as fine-tuned models or LoRA adapters.

Contact our dedicated neural networks support team at nn@immers.cloud or send your request to the sales department at sale@immers.cloud.