Qwen2.5-7B

Qwen2.5-7B is a 7-billion-parameter model featuring 28 layers and a 28/4 attention head architecture, delivering substantially expanded capabilities compared to smaller versions. Its key advantage lies in supporting an extended 128K-token context window while maintaining 8K-token generation capacity, enabling it to process lengthy documents and retain complex multi-step tasks in memory.

The model demonstrates significantly enhanced capabilities in mathematical computations, programming, and logical reasoning due to the incorporation of specialized data during pretraining. It also shows substantial progress in understanding structured data formats including tables and JSON.

Qwen2.5-7B represents the optimal choice for most business applications requiring high-quality natural language processing without excessive infrastructure demands. The model excels at workflow automation, long-document analysis, and building intelligent decision-support systems.

Announce Date: 19.09.2024
Parameters: 7B
Context: 131K
Attention Type: Full Attention
VRAM requirements: 10.3 GB using 4 bits quantization
Developer: Alibaba
Transformers Version: 4.43.1
License: Apache 2.0

Use our pre-built public endpoints to test inference and explore Qwen2.5-7B 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.