Qwen2-1.5B

Qwen2-1.5B is a lightweight model with 1.5 billion parameters, offering an optimal balance between performance and resource efficiency. The model includes 28 layers and employs Grouped Query Attention (GQA) with 12 query heads and 2 shared key-value heads, enabling efficient management of the KV-cache memory.

Trained on 7 trillion tokens of high-quality multilingual data, the model demonstrates significantly enhanced capabilities compared to previous versions, particularly in programming and mathematics. It supports a wide range of languages and delivers competitive results on standard benchmarks, while maintaining relatively low computational resource requirements.

Qwen2-1.5B stands out for its versatility and ability to perform efficiently on both consumer-grade hardware and small servers. The model supports a context window of 32K tokens, allowing it to process long documents and support complex conversations. It is ideally suited for information extraction, document analysis, entry-level programming tasks, educational applications, and enterprise chatbots. Qwen2-1.5B is an excellent choice for users seeking a reliable language model for simple tasks without the need for expensive GPU infrastructure.

Announce Date: 24.07.2024
Parameters: 1.5B
Context: 32K
Attention Type: Full Attention
VRAM requirements: 1.6 GB using 4 bits quantization
Developer: Alibaba
Transformers Version: 4.40.1
License: Apache 2.0

Use our pre-built public endpoints to test inference and explore Qwen2-1.5B 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.