Qwen1.5 is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. In comparison with the previous released Qwen, the improvements include:

• 8 model sizes, including 0.5B, 1.8B, 4B, 7B, 14B, 32B and 72B dense models, and an MoE model of 14B with 2.7B activated;
• Significant performance improvement in human preference for chat models;
• Multilingual support of both base and chat models;
• Stable support of 32K context length for models of all sizes
• No need of trust_remote_code.

For more details, please refer to blog post and GitHub repo.

Qwen1.5 is a language model series including decoder language models of different model sizes. For each size, Qwen1.5 release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, mixture of sliding window attention and full attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes. For the beta version, temporarily we did not include GQA (except for 32B) and the mixture of SWA and full attention.

Note: This table showed source model instead of quantized model evaluation. Source Model Evaluation refer to Qwen1.5-1.8B-Chat Evaluation Result

Users can run large language models on Qualcomm chips using either of the following methods:

• Run large models with APLUX AidGen: Please refer to the APLUX AidGen Developer Documentation

• Run large models with Qualcomm Genie: Please refer to the Qualcomm Genie Documentation