The Qwen3-235B-A22B-Instruct-2507 model has several key features, including a causal language model type, pretraining and post-training stages, and 235 billion parameters in total, with 22 billion activated. It also has 94 layers, 64 attention heads for Q and 4 for KV, and 128 experts, with 8 activated. The model supports a context length of 262,144 natively and does not generate think blocks in its output. Additionally, specifying enable_thinking=False is no longer required.
To achieve optimal performance with Qwen3-235B-A22B-Instruct-2507, it is recommended to use specific settings, such as temperature=0.7, top_p=0.8, top_k=20, and min_p=0. The model also supports adjusting the presence_penalty parameter between 0 and 2 to reduce endless repetitions. Furthermore, an output length of 16,384 tokens is recommended for most queries, and using prompts to standardize model outputs is suggested when benchmarking. The model can be used for various tasks, including math problems and multiple-choice questions, with specific prompt structures.