qwen.ai2026-02-16officialauthorityHN front pageabout the Qwen3.5 familyHN 434 pts · 214 comments17,678 words
Qwen3.5-397B-A17B is the first open-weight native vision-language model in the Qwen3.5 series, released on 2026/02/15. Built on a hybrid architecture fusing linear attention via Gated Delta Networks with a sparse mixture-of-experts, it activates just 17 billion parameters per forward pass despite comprising 397 billion total parameters. The model expanded language support from 119 to 201 languages while matching the >1T-parameter Qwen3-Max-Base capability.written, reviewed, cited
- The hosted Qwen3.5-Plus model defaults to a 1M context window and includes official built-in tools with adaptive tool use.
- For the BrowseComp benchmark, the model achieved a score of 69.0 using simple context-folding, while the discard-all strategy reached 78.6.
unsloth.ai2026-03-04authorityHN front pageabout the Qwen3.5 familyHN 416 pts · 107 comments729 words
Engineers can fine-tune the Qwen3.5 model family (0.8B, 2B, 4B, 9B, 27B, 35B-A3B, 122B-A10B) with Unsloth, which trains models 1.5 times faster using 50% less VRAM than FA2 setups. VRAM requirements for bf16 LoRA range from 3GB for the 0.8B model to 56GB for the 27B model, while the 35B-A3B variant operates on 74GB VRAM and the 122B-A10B on 256GB VRAM. MoE fine-tuning leverages a recent update delivering over 35% less VRAM usage and approximately 6x longer context support.written, reviewed, cited
- To preserve reasoning ability, mix reasoning-style examples with direct answers while keeping a minimum of 75% reasoning.
- Router-layer fine-tuning is disabled by default for stability on MoE models like Qwen3.5‑35B‑A3B.
- You can run Qwen3.5 RL with Unsloth even though it is not supported by vLLM by setting fast_inference=False.
