OmniCoder-9B packages agent-style coding behavior into a smaller open model by training on more than 425,000 curated trajectories from real tool-using workflows.
#qwen
A r/LocalLLaMA post pointed Mac users to llama.cpp pull request #20361, merged on March 11, 2026, adding a fused GDN recurrent Metal kernel. The PR shows around 12-36% throughput gains on Qwen 3.5 variants, while Reddit commenters noted the change is merged but can still trail MLX on some local benchmarks.
A high-scoring r/MachineLearning post resurfaced David Noel Ng's long-form write-up, centering on the claim that duplicating a seven-layer middle block in Qwen2-72B, without changing weights, was enough to reach the top of the open leaderboard.
A high-scoring LocalLLaMA post says Qwen 3.5 9B on a 16GB M1 Pro handled memory recall and basic tool calling well enough for real agent work, even though creative reasoning still trailed frontier models.
A LocalLLaMA thread reported a large prompt-processing speedup on Qwen3.5-27B by lowering llama.cpp `--ubatch-size` to 64 on an RX 9070 XT. The interesting part is not a universal magic number, but the reminder that prompt ingestion and token generation can respond very differently to `n_ubatch` tuning.
A r/LocalLLaMA thread is drawing attention to `llama.cpp` pull request #19504, which adds a `GATED_DELTA_NET` op for Qwen3Next-style models. Reddit users reported better token-generation speed after updating, while the PR itself includes early CPU/CUDA benchmark data.
A Hacker News post surfaced Unsloth's Qwen3.5 local guide, which lays out memory targets, reasoning-mode controls, and llama.cpp commands for running 27B and 35B-A3B models on local hardware.
A high-scoring LocalLLaMA post highlights Open WebUI’s Open Terminal: a Docker or bare-metal execution layer that lets local models run commands, edit files, and return artifacts through chat.
A high-ranking Hacker News thread highlighted a two-sided Qwen story: rapid model quality gains and potential organizational instability. As Qwen 3.5 expands across model sizes, reported leadership departures raise questions about roadmap continuity in the open-weight LLM ecosystem.
A high-scoring LocalLLaMA post benchmarked Qwen3.5-27B Q4 GGUF variants against BF16, separating “closest-to-baseline” choices from “best efficiency” picks for constrained VRAM setups.
A high-signal Hacker News thread surfaced Unsloth’s Qwen3.5 guide, which maps model sizes to bf16 LoRA VRAM budgets and clarifies MoE, vision, and export paths for production workflows.
A LocalLLaMA post reports that a simple “verify after every edit” loop raised Qwen3.5-35B-A3B from 22.2% to 37.8% on SWE-bench Verified Hard, approaching a cited 40% reference for Claude Opus 4.6.