Here's what's new 🎉
🤖 New Model: Qwen3.6-35B-A3B
Say hello to Qwen3.6-35B-A3B — the second MoE model in FLM, joining GPT-OSS. It packs 35B total parameters with only 3B activated per forward pass, so you get strong reasoning quality at a fraction of the compute cost.
Tag: qwen3.6-moe:35b-a3b
Run in CLI mode:
flm run qwen3.6-moe:35b-a3b
Run in server mode:
flm serve qwen3.6-moe:35b-a3b
Check out the model card and benchmark results for more details.
⚡ Smoother KV Cache Through Multi-Backend Support
FLM now supports per-round KV cache checks, making context management more precise and robust when mixing inference backends.
Here's a real example — imagine a Lemonade user running gemma4-it:e2b on both NPU (via FLM) and GPU (via llama.cpp):
- They send an initial prompt to the NPU and get a response.
- They continue on the GPU and get a second response.
- They switch back to the NPU with the full conversation history.
Previously, the NPU would see gaps from the GPU round, fail the KV cache check, and re-prefill everything from scratch. Now, with per-round KV cache checks, it knows the first round is already cached — so it only prefills the GPU round and the new prompt. Mixing backends is no longer a headache! 🙌
🌟 Summary
- New model:
Qwen3.6-35B-A3B— our second MoE model, with 35B total parameters and just 3B activated per token 🧠 - Smarter KV cache — per-round checks let you mix FLM and other backends without losing cache or re-prefilling the whole context 🔄
Thanks for your support — more good stuff is on the way. See you in the next one! 🚀