🚀 FastFlowLM v0.9.45 — Qwen3.6-35B-A3B & Smoother KV Cache Through Multi-Backend Support

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.

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⚡ 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):

1. They send an initial prompt to the NPU and get a response.
2. They continue on the GPU and get a second response.
3. 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! 🙌

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🌟 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! 🚀