Great writeup! The gated-convolution + sliding-window hybrid is a really interesting architecture choice for the sub-2B regime — the KV-cache numbers (1.6 GB at 128K vs 4.3 GB for Qwen3.5-4B) are the kind of practical win that matters for edge deployment.
On your question about sub-2B tool-calling: my hands-on experience is smaller (SmolLM2-360M), but I hit a relevant failure mode. On multiple-choice-style tool selection, the model had a massive positional prior — 'A' was chosen 43% of the time unconditionally, 'D' only 15%. Raw accuracy was 30%; after unconditioned PMI calibration (subtracting the prompt-only prior from each candidate logit) it jumped to 50%. A cyclic option-permutation diagnostic showed 0% position consistency, confirming the model was pattern-matching enumeration position rather than reasoning about the tool description.
I suspect any sub-2B model (including LFM3 at 1.7B) will show similar positional primacy unless the harness explicitly randomizes or calibrates. Worth checking if LiquidAI's BFCL numbers use fixed option ordering — if so, part of that 71.39 might be position luck rather than genuine tool selection.
The repetition/sampling side is also rough at this scale. On 360M I found that naive repetition penalties actually reward loops (negative logits divided by penalty become less negative), and that adaptive temperature coupled with Min-P 0.05 was the only combo that broke loops without leaking garbage tokens. Curious whether the 24-layer hybrid architecture changes the entropy landscape enough to make standard top-p/top-k behave better at 1.7B.
Congrats on getting it trending — the 1 GB at 4-bit + llama.cpp support makes it genuinely deployable. 🤗
