LFM2.5-350M-ToMoE-GGUF

GGUF quantizations of Nichonauta/LFM2.5-350M-ToMoE — the ToMoE Mixture-of-Experts conversion of LiquidAI/LFM2.5-350M.

Files

File	Quantization	Size	BPW
LFM2.5-350M-ToMoE-BF16.gguf	BF16	678 MB	16.0
LFM2.5-350M-ToMoE-Q8_0.gguf	Q8_0	362 MB	8.50
LFM2.5-350M-ToMoE-Q4_K_M.gguf	Q4_K_M	219 MB	5.12

Important note about the conversion

The ToMoE channel-MoE experts and the per-token dynamic V masks have no native representation in llama.cpp's LFM2 architecture. To produce a runnable GGUF, the pruned model was rebuilt as a dense-equivalent:

• The cut conv layers were restored to their full width using the original dense weights (the conv weights were never modified by ToMoE — only masked at runtime).
• The MLP union cut (~99% of the FFN was already preserved) was restored to the full FFN.
• The attention Q/K were already full-width in the pruned model.

As a result, the GGUF behaves like the dense base model rather than the pruned MoE. For the actual MoE behavior, use the safetensors version with trust_remote_code.

Usage (llama.cpp, CUDA)

llama-server.exe ^
  --model LFM2.5-350M-ToMoE-Q4_K_M.gguf ^
  --gpu-layers all ^
  --ctx-size 32768 ^
  --alias LFM2.5-350M-ToMoE

Or with llama-cli:

llama-cli.exe -m LFM2.5-350M-ToMoE-Q4_K_M.gguf -ngl 99 -p "The capital of France is" -n 30

Measured on an RTX 3060 (CUDA 13.3 build, llama.cpp b10603): ~470 t/s generation for Q4_K_M, ~370 t/s for Q8_0, ~270 t/s for BF16.

Base model

• MoE source: Nichonauta/LFM2.5-350M-ToMoE
• Dense base: LiquidAI/LFM2.5-350M (LFM Open License v1.0)

License

Derivative of LiquidAI/LFM2.5-350M — released under the LFM Open License v1.0 (see LICENSE).