LFM2.5-350M-ToMoE
A Mixture-of-Experts (MoE) conversion of LiquidAI/LFM2.5-350M, obtained with the ToMoE method (Dynamic Structural Pruning + Hypernetwork).
The dense 350M instruct model is converted into a channel-MoE where:
- feed_forward / MLP (16 layers): per-token expert routing over 8 channel-experts (top-1 via Gumbel-softmax), the FFN is cut to the union of the expert channel masks.
- Full attention (6 layers): Q/K kept at full width; the V channels are dynamically masked per token (deterministic masks).
- ShortConv (10 layers): statically pruned (a single channel index shared by the B/C/x projections, the conv and the output projection).
Metrics
| Metric | Dense base | ToMoE MoE |
|---|---|---|
| Total params | 354.5M | 321.4M stored |
| Active params per token | 354.5M | ~205M |
| PPL wikitext-2 (raw, 1810 tok) | 919.9 | 994.4 |
| PPL chat-formatted (prompt + answer) | 21.7 | 314.2 |
| Embeddings (always active) | 67.1M | 67.1M |
Note: LFM2.5-350M is an instruction-tuned model. Its raw-text perplexity is dominated by a strong special-token prior, so the chat-formatted PPL is the more meaningful fidelity metric (14x degradation vs 40x for the 230M conversion).
The pruned budget is p = 0.05 with the ToMoE regularization (lam 64→128, Gumbel base annealed 1.0→−0.25, 8 experts). The attention QK is excluded from the budget (kept full) because the small head-dim (64) collapses under aggressive QK pruning. The hypernetwork was trained with chat-formatted wikitext-103 turns (user/assistant split of the articles) so the KD signal follows the instruct distribution.
Usage (transformers, trust_remote_code)
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained(
"Nichonauta/LFM2.5-350M-ToMoE",
trust_remote_code=True,
torch_dtype=torch.bfloat16,
device_map="cuda:0",
attn_implementation="eager",
)
tokenizer = AutoTokenizer.from_pretrained("Nichonauta/LFM2.5-350M-ToMoE")
messages = [{"role": "user", "content": "What is the capital of France?"}]
prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_tensors="pt")
out = model.generate(**prompt, max_new_tokens=64)
print(tokenizer.decode(out[0], skip_special_tokens=True))
Note: this model requires
trust_remote_code=True— the custommodeling_lfm2_moe_final.pydefines theLfm2MoEForCausalLMclass.
Conversion details
- Base: LiquidAI/LFM2.5-350M (LFM Open License v1.0,
lfm1.0) - Method: ToMoE — a bidirectional-GRU hypernetwork generates differentiable channel masks per sublayer; the model weights are frozen and only the hypernetwork is trained (online KD against the dense teacher). The final pruned weights are extracted from the trained masks.
- Training: 2,400 steps, seq 2048, online knowledge distillation (2× forward KL), gradient checkpointing, chat-formatted wikitext-103 turns (4M tokens).
- Architecture deltas: 16 layers (10 ShortConv + 6 full attention), FFN 4608-wide per layer (auto-adjusted from 6656), head_dim 64, tied embeddings.
Limitations
- The model is significantly degraded versus the dense base (chat PPL ~314 vs ~22) and generation tends to repeat — the conv layers are cut to ~5–36% width and each token activates ~50–70% of the MLP channels through a single expert.
- This is a research artifact demonstrating the ToMoE pipeline on a small LFM2 architecture.
License
This model is a derivative of LiquidAI/LFM2.5-350M and is released under the LFM Open License v1.0 (see LICENSE).
Files
model.safetensors— the pruned MoE weights (fp32)modeling_lfm2_moe_final.py— the custom model definition (trust_remote_code)config.json— model configuration (withauto_map)tokenizer.json,tokenizer_config.json,chat_template.jinja,generation_config.json
GGUF quantizations
Quantized GGUF files (BF16, Q8_0, Q4_K_M) are available in the companion repository: Nichonauta/LFM2.5-350M-ToMoE-GGUF.