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# Lfm2Moe

LFM2-MoE is a Mixture-of-Experts version of the LFM2 architecture, designed for efficient on-device inference. It combines gated convolutions for local context with grouped-query attention (GQA) for efficient global reasoning. By adding sparse MoE feed-forward layers, it boosts representational power while keeping computational costs low. The initial model, LFM2-8B-A1B, has 8.3B total parameters with 1.5B active per inference, matching the quality of 3–4B dense models while running faster than typical 1.5B models.

<hfoptions id="usage">
<hfoption id="Pipeline">

```py
import torch
from transformers import pipeline

pipeline = pipeline(task="text-generation", model="LiquidAI/LFM2-8B-A1B", dtype="auto",)
pipeline("Plants create energy through a process known as photosynthesis.")
```

</hfoption>
<hfoption id="AutoModel">

```py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("LiquidAI/LFM2-8B-A1B")
model = AutoModelForCausalLM.from_pretrained("LiquidAI/LFM2-8B-A1B", dtype="auto",)

inputs = tokenizer("Plants create energy through a process known as photosynthesis.", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0]))
```

</hfoption>
</hfoptions>

## Lfm2MoeConfig

[[autodoc]] Lfm2MoeConfig

## Lfm2MoeForCausalLM

[[autodoc]] Lfm2MoeForCausalLM

## Lfm2MoeModel

[[autodoc]] Lfm2MoeModel
    - forward

## Lfm2MoePreTrainedModel

[[autodoc]] Lfm2MoePreTrainedModel
    - forward
