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*This model was published in HF papers on 2024-08-28 and contributed to Hugging Face Transformers on 2026-04-28.*

<div style="float: right;">
    <div class="flex flex-wrap space-x-1">
        <img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
        <img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
        <img alt="Tensor parallelism" src="https://img.shields.io/badge/Tensor%20parallelism-06b6d4?style=flat&logoColor=white">
    </div>
</div>

# Laguna

Laguna is Poolside's mixture-of-experts language model family. The Laguna-specific
deltas vs a standard SwiGLU MoE transformer are:

- **Per-layer head counts** via `num_attention_heads_per_layer` — different decoder
  layers can have different query-head counts while sharing the same KV cache shape.
- **Sigmoid MoE router with auxiliary-loss-free load balancing**
  ([arXiv:2408.15664](https://huggingface.co/papers/2408.15664)) and optional logit
  soft-capping (`moe_router_logit_softcapping`) — router scores are the element-wise
  sigmoid of the gate logits plus a learned per-expert bias (`e_score_correction_bias`)
  that is added at selection time only.

## Usage

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

```python
from transformers import pipeline

pipe = pipeline(
    "text-generation",
    model="poolside/Laguna-XS.2",
    dtype="auto",
    device_map="auto",
)
print(pipe("The capital of France is", max_new_tokens=20, do_sample=False)[0]["generated_text"])
```

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

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

model_id = "poolside/Laguna-XS.2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    dtype=torch.bfloat16,
    device_map="auto",
)

prompt = "The capital of France is"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
generated = model.generate(**inputs, max_new_tokens=20, do_sample=False)
print(tokenizer.decode(generated[0], skip_special_tokens=True))
```

</hfoption>
</hfoptions>

## Notes

- **Attention backends.** SDPA (default), FlashAttention-2, and flex attention are
  supported. Attention-output gating is applied outside the kernel call and
  therefore works with all backends.
- **`num_attention_heads_per_layer`.** When provided, its length must equal
  `num_hidden_layers`. Each entry must be divisible by `num_key_value_heads`.
- **`layer_types`.** Defaults to `["full_attention"] * num_hidden_layers` when left
  unset. To enable sliding-window attention, pass a list of
  `"full_attention"` / `"sliding_attention"` values.
- **`mlp_layer_types`.** Per-layer MLP type, values `"dense"` or `"sparse"`. Length must
  equal `num_hidden_layers`. Defaults to `["dense"] + ["sparse"] * (num_hidden_layers - 1)`
  (first layer dense, rest MoE) when left unset.
- **`moe_apply_router_weight_on_input=True`** is not currently supported alongside the
  fused experts kernel (`grouped_mm_experts_forward`); `validate_architecture` raises at
  config-construction time. Set it to `False` (the default).

## LagunaConfig

[[autodoc]] LagunaConfig

## LagunaModel

[[autodoc]] LagunaModel
    - forward

## LagunaForCausalLM

[[autodoc]] LagunaForCausalLM
    - forward
