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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