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108 lines
4.0 KiB
Markdown
108 lines
4.0 KiB
Markdown
<!--Copyright 2026 Poolside and The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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⚠️ Note that this file is in Markdown but contains specific syntax for our doc-builder (similar to MDX) that may not be
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rendered properly in your Markdown viewer.
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-->
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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.*
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<div style="float: right;">
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<div class="flex flex-wrap space-x-1">
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<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
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<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
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<img alt="Tensor parallelism" src="https://img.shields.io/badge/Tensor%20parallelism-06b6d4?style=flat&logoColor=white">
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</div>
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</div>
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# Laguna
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Laguna is Poolside's mixture-of-experts language model family. The Laguna-specific
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deltas vs a standard SwiGLU MoE transformer are:
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- **Per-layer head counts** via `num_attention_heads_per_layer` — different decoder
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layers can have different query-head counts while sharing the same KV cache shape.
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- **Sigmoid MoE router with auxiliary-loss-free load balancing**
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([arXiv:2408.15664](https://huggingface.co/papers/2408.15664)) and optional logit
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soft-capping (`moe_router_logit_softcapping`) — router scores are the element-wise
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sigmoid of the gate logits plus a learned per-expert bias (`e_score_correction_bias`)
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that is added at selection time only.
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## Usage
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<hfoptions id="usage">
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<hfoption id="Pipeline">
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```python
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from transformers import pipeline
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pipe = pipeline(
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"text-generation",
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model="poolside/Laguna-XS.2",
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dtype="auto",
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device_map="auto",
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)
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print(pipe("The capital of France is", max_new_tokens=20, do_sample=False)[0]["generated_text"])
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```
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</hfoption>
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<hfoption id="AutoModel">
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "poolside/Laguna-XS.2"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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dtype=torch.bfloat16,
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device_map="auto",
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)
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prompt = "The capital of France is"
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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generated = model.generate(**inputs, max_new_tokens=20, do_sample=False)
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print(tokenizer.decode(generated[0], skip_special_tokens=True))
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```
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</hfoption>
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</hfoptions>
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## Notes
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- **Attention backends.** SDPA (default), FlashAttention-2, and flex attention are
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supported. Attention-output gating is applied outside the kernel call and
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therefore works with all backends.
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- **`num_attention_heads_per_layer`.** When provided, its length must equal
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`num_hidden_layers`. Each entry must be divisible by `num_key_value_heads`.
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- **`layer_types`.** Defaults to `["full_attention"] * num_hidden_layers` when left
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unset. To enable sliding-window attention, pass a list of
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`"full_attention"` / `"sliding_attention"` values.
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- **`mlp_layer_types`.** Per-layer MLP type, values `"dense"` or `"sparse"`. Length must
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equal `num_hidden_layers`. Defaults to `["dense"] + ["sparse"] * (num_hidden_layers - 1)`
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(first layer dense, rest MoE) when left unset.
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- **`moe_apply_router_weight_on_input=True`** is not currently supported alongside the
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fused experts kernel (`grouped_mm_experts_forward`); `validate_architecture` raises at
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config-construction time. Set it to `False` (the default).
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## LagunaConfig
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[[autodoc]] LagunaConfig
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## LagunaModel
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[[autodoc]] LagunaModel
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- forward
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## LagunaForCausalLM
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[[autodoc]] LagunaForCausalLM
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- forward
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