Instructions to use poolside/Laguna-S-2.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use poolside/Laguna-S-2.1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="poolside/Laguna-S-2.1", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("poolside/Laguna-S-2.1", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("poolside/Laguna-S-2.1", trust_remote_code=True, device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use poolside/Laguna-S-2.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "poolside/Laguna-S-2.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "poolside/Laguna-S-2.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/poolside/Laguna-S-2.1

SGLang

How to use poolside/Laguna-S-2.1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "poolside/Laguna-S-2.1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "poolside/Laguna-S-2.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "poolside/Laguna-S-2.1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "poolside/Laguna-S-2.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use poolside/Laguna-S-2.1 with Docker Model Runner:
```
docker model run hf.co/poolside/Laguna-S-2.1
```

Laguna-S-2.1 / README.md

joerowell

docs: add Ollama usage example

00af5a5 verified 3 days ago

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	---
	library_name: transformers
	inference: false
	extra_gated_description: >-
	To learn more about how we process your personal data, please read our <a
	href="https://poolside.ai/legal/privacy">Privacy Policy</a>.
	tags:
	- laguna-s-2.1
	- vllm
	license: openmdw-1.1
	pipeline_tag: text-generation
	---

	<p align="center">
	<img alt="poolside-banner" src="https://poolside.ai/assets/laguna/laguna-s-2-1-banner.svg" width="800px">
	</p>

	<p align="center">
	<a href="https://openrouter.ai/poolside/laguna-s-2.1"><strong>Use on OpenRouter</strong></a> ·
	<a href="https://vercel.com/ai-gateway/models/laguna-s-2.1"><strong>Use on Vercel AI Gateway</strong></a> ·
	<a href="https://poolside.ai/blog/introducing-laguna-s-2-1"><strong>Release blog post</strong></a>
	</p>

	<br>

	# Laguna S 2.1

	Laguna S 2.1 is a 118B total parameter Mixture-of-Experts model with 8B activated
	parameters per token, designed for agentic coding and long-horizon work. It sits
	between [Laguna XS 2.1](https://huggingface.co/poolside/Laguna-XS-2.1) (33B-A3B) and
	Laguna M.1 (225B-A23B) in the Laguna series and shares the family recipe: a
	token-choice router with softplus gating over 256 routed experts plus one shared
	expert, grouped-query attention, and interleaved full/sliding-window attention.

	## Highlights

	- Mixed SWA and global attention layout: 48 layers in a 1:3 global-to-SWA ratio
	(12 global attention layers, 36 sliding-window layers, window 512), with softplus
	attention gating and per-layer-type rotary scales
	- 1M context: 1,048,576-token context window
	- Native reasoning support: interleaved thinking between tool calls, with
	per-request control via `enable_thinking`
	- Speculative decoding: a trained
	[DFlash draft model](https://huggingface.co/poolside/Laguna-S-2.1-DFlash) is available
	for lower-latency serving
	- Quantized variants:
	[FP8](https://huggingface.co/poolside/Laguna-S-2.1-FP8),
	[NVFP4](https://huggingface.co/poolside/Laguna-S-2.1-NVFP4),
	[INT4](https://huggingface.co/poolside/Laguna-S-2.1-INT4) and
	[GGUF](https://huggingface.co/poolside/Laguna-S-2.1-GGUF)
	- OpenMDW-1.1 license: Use and modify the model and associated materials freely
	for commercial and non-commercial purposes
	([learn more about OpenMDW](https://openmdw.ai/))

	## Model overview

	- Number of parameters: 118B total, ~8B activated per token
	- Layers: 48 (12 global attention, 36 sliding-window attention)
	- Experts: 256 routed (top-10) plus 1 shared expert
	- Attention: grouped-query, 8 KV heads, head dim 128; per-head softplus output gating
	- Sliding window: 512 tokens
	- Context window: 1,048,576 tokens
	- Vocabulary: 100,352 tokens (Laguna family tokenizer)
	- Modality: text-to-text
	- Reasoning: interleaved thinking with preserved thinking

	## Benchmark results

	<p align="center">
	<img alt="benchmarks" src="https://poolside.ai/assets/laguna/laguna-s-2-1-chart.svg" width="800px">
	</p>

	\| Model \| Size \| Terminal-Bench 2.1 \| SWE-bench Multilingual \| SWE-Bench Pro (Public Dataset) \| DeepSWE \| SWE Atlas (Codebase QnA) \| Toolathlon Verified \|
	\|---\|---\|---\|---\|---\|---\|---\|---\|
	\| Laguna S 2.1 \| 118B-A8B \| 70.2% \| 78.5% \| 59.4% \| 40.4% \| 46.2% \| 49.7% \|
	\| Tencent Hy3 \| 295B-A21B \| 71.7% \| 75.8% \| 57.9% \| - \| - \| - \|
	\| Inkling \| 975B-A41B \| 63.8% \| - \| 54.3% \| - \| - \| 45.5%* \|
	\| Nemotron 3 Ultra \| 550B-A55B \| 56.4% \| 67.7% \| - \| - \| - \| 34.3%* \|
	\| DeepSeek-V4-Pro Max \| 1.6T-A49B \| 64.0%* \| 76.2% \| 55.4% \| 9.0%* \| 27.2%* \| 55.9%* \|
	\| Kimi K3 \| 2800B-A50B \| 88.3% \| - \| - \| 69% \| - \| - \|
	\| Qwen 3.7 Max \| - \| 74.5%* \| 78.3% \| 60.6% \| - \| - \| - \|
	\| Muse Spark 1.1 \| - \| 80% \| - \| 61.5% \| 53.3% \| 42.2%* \| 75.6% \|
	\| Claude Fable 5 \| - \| 88% \| - \| 80.3% \| 70% \| - \| - \|

	Benchmarks as of 21 July 2026. Laguna S 2.1 in bold; a dash (-) marks a benchmark a model was not evaluated on. Scores marked * are as reported by third parties: Terminal-Bench 2.1 and DeepSWE via Artificial Analysis, SWE Atlas via Scale AI's official leaderboard, and Toolathlon Verified via its official leaderboard. Full evaluation trajectories: [trajectories.poolside.ai](https://trajectories.poolside.ai).

	## Usage

	Laguna S 2.1 uses the same `laguna` architecture as Laguna XS 2.1, so the same
	engine integrations apply (vLLM, SGLang, Transformers, TRT-LLM, llama.cpp). At 118B
	parameters the BF16 checkpoint needs multiple GPUs (roughly 236GB of weights);
	quantized variants reduce this substantially.

	### vLLM

	```shell
	vllm serve \
	--model poolside/Laguna-S-2.1 \
	--tensor-parallel-size 4 \
	--tool-call-parser poolside_v1 \
	--reasoning-parser poolside_v1 \
	--enable-auto-tool-choice \
	--served-model-name laguna \
	--default-chat-template-kwargs '{"enable_thinking": true}'
	```

	> [!NOTE]
	> Optional: speculative decoding with DFlash. Pair with the
	> [Laguna S 2.1 DFlash draft model](https://huggingface.co/poolside/Laguna-S-2.1-DFlash)
	> by adding
	> `--speculative-config '{"model":"poolside/Laguna-S-2.1-DFlash","num_speculative_tokens":7,"method":"dflash"}'`.

	### SGLang

	```shell
	python -m sglang.launch_server \
	--model-path poolside/Laguna-S-2.1 \
	--tp-size 4 \
	--reasoning-parser poolside_v1 \
	--tool-call-parser poolside_v1 \
	--trust-remote-code
	```

	### TRT-LLM

	```shell
	trtllm-serve poolside/Laguna-S-2.1 --trust-remote-code \
	--tool_parser poolside_v1 --reasoning_parser laguna
	```

	Note the flag names differ from vLLM's (`--tool_parser`, and the reasoning parser
	is `laguna`, not `poolside_v1`).

	### llama.cpp

	GGUF conversions are available at
	[poolside/Laguna-S-2.1-GGUF](https://huggingface.co/poolside/Laguna-S-2.1-GGUF).
	Serve with poolside's llama.cpp fork, branch
	[`laguna`](https://github.com/poolsideai/llama.cpp/tree/laguna), which carries
	full Laguna support including DFlash speculative decoding. (Base Laguna support
	is also in upstream review:
	[ggml-org/llama.cpp#25165](https://github.com/ggml-org/llama.cpp/pull/25165).)

	```shell
	git clone --branch laguna https://github.com/poolsideai/llama.cpp
	cd llama.cpp && cmake -B build && cmake --build build -j

	./build/bin/llama-server -m laguna-s-2.1-Q4_K_M.gguf --jinja --port 8000

	# with DFlash speculative decoding:
	./build/bin/llama-server -m laguna-s-2.1-Q4_K_M.gguf \
	-md laguna-s-2.1-DFlash-BF16.gguf \
	--spec-type draft-dflash --spec-draft-n-max 7 -fa on --jinja --port 8000
	```

	### Ollama

	Run directly from the [Ollama library](https://ollama.com/library/laguna-s-2.1):

	```shell
	ollama run laguna-s-2.1
	```

	Quantization variants are available as tags (`q4_K_M`, `q8_0`, `f16`, `mxfp8`,
	`nvfp4`, `mlx-bf16`), for example `ollama run laguna-s-2.1:q8_0`. The Laguna chat
	template is baked into the model, so tool-calling and interleaved reasoning work
	automatically.

	## Controlling reasoning

	Laguna S 2.1 has native reasoning support and works best with preserved thinking:
	keep `reasoning_content` from prior assistant messages in the message history.
	The model will generally reason before calling tools and between tool calls, and
	may stop reasoning in follow-up steps if prior thinking blocks are dropped.

	Thinking is controlled per request via the chat template:

	```python
	extra_body={"chat_template_kwargs": {"enable_thinking": False}}
	```

	or at the server level with
	`--default-chat-template-kwargs '{"enable_thinking": true}'`. For agentic coding
	use cases we recommend enabling thinking and preserving reasoning in the message
	history.

	## License

	This model is licensed under the [OpenMDW-1.1 License](https://huggingface.co/poolside/Laguna-S-2.1/blob/main/LICENSE.md).

	## Intended and Responsible Use

	Laguna S 2.1 is designed for software engineering and agentic coding use cases, and you are responsible for confirming that it is appropriate for your intended application. Laguna S 2.1 is subject to the [OpenMDW-1.1 License](https://huggingface.co/poolside/Laguna-S-2.1/blob/main/LICENSE.md), and should be used consistently with Poolside's [Acceptable Use Policy](https://poolside.ai/legal/acceptable-use-policy). We advise against circumventing Laguna S 2.1 safety guardrails without implementing substantially equivalent mitigations appropriate for your use case.

	Please report security vulnerabilities or safety concerns to [security@poolside.ai](mailto:security@poolside.ai).