Instructions to use poolside/Laguna-XS.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use poolside/Laguna-XS.2 with Transformers:

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

pipe = pipeline("text-generation", model="poolside/Laguna-XS.2", 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-XS.2", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("poolside/Laguna-XS.2", 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]:]))

Notebooks
Google Colab
Kaggle
AMD Developer Cloud
Local Apps Settings

vLLM

How to use poolside/Laguna-XS.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "poolside/Laguna-XS.2"
# 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-XS.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/poolside/Laguna-XS.2

SGLang

How to use poolside/Laguna-XS.2 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-XS.2" \
    --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-XS.2",
		"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-XS.2" \
        --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-XS.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use poolside/Laguna-XS.2 with Docker Model Runner:
```
docker model run hf.co/poolside/Laguna-XS.2
```

joerowell commited on Jun 18

Commit

0bab366

verified ·

1 Parent(s): 0f4935b

Document SGLang support (sgl-project/sglang#24204)

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README.md +5 -1

README.md CHANGED Viewed

@@ -124,7 +124,7 @@ Submit feedback with `/feedback` and read the [full documentation on GitHub](htt
 ### Local deployment
-Laguna XS.2 is supported in vLLM and Transformers, and TRT-LLM thanks to the support of the team at NVIDIA. Use Laguna-XS.2 with Ollama (with MLX support) and the mlx-lm framework for the best experience on your local machine.
 #### vLLM
@@ -147,6 +147,10 @@ vllm serve \
 See the [vLLM recipes page](https://recipes.vllm.ai/poolside/Laguna-XS.2) for additional deployment guidance.
 #### Speculative decoding (DFlash)
 For lower latency, serve Laguna XS.2 with the [Laguna-XS.2 DFlash speculator](https://huggingface.co/poolside/Laguna-XS.2-speculator.dflash) — a 5-layer Llama-style draft model that proposes up to 7 tokens per step at ~70% per-position acceptance on coding tasks.

 ### Local deployment
+Laguna XS.2 is supported in vLLM, SGLang, and Transformers, and TRT-LLM thanks to the support of the team at NVIDIA. Use Laguna-XS.2 with Ollama (with MLX support) and the mlx-lm framework for the best experience on your local machine.
 #### vLLM
 See the [vLLM recipes page](https://recipes.vllm.ai/poolside/Laguna-XS.2) for additional deployment guidance.
+#### SGLang
+Laguna XS.2 is supported in SGLang via [sgl-project/sglang#24204](https://github.com/sgl-project/sglang/pull/24204). See the [SGLang cookbook entry](https://github.com/sgl-project/sglang/pull/24730) for a serving recipe.
 #### Speculative decoding (DFlash)
 For lower latency, serve Laguna XS.2 with the [Laguna-XS.2 DFlash speculator](https://huggingface.co/poolside/Laguna-XS.2-speculator.dflash) — a 5-layer Llama-style draft model that proposes up to 7 tokens per step at ~70% per-position acceptance on coding tasks.