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
```

varunrandery commited on Apr 24

Commit

a4fde8e

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1 Parent(s): 0fb0c2d

Update README.md

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@@ -37,7 +37,7 @@ For more details on how we trained this model, including on data automixing and
 - **KV cache in FP8**: All quantization formats use a KV cache quantized to FP8, reducing memory per token
 - **Native reasoning support**: Interleaved thinking enabled by default
 - **Local-ready**: At 33B total parameters and 3B activated, Laguna XS.2 is compact enough to run on a Mac with 36 GB of RAM. [Available on Ollama](https://ollama.com/library/laguna-xs.2)
-- **Apache 2.0 license**: Use and modify freely for commerical and non-commercial purposes
 ---
@@ -120,7 +120,7 @@ Submit feedback with `/feedback` and read the [full documentation on GitHub](htt
 *By downloading and using pool, you agree to the Poolside [End User License Agreement (EULA)](https://poolside.ai/legal/eula).*
-### Local deployment
 [vLLM, Transformers v5, TRT-LLM, SGLang, ...]
@@ -128,15 +128,15 @@ Thanks to support from Ollama and the mlx-lm team...
 [Device frameworks: Ollama, mlx-lm, ...]
-#### vLLM
 [...]
-#### Transformers
 [...]
-#### [Other frameworks]
 ## Controlling reasoning

 - **KV cache in FP8**: All quantization formats use a KV cache quantized to FP8, reducing memory per token
 - **Native reasoning support**: Interleaved thinking enabled by default
 - **Local-ready**: At 33B total parameters and 3B activated, Laguna XS.2 is compact enough to run on a Mac with 36 GB of RAM. [Available on Ollama](https://ollama.com/library/laguna-xs.2)
+- **Apache 2.0 license**: Use and modify freely for commercial and non-commercial purposes
 ---
 *By downloading and using pool, you agree to the Poolside [End User License Agreement (EULA)](https://poolside.ai/legal/eula).*
+## Local deployment
 [vLLM, Transformers v5, TRT-LLM, SGLang, ...]
 [Device frameworks: Ollama, mlx-lm, ...]
+### vLLM
 [...]
+### Transformers
 [...]
+### [Other frameworks]
 ## Controlling reasoning