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)
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
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
```

Update README.md

by baranowskiadam - opened Jun 1

base: refs/heads/main

←

from: refs/pr/9

Discussion Files changed

-1

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -152,7 +152,7 @@ See the [vLLM recipes page](https://recipes.vllm.ai/poolside/Laguna-XS.2) for ad
 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.
 > [!NOTE]
-> DFlash support landed in vLLM via [vllm-project/vllm#41880](https://github.com/vllm-project/vllm/pull/41880) and is available in vLLM 0.21.0 and later. `VLLM_USE_DEEP_GEMM=0` is required: DeepGEMM is currently incompatible with the DFlash draft path.
 ```shell
 VLLM_USE_DEEP_GEMM=0 vllm serve poolside/Laguna-XS.2 \

 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.
 > [!NOTE]
+> DFlash support landed in vLLM via [vllm-project/vllm#41880](https://github.com/vllm-project/vllm/pull/41880) and is available in vLLM 0.21.0 and later.
 ```shell
 VLLM_USE_DEEP_GEMM=0 vllm serve poolside/Laguna-XS.2 \