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

Libraries

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

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

pipe = pipeline("text-generation", model="poolside/Laguna-XS-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-XS-2.1", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("poolside/Laguna-XS-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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use poolside/Laguna-XS-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-XS-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-XS-2.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use poolside/Laguna-XS-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-XS-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-XS-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-XS-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-XS-2.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

nikivdev commited on about 1 month ago

Commit

64e3aa3

verified ·

1 Parent(s): 2fa307b

Fix relative imports in modeling_laguna.py that break trust_remote_code loading

Browse files

Fixes the `trust_remote_code` loading failure reported in discussion #3.

`modeling_laguna.py` keeps two function-local in-tree relative imports inside `LagunaModel.forward`:

```python
from ...cache_utils import DynamicCache
from ...masking_utils import create_causal_mask, create_sliding_window_causal_mask
```

Inside the transformers source tree these resolve to the package root, but as Hub custom code transformers' dynamic-module scanner (`dynamic_module_utils.get_relative_imports`) collects them as sibling snapshot files to fetch, and loading dies before the model class is even built:

```
FileNotFoundError: [Errno 2] No such file or directory: '.../snapshots/.../..cache_utils.py'
```

Repro: `AutoModelForCausalLM.from_pretrained("poolside/Laguna-XS-2.1", trust_remote_code=True)` on transformers 5.12.1. (vLLM is unaffected — it never runs the scanner.)

This PR rewrites the two lines to the absolute imports they resolved to in-tree; both modules are public transformers API, so behavior is identical. Verified locally: with this change the model loads and runs forward/backward (transformers 5.12.1, torch 2.12.1, bf16).

Files changed (1) hide show

modeling_laguna.py +2 -2

modeling_laguna.py CHANGED Viewed

@@ -633,8 +633,8 @@ class LagunaModel(LagunaPreTrainedModel):
         use_cache: bool | None = None,
         **kwargs: Unpack[TransformersKwargs],
     ) -> MoeModelOutputWithPast:
-        from ...cache_utils import DynamicCache
-        from ...masking_utils import create_causal_mask, create_sliding_window_causal_mask
         if (input_ids is None) ^ (inputs_embeds is not None):
             raise ValueError("You must specify exactly one of input_ids or inputs_embeds")

         use_cache: bool | None = None,
         **kwargs: Unpack[TransformersKwargs],
     ) -> MoeModelOutputWithPast:
+        from transformers.cache_utils import DynamicCache
+        from transformers.masking_utils import create_causal_mask, create_sliding_window_causal_mask
         if (input_ids is None) ^ (inputs_embeds is not None):
             raise ValueError("You must specify exactly one of input_ids or inputs_embeds")