Instructions to use poolside-laguna-hackathon/laguna-xs2-dense-stage2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use poolside-laguna-hackathon/laguna-xs2-dense-stage2 with Transformers:

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

pipe = pipeline("text-generation", model="poolside-laguna-hackathon/laguna-xs2-dense-stage2", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("poolside-laguna-hackathon/laguna-xs2-dense-stage2", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("poolside-laguna-hackathon/laguna-xs2-dense-stage2", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use poolside-laguna-hackathon/laguna-xs2-dense-stage2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "poolside-laguna-hackathon/laguna-xs2-dense-stage2"
# 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-hackathon/laguna-xs2-dense-stage2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/poolside-laguna-hackathon/laguna-xs2-dense-stage2

SGLang

How to use poolside-laguna-hackathon/laguna-xs2-dense-stage2 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-hackathon/laguna-xs2-dense-stage2" \
    --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-hackathon/laguna-xs2-dense-stage2",
		"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-hackathon/laguna-xs2-dense-stage2" \
        --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-hackathon/laguna-xs2-dense-stage2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use poolside-laguna-hackathon/laguna-xs2-dense-stage2 with Docker Model Runner:
```
docker model run hf.co/poolside-laguna-hackathon/laguna-xs2-dense-stage2
```

duico commited on May 30

Commit

78009fd

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1 Parent(s): 174e76e

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@@ -29,6 +29,10 @@ Data: 50% [DCLM](https://huggingface.co/datasets/mlfoundations/dclm-baseline-1.0
 ![Stage-1 per-layer NMSE curves](stage1_nmse_curves.png)
 ## Architecture / loading note
 The dense FFNs are intermediate 4608 (layers 1–39) and 8192 (layer 0). For a uniform config that loads with the **stock** `modeling_laguna.py`, the 4608 FFNs are **zero-padded to 8192** (numerically identical — `silu(0)·0 = 0`). So the exported checkpoint reports ~3.8B params (padded); the true model is ~3.0B.

 ![Stage-1 per-layer NMSE curves](stage1_nmse_curves.png)
+**Stage-2 KD loss** (forward-KL teacher‖student over training, 2.5 → 1.40):
+![Stage-2 KD KL loss](stage2_kl_loss.png)
 ## Architecture / loading note
 The dense FFNs are intermediate 4608 (layers 1–39) and 8192 (layer 0). For a uniform config that loads with the **stock** `modeling_laguna.py`, the 4608 FFNs are **zero-padded to 8192** (numerically identical — `silu(0)·0 = 0`). So the exported checkpoint reports ~3.8B params (padded); the true model is ~3.0B.