Instructions to use poolside-laguna-hackathon/laguna-xs2-dense-stage1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use poolside-laguna-hackathon/laguna-xs2-dense-stage1 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-stage1", 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-hackathon/laguna-xs2-dense-stage1", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("poolside-laguna-hackathon/laguna-xs2-dense-stage1", 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

vLLM

How to use poolside-laguna-hackathon/laguna-xs2-dense-stage1 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-stage1"
# 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-stage1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/poolside-laguna-hackathon/laguna-xs2-dense-stage1

SGLang

How to use poolside-laguna-hackathon/laguna-xs2-dense-stage1 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-stage1" \
    --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-stage1",
		"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-stage1" \
        --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-stage1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use poolside-laguna-hackathon/laguna-xs2-dense-stage1 with Docker Model Runner:
```
docker model run hf.co/poolside-laguna-hackathon/laguna-xs2-dense-stage1
```

Laguna-XS.2-dense (Stage 1)

The Stage-1 init for a dense distillation of poolside/Laguna-XS.2 (33B MoE, ≈3B active) into a ≈3B dense model. Each of the 39 sparse MoE blocks is replaced by a single dense SwiGLU FFN (intermediate 4608) and trained per-layer, in parallel to match the teacher MoE block's output (RADLADS-style; teacher-fed inputs → no cross-layer error compounding). ≈90M tokens.

⚠️ Intermediate research artifact. This is the rough init — cross-layer error compounding is deliberately not corrected here (that's Stage 2's job). Held-out perplexity ≈25 (teacher ≈4.4); HumanEval pass@1 = 0.0%. Use laguna-xs2-dense-stage2 (KD-recovered) as the more capable checkpoint.

Loading

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
m = AutoModelForCausalLM.from_pretrained("poolside-laguna-hackathon/laguna-xs2-dense-stage1",
        trust_remote_code=True, torch_dtype=torch.bfloat16, device_map="cuda")
tok = AutoTokenizer.from_pretrained("poolside-laguna-hackathon/laguna-xs2-dense-stage1", trust_remote_code=True)

Dense FFNs (intermediate 4608) are zero-padded to 8192 so the stock modeling_laguna.py loads it (numerically identical); exported reports ≈3.8B, true model ≈3.0B. last.pt (raw Stage-1 FFN weights) is also in this repo. Footprint: ≈6 GB bf16 vs ≈67 GB for the 33B MoE (≈11× less weight VRAM).

See the Stage-2 card for the full method, results, and next steps. Code: https://github.com/postscarcity-inc/laguna-xs.2-dense

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Safetensors

Model size

4B params

Tensor type

BF16

Model tree for poolside-laguna-hackathon/laguna-xs2-dense-stage1

Base model

poolside/Laguna-XS.2

Finetuned

(23)

this model