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

Libraries

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

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

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

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

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

SGLang

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

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

BenchMAXXXED

#28

by TawusGames - opened 3 days ago

Discussion

TawusGames

3 days ago

BenchMAXXXED for investors.

Danne980

3 days ago

I dont think so. I did some of my own benchmarking on this. I don+t know what would happen if test was harder on Agentic and tools, the difference might be bigger. It for sure has some downsides but maybe its a good thing all models are not mediokre on all areas. Running Laguna together with another model might be great. For my particular hardware its crazy fast. 180+t/s in concurrent 1 on TG and 6000t/s in PP.

Scores per Benchmark
Bold = best in row.

Benchmark	Qwen3.5-122B	Laguna	Solar-250B	Qwen3.6-27B	MiniMax-M2.7
Internal Tool Use	98.6	100.0	100.0	96.9	96.6
OpenClaw Agentic	87.7	93.0	81.6	85.1	85.1
MATH Hard	83.0	57.0	80.0	78.0	78.0
MuSR	91.0	76.0	72.0	88.0	88.0
GPQA Diamond	57.0	59.0	62.0	64.0	58.0
MMLU-Pro	82.0	68.7	79.0	82.3	60.3
HumanEval	94.0	93.0	91.0	81.0	81.0
BFCL	93.0	90.0	94.0	84.0	92.0
HellaSwag	96.7	90.0	94.0	96.7	82.7
GSM8K	96.0	88.0	91.0	92.0	93.0

weisunding

3 days ago

@Danne980 great benchmark, but no any SWE (coding skills)? Qwen3.6-27B is very strong in coding and math but with tool calling issues, it seems the Laguna is slightly better in coding than Qwen3.6-27B.

pegasus912

2 days ago

I dont think so. I did some of my own benchmarking on this. I don+t know what would happen if test was harder on Agentic and tools, the difference might be bigger. It for sure has some downsides but maybe its a good thing all models are not mediokre on all areas. Running Laguna together with another model might be great. For my particular hardware its crazy fast. 180+t/s in concurrent 1 on TG and 6000t/s in PP.

Scores per Benchmark
Bold = best in row.

Benchmark Qwen3.5-122B Laguna Solar-250B Qwen3.6-27B MiniMax-M2.7

Internal Tool Use 98.6 100.0 100.0 96.9 96.6

OpenClaw Agentic 87.7 93.0 81.6 85.1 85.1

MATH Hard 83.0 57.0 80.0 78.0 78.0

MuSR 91.0 76.0 72.0 88.0 88.0

GPQA Diamond 57.0 59.0 62.0 64.0 58.0

MMLU-Pro 82.0 68.7 79.0 82.3 60.3

HumanEval 94.0 93.0 91.0 81.0 81.0

BFCL 93.0 90.0 94.0 84.0 92.0

HellaSwag 96.7 90.0 94.0 96.7 82.7

GSM8K 96.0 88.0 91.0 92.0 93.0

Which sampler settings? This is a big point of contention currently.

Danne980

2 days ago

•

edited 2 days ago

Sample sizes vary per benchmark (1,079 total): Internal Tool Use 10, OpenClaw Agentic 19, MATH/MuSR/GPQA/HumanEval/BFCL/GSM8K 100 each, HellaSwag 150, MMLU-Pro 300.

M2.7 was 4 bit. Others 8.

I think the model are sensetive on quant and setup. Reults sems to vary.

TawusGames

2 days ago

I don't give a fuck about those fucking benchmarks. I tested it with some prompts on my own app, and it's definitely worse than Qwen (Thinking Cap fine tune) 3.6 27b and Qwen (base model) 3.6 35b a3b. My tests mostly involve Blender MCP, Unity MCP, and HTML game code. The model overthinks too much and has almost zero sense of design.

Danne980

1 day ago

I don't give a fuck about those fucking benchmarks. I tested it with some prompts on my own app, and it's definitely worse than Qwen (Thinking Cap fine tune) 3.6 27b and Qwen (base model) 3.6 35b a3b. My tests mostly involve Blender MCP, Unity MCP, and HTML game code. The model overthinks too much and has almost zero sense of design.

And i guess you applied the fixes and ran 8 bits?

TawusGames

1 day ago

I don't give a fuck about those fucking benchmarks. I tested it with some prompts on my own app, and it's definitely worse than Qwen (Thinking Cap fine tune) 3.6 27b and Qwen (base model) 3.6 35b a3b. My tests mostly involve Blender MCP, Unity MCP, and HTML game code. The model overthinks too much and has almost zero sense of design.

And i guess you applied the fixes and ran 8 bits?

I use open router for this kind of test.

TawusGames

1 day ago

You dont need defend this much. Here some of test on youtube community. Everyone knows this model worse than qwen 3.6 27b (iq4 xs) run on my 16gb vram machine.

https://www.youtube.com/watch?v=SoRla9IxttE
https://www.youtube.com/watch?v=t6uhTLEOpzU&t=1800s

Danne980

1 day ago

Defend? I was just asking. I see very mixed reviews. And i know they have done a lot of changes. My first results was not good. One thing I have noticed is that the models does not seem to quant well, at least not at 4 bits.

TawusGames

1 day ago

•

edited 1 day ago

Without doing any real testing (aka real world scenario), people try a couple of prompts and claim a 120B parameter model is the best—without even checking if it actually performs worse than a 27B or 35b a3b model. Some of the most downloaded models include nonsensical fine-tunes like fable vibe. We need to cut through the hype and focus on what AI can actually do for us and which model is genuinely better. Everyone just jumps on the hype train, looks at benchmark scores without proper testing, and calls it a day.

Edit: I forget to say this model even can't talk my native language's.

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment