Instructions to use SearchSwarm/SearchSwarm-30B-A3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SearchSwarm/SearchSwarm-30B-A3B with Transformers:

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

pipe = pipeline("text-generation", model="SearchSwarm/SearchSwarm-30B-A3B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SearchSwarm/SearchSwarm-30B-A3B")
model = AutoModelForCausalLM.from_pretrained("SearchSwarm/SearchSwarm-30B-A3B")
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 SearchSwarm/SearchSwarm-30B-A3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SearchSwarm/SearchSwarm-30B-A3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SearchSwarm/SearchSwarm-30B-A3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SearchSwarm/SearchSwarm-30B-A3B

SGLang

How to use SearchSwarm/SearchSwarm-30B-A3B 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 "SearchSwarm/SearchSwarm-30B-A3B" \
    --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": "SearchSwarm/SearchSwarm-30B-A3B",
		"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 "SearchSwarm/SearchSwarm-30B-A3B" \
        --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": "SearchSwarm/SearchSwarm-30B-A3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SearchSwarm/SearchSwarm-30B-A3B with Docker Model Runner:
```
docker model run hf.co/SearchSwarm/SearchSwarm-30B-A3B
```

Update model card with paper, code and project page links

by nielsr HF Staff - opened Jun 11

base: refs/heads/main

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from: refs/pr/1

Discussion Files changed

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nielsr

Jun 11

This PR improves the model card for SearchSwarm-30B-A3B by adding:

A link to the paper: SearchSwarm: Towards Delegation Intelligence in Agentic LLMs for Long-Horizon Deep Research.
Links to the GitHub repository and project page.
The library_name: transformers metadata tag for better discoverability.
A summary of the model's "delegation intelligence" framework and performance results on benchmarks like BrowseComp.

Update model card with paper, code and project page linkse67ddfde

QQqqrabit changed pull request status to merged Jun 11

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