Instructions to use InternScience/Agents-A1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use InternScience/Agents-A1 with Transformers:

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

pipe = pipeline("text-generation", model="InternScience/Agents-A1")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("InternScience/Agents-A1")
model = AutoModelForMultimodalLM.from_pretrained("InternScience/Agents-A1")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use InternScience/Agents-A1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "InternScience/Agents-A1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "InternScience/Agents-A1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/InternScience/Agents-A1

SGLang

How to use InternScience/Agents-A1 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 "InternScience/Agents-A1" \
    --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": "InternScience/Agents-A1",
		"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 "InternScience/Agents-A1" \
        --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": "InternScience/Agents-A1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use InternScience/Agents-A1 with Docker Model Runner:
```
docker model run hf.co/InternScience/Agents-A1
```

benchmaxxed

#11

by Tom-Neverwinter - opened 2 days ago

Discussion

Tom-Neverwinter

2 days ago

the too good to be true alarms are going off. has to be another benchmaxxed or misleading model. I doubt it has the specs claimed.

plusgiant5

1 day ago

it's an extremely impressive agentic model for its size, I have been using it

seovendorco

about 20 hours ago

this model is impressive. I use Qwen 3.6 35b-a3b and Qwen 3.6 27b every day, and I can tell you this model is here to stay. It beats the 35b handsdown, but is now also progressing through 14-stage workflows that have been challenging for the 27b. I'm now putting it up to long-term challenges.

NatsuGatsu

about 18 hours ago

this model is impressive. I use Qwen 3.6 35b-a3b and Qwen 3.6 27b every day, and I can tell you this model is here to stay. It beats the 35b handsdown, but is now also progressing through 14-stage workflows that have been challenging for the 27b. I'm now putting it up to long-term challenges.

I hope you update us about the results of your tests. I hope this model really is something worth using.

sHEL1562

about 6 hours ago

What about comparison with Qwen's own AgentWorld, which was released like two days prior? It is identically insanely good at toolcalls, and has not had a miss on anything thrown at it on my end. Is this a secondary party's implementation of what Qwen (apparently) set out to build with AgentWorld? Both look insanely good, but I have only tried AW and not AA1... apparently models trained on simulating a terminal-output aware environment is such a game changer. As even AW under a harness of any sort is like 10 lightyears ahead of what we just had a few months ago with normal (non-agent) models. Actually incredibly impressive for Hermes/Openclaw/Pis/etc

sY713

Intern Science org about 6 hours ago

What about comparison with Qwen's own AgentWorld, which was released like two days prior? It is identically insanely good at toolcalls, and has not had a miss on anything thrown at it on my end. Is this a secondary party's implementation of what Qwen (apparently) set out to build with AgentWorld? Both look insanely good, but I have only tried AW and not AA1... apparently models trained on simulating a terminal-output aware environment is such a game changer. As even AW under a harness of any sort is like 10 lightyears ahead of what we just had a few months ago with normal (non-agent) models. Actually incredibly impressive for Hermes/Openclaw/Pis/etc

Thanks for the attention! I also think AgentWorld is very impressive. My understanding is that AgentWorld starts from the idea of training models with simulated real-world feedback, and surprisingly obtains a base model with quite strong agentic capabilities.

Agents-A1 is a bit different: as we mentioned in our technical report, it is post-trained based on Qwen3.5, and is mainly optimized for agentic search and scientific reasoning tasks.

Both directions are very exciting, and I’d love to hear your real hands-on feedback if you get a chance to try Agents-A1.

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