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", device_map="auto")
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
```

Lonepic commited on Jun 30

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@@ -11,20 +11,20 @@ pipeline_tag: text-generation
   <a href="https://internscience.github.io/Agents-A1/"><b>Homepage</b></a>
   <span style="color: #ccc;">|</span>
   <img src="./figures/hf-logo.svg" width="16" height="16" alt="Hugging Face">
   <a href="https://huggingface.co/InternScience/Agents-A1"><b>Hugging Face</b></a>
   <span style="color: #ccc;">|</span>
   <img src="./figures/github-logo.svg" width="16" height="16" alt="GitHub">
-  <a href="https://github.com/InternScience/Agents-A1"><b>Github</b></a>
   <span style="color: #ccc;">|</span>
-  <!-- <img src="./figures/modelscope-logo.svg" width="16" height="16" alt="Model Scope">
-  <a href="https://modelscope.cn/models/Shanghai_AI_Laboratory/Agents-A1"><b>ModelScope</b></a>
-  <span style="color: #ccc;">|</span> -->
-  <img src="./figures/24px.svg" width="16" height="16" alt="Technical Report" style="filter: invert(0.5);">
-  <a href="https://arxiv.org/abs/2606.30616"><b>Technical Report</b></a>
 </div>
 > [!Note]

   <a href="https://internscience.github.io/Agents-A1/"><b>Homepage</b></a>
   <span style="color: #ccc;">|</span>
+  <img src="./figures/24px.svg" width="16" height="16" alt="Technical Report" style="filter: invert(0.5);">
+  <a href="https://arxiv.org/abs/2606.30616"><b>Technical Report</b></a>
+  <span style="color: #ccc;">|</span>
   <img src="./figures/hf-logo.svg" width="16" height="16" alt="Hugging Face">
   <a href="https://huggingface.co/InternScience/Agents-A1"><b>Hugging Face</b></a>
   <span style="color: #ccc;">|</span>
   <img src="./figures/github-logo.svg" width="16" height="16" alt="GitHub">
+  <a href="GitHub - InternScience/Agents-A1: Reaching Trillion-Parameter Performance with a 35B Agent"><b>Github</b></a>
   <span style="color: #ccc;">|</span>
+  <img src="./figures/modelscope-logo.svg" width="16" height="16" alt="Model Scope">
+  <a href="https://modelscope.cn/models/Shanghai_AI_Laboratory/Agents-A1"><b>ModelScope</b></a>
 </div>
 > [!Note]