Text Generation
Transformers
Safetensors
qwen3_5
image-text-to-text
vlm
vision
agentic
conversational
Instructions to use InternScience/Agents-A1-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use InternScience/Agents-A1-4B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="InternScience/Agents-A1-4B") 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-A1-4B") model = AutoModelForMultimodalLM.from_pretrained("InternScience/Agents-A1-4B", 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]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use InternScience/Agents-A1-4B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "InternScience/Agents-A1-4B" # 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-A1-4B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/InternScience/Agents-A1-4B
- SGLang
How to use InternScience/Agents-A1-4B 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-A1-4B" \ --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-A1-4B", "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-A1-4B" \ --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-A1-4B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use InternScience/Agents-A1-4B with Docker Model Runner:
docker model run hf.co/InternScience/Agents-A1-4B
| {%- set image_count = namespace(value=0) %} | |
| {%- set video_count = namespace(value=0) %} | |
| {%- macro render_content(content, do_vision_count, is_system_content=false) %} | |
| {%- if content is string %} | |
| {{- content }} | |
| {%- elif content is iterable and content is not mapping %} | |
| {%- for item in content %} | |
| {%- if 'image' in item or 'image_url' in item or item.type == 'image' %} | |
| {%- if is_system_content %} | |
| {{- raise_exception('System message cannot contain images.') }} | |
| {%- endif %} | |
| {%- if do_vision_count %} | |
| {%- set image_count.value = image_count.value + 1 %} | |
| {%- endif %} | |
| {%- if add_vision_id %} | |
| {{- 'Picture ' ~ image_count.value ~ ': ' }} | |
| {%- endif %} | |
| {{- '<|vision_start|><|image_pad|><|vision_end|>' }} | |
| {%- elif 'video' in item or item.type == 'video' %} | |
| {%- if is_system_content %} | |
| {{- raise_exception('System message cannot contain videos.') }} | |
| {%- endif %} | |
| {%- if do_vision_count %} | |
| {%- set video_count.value = video_count.value + 1 %} | |
| {%- endif %} | |
| {%- if add_vision_id %} | |
| {{- 'Video ' ~ video_count.value ~ ': ' }} | |
| {%- endif %} | |
| {{- '<|vision_start|><|video_pad|><|vision_end|>' }} | |
| {%- elif 'text' in item %} | |
| {{- item.text }} | |
| {%- else %} | |
| {{- raise_exception('Unexpected item type in content.') }} | |
| {%- endif %} | |
| {%- endfor %} | |
| {%- elif content is none or content is undefined %} | |
| {{- '' }} | |
| {%- else %} | |
| {{- raise_exception('Unexpected content type.') }} | |
| {%- endif %} | |
| {%- endmacro %} | |
| {%- set default_system_prompt -%} | |
| You are Intern-A1, a deep research assistant developed by InternAgent Team, Shanghai Artificial Intelligence Laboratory. 你是Intern-A1, 一个由上海人工智能实验室的InternAgent团队开发的深度研究人工智能助手。 You can have natural multi-turn conversations with users on any topic. | |
| ## Daily Chat & Simple Questions | |
| For everyday conversations, greetings, opinions, coding help, factual lookups, definitions, calculations, explanations, and any question you can confidently answer from your knowledge — just respond directly and naturally in the user's language as Intern-A1. Do NOT use any tools for these. | |
| ## Research & Search Questions | |
| Only when the user's question requires up-to-date information, in-depth investigation, multi-source verification, or involves recent events, niche topics, or anything you are uncertain about, use the available tools. | |
| Research strategy: | |
| - Start with a focused search query to get an overview. | |
| - If the initial search is insufficient, refine your query with more specific terms. | |
| - Stop searching once you have enough information to provide a comprehensive answer. Do not over-research. | |
| Current date: 2026-07-14 | |
| {%- endset -%} | |
| {%- if not messages %} | |
| {{- raise_exception('No messages provided.') }} | |
| {%- endif %} | |
| {%- set user_system_content = render_content(messages[0].content, false, true)|trim if messages[0].role == 'system' else '' %} | |
| {%- set system_content = user_system_content if user_system_content else default_system_prompt %} | |
| {%- if tools and tools is iterable and tools is not mapping %} | |
| {{- '<|im_start|>system\n' }} | |
| {{- "# Tools\n\nYou have access to the following functions:\n\n<tools>" }} | |
| {%- for tool in tools %} | |
| {{- "\n" }} | |
| {{- tool | tojson }} | |
| {%- endfor %} | |
| {{- "\n</tools>" }} | |
| {{- '\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n<tool_call>\n<function=example_function_name>\n<parameter=example_parameter_1>\nvalue_1\n</parameter>\n<parameter=example_parameter_2>\nThis is the value for the second parameter\nthat can span\nmultiple lines\n</parameter>\n</function>\n</tool_call>\n\n<IMPORTANT>\nReminder:\n- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\n- Required parameters MUST be specified\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\n</IMPORTANT>' }} | |
| {%- if system_content %} | |
| {{- '\n\n' + system_content }} | |
| {%- endif %} | |
| {{- '<|im_end|>\n' }} | |
| {%- else %} | |
| {%- if system_content %} | |
| {{- '<|im_start|>system\n' + system_content + '<|im_end|>\n' }} | |
| {%- endif %} | |
| {%- endif %} | |
| {%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %} | |
| {%- for message in messages[::-1] %} | |
| {%- set index = (messages|length - 1) - loop.index0 %} | |
| {%- if ns.multi_step_tool and message.role == "user" %} | |
| {%- set content = render_content(message.content, false)|trim %} | |
| {%- if not(content.startswith('<tool_response>') and content.endswith('</tool_response>')) %} | |
| {%- set ns.multi_step_tool = false %} | |
| {%- set ns.last_query_index = index %} | |
| {%- endif %} | |
| {%- endif %} | |
| {%- endfor %} | |
| {%- if ns.multi_step_tool %} | |
| {{- raise_exception('No user query found in messages.') }} | |
| {%- endif %} | |
| {%- for message in messages %} | |
| {%- set content = render_content(message.content, true)|trim %} | |
| {%- if message.role == "system" %} | |
| {%- if not loop.first %} | |
| {{- raise_exception('System message must be at the beginning.') }} | |
| {%- endif %} | |
| {%- elif message.role == "user" %} | |
| {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }} | |
| {%- elif message.role == "assistant" %} | |
| {%- set reasoning_content = '' %} | |
| {%- if message.reasoning_content is string %} | |
| {%- set reasoning_content = message.reasoning_content %} | |
| {%- else %} | |
| {%- if '</think>' in content %} | |
| {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %} | |
| {%- set content = content.split('</think>')[-1].lstrip('\n') %} | |
| {%- endif %} | |
| {%- endif %} | |
| {%- set reasoning_content = reasoning_content|trim %} | |
| {%- if loop.index0 > ns.last_query_index %} | |
| {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content + '\n</think>\n\n' + content }} | |
| {%- else %} | |
| {{- '<|im_start|>' + message.role + '\n' + content }} | |
| {%- endif %} | |
| {%- if message.tool_calls and message.tool_calls is iterable and message.tool_calls is not mapping %} | |
| {%- for tool_call in message.tool_calls %} | |
| {%- if tool_call.function is defined %} | |
| {%- set tool_call = tool_call.function %} | |
| {%- endif %} | |
| {%- if loop.first %} | |
| {%- if content|trim %} | |
| {{- '\n\n<tool_call>\n<function=' + tool_call.name + '>\n' }} | |
| {%- else %} | |
| {{- '<tool_call>\n<function=' + tool_call.name + '>\n' }} | |
| {%- endif %} | |
| {%- else %} | |
| {{- '\n<tool_call>\n<function=' + tool_call.name + '>\n' }} | |
| {%- endif %} | |
| {%- if tool_call.arguments is defined %} | |
| {%- for args_name, args_value in tool_call.arguments|items %} | |
| {{- '<parameter=' + args_name + '>\n' }} | |
| {%- set args_value = args_value | tojson | safe if args_value is mapping or (args_value is sequence and args_value is not string) else args_value | string %} | |
| {{- args_value }} | |
| {{- '\n</parameter>\n' }} | |
| {%- endfor %} | |
| {%- endif %} | |
| {{- '</function>\n</tool_call>' }} | |
| {%- endfor %} | |
| {%- endif %} | |
| {{- '<|im_end|>\n' }} | |
| {%- elif message.role == "tool" %} | |
| {%- if loop.previtem and loop.previtem.role != "tool" %} | |
| {{- '<|im_start|>user' }} | |
| {%- endif %} | |
| {{- '\n<tool_response>\n' }} | |
| {{- content }} | |
| {{- '\n</tool_response>' }} | |
| {%- if not loop.last and loop.nextitem.role != "tool" %} | |
| {{- '<|im_end|>\n' }} | |
| {%- elif loop.last %} | |
| {{- '<|im_end|>\n' }} | |
| {%- endif %} | |
| {%- else %} | |
| {{- raise_exception('Unexpected message role.') }} | |
| {%- endif %} | |
| {%- endfor %} | |
| {%- if add_generation_prompt %} | |
| {{- '<|im_start|>assistant\n' }} | |
| {%- if enable_thinking is defined and enable_thinking is false %} | |
| {{- '<think>\n\n</think>\n\n' }} | |
| {%- else %} | |
| {{- '<think>\n' }} | |
| {%- endif %} | |
| {%- endif %} |