Intern-S1

Intern-S1

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Introduction

We introduce Intern-S1, our most advanced open-source multimodal reasoning model to date. Intern-S1 combines strong general-task capabilities with state-of-the-art performance on a wide range of scientific tasks, rivaling leading closed-source commercial models. Built upon a 235B MoE language model (Qwen3) and a 6B Vision encoder (InternViT), Intern-S1 has been further pretrained on 5 trillion tokens of multimodal data, including over 2.5 trillion scientific-domain tokens. This enables the model to retain strong general capabilities while excelling in specialized scientific domains such as interpreting chemical structures, understanding protein sequences, and planning compound synthesis routes, making Intern-S1 to be a capable research assistant for real-world scientific applications. Features

• Strong performance across language and vision reasoning benchmarks, especially scientific tasks.

• Continuously pretrained on a massive 5T token dataset, with over 50% specialized scientific data, embedding deep domain expertise.

• Dynamic tokenizer enables native understanding of molecular formulas, protein sequences, and seismic signals.

Performance

We evaluate the Intern-S1 on various benchmarks including general datasets and scientifc datasets. We report the performance comparsion with the recent VLMs and LLMs below.

Note: ✅ means the best performance among open-sourced models, 👑 indicates the best performance among all models.

We use the OpenCompass and VLMEvalkit to evaluate all models. Please refer to this page to quickly start the text-only evaluation task.

Quick Start

Sampling Parameters

We recommend using the following hyperparameters to ensure better results

top_p = 1.0
top_k = 50
min_p = 0.0
temperature = 0.7

Transformers

The following provides demo code illustrating how to generate based on text and multimodal inputs.

Please use transformers>=4.53.0 to ensure the model works normally.

Text input

from transformers import AutoProcessor, AutoModelForCausalLM
import torch

model_name = "internlm/Intern-S1"
processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype="auto", trust_remote_code=True)

messages = [
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "tell me about an interesting physical phenomenon."},
        ],
    }
]

inputs = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt").to(model.device, dtype=torch.bfloat16)

generate_ids = model.generate(**inputs, max_new_tokens=32768)
decoded_output = processor.decode(generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True)
print(decoded_output)

Image input

from transformers import AutoProcessor, AutoModelForCausalLM
import torch

model_name = "internlm/Intern-S1"
processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype="auto", trust_remote_code=True)

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "http://images.cocodataset.org/val2017/000000039769.jpg"},
            {"type": "text", "text": "Please describe the image explicitly."},
        ],
    }
]

inputs = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt").to(model.device, dtype=torch.bfloat16)

generate_ids = model.generate(**inputs, max_new_tokens=32768)
decoded_output = processor.decode(generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True)
print(decoded_output)

Video input

Please ensure that the decord video decoding library is installed via pip install decord.

from transformers import AutoProcessor, AutoModelForCausalLM
import torch

model_name = "internlm/Intern-S1"
processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype="auto", trust_remote_code=True)

messages = [
        {
            "role": "user",
            "content": [
                {
                    "type": "video",
                    "url": "https://huggingface.co/datasets/hf-internal-testing/fixtures_videos/resolve/main/tennis.mp4",
                },
                {"type": "text", "text": "What type of shot is the man performing?"},
            ],
        }
    ]

inputs = processor.apply_chat_template(
        messages,
        return_tensors="pt",
        add_generation_prompt=True,
        video_load_backend="decord",
        tokenize=True,
        return_dict=True,
    ).to(model.device, dtype=torch.float16)

generate_ids = model.generate(**inputs, max_new_tokens=32768)
decoded_output = processor.decode(generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True)
print(decoded_output)

Serving

The minimum hardware requirements for deploying Intern-S1 series models are: