Intern-S1-mini

Intern-S1-mini

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Introduction

We introduce Intern-S1-mini, a lightweight open-source multimodal reasoning model based on the same techniques as Intern-S1. Built upon an 8B dense language model (Qwen3) and a 0.3B Vision encoder (InternViT), Intern-S1-mini 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-mini 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 and protein sequences.

Performance

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

We use the OpenCompass and VLMEvalkit to evaluate all models.

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 = 0.8

Transformers

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

Please use transformers>=4.55.2 to ensure the model works normally.

Text input

from transformers import AutoProcessor, AutoModelForCausalLM
import torch

model_name = "internlm/Intern-S1-mini"
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-mini"
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

Pl