--- license: apache-2.0 pipeline_tag: image-text-to-text --- ## 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](https://huggingface.co/internlm/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. | | | Intern-S1-mini | Qwen3-8B | GLM-4.1V | MiMo-VL-7B-RL-2508 | |------------|----------------|-------------------|----------|----------|--------------------| | General | MMLU-Pro | **74.78** | 73.7 | 57.1 | 73.93 | | 　 | MMMU | **72.33** | N/A | 69.9 | 70.4 | | 　 | MMStar | 65.2 | N/A | 71.5 | 72.9 | | 　 | GPQA | **65.15** | 62 | 50.32 | 60.35 | | 　 | AIME2024 | **84.58** | 76 | 36.2 | 72.6 | | 　 | AIME2025 | **80** | 67.3 | 32 | 64.4 | | 　 | MathVision | 51.41 | N/A | 53.9 | 54.5 | | 　 | MathVista | 70.3 | N/A | 80.7 | 79.4 | | 　 | IFEval | 81.15 | 85 | 71.53 | 71.4 | | | | | | | | | Scientific | SFE | 35.84 | N/A | 43.2 | 43.9 | | 　 | Physics | **28.76** | N/A | 4.3 | 23.9 | | 　 | SmolInstruct | **32.2** | 17.6 | 18.1 | 16.11 | | 　 | ChemBench | **76.47** | 61.1 | 56.2 | 66.78 | | 　 | MatBench | **61.55** | 45.24 | 54.3 | 46.9 | | 　 | MicroVQA | **56.62** | N/A | 50.2 | 50.96 | | 　 | ProteinLMBench | 58.47 | 59.1 | 58.3 | 59.8 | | 　 | MSEarthMCQ | **58.12** | N/A | 50.3 | 47.3 | | 　 | XLRS-Bench | **51.63** | N/A | 49.8 | 12.29 | We use the [OpenCompass](https://github.com/open-compass/OpenCompass/) and [VLMEvalkit](https://github.com/open-compass/vlmevalkit) to evaluate all models. ## Quick Start ### Sampling Parameters We recommend using the following hyperparameters to ensure better results ```python 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 ```python 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 ```python 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 Please ensure that the decord video decoding library is installed via `pip install decord`. To avoid OOM, please install flash_attention and use at least 2 GPUS. ```python 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": "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: | Model | A100(GPUs) | H800(GPUs) | H100(GPUs) | H200(GPUs) | | :---------------------------------------------------------------------: | :--------: | :--------: | :--------: | :--------: | | [internlm/Intern-S1-mini](https://huggingface.co/internlm/Intern-S1-mini) | 1 | 1 | 1 | 1 | | [internlm/Intern-S1-mini-FP8](https://huggingface.co/internlm/Intern-S1-mini-FP8) | - | 1 | 1 | 1 | You can utilize one of the following LLM inference frameworks to create an OpenAI compatible server: #### [lmdeploy(>=0.9.2)](https://github.com/InternLM/lmdeploy) ```bash lmdeploy serve api_server internlm/Intern-S1-mini --reasoning-parser intern-s1 --tool-call-parser intern-s1 ``` #### [vllm](https://github.com/vllm-project/vllm) ```bash vllm serve internlm/Intern-S1-mini --trust-remote-code ``` #### [sglang](https://github.com/sgl-project/sglang) ```bash python3 -m sglang.launch_server \ --model-path internlm/Intern-S1-mini \ --trust-remote-code \ --grammar-backend none ``` #### ollama for local deployment: ```bash # install ollama curl -fsSL https://ollama.com/install.sh | sh # fetch model ollama pull internlm/interns1-mini # run model ollama run internlm/interns1-mini # then use openai client to call on http://localhost:11434/v1 ``` ## Advanced Usage ### Tool Calling Many Large Language Models (LLMs) now feature **Tool Calling**, a powerful capability that allows them to extend their functionality by interacting with external tools and APIs. This enables models to perform tasks like fetching up-to-the-minute information, running code, or calling functions within other applications. A key advantage for developers is that a growing number of open-source LLMs are designed to be compatible with the OpenAI API. This means you can leverage the same familiar syntax and structure from the OpenAI library to implement tool calling with these open-source models. As a result, the code demonstrated in this tutorial is versatile—it works not just with OpenAI models, but with any model that follows the same interface standard. To illustrate how this works, let's dive into a practical code example that uses tool calling to get the latest weather forecast (based on lmdeploy api server). ```python from openai import OpenAI import json def get_current_temperature(location: str, unit: str = "celsius"): """Get current temperature at a location. Args: location: The location to get the temperature for, in the format "City, State, Country". unit: The unit to return the temperature in. Defaults to "celsius". (choices: ["celsius", "fahrenheit"]) Returns: the temperature, the location, and the unit in a dict """ return { "temperature": 26.1, "location": location, "unit": unit, } def get_temperature_date(location: str, date: str, unit: str = "celsius"): """Get temperature at a location and date. Args: location: The location to get the temperature for, in the format "City, State, Country". date: The date to get the temperature for, in the format "Year-Month-Day". unit: The unit to return the temperature in. Defaults to "celsius". (choices: ["celsius", "fahrenheit"]) Returns: the temperature, the location, the date and the unit in a dict """ return { "temperature": 25.9, "location": location, "date": date, "unit": unit, } def get_function_by_name(name): if name == "get_current_temperature": return get_current_temperature if name == "get_temperature_date": return get_temperature_date tools = [{ 'type': 'function', 'function': { 'name': 'get_current_temperature', 'description': 'Get current temperature at a location.', 'parameters': { 'type': 'object', 'properties': { 'location': { 'type': 'string', 'description': 'The location to get the temperature for, in the format \'City, State, Country\'.' }, 'unit': { 'type': 'string', 'enum': [ 'celsius', 'fahrenheit' ], 'description': 'The unit to return the temperature in. Defaults to \'celsius\'.' } }, 'required': [ 'location' ] } } }, { 'type': 'function', 'function': { 'name': 'get_temperature_date', 'description': 'Get temperature at a location and date.', 'parameters': { 'type': 'object', 'properties': { 'location': { 'type': 'string', 'description': 'The location to get the temperature for, in the format \'City, State, Country\'.' }, 'date': { 'type': 'string', 'description': 'The date to get the temperature for, in the format \'Year-Month-Day\'.' }, 'unit': { 'type': 'string', 'enum': [ 'celsius', 'fahrenheit' ], 'description': 'The unit to return the temperature in. Defaults to \'celsius\'.' } }, 'required': [ 'location', 'date' ] } } }] messages = [ {'role': 'user', 'content': 'Today is 2024-11-14, What\'s the temperature in San Francisco now? How about tomorrow?'} ] openai_api_key = "EMPTY" openai_api_base = "http://0.0.0.0:23333/v1" client = OpenAI( api_key=openai_api_key, base_url=openai_api_base, ) model_name = client.models.list().data[0].id response = client.chat.completions.create( model=model_name, messages=messages, max_tokens=32768, temperature=0.8, top_p=0.8, stream=False, extra_body=dict(spaces_between_special_tokens=False, enable_thinking=False), tools=tools) print(response.choices[0].message) messages.append(response.choices[0].message) for tool_call in response.choices[0].message.tool_calls: tool_call_args = json.loads(tool_call.function.arguments) tool_call_result = get_function_by_name(tool_call.function.name)(**tool_call_args) tool_call_result = json.dumps(tool_call_result, ensure_ascii=False) messages.append({ 'role': 'tool', 'name': tool_call.function.name, 'content': tool_call_result, 'tool_call_id': tool_call.id }) response = client.chat.completions.create( model=model_name, messages=messages, temperature=0.8, top_p=0.8, stream=False, extra_body=dict(spaces_between_special_tokens=False, enable_thinking=False), tools=tools) print(response.choices[0].message.content) ``` ### Switching Between Thinking and Non-Thinking Modes Intern-S1-mini enables thinking mode by default, enhancing the model's reasoning capabilities to generate higher-quality responses. This feature can be disabled by setting `enable_thinking=False` in `tokenizer.apply_chat_template` ```python text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=False # think mode indicator ) ``` With LMDeploy serving Intern-S1-mini models, you can dynamically control the thinking mode by adjusting the `enable_thinking` parameter in your requests. ```python from openai import OpenAI import json messages = [ { 'role': 'user', 'content': 'who are you' }, { 'role': 'assistant', 'content': 'I am an AI' }, { 'role': 'user', 'content': 'AGI is?' }] openai_api_key = "EMPTY" openai_api_base = "http://0.0.0.0:23333/v1" client = OpenAI( api_key=openai_api_key, base_url=openai_api_base, ) model_name = client.models.list().data[0].id response = client.chat.completions.create( model=model_name, messages=messages, temperature=0.8, top_p=0.8, max_tokens=2048, extra_body={ "enable_thinking": False, } ) print(json.dumps(response.model_dump(), indent=2, ensure_ascii=False)) ``` For vllm and sglang users, configure this through, ```python extra_body={ "chat_template_kwargs": {"enable_thinking": False} } ```