Improve Intern-S1-mini-GGUF model card: Add Transformers usage, paper/GitHub links, and `library_name`

README.md

@@ -1,21 +1,24 @@
 ---
-license: apache-2.0
+base_model:
+- internlm/Intern-S1-mini
 language:
 - en
-base_model:
-  - internlm/Intern-S1-mini
-
-base_model_relation: quantized
-
+license: apache-2.0
 pipeline_tag: image-text-to-text
 tags:
 - chat
+base_model_relation: quantized
+library_name: transformers
 ---
 
 # Intern-S1-mini-GGUF Model
 
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/642695e5274e7ad464c8a5ba/E43cgEXBRWjVJlU_-hdh6.png)
+This repository contains the `Intern-S1-mini` model in GGUF format, which is part of the **Intern-S1** family of scientific multimodal foundation models as introduced in the paper [Intern-S1: A Scientific Multimodal Foundation Model](https://huggingface.co/papers/2508.15763) ([arXiv:2508.15763](https://arxiv.org/abs/2508.15763)).
 
+For more details about the project, visit the [official GitHub repository](https://github.com/InternLM/Intern-S1).
+You can also try the [online chat demo](https://chat.intern-ai.org.cn/).
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/642695e5274e7ad464c8a5ba/E43cgEXBRWjVJlU_-hdh6.png)
 
 <p align="center">
     👋 join us on <a href="https://discord.gg/xa29JuW87d" target="_blank">Discord</a> and <a href="https://cdn.vansin.top/intern-s1.jpg" target="_blank">WeChat</a>
@@ -64,7 +67,7 @@ pip install huggingface-hub
 huggingface-cli download internlm/Intern-S1-mini-GGUF --include *-f16.gguf  --local-dir Intern-S1-mini-GGUF --local-dir-use-symlinks False
 ```
 
-## Inference
+## Inference (llama.cpp)
 
 You can use `build/bin/llama-mtmd-cli` for conducting inference. For a detailed explanation of `build/bin/llama-mtmd-cli`, please refer to [this guide](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
 
@@ -74,7 +77,10 @@ Here is an example of using the thinking system prompt.
 
 ```shell
 
-system_prompt="<|im_start|>system\nYou are an expert reasoner with extensive experience in all areas. You approach problems through systematic thinking and rigorous reasoning. Your response should reflect deep understanding and precise logical thinking, making your solution path and reasoning clear to others. Please put your thinking process within <think>...</think> tags.\n<|im_end|>\n"
+system_prompt="<|im_start|>system
+You are an expert reasoner with extensive experience in all areas. You approach problems through systematic thinking and rigorous reasoning. Your response should reflect deep understanding and precise logical thinking, making your solution path and reasoning clear to others. Please put your thinking process within <think>...</think> tags.
+<|im_end|>
+"
 
 build/bin/llama-mtmd-cli \
     --model Intern-S1-mini-GGUF/f16/Intern-S1-mini-f16.gguf  \
@@ -90,7 +96,7 @@ build/bin/llama-mtmd-cli \
 
 Then input your question with image input as `/image xxx.jpg`.
 
-## Serving
+## Serving (llama.cpp)
 
 `llama.cpp` provides an OpenAI API compatible server - `llama-server`. You can deploy the model as a service like this:
 
@@ -140,3 +146,357 @@ ollama pull internlm/interns1:mini
 ollama run internlm/interns1:mini
 # then use openai client to call on http://localhost:11434/v1
 ```
+
+## Quick Start (Transformers)
+
+### Sampling Parameters
+
+We recommend using the following hyperparameters to ensure better results
+
+For Intern-S1-mini:
+
+```python
+top_p = 1.0
+top_k = 50
+min_p = 0.0
+temperature = 0.8
+```
+
+### 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`.
+
+```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)
+```
+
+## 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 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 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=0.7,
+    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}
+}
+```
+
+## Fine-tuning
+
+See this [documentation](docs/sft.md) for more details.
+
+## License
+
+This project is released under the Apache 2.0 license.
+
+## Citation
+
+If you find this work useful, feel free to give us a cite.
+
+```bibtex
+@misc{bai2025interns1scientificmultimodalfoundation,
+      title={Intern-S1: A Scientific Multimodal Foundation Model},
+      author={Lei Bai and Zhongrui Cai and Maosong Cao and Weihan Cao and Chiyu Chen and Haojiong Chen and Kai Chen and Pengcheng Chen and Ying Chen and Yongkang Chen and Yu Cheng and Yu Cheng and Pei Chu and Tao Chu and Erfei Cui and Ganqu Cui and Long Cui and Ziyun Cui and Nianchen Deng and Ning Ding and Nanqin Dong and Peijie Dong and Shihan Dou and Sinan Du and Haodong Duan and Caihua Fan and Ben Gao and Changjiang Gao and Jianfei Gao and Songyang Gao and Yang Gao and Zhangwei Gao and Jiaye Ge and Qiming Ge and Lixin Gu and Yuzhe Gu and Aijia Guo and Qipeng Guo and Xu Guo and Conghui He and Junjun He and Yili Hong and Siyuan Hou and Caiyu Hu and Hanglei Hu and Jucheng Hu and Ming Hu and Zhouqi Hua and Haian Huang and Junhao Huang and Xu Huang and Zixian Huang and Zhe Jiang and Lingkai Kong and Linyang Li and Peiji Li and Pengze Li and Shuaibin Li and Tianbin Li and Wei Li and Yuqiang Li and Dahua Lin and Junyao Lin and Tianyi Lin and Zhishan Lin and Hongwei Liu and Jiangning Liu and Jiyao Liu and Junnan Liu and Kai Liu and Kaiwen Liu and Kuikun Liu and Shichun Liu and Shudong Liu and Wei Liu and Xinyao Liu and Yuhong Liu and Zhan Liu and Yinquan Lu and Haijun Lv and Hongxia Lv and Huijie Lv and Qidang Lv and Ying Lv and Chengqi Lyu and Chenglong Ma and Jianpeng Ma and Ren Ma and Runmin Ma and Runyuan Ma and Xinzhu Ma and Yichuan Ma and Zihan Ma and Sixuan Mi and Junzhi Ning and Wenchang Ning and Xinle Pang and Jiahui Peng and Runyu Peng and Yu Qiao and Jiantao Qiu and Xiaoye Qu and Yuan Qu and Yuchen Ren and Fukai Shang and Wenqi Shao and Junhao Shen and Shuaike Shen and Chunfeng Song and Demin Song and Diping Song and Chenlin Su and Weijie Su and Weigao Sun and Yu Sun and Qian Tan and Cheng Tang and Huanze Tang and Kexian Tang and Shixiang Tang and Jian Tong and Aoran Wang and Bin Wang and Dong Wang and Lintao Wang and Rui Wang and Weiyun Wang and Wenhai Wang and Yi Wang and Ziyi Wang and Ling-I Wu and Wen Wu and Yue Wu and Zijian Wu and Linchen Xiao and Shuhao Xing and Chao Xu and Huihui Xu and Jun Xu and Ruiliang Xu and Wanghan Xu and GanLin Yang and Yuming Yang and Haochen Ye and Jin Ye and Shenglong Ye and Jia Yu and Jiashuo Yu and Jing Yu and Fei Yuan and Bo Zhang and Chao Zhang and Chen Zhang and Hongjie Zhang and Jin Zhang and Qiaosheng Zhang and Qiuyinzhe Zhang and Songyang Zhang and Taolin Zhang and Wenlong Zhang and Wenwei Zhang and Yechen Zhang and Ziyang Zhang and Haiteng Zhao and Qian Zhao and Xiangyu Zhao and Xiangyu Zhao and Bowen Zhou and Dongzhan Zhou and Peiheng Zhou and Yuhao Zhou and Yunhua Zhou and Dongsheng Zhu and Lin Zhu and Yicheng Zou},
+      year={2025},
+      eprint={2508.15763},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2508.15763},
+}
+```