Duplicate from internlm/Intern-S2-397B

Co-authored-by: henry <haijunlv@users.noreply.huggingface.co>

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README.md

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+---
+library_name: transformers
+license: apache-2.0
+license_link: https://huggingface.co/internlm/Intern-S2/blob/main/LICENSE
+pipeline_tag: image-text-to-text
+---
+
+## Intern-S2-397B
+
+<div align="center">
+<img src="./figs/title.png" />
+
+  <div>&nbsp;</div>
+
+[💻Github Repo](https://github.com/InternLM/Intern-S1) • [🤗HF Model Collections](https://huggingface.co/collections/internlm/intern-s2) • [🤖ModelScope Collections](https://modelscope.cn/collections/Shanghai_AI_Laboratory/intern-s2) • [💬Online Chat](https://chat.intern-ai.org.cn/)
+
+</div>
+
+<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>
+</p>
+
+
+
+## Introduction
+
+We introduce **Intern-S2-397B**, our most capable multimodal foundation model for scientific intelligence and long-horizon agents. Intern-S2-397B scales along three critical dimensions: pre-training, reinforcement-learning task coverage, and interactive agent environments. By combining a new vision-language pre-training paradigm with large-scale multi-task reinforcement learning and long-horizon agent reinforcement learning, Intern-S2-397B delivers a step change in general reasoning, scientific problem solving, and agentic capabilities.
+
+### Features
+
+- **New Pre-training Paradigm.** Via visual pretraining, Intern-S2-397B learns directly from raw pages of scientific literature, jointly modeling symbolic semantics and visual relationships in a shared representation space without intermediate parsing. This preserves text-visual correspondence, strengthens spatial and visual reasoning, and improves data efficiency.
+
+- **Scientific Modality Reasoning and Generation.** By scaling diverse scientific reinforcement-learning tasks across more than 20 domains and training them jointly, Intern-S2-397B achieves leading general-reasoning performance among open-source models and strong results in specialized scientific tasks such as biomolecular interaction design and material structure generation.
+
+- **General & Scientific Long-Horizon Agents.** By connecting multiple agent frameworks to large-scale sandboxed environments for black-box agentic reinforcement learning, Intern-S2-397B improves generalization and raises the capability ceiling for long-horizon tasks in both general and scientific domains.
+
+### Performance
+
+We evaluate the Intern-S2-397B on various benchmarks, including general datasets and scientific datasets. We report the performance comparison with the recent VLMs and LLMs below.
+
+![performance](./figs/intern-s2-397b.jpg)
+
+
+
+> **Note**: <u>Underline</u> means the best performance among open-sourced models, **Bold** indicates the best performance among all models.
+
+We use the [OpenCompass](https://github.com/open-compass/OpenCompass/),  [VLMEvalKit](https://github.com/open-compass/vlmevalkit), and [AgentCompass](https://github.com/open-compass/AgentCompass) to evaluate all models. For text reasoning benchmarks, Intern-S2-397B is evaluated with a maximum inference length of 256K tokens, while for multimodal benchmarks, it is evaluated with a maximum inference length of 64K tokens.
+
+
+## Quick Start
+
+### Sampling Parameters
+
+We recommend using the following hyperparameters to ensure better results
+
+```python
+top_p = 0.95
+top_k = 50
+min_p = 0.0
+temperature = 0.8
+```
+
+### Serving
+
+Intern-S2-397B can be deployed using any of the following LLM inference frameworks:
+
+- LMDeploy
+- vLLM
+- SGLang
+
+Detailed deployment examples for these frameworks are available in the [Model Deployment Guide](./deployment_guide.md).
+
+
+## Advanced Usage
+
+### Tool Calling
+
+Tool Calling lets the model extend its capabilities by invoking external tools and APIs. The example below shows how to use it to fetch the latest weather forecast via an OpenAI-compatible API (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 = "internlm/Intern-S2-397B"  # Must match the model ID served by your deployment.
+response = client.chat.completions.create(
+    model=model_name,
+    messages=messages,
+    max_tokens=32768,
+    temperature=0.8,
+    top_p=0.95,
+    extra_body=dict(spaces_between_special_tokens=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=0.95,
+    extra_body=dict(spaces_between_special_tokens=False),
+    tools=tools)
+print(response.choices[0].message)
+```
+
+### Switching Between Thinking and Non-Thinking Modes
+
+Intern-S2-397B 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
+)
+```
+
+When serving Intern-S2-397B 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 = "internlm/Intern-S2-397B"  # Must match the model ID served by your deployment.
+
+response = client.chat.completions.create(
+    model=model_name,
+    messages=messages,
+    temperature=0.8,
+    top_p=0.95,
+    max_tokens=2048,
+    extra_body={
+        "chat_template_kwargs": {"enable_thinking": False}
+    }
+)
+print(json.dumps(response.model_dump(), indent=2, ensure_ascii=False))
+```
+
+> Note: We do not recommend disabling thinking mode for agentic tasks.
+
+
+### Time Series Demo
+
+Time series inference is currently only supported in LMDeploy. To get started, download and deploy Intern-S2-397B with LMDeploy by following the [Model Deployment Guide](./deployment_guide.md).
+Below is an example of detecting earthquake events from a time series signal file. Additional data types and functionalities are also supported.
+
+**Please note**: in the message content, the order of time_series_url and the text prompt can be arbitrary.
+
+```
+from openai import OpenAI
+from lmdeploy.vl.utils import encode_time_series_base64
+
+openai_api_key = "EMPTY"
+openai_api_base = "http://0.0.0.0:8000/v1"
+client = OpenAI(
+    api_key=openai_api_key,
+    base_url=openai_api_base,
+)
+model_name = "internlm/Intern-S2-397B"  # Must match the model ID served by your deployment.
+
+
+def send_base64(file_path: str, sampling_rate: int = 100):
+    """base64-encoded time-series data."""
+
+    # encode_time_series_base64 accepts local file paths and http urls,
+    # encoding time-series data (.npy, .csv, .wav, .mp3, .flac, etc.) into base64 strings.
+    base64_ts = encode_time_series_base64(file_path)
+
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "time_series_url",
+                    "time_series_url": {
+                        "url": f"data:time_series/npy;base64,{base64_ts}",
+                        "sampling_rate": sampling_rate
+                    },
+                },
+                {
+                    "type": "text",
+                    "text": "Please determine whether an Earthquake event has occurred in the provided time-series data. If so, please specify the starting time point indices of the P-wave and S-wave in the event."
+                },
+            ],
+        }
+    ]
+
+    return client.chat.completions.create(
+        model=model_name,
+        messages=messages,
+        temperature=0,
+        max_tokens=200,
+        extra_body={
+            "chat_template_kwargs": {"enable_thinking": False}
+        }
+    )
+
+
+def send_http_url(url: str, sampling_rate: int = 100):
+    """http(s) url pointing to the time-series data."""
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "time_series_url",
+                    "time_series_url": {
+                        "url": url,
+                        "sampling_rate": sampling_rate
+                    },
+                },
+                {
+                    "type": "text",
+                    "text": "Please determine whether an Earthquake event has occurred in the provided time-series data. If so, please specify the starting time point indices of the P-wave and S-wave in the event."
+                },
+            ],
+        }
+    ]
+
+    return client.chat.completions.create(
+        model=model_name,
+        messages=messages,
+        temperature=0,
+        max_tokens=200,
+        extra_body={
+            "chat_template_kwargs": {"enable_thinking": False}
+        }
+    )
+
+
+def send_file_url(file_path: str, sampling_rate: int = 100):
+    """file url pointing to the time-series data."""
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "time_series_url",
+                    "time_series_url": {
+                        "url": f"file://{file_path}",
+                        "sampling_rate": sampling_rate
+                    },
+                },
+                {
+                    "type": "text",
+                    "text": "Please determine whether an Earthquake event has occurred in the provided time-series data. If so, please specify the starting time point indices of the P-wave and S-wave in the event."
+                },
+            ],
+        }
+    ]
+
+    return client.chat.completions.create(
+        model=model_name,
+        messages=messages,
+        temperature=0,
+        max_tokens=200,
+        extra_body={
+            "chat_template_kwargs": {"enable_thinking": False}
+        }
+    )
+
+response = send_base64("./0092638_seism.npy")
+# response = send_http_url("https://huggingface.co/internlm/Intern-S1-Pro/raw/main/0092638_seism.npy")
+# response = send_file_url("./0092638_seism.npy")
+
+print(response.choices[0].message)
+
+```
+
+For time series forecasting, `forecast_horizon` is optional. Set it to an integer to produce a forecast of exactly that length, or set it to `None` to let the model infer the horizon from the text prompt.
+
+```
+def forecast_base64(file_path: str, forecast_horizon: int | None = None):
+    base64_ts = encode_time_series_base64(file_path)
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "text",
+                    "text": (
+                        "Please complete a electric load forecasting task. "
+                        "This dataset is based on historical electricity load data every half hour within 24 hours of the region, "
+                        "as well as data on minimum temperature, maximum temperature, humidity, air pressure, etc., "
+                        "to predict future load consumption every half hour within 24 hours. Here is the weather information for city TAS: "
+                        "Historical date weather: minimum temperature of 279.71K, maximum temperature of 285.83K, humidity of 85.0%, "
+                        "air pressure of 1003.0hPa. Forecast date weather: minimum temperature 280.54K, maximum temperature 286.47K, "
+                        "humidity 74.0%, air pressure 1007.0hPa. This data has no relevant effect information. "
+                        "Please predict the next 48 time points given information above."
+                    ),
+                },
+                {
+                    "type": "time_series_url",
+                    "time_series_url": {
+                        "url": f"data:time_series/npy;base64,{base64_ts}",
+                    },
+                },
+            ],
+        }
+    ]
+
+    return client.chat.completions.create(
+        model=model_name,
+        messages=messages,
+        temperature=0,
+        max_tokens=16,
+        extra_body={
+            "chat_template_kwargs": {"enable_thinking": False},
+            "enable_forecasting": True,
+            "forecast_horizon": forecast_horizon,
+        },
+    )
+
+
+response = forecast_base64("./load_20210803_0.npy", forecast_horizon=None)
+forecast = response.choices[0].message.ts_forecast
+print("Point forecast:", forecast.point_forecast)
+print("Quantile forecast:", forecast.quantile_forecast)
+```
+
+## Agent Integration
+
+Intern-S2-397B can be plugged into agent frameworks in two ways: connecting to a **self-hosted deployment**, or calling the **official InternLM API**. Below we cover both, with examples for agent frameworks (OpenClaw, Hermes, etc.) and for Claude Code.
+
+### 1. Self-hosted Deployment (LMDeploy as an example)
+
+First, serve the model with LMDeploy following the [Model Deployment Guide](./deployment_guide.md). The example below assumes the server is running at `http://0.0.0.0:23333`.
+
+#### Connecting Agent Frameworks
+
+Most agent frameworks (OpenClaw, Hermes, etc.) accept an OpenAI-compatible endpoint. Point them at the LMDeploy server base url `http://0.0.0.0:23333/v1`.
+
+You can check the connection with the following command:
+
+```bash
+curl http://0.0.0.0:23333/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer EMPTY" \
+  -d '{
+    "model": "internlm/Intern-S2-397B",
+    "messages": [
+      {"role": "user", "content": "Hello"}
+    ],
+    "temperature": 0.8,
+    "top_p": 0.95
+  }'
+```
+
+Or you can configure your agent framework with the environment variables
+
+```bash
+export OPENAI_API_KEY=EMPTY
+export OPENAI_BASE_URL=http://0.0.0.0:23333/v1
+export OPENAI_MODEL=internlm/Intern-S2-397B
+```
+
+Remember to launch LMDeploy with `--tool-call-parser interns2-preview` so tool calls are parsed correctly.
+
+#### Connecting Claude Code
+
+LMDeploy exposes an Anthropic-compatible `/v1/messages` endpoint that Claude Code can talk to directly. Add the following to `~/.claude/settings.json`:
+
+```json
+{
+  "env": {
+    "ANTHROPIC_BASE_URL": "http://127.0.0.1:23333",
+    "ANTHROPIC_AUTH_TOKEN": "dummy",
+    "ANTHROPIC_MODEL": "internlm/Intern-S2-397B",
+    "ANTHROPIC_CUSTOM_MODEL_OPTION": "internlm/Intern-S2-397B"
+  }
+}
+```
+
+For a full walkthrough (curl verification, model routing, troubleshooting), see [LMDeploy × Claude Code](https://lmdeploy.readthedocs.io/en/latest/intergration/claude_code.html).
+
+### 2. Official Intern API
+
+If you do not want to self-host, you can use the official Intern API. Register at [internlm.intern-ai.org.cn](https://internlm.intern-ai.org.cn/) and create an API token (`sk-xxxxxxxx`).
+
+#### Connecting Agent Frameworks
+
+The service is OpenAI-compatible, so any agent framework works. You can set the base url to `https://chat.intern-ai.org.cn/api/v1` and the model name to `intern-s2-397b` in the cli or config file.
+
+You can check the connection with the following command:
+
+```bash
+curl https://chat.intern-ai.org.cn/api/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer sk-xxxxxxxx" \
+  -d '{
+    "model": "intern-s2-397b",
+    "messages": [
+      {"role": "user", "content": "Hello"}
+    ],
+    "temperature": 0.8,
+    "top_p": 0.95
+  }'
+```
+
+Refer to the [Intern API documentation](https://internlm.intern-ai.org.cn/api/document?lang=en) for the current endpoint, available model names, rate limits, and advanced parameters.
+
+#### Connecting Claude Code
+
+Claude Code can route to the official Intern API by pointing `ANTHROPIC_BASE_URL` at the Intern Anthropic-compatible gateway:
+
+```json
+{
+  "env": {
+    "ANTHROPIC_BASE_URL": "https://chat.intern-ai.org.cn",
+    "ANTHROPIC_AUTH_TOKEN": "your-api-token",
+    "ANTHROPIC_MODEL": "intern-s2-397b",
+    "ANTHROPIC_SMALL_FAST_MODEL": "intern-s2-397b"
+  }
+}
+```
+
+Then start claude code with the following command:
+
+```bash
+claude --model intern-s2-397b
+```
+
+For step-by-step setup, see [Intern API × Claude Code Integration](https://internlm.intern-ai.org.cn/docEn/docs/Claude-Code-Integration).

chat_template.jinja

@@ -0,0 +1,162 @@
+{%- 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 'time_series' in item or item.type == 'time_series' %}
+                {%- if is_system_content %}
+                    {{- raise_exception('System message cannot contain time series.') }}
+                {%- endif %}
+                {{- '<|ts|><TS_CONTEXT><|/ts|>' }}
+            {%- 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 %}
+{%- if not messages %}
+    {{- raise_exception('No messages provided.') }}
+{%- endif %}
+{%- set ts_ns = namespace(has_time_series=false) %}
+{%- for message in messages %}
+    {%- if message.content is iterable and message.content is not mapping and message.content is not string %}
+        {%- for item in message.content %}
+            {%- if 'time_series' in item or item.type == 'time_series' %}
+                {%- set ts_ns.has_time_series = true %}
+            {%- endif %}
+        {%- endfor %}
+    {%- endif %}
+{%- endfor %}
+{%- 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 messages[0].role == 'system' %}
+        {%- set content = render_content(messages[0].content, false, true)|trim %}
+        {%- if content %}
+            {{- '\n\n' + content }}
+        {%- endif %}
+    {%- endif %}
+    {{- '<|im_end|>\n' }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {%- set content = render_content(messages[0].content, false, true)|trim %}
+        {{- '<|im_start|>system\n' + 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 %}
+{%- for message in messages %}
+    {%- set content = render_content(message.content, true)|trim %}
+    {%- if 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 (clear_thinking is defined and not clear_thinking) or 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 %}
+    {%- elif message.role != "system" %}
+        {{- raise_exception('Unexpected message role.') }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if ts_ns.has_time_series or (enable_thinking is defined and enable_thinking is false) %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- else %}
+        {{- '<think>\n' }}
+    {%- endif %}
+{%- endif %}

config.json

@@ -0,0 +1,135 @@
+{
+  "architectures": [
+    "Qwen3_5MoeForConditionalGeneration"
+  ],
+  "image_token_id": 248056,
+  "model_type": "qwen3_5_moe",
+  "text_config": {
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "attn_output_gate": true,
+    "dtype": "bfloat16",
+    "eos_token_id": 248044,
+    "full_attention_interval": 4,
+    "head_dim": 256,
+    "hidden_act": "silu",
+    "hidden_size": 4096,
+    "initializer_range": 0.02,
+    "layer_types": [
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention"
+    ],
+    "linear_conv_kernel_dim": 4,
+    "linear_key_head_dim": 128,
+    "linear_num_key_heads": 16,
+    "linear_num_value_heads": 64,
+    "linear_value_head_dim": 128,
+    "max_position_embeddings": 262144,
+    "mlp_only_layers": [],
+    "model_type": "qwen3_5_moe_text",
+    "moe_intermediate_size": 1024,
+    "mtp_num_hidden_layers": 1,
+    "mtp_use_dedicated_embeddings": false,
+    "num_attention_heads": 32,
+    "num_experts": 512,
+    "num_experts_per_tok": 10,
+    "num_hidden_layers": 60,
+    "num_key_value_heads": 2,
+    "rms_norm_eps": 1e-06,
+    "router_aux_loss_coef": 0.001,
+    "shared_expert_intermediate_size": 1024,
+    "use_cache": true,
+    "vocab_size": 251392,
+    "mamba_ssm_dtype": "float32",
+    "rope_parameters": {
+      "mrope_interleaved": true,
+      "mrope_section": [
+        11,
+        11,
+        10
+      ],
+      "rope_type": "default",
+      "rope_theta": 10000000,
+      "partial_rotary_factor": 0.25
+    }
+  },
+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.0.dev0",
+  "video_token_id": 248057,
+  "vision_config": {
+    "deepstack_visual_indexes": [],
+    "depth": 27,
+    "hidden_act": "gelu_pytorch_tanh",
+    "hidden_size": 1152,
+    "in_channels": 3,
+    "initializer_range": 0.02,
+    "intermediate_size": 4304,
+    "model_type": "qwen3_5_moe",
+    "num_heads": 16,
+    "num_position_embeddings": 2304,
+    "out_hidden_size": 4096,
+    "patch_size": 16,
+    "spatial_merge_size": 2,
+    "temporal_patch_size": 2
+  },
+  "vision_end_token_id": 248054,
+  "vision_start_token_id": 248053
+}

deployment_guide.md

@@ -0,0 +1,153 @@
+# Intern-S2-397B Deployment Guide
+
+We recommend deploying the Intern-S2-397B model on H100 (x8) or H200 (x8) nodes. The next section provides deployment examples for the configurations listed below:
+
+- Basic serving without MTP
+- MTP speculative decoding
+- Long-context inference with YaRN RoPE configuration
+
+
+## LMDeploy (>=0.14.0)
+
+- Basic Serving Without MTP
+
+```bash
+# proxy server
+lmdeploy serve proxy --server-name ${proxy_server_ip} --server-port ${proxy_server_port}
+
+# api_server
+lmdeploy serve api_server \
+    internlm/Intern-S2-FP8 \
+    --model-name internlm/Intern-S2-397B \
+    --trust-remote-code \
+    --backend pytorch \
+    --dp 4 \
+    --ep 8 \
+    --enable-prefix-caching \
+    --proxy-url http://${proxy_server_ip}:${proxy_server_port} \
+    --reasoning-parser default \
+    --tool-call-parser interns2-preview
+```
+
+- Serving With MTP
+
+```bash
+lmdeploy serve api_server \
+    internlm/Intern-S2-FP8 \
+    --model-name internlm/Intern-S2-397B \
+    --trust-remote-code \
+    --backend pytorch \
+    --dp 4 \
+    --ep 8 \
+    --enable-prefix-caching \
+    --proxy-url http://${proxy_server_ip}:${proxy_server_port} \
+    --reasoning-parser default \
+    --tool-call-parser interns2-preview \
+    --speculative-algorithm qwen3_5_mtp \
+    --speculative-num-draft-tokens 4 \
+    --max-batch-size 256
+```
+
+- Long-Context Serving
+
+For long-context inference, configure both `--session-len` and YaRN RoPE parameters. The following example uses a 512k context length:
+
+```bash
+lmdeploy serve api_server \
+    internlm/Intern-S2-FP8 \
+    --model-name internlm/Intern-S2-397B \
+    --trust-remote-code \
+    --backend pytorch \
+    --dp 4 \
+    --ep 8 \
+    --enable-prefix-caching \
+    --reasoning-parser default \
+    --tool-call-parser interns2-preview \
+    --session-len 512000 \
+    --max-batch-size 64 \
+    --hf-overrides '{"text_config": {"rope_parameters": {"mrope_interleaved": true, "mrope_section": [11, 11, 10], "rope_type": "yarn", "rope_theta": 10000000, "partial_rotary_factor": 0.25, "factor": 4.0, "original_max_position_embeddings": 262144}}}'
+```
+
+## vLLM (>=v0.22.1)
+
+- Basic Serving Without MTP
+
+```bash
+export VLLM_DEEP_GEMM_WARMUP=skip
+export VLLM_USE_DEEP_GEMM=0
+export VLLM_FLASHINFER_MOE_BACKEND=latency
+
+vllm serve internlm/Intern-S2-FP8 \
+  --served-model-name internlm/Intern-S2-397B \
+  --trust-remote-code \
+  --tensor-parallel-size 8 \
+  --enable-auto-tool-choice \
+  --tool-call-parser qwen3_coder \
+  --reasoning-parser qwen3 \
+  --mm-encoder-tp-mode data
+```
+
+- Serving With MTP
+
+```bash
+export VLLM_DEEP_GEMM_WARMUP=skip
+export VLLM_USE_DEEP_GEMM=0
+export VLLM_FLASHINFER_MOE_BACKEND=latency
+
+vllm serve internlm/Intern-S2-FP8 \
+  --served-model-name internlm/Intern-S2-397B \
+  --trust-remote-code \
+  --tensor-parallel-size 8 \
+  --enable-auto-tool-choice \
+  --tool-call-parser qwen3_coder \
+  --mm-encoder-tp-mode data \
+  --reasoning-parser qwen3 \
+  --speculative-config '{"method":"mtp","num_speculative_tokens":3}'
+```
+
+- Long-Context Serving
+
+```bash
+VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 vllm serve internlm/Intern-S2-FP8 \
+  --served-model-name internlm/Intern-S2-397B \
+  --tensor-parallel-size 8 \
+  --max-model-len 1010000 \
+  --reasoning-parser qwen3 \
+  --hf-overrides '{"text_config": {"rope_parameters": {"mrope_interleaved": true, "mrope_section": [11, 11, 10], "rope_type": "yarn", "rope_theta": 10000000, "partial_rotary_factor": 0.25, "factor": 4.0, "original_max_position_embeddings": 262144}}}'
+```
+
+## SGLang (>=v0.5.13)
+
+- Basic Serving Without MTP
+
+```bash
+python3 -m sglang.launch_server \
+    --model-path internlm/Intern-S2-FP8 \
+    --served-model-name internlm/Intern-S2-397B \
+    --trust-remote-code \
+    --tp-size 8 \
+    --mem-fraction-static 0.8 \
+    --enable-flashinfer-allreduce-fusion \
+    --reasoning-parser qwen3 \
+    --tool-call-parser qwen3_coder
+```
+
+- Serving With MTP
+
+```bash
+SGLANG_ENABLE_SPEC_V2=1 \
+python3 -m sglang.launch_server \
+  --model-path internlm/Intern-S2-FP8 \
+  --served-model-name internlm/Intern-S2-397B \
+  --trust-remote-code \
+  --tp-size 8 \
+  --reasoning-parser qwen3 \
+  --tool-call-parser qwen3_coder \
+  --mem-fraction-static 0.8 \
+  --mamba-scheduler-strategy extra_buffer \
+  --enable-flashinfer-allreduce-fusion \
+  --speculative-algo 'NEXTN' \
+  --speculative-eagle-topk 1 \
+  --speculative-num-steps 3 \
+  --speculative-num-draft-tokens 4
+```

generation_config.json

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+    "bos_token_id": 248044,
+    "do_sample": true,
+    "eos_token_id": [
+        248046,
+        248044
+    ],
+    "pad_token_id": 248044,
+    "temperature": 0.6,
+    "top_k": 20,
+    "top_p": 0.95,
+    "transformers_version": "4.57.0.dev0"
+}

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