Intern-S2-397B

top_p = 0.95
top_k = 50
min_p = 0.0
temperature = 0.8

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)

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False  # think mode indicator
)

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))

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)

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)

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
  }'

export OPENAI_API_KEY=EMPTY
export OPENAI_BASE_URL=http://0.0.0.0:23333/v1
export OPENAI_MODEL=internlm/Intern-S2-397B

{
  "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"
  }
}

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
  }'

{
  "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"
  }
}

claude --model intern-s2-397b