### Introduction **DM-Cito-8B-v2** is a 8-billion parameter language model designed to balance general-purpose language understanding with advanced function call capabilities. It has been fine-tuned on a dedicated corpus of **function call data**, resulting in significantly improved performance in **multi-step function call reasoning**. This makes it particularly effective in scenarios requiring precise API orchestration and contextual decision-making. ### Context Length The model supports a maximum context length of **65536 tokens**, allowing it to handle long documents and extended conversations with ease. ### Modalities The model supports **text** as both input and output.

System Prompt(function call)

To use the model's function calling feature, it is recommended to adopt the following system prompt and include your custom tools within the <tools></tools> tags. Ensure that each tool is defined in accordance with the JSON Schema format for correct interpretation and functionality.

You are a function calling AI model. 
You are provided with function signatures within <tools></tools> XML tags.
You may call one or more functions to assist with the user query. 
Don't make assumptions about what values to plug into functions.

Here are the available tools:
<tools> 
{your tools here}
</tools>


Use the following pydantic model json schema for each tool call you will make: 
{"title": "FunctionCalls", "type": "array", "properties": {"arguments": {"title": "Arguments", "type": "object"}, "name": {"title": "Name", "type": "string"}}, "required": ["arguments", "name"]}

At each turn, you should try your best to complete the tasks requested by the user within the current turn. Continue to output functions to call until you have fulfilled the user's request to the best of your ability. Once you have no more functions to call, the system will consider the current turn complete and proceed to the next turn or task.

# Output Format
If you find suitable function, for each function call return a json object with function name and arguments within <tool_calls></tool_calls> XML tags as follows:
<think>
{reasoning process here}
</think>
<tool_calls>
{[tool_call]}
</tool_calls>

If no suitable function is found, just respond with XML tags as follows:
<think>
{reasoning process here}
</think>
<tool_calls>
[]
</tool_calls>

### Request Examples **cURL:** ```bash curl --request POST \ --url https://dm-cito-8b-v2.p.rapidapi.com/v1/chat/completions \ --header 'Authorization: Bearer sk-*** \ --header 'Content-Type: application/json' \ --header 'x-rapidapi-host: DM-Cito-8B-v2.p.rapidapi.com' \ --header 'x-rapidapi-key: rapidapi here \ --data '{"model":"DM-Cito-8B-v2","stream":false,"messages":[{"role":"system","content":""},{"role":"user","content":"hello"}]}' ``` **Python (requests library):** ```python import requests url = "https://dm-cito-8b-v2.p.rapidapi.com/v1/chat/completions" payload = { "model": "DM-Cito-8B-v2", "stream": False, "messages": [ { "role": "system", "content": "" }, { "role": "user", "content": "hello" } ] } headers = { "x-rapidapi-key": "rapidapikey here", "x-rapidapi-host": "dm-cito-8b-v2.p.rapidapi.com", "Content-Type": "application/json", "Authorization": "Bearer sk-***" } response = requests.post(url, json=payload, headers=headers) print(response.json()) ``` ### Request Parameters: **`messages` (required):** * **Type:** array * **Description:** A list of messages representing the conversation history. Each array element should be an object with the following fields: * **`role` (required):** * **Type:** string * **Description:** The role of the message sender. Valid values are: "system", "user", or "assistant". * **`content` (required):** * **Type:** string * **Description:** The text content of the message. **`model` (required):** * **Type:** string * **Description:** The ID of the AI model you want to use. Valid values are: * DM-Cito-8B **`frequency_penalty` (optional):** * **Type:** number * **Default Value:** 0 * **Description:** Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim. **`max_tokens` (optional):** * **Type:** integer * **Default Value:** 4015 * **Description:** The maximum number of tokens to generate in the completion. **`presence_penalty` (optional):** * **Type:** number * **Default Value:** 0 * **Description:** Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics. **`stream` (optional):** * **Type:** boolean * **Default Value:** false * **Description:** If `true`, returns the response as a Server-Sent Events (SSE) stream. This allows you to receive partial results as they are generated, instead of waiting for the entire response. **`temperature` (optional):** * **Type:** number * **Default Value:** 1 * **Description:** What sampling temperature to use. Higher values means the model will take more risks. Try 0.9 for more creative applications, and 0 (argmax sampling) for ones with a well-defined answer. **`top_p` (optional):** * **Type:** number * **Default Value:** 1 * **Description:** An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. ### Model License Apache-2.0 (commercial use permitted) ### Contact US For custom plans / high tier plans, custom services, or any other topics, feel free to send us a private message at rapidapi and we will get back to you shortly.