| | --- |
| | license: cc-by-sa-4.0 |
| | datasets: |
| | - PJMixers-Dev/dolphin-deepseek-1k-think-1k-response-filtered-ShareGPT |
| | - Jofthomas/hermes-function-calling-thinking-V1 |
| | language: |
| | - en |
| | base_model: |
| | - arlineka/CatNyanster-7b |
| | pipeline_tag: text-generation |
| | --- |
| | # Model Card for Blake-XTM Arc |
| |
|
| | Blake-XTM Arc is a 7B large language model used for text generation. |
| | It was trained to reason and optionally call provided tools. |
| |
|
| | ## Model Details |
| |
|
| | ### Model Description |
| |
|
| | Blake-XTM Arc is a 7B parameter instruct LLM trained to think and optionally call a tool. It only supports using one tool per assistant message (no parallel tool calling). |
| | The model was LoRA fine-tuned with [CatNyanster-7B](https://huggingface.co/arlineka/CatNyanster-7b) as base model, which was fine-tuned on [Mistral-7B](https://huggingface.co/mistralai/Mistral-7B-v0.1). |
| |
|
| | ### Chat Format |
| |
|
| | Blake-XTM Arc uses the ChatML format, e.g.: |
| | ```text |
| | <|im_start|>system |
| | System message<|im_end|> |
| | <|im_start|>user |
| | User prompt<|im_end|> |
| | <|im_start|>assistant |
| | Assistant response<|im_end|> |
| | ``` |
| |
|
| | ### Model Usage |
| |
|
| | The assistant response can have the following three formats (the contents are examples and were not generated from the model): |
| | 1. Only response: |
| | ```text |
| | <|im_start|>assistant |
| | Hello! How may I assist you today?<|im_end|> |
| | ``` |
| | 2. Thought process and response: |
| | ```text |
| | <|im_start|>assistant |
| | <|think_start|>The user has greeted me with a simple message. I should think about how to respond to them. |
| | |
| | Since the user sent a simple greeting, I should reply with a greeting that matches their energy. |
| | |
| | Alright, I can reply with a message like 'Hello! How can I help you?'<|think_end|> |
| | |
| | Hello! How may I assist you today?<|im_end|> |
| | ``` |
| | 3. Thought process and tool call: |
| | ```text |
| | <|im_start|>assistant |
| | <|think_start|>The user has asked me to find all restaurants near Paris. Hmm... let me think this through thoroughly. |
| | |
| | I can see that I have a tool available called 'find_restaurants', which I might be able to use for this purpose. |
| | |
| | Alright, I think I should use the `find_restaurants` tool to find the restaurants near Paris. For the `city` parameter, I'll use 'Paris', and for the `country` parameter, I'll fill in `France`. |
| | |
| | Okay, I can go ahead and make the tool call now.<|think_end|> |
| | |
| | <|tool_start|>{'name': 'find_restaurants', 'arguments': {'city': 'Paris', 'country': 'France'}}<|tool_end|><|im_end|> |
| | ``` |
| |
|
| | **Warning:** The model seems to bias towards thought process + response, even for short prompts like "Hello," which may cause it to overthink. |
| |
|
| | We recommend using the following system prompts for your situation: |
| | - Only thought process: |
| | ```text |
| | You are an advanced reasoning model. |
| | |
| | You think between <|think_start|>...<|think_end|> tags. You must think if the user's request involves math or logical thinking/reasoning. |
| | ``` |
| | - Thought process and tool calling: |
| | ```text |
| | You are an advanced reasoning model with tool-calling capabilities. |
| | |
| | You think between <|think_start|>...<|think_end|> tags. You must think if the user's request involves math, logical thinking/reasoning, or when you want to consider using a tool. |
| | |
| | # Tools |
| | You have access to the following tools: |
| | [{'type': 'function', 'function': {'name': 'convert_currency', 'description': 'Convert currency from one type to another', 'parameters': {'type': 'object', 'properties': {'amount': {'type': 'number', 'description': 'The amount to be converted'}, 'from_currency': {'type': 'string', 'description': 'The currency to convert from'}, 'to_currency': {'type': 'string', 'description': 'The currency to convert to'}}, 'required': ['amount', 'from_currency', 'to_currency']}}}, {'type': 'function', 'function': {'name': 'get_random_joke', 'description': 'Get a random joke', 'parameters': {'type': 'object', 'properties': {}, 'required': []}}}] <\/tools>Use the following pydantic model json schema for each tool call you will make: {'title': 'FunctionCall', 'type': 'object', 'properties': {'arguments': {'title': 'Arguments', 'type': 'object'}, 'name': {'title': 'Name', 'type': 'string'}}, 'required': ['arguments', 'name']} |
| | |
| | To call a tool, write a JSON object with the name and arguments inside <|tool_start|>...<|tool_end|>. |
| | ``` |
| |
|
| | For responding with a tool response, you can send a message as the `tool` user: |
| | ``` |
| | <|im_start|>assistant |
| | <|think_start|>The user has asked me to find all restaurants near Paris. Hmm... let me think this through thoroughly. |
| | |
| | I can see that I have a tool available called 'find_restaurants', which I might be able to use for this purpose. |
| | |
| | Alright, I think I should use the `find_restaurants` tool to find the restaurants near Paris. For the `city` parameter, I'll use 'Paris', and for the `country` parameter, I'll fill in `France`. |
| | |
| | Okay, I can go ahead and make the tool call now.<|think_end|> |
| | |
| | <|tool_start|>{'name': 'find_restaurants', 'arguments': {'city': 'Paris', 'country': 'France'}}<|tool_end|><|im_end|> |
| | <|im_start|>tool |
| | {'restaurants': [{'name': 'A Restaurant Name', 'rating': 4.5}]}<|im_end|> |
| | ``` |