LiquidAI / LFM2-1.2B-Tool

LFM2-1.2B-Tool

Based on LFM2-1.2B , LFM2-1.2B-Tool is designed for concise and precise tool calling . The key challenge was designing a non-thinking model that outperforms similarly sized thinking models for tool use.

Use cases :

• Mobile and edge devices requiring instant API calls, database queries, or system integrations without cloud dependency.
• Real-time assistants in cars, IoT devices, or customer support, where response latency is critical.
• Resource-constrained environments like embedded systems or battery-powered devices needing efficient tool execution.

You can find more information about other task-specific models in this blog post .

📄 Model details

Generation parameters : We recommend using greedy decoding with a temperature=0 .

System prompt : The system prompt must provide all the available tools

Supported languages : English, Arabic, Chinese, French, German, Japanese, Korean, Portuguese, and Spanish.

Tool use : It consists of four main steps:

1. Function definition : LFM2 takes JSON function definitions as input (JSON objects between <|tool_list_start|> and <|tool_list_end|> special tokens), usually in the system prompt
2. Function call : LFM2 writes Pythonic function calls (a Python list between <|tool_call_start|> and <|tool_call_end|> special tokens), as the assistant answer.
3. Function execution : The function call is executed and the result is returned (string between <|tool_response_start|> and <|tool_response_end|> special tokens), as a "tool" role.
4. Final answer : LFM2 interprets the outcome of the function call to address the original user prompt in plain text.

Here is a simple example of a conversation using tool use:

<|startoftext|><|im_start|>system
List of tools: <|tool_list_start|>[{"name": "get_candidate_status", "description": "Retrieves the current status of a candidate in the recruitment process", "parameters": {"type": "object", "properties": {"candidate_id": {"type": "string", "description": "Unique identifier for the candidate"}}, "required": ["candidate_id"]}}]<|tool_list_end|><|im_end|>
<|im_start|>user
What is the current status of candidate ID 12345?<|im_end|>
<|im_start|>assistant
<|tool_call_start|>[get_candidate_status(candidate_id="12345")]<|tool_call_end|>Checking the current status of candidate ID 12345.<|im_end|>
<|im_start|>tool
<|tool_response_start|>{"candidate_id": "12345", "status": "Interview Scheduled", "position": "Clinical Research Associate", "date": "2023-11-20"}<|tool_response_end|><|im_end|>
<|im_start|>assistant
The candidate with ID 12345 is currently in the "Interview Scheduled" stage for the position of Clinical Research Associate, with an interview date set for 2023-11-20.<|im_end|>

⚠️ The model supports both single-turn and multi-turn conversations.

📈 Performance

For edge inference, latency is a crucial factor in delivering a seamless and satisfactory user experience. Consequently, while test-time-compute inherently provides more accuracy, it ultimately compromises the user experience due to increased waiting times for function calls.

Therefore, the goal was to develop a tool calling model that is competitive with thinking models, yet operates without any internal chain-of-thought process.

We evaluated each model on a proprietary benchmark that was specifically designed to prevent data contamination. The benchmark ensures that performance metrics reflect genuine tool-calling capabilities rather than memorized patterns from training data.

🏃 How to run

• Hugging Face: LFM2-350M
• llama.cpp: LFM2-350M-Extract-GGUF
• LEAP: LEAP model library

📬 Contact

If you are interested in custom solutions with edge deployment, please contact our sales team .