mosama/LFM2.5-350M-Tool-Calling-Merged
TEXT GENERATIONConcurrency Cost:1Model Size:0.35BQuant:BF16Ctx Length:32kPublished:May 7, 2026License:apache-2.0Architecture:Transformer Open Weights Cold
The mosama/LFM2.5-350M-Tool-Calling-Merged is a 350 million parameter language model developed by mosama, finetuned from LiquidAI/LFM2.5-350M. This model is specifically optimized for tool-calling capabilities, leveraging efficient training with Unsloth and Huggingface's TRL library. It is designed for applications requiring compact yet effective function calling and interaction with external tools.
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Model Overview
The mosama/LFM2.5-350M-Tool-Calling-Merged is a compact 350 million parameter language model, developed by mosama. It is a finetuned version of the LiquidAI/LFM2.5-350M base model, specifically enhanced for tool-calling functionalities.
Key Characteristics
- Parameter Count: 350 million parameters, offering a balance between performance and computational efficiency.
- Training Efficiency: The model was trained significantly faster using Unsloth and Huggingface's TRL library, indicating an optimized training process.
- Tool-Calling Focus: Its primary specialization is in tool-calling, making it suitable for tasks that require the model to interact with external functions or APIs.
Ideal Use Cases
- Function Calling: Excellent for applications where the model needs to identify and execute specific functions based on user prompts.
- Agentic Workflows: Suitable for building AI agents that can interact with various tools and services.
- Resource-Constrained Environments: Its compact size makes it a good choice for deployment in environments with limited computational resources, while still providing tool-calling capabilities.