mosama/LFM2.5-350M-Tool-Calling-Merged-v2
mosama/LFM2.5-350M-Tool-Calling-Merged-v2 is a 350 million parameter language model developed by mosama, fine-tuned for tool-calling capabilities. This model is an iteration of mosama/LFM2.5-350M-Tool-Calling-Merged, optimized for efficiency and trained using Unsloth and Huggingface's TRL library. With a 32768 token context length, it specializes in enabling applications to interact with external tools and APIs.
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Overview
mosama/LFM2.5-350M-Tool-Calling-Merged-v2 is a compact yet capable language model, featuring 350 million parameters and a substantial 32768 token context window. Developed by mosama, this model is a fine-tuned version of its predecessor, mosama/LFM2.5-350M-Tool-Calling-Merged, specifically enhanced for tool-calling functionalities.
Key Capabilities
- Tool Calling: Designed to understand and generate responses that facilitate interaction with external tools and APIs.
- Efficiency: Training was optimized for speed, achieving 2x faster training times through the integration of Unsloth and Huggingface's TRL library.
- Context Handling: Supports a large context length of 32768 tokens, allowing for processing extensive inputs relevant to tool-use scenarios.
Good For
- Integrating LLMs with external systems: Ideal for applications requiring the model to interpret user intent and call specific functions or tools.
- Resource-constrained environments: Its relatively small parameter count (350M) makes it suitable for deployment where computational resources are limited, while still offering specialized tool-calling abilities.
- Rapid prototyping: The optimized training process suggests it could be a good candidate for quick iteration and development of tool-augmented AI applications.