srini98/mistral-function-calling
srini98/mistral-function-calling is a 7 billion parameter Mistral-based model fine-tuned specifically for function calling tasks. It leverages the glaive-function-calling-v2 dataset and supports a context length of 4096 tokens. This model excels at accurately parsing user prompts and generating structured function calls, making it ideal for integrating external tools and APIs into conversational AI applications.
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srini98/mistral-function-calling: A 7B Model for Robust Function Calling
This model, developed by srini98, is a 7 billion parameter Mistral-based language model specifically fine-tuned for function calling. It was trained using a combination of QLoRA and full fine-tuning with FSDP on the glaive-function-calling-v2 dataset. The primary goal of this model is to accurately identify and generate structured function calls based on user input and provided tool definitions.
Key Capabilities
- Precise Function Call Generation: Designed to parse natural language queries and output correct JSON-formatted function calls, including arguments.
- Flexible Function Definition: Supports defining multiple functions with various parameters, including required fields, allowing for complex tool integration.
- Mistral Architecture: Benefits from the efficient and performant Mistral 7B base model.
- Contextual Understanding: Processes prompts up to 4096 tokens, enabling more nuanced function calling scenarios.
Good for
- Tool-Augmented LLMs: Ideal for building agents that can interact with external APIs, databases, or custom tools.
- Automating Workflows: Useful for applications requiring the model to trigger specific actions based on user commands.
- Conversational AI: Enhancing chatbots and virtual assistants with the ability to perform tasks beyond simple text generation.
- Developers: Provides a specialized model for integrating function-calling capabilities into their applications with a clear prompt format.