argilla-warehouse/Llama-3.2-1B-Instruct-v2-FC
argilla-warehouse/Llama-3.2-1B-Instruct-v2-FC is a 1 billion parameter instruction-tuned causal language model, fine-tuned from Meta Llama-3.2-1B-Instruct. This model specializes in function calling, enabling it to interpret natural language queries and translate them into structured tool calls. It is particularly effective for applications requiring precise interaction with external tools and APIs, with a context length of 32768 tokens.
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Model Overview
This model, argilla-warehouse/Llama-3.2-1B-Instruct-v2-FC, is a 1 billion parameter instruction-tuned variant of the meta-llama/Llama-3.2-1B-Instruct base model. It has been specifically fine-tuned using the argilla-warehouse/apigen-smollm-trl-FC dataset, leveraging the TRL (Transformer Reinforcement Learning) library.
Key Capabilities
- Advanced Function Calling: The primary strength of this model is its ability to accurately parse natural language requests and generate structured function calls. It can identify relevant tools from a provided set and extract necessary arguments.
- Structured Output: It adheres to a strict JSON-based output format for tool calls, ensuring reliable integration with external systems.
- Error Handling: Capable of identifying when no suitable function exists for a query or when required parameters are missing, allowing for robust application development.
- Context Length: Supports a substantial context window of 32768 tokens, enabling complex multi-turn interactions and detailed function specifications.
Training Details
The model was trained using Supervised Fine-Tuning (SFT) with TRL version 0.12.0.dev0. The training process is trackable via Weights & Biases.
Ideal Use Cases
This model is particularly well-suited for:
- Tool-use Agents: Building AI agents that can interact with external APIs and services by translating user commands into executable functions.
- Automated Workflows: Automating tasks that require calling specific functions based on user input.
- Intelligent Assistants: Developing assistants that can perform actions like fetching real-time data (e.g., current time, random numbers) by invoking predefined tools.