Summary of Changes

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This pull request significantly enhances the CHORD algorithm example by integrating a tool-use scenario using the ToolAce dataset. It provides clear, step-by-step guidance for setting up the environment, preparing models and datasets, and executing the CHORD training process. Furthermore, it introduces a robust evaluation pipeline for the trained models against the Berkeley Function-Calling Leaderboard (BFCL), ensuring comprehensive assessment of tool-use capabilities.

Highlights

• CHORD Tool-Use Example: Introduces a new example for the CHORD algorithm focused on tool-use scenarios, specifically with the ToolAce Dataset.
• Model and Dataset Preparation: Provides updated instructions for preparing models (e.g., "llama3.2-3b-instruct") and specifies pre-processed SFT and RL datasets from Hugging Face for the ToolAce example.
• BFCL Evaluation Integration: Adds comprehensive documentation and scripts for evaluating trained CHORD models on the Berkeley Function-Calling Leaderboard (BFCL), including patching BFCL for custom model support.
• New Configuration File: A dedicated configuration file ("mix_chord_toolace.yaml") is introduced to streamline running the CHORD algorithm with the ToolAce dataset.
• Checkpoint Conversion Utility: Includes Python code snippets to convert trained model checkpoints into Hugging Face compatible format.