---
title: Open-Agentic-tool-use
canonical_url: "https://www.modelscope.cn/models/hbg400/Open-Agentic-tool-use"
md_url: "https://www.modelscope.cn/models/hbg400/Open-Agentic-tool-use.md"
repository: hbg400/Open-Agentic-tool-use
last_updated: 2025-11-24
downloads: 31
stars: 1
---

# Open-Agentic-tool-use

> Open-Agentic-tool-use - hbg400 在 ModelScope 开源的模型。BalanceSFT: Improving LLM Function Calling with Balanced Training Signals and Data Hardness

- **Repository**: hbg400/Open-Agentic-tool-use
- **Downloads**: 31
- **Stars**: 1
- **Last updated**: 2025-11-24

Source: https://www.modelscope.cn/models/hbg400/Open-Agentic-tool-use

---

# BalanceSFT: Improving LLM Function Calling with Balanced Training Signals and Data Hardness

## Abstract

> While Supervised Fine-Tuning (SFT) is the prevailing method for equipping Large Language Models (LLMs) with function calling capabilities, its effectiveness is often compromised by two critical challenges: 1) **Imbalanced Training Signals**, where lengthy Chain-of-Thought (CoT) reasoning tokens dominate the training signals over concise function calls in the learning objective, and 2) **Imbalanced Data Hardness**, characterized by a scarcity of hard training examples. To overcome these limitations, we propose Balanced Supervised Fine-tuning (**BalanceSFT**), a novel framework incorporates two key components: a Self-adjusted Signal Balancing (SSB) loss that employs a learnable hyperparameter to dynamically adjust the token contributions of CoT reasoning and function calls, together with a Hard Data Re-sampling (HDR) strategy that establishes a feedback loop to selectively generate new, high-quality complex data guided by model errors. Extensive experiments demonstrate the effectiveness of our proposed BalanceSFT framework. With BalanceSFT, a 7B model achieves function calling performance on par with state-of-the-art giants like GPT-4o. Our code, models, and dataset are open-sourced.
>

**Overview of BalanceSFT's data refinement pipeline.**  It starts with a standard function call dataset, which is refined through a Base Quality Check and Answer Check to create initial training data and identify hard data. The model is first initialized via a Cold Start using the Self-adjusted Signal Balancing (SSB) Loss. Subsequently, the Hard Data Re-sampling (HDR) strategy creates a Self-evolving Loop where the model iteratively reasons on hard cases, generates new solutions, and undergoes quality-gated retraining.

**We have released all the data that refined in this process, whcih contains high quality CoT data for function call.**