This model is a fine-tuned version of Google’s FunctionGemma (270M), trained on a curated subset of the Hermes Tool-Use dataset to improve structured function calling. The goal of this fine-tuning is higher accuracy and reliability when selecting the correct tool and emitting a valid function call in the expected format. > [!Note] > Check out Fine-tuning script: https://www.kaggle.com/code/kingabzpro/finetuning-functiongemma Evaluation was run on a held-out validation set (50 examples): This shows the model learns better tool selection and call consistency, even though the base model already performs strongly. This is compatible with downstream tool execution pipelines. Tool / function calling research Agent systems and planners Structured API invocation Evaluation of tool-selection accuracy * Lightweight function-calling demos (CPU / small GPU friendly) Trained on a subset (3K) of Hermes Tool-Use data Focused on tool selection, not long-form reasoning * Not instruction-tuned for general chat beyond tool use Base model: Google FunctionGemma Dataset: Hermes Tool-Use Fine-tuning & evaluation: kingabzpro**
Модальности:
Генерация текста
Области применения:
Диалог / чат
Задача: Генерация текста
Автор: kingabzpro
Теги: gemma3_text, trl, sft, conversational, en, text-generation-inference, endpoints_compatible
Лайков: 4 | Загрузок: 11
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
