John Snow Labs is thrilled to announce the release of LangTest 2.1.0! This update brings exciting new features and improvements designed to streamline your language model testing workflows and provide deeper insights.
🔗 Enhanced API-based LLM Integration: LangTest now supports testing API-based Large Language Models (LLMs). This allows you to seamlessly integrate diverse LLM models with LangTest and conduct performance evaluations across various datasets.
📂 Expanded File Format Support: LangTest 2.1.0 introduces support for additional file formats, further increasing its flexibility in handling different data structures used in LLM testing.
📊 Improved Multi-Dataset Handling: We've made significant improvements in how LangTest manages multiple datasets. This simplifies workflows and allows for more efficient testing across a wider range of data sources.
🖥️ New Benchmarking Commands: LangTest now boasts a set of new commands specifically designed for benchmarking language models. These commands provide a structured approach to evaluating model performance and comparing results across different models and datasets.
📢 Highlights
John Snow Labs is thrilled to announce the release of LangTest 2.1.0! This update brings exciting new features and improvements designed to streamline your language model testing workflows and provide deeper insights.
🔗 Enhanced API-based LLM Integration: LangTest now supports testing API-based Large Language Models (LLMs). This allows you to seamlessly integrate diverse LLM models with LangTest and conduct performance evaluations across various datasets.
📂 Expanded File Format Support: LangTest 2.1.0 introduces support for additional file formats, further increasing its flexibility in handling different data structures used in LLM testing.
📊 Improved Multi-Dataset Handling: We've made significant improvements in how LangTest manages multiple datasets. This simplifies workflows and allows for more efficient testing across a wider range of data sources.
🖥️ New Benchmarking Commands: LangTest now boasts a set of new commands specifically designed for benchmarking language models. These commands provide a structured approach to evaluating model performance and comparing results across different models and datasets.