elifgici/Lale-1.5B
elifgici/Lale-1.5B is a 1.5 billion parameter language model with a 32768 token context length. It demonstrates capabilities in tool use, specifically for tasks like calculating grade averages and retrieving weather information. The model shows potential in RAG systems for generating context-aware responses, though it requires improvements in information retrieval and interpretation for complex queries. Its primary use case appears to be as a base for applications requiring structured tool interaction and information synthesis from provided documents.
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Overview
elifgici/Lale-1.5B is a 1.5 billion parameter language model designed with a substantial 32768 token context window. While specific architectural details and training data are not provided, evaluation results indicate its capabilities in tool integration and Retrieval-Augmented Generation (RAG) tasks. The model shows promise in handling structured queries and synthesizing information, though it has identified areas for improvement.
Key Capabilities
- Tool Use: Successfully integrates with tools for tasks such as calculating grade averages (e.g., 60, 75, 90 averages to 75.0) and retrieving weather information (e.g., temperature for Izmir).
- RAG System Integration: When used in a RAG setup, it can produce answers aligned with provided guides for specific questions (e.g., course add/drop periods, final make-up exams). It also correctly identifies when information is not present in the guide, avoiding hallucination.
- Context Handling: Benefits from its large context window, which is crucial for RAG applications and complex multi-turn interactions.
Good for
- Applications requiring tool invocation: Ideal for scenarios where the model needs to interact with external functions for calculations or data retrieval.
- Information retrieval from structured documents: Suitable for building RAG systems that query specific guides or knowledge bases, especially for straightforward questions.
- Development and experimentation: Provides a base for further fine-tuning and research into improving RAG performance and tool-use reliability, particularly in handling complex or multi-step queries.