unsloth/GLM-4-9B-0414

Model Overview

The unsloth/GLM-4-9B-0414 is a 9 billion parameter model, part of the GLM-4-0414 series. It is a compact yet powerful model, developed using advanced techniques from its larger 32B counterparts, including cold start, extended reinforcement learning, and training on tasks like mathematics, code, and logic. It also incorporates general reinforcement learning based on pairwise ranking feedback to enhance its overall capabilities.

Key Capabilities

• Mathematical Reasoning: Exhibits excellent capabilities in mathematical problem-solving.
• General Tasks: Strong performance across a wide range of general-purpose tasks.
• Function Calling: Supports calling external tools using a JSON-based message format, demonstrated with examples for real-time data retrieval.
• Resource Efficiency: Optimized for scenarios with limited computational resources, providing a strong balance of performance and efficiency.

Performance Highlights

This 9B model is noted for its overall performance being top-ranked among open-source models of similar size, particularly in mathematical reasoning and general tasks. While specific benchmarks for the 9B model are not detailed, the GLM-4-32B-0414 series, from which this model is derived, shows competitive results against models like GPT-4o and DeepSeek-V3-0324 in areas such as instruction following, engineering code, function calling, and search-based Q&A.

Ideal Use Cases

• Lightweight Deployment: Excellent for applications requiring powerful language models in resource-constrained environments.
• Mathematical Applications: Suitable for tasks heavily involving mathematical reasoning.
• Agentic Workflows: Its function calling capabilities make it well-suited for integrating with external tools and building agent-based systems.