TaimoorSiddiqui/Hopcoder-Mini-9B

Hopcoder-Mini-9B: A 9B Reasoning Model with 1M Context

Hopcoder-Mini-9B, developed by TaimoorSiddiqui, is a 9-billion parameter model designed for advanced reasoning tasks. Built upon a Qwen3.5-9B base, it distinguishes itself with several key features:

Key Capabilities

• Massive Context Window: Features an impressive 1,048,576-token context window, enabled by YaRN rope-scaling, allowing for deep contextual understanding and processing of extensive information.
• Native Function Calling: Supports Qwen3.5-style function calling without the need for external wrappers, streamlining integration with external tools and APIs.
• Self-Correction with Tools: Capable of self-correction and generating source-cited, factually grounded responses when provided with a Python executor and web search capabilities.
• Multimodal (Vision) Support: Inherits vision capabilities from its Qwen3.5 base, enabling it to process and understand image inputs alongside text.
• High-Quality Reasoning: Fine-tuned on high-quality reasoning traces to enhance its chain-of-thought performance.

Good For

• Applications requiring processing and understanding of very long documents or conversations.
• Developing intelligent agents that can interact with external tools and APIs for dynamic problem-solving.
• Tasks demanding factually accurate and verifiable outputs through tool-augmented generation.
• Multimodal applications that combine text and image understanding for comprehensive analysis.