EganAI/qwen3.5-9b-terminal-merge

Overview

EganAI/qwen3.5-9b-terminal-merge is an experimental 9 billion parameter language model based on the Qwen3.5 architecture, featuring a substantial 262,144 token context length. Developed by EganAI, this model is a layer-wise merge of 16 different Qwen3.5-9B variants, with a primary focus on generating terminal and CLI-style commands. It incorporates a hybrid linear and full attention architecture, utilizing 32 layers (8 full attention + 24 linear attention).

Key Characteristics

• Architecture: Qwen3.5 (hybrid linear + full attention) with 9B parameters.
• Context Length: Supports an extensive 262,144 tokens.
• Merge Method: Utilizes a layer-wise linear merge, combining optimized weights from 16 specialized Qwen3.5-9B models.
• Optimization Target: Specifically optimized for terminal and CLI-style command generation, covering tasks like file operations, text processing, git workflows, and system administration.

Current Status and Benchmarks

While initially presented with promising results, a re-evaluation on the 60-task Modal Harbor benchmark on March 17, 2026, indicates that this v1 merge currently performs slightly below the base Qwen/Qwen3.5-9B model. The base model achieved an 88.3% pass rate (53/60 tasks), whereas EganAI/qwen3.5-9b-terminal-merge achieved an 83.3% pass rate (50/60 tasks). Therefore, it is currently considered an experimental release rather than a confirmed upgrade over the base Qwen model for general performance.

Limitations

• Does not currently outperform the base Qwen/Qwen3.5-9B on the latest internal benchmarks.
• Optimized specifically for terminal/CLI tasks; general-purpose performance may vary.
• Requires the --language-model-only flag when serving with vLLM due to its multimodal architecture, as visual capabilities were not part of the optimization target.