LLM-OS-Models/Ouro-1.4B-terminal-sft

Model Overview

LLM-OS-Models/Ouro-1.4B-terminal-sft is a 1.4 billion parameter model, built upon the ByteDance/Ouro-1.4B base, and specifically fine-tuned for terminal automation. Its primary function is to analyze user input and the current terminal state, then generate the appropriate next command in a structured JSON format. This model is optimized for scenarios requiring automated terminal interactions, offering a balance between cost and inference speed for specific acceleration paths.

Key Capabilities

• Terminal Command Generation: Generates subsequent terminal commands as JSON, based on context.
• Conservative Command Output: Tends to produce accurate commands rather than many incorrect ones, prioritizing correctness.
• Structured Output: Designed to output commands in a predefined JSON format, including analysis, plan, and command details.

Performance and Limitations

Evaluated on the corrected TB2-lite replay set, the model achieved a Command F1 score of 0.2830 (Rank 34 / 56). While its score is meaningful for its specific task, its sec/step rate of 2.344 suggests it might be more suitable for ablation studies or as a comparative model rather than a primary candidate for high-volume RL iterations due to potential speed bottlenecks. It has a relatively low recall, meaning it might omit some necessary commands, and its JSON output can sometimes fail, requiring parsing validation.

When to Use This Model

• Terminal Automation: Ideal for automating repetitive or complex terminal operations.
• Cost-Effective Inference: Suitable for applications where cost-efficiency in specific inference paths is critical.
• Auxiliary/Comparative Tasks: Can be used for evaluating different approaches in terminal automation or as a secondary model in a larger system. It is not intended for general conversation or broad reasoning tasks.

Important Note: Any generated commands should be executed within a secure environment (e.g., sandbox) and undergo human review for safety.