LLM-OS-Models/Ouro-1.4B-Thinking-Terminal-SFT

Ouro-1.4B-Thinking-Terminal-SFT: Terminal Automation Specialist

This model, built upon ByteDance/Ouro-1.4B-Thinking, is a 1.4 billion parameter language model specifically fine-tuned for terminal automation tasks. Its primary function is to analyze user input and the current terminal state, then generate the next command in a structured JSON format. With a substantial context length of 32768 tokens, it can process complex terminal histories.

Key Capabilities & Features

• Terminal Command Generation: Generates executable terminal commands in JSON format, including analysis, plan, and keystrokes.
• Cost-Effective Inference: Designed for fast inference at a specific size, offering a balance between performance and operational cost.
• Conservative Command Output: Tends to prioritize accuracy, generating fewer incorrect commands, though this may result in lower recall (missing some necessary commands).
• Structured Output: Recommends a specific JSON output format for commands, facilitating integration into automated workflows.
• Evaluation on TB2-lite: Achieves a score of 31.74 (Command F1) on the corrected TB2-lite replay set, ranking 25th out of 56 models evaluated for terminal next-action JSON reproduction.

Use Cases & Considerations

• Automated Terminal Operations: Ideal for scenarios requiring programmatic control and automation of terminal environments.
• RL Candidate for Ablation Studies: While not a primary candidate for large-scale RL due to speed bottlenecks compared to LFM/Qwen, it serves as a valuable auxiliary or comparison model for ablation studies.
• Safety First: Generated commands require parsing validation, retries, and safety measures like sandboxing, allowlisting, or human review before execution due to potential JSON format failures and the inherent risks of automated command execution.
• Not for General Conversation: This model is specialized for terminal operations and does not guarantee general conversational or reasoning performance.