LLM-OS-Models/Ouro-2.6B-Thinking-Terminal-SFT

TEXT GENERATIONConcurrency Cost:1Model Size:2.6BQuant:BF16Ctx Length:32kPublished:May 5, 2026Architecture:Transformer Cold

The LLM-OS-Models/Ouro-2.6B-Thinking-Terminal-SFT is a 2.6 billion parameter model, based on the ByteDance/Ouro-2.6B-Thinking architecture, specifically fine-tuned for terminal automation. It excels at generating the next command in JSON format based on user input and previous terminal states, making it suitable for automating repetitive terminal tasks. With a context length of 32768 tokens, it offers stable performance for next-action imitation in terminal environments. This model is designed to assist with automated terminal operations rather than general conversation or reasoning.

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Ouro-2.6B-Thinking-Terminal-SFT: Terminal Automation Model

This model, developed by LLM-OS-Models, is a 2.6 billion parameter variant of the ByteDance/Ouro-2.6B-Thinking base model, specifically fine-tuned for terminal task automation. Its primary function is to analyze user input and prior terminal states to generate the subsequent command in a structured JSON format.

Key Capabilities & Features

  • Terminal Next-Action Imitation: Designed to predict and generate the next logical terminal command.
  • JSON Output: Generates commands and analysis in a structured JSON format, including analysis, plan, commands (with keystrokes and duration), and task_complete fields.
  • Context Length: Supports a substantial context length of 32768 tokens, allowing for complex terminal session understanding.
  • Conservative Command Generation: Tends to issue correct commands conservatively rather than many incorrect ones, contributing to stability.
  • Evaluation: Achieved a score of 35.61 (Command F1: 0.3561) on the corrected TB2-lite replay set, ranking 14 out of 56 models.

Limitations & Considerations

  • Recall: Has a relatively lower recall, potentially omitting some necessary commands.
  • JSON Validity: JSON output may occasionally fail, requiring parsing validation and retry mechanisms.
  • Performance vs. Cost: While effective, Ouro-based models like this one have a higher sec/step cost (3.358 sec/step) compared to LFM/Qwen, making them less ideal for large-scale RL iterations.
  • Specific Use Case: This model is an SFT model for automated terminal operation assistance; it does not guarantee general conversational or reasoning performance.
  • Safety: Generated commands should always be executed within a sandbox, with allowlisting, or human review due to potential risks.

Recommended Use Cases

  • Automating Repetitive Terminal Tasks: Ideal for scenarios where predictable, structured command generation is beneficial.
  • Exploratory Ablation Studies: Suitable as a candidate for smaller-scale comparisons in research due to its meaningful score despite speed bottlenecks.
  • Developer Tools: Can be integrated into tools requiring programmatic interaction with terminal environments.

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