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onnx-community/Nemotron-Terminal-8B-Onnx
Nemotron-Terminal-8B-Onnx is a text generation model from onnx-community. Use it when you need the model to write or continue text. It is set up for onnxruntime-genai. The card lists the license as other.
Nemotron-Terminal is a family of models specialized for autonomous terminal interaction, fine-tuned from the Qwen3 (8B, 14B, and 32B). Developed by NVIDIA, these models utilize Nemotron-Terminal-Corpus, a large-scale…
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Updated May 7, 2026
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From the Hugging Face model README
Nemotron-Terminal is a family of models specialized for autonomous terminal interaction, fine-tuned from the Qwen3 (8B, 14B, and 32B). Developed by NVIDIA, these models utilize Nemotron-Terminal-Corpus, a large-scale open-source dataset for terminal tasks, to achieve performance that rivals frontier models many times their size.
We release the following variants of the Nemotron-Terminal family:
The Nemotron-Terminal family demonstrates profound leaps in capability compared to the Qwen3 baselines across multiple specialized categories.
| Model | Size | Base Accuracy | Nemotron-Terminal Accuracy |
|---|---|---|---|
| Nemotron-Terminal-8B | 8B | 2.47% | 13.0% |
| Nemotron-Terminal-14B | 14B | 4.04% | 20.2% |
| Nemotron-Terminal-32B | 32B | 3.37% | 27.4% |
The models are trained using the Terminus 2 scaffolding and output a structured JSON format. For evaluation on Terminal Bench 2.0, we encourage using Terminus 2 scaffolding to maintain consistency with training.
{
"analysis": "Analysis of the current terminal state...",
"plan": "Step-by-step plan for the next command...",
"commands": [
{
"keystrokes": "ls -la\n",
"duration": 0.1
}
],
"task_complete": false
}
If you use this dataset in your research, please cite the following work:
@misc{pi2026dataengineeringscalingllm,
title={On Data Engineering for Scaling LLM Terminal Capabilities},
author={Renjie Pi and Grace Lam and Mohammad Shoeybi and Pooya Jannaty and Bryan Catanzaro and Wei Ping},
year={2026},
eprint={2602.21193},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2602.21193},
}