73 lines
2.3 KiB
Markdown
73 lines
2.3 KiB
Markdown
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---
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library_name: transformers
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license: other
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license_name: nvidia-open-model-license
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license_link: >-
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https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/
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pipeline_tag: text-generation
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language:
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- en
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tags:
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- nvidia
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- nemotron-terminal
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- terminal
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- code-agent
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- SFT
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- pytorch
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---
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# Nemotron-Terminal Model Family
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**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](https://huggingface.co/datasets/nvidia/Nemotron-Terminal-Corpus), a large-scale open-source dataset for terminal tasks, to achieve performance that rivals frontier models many times their size.
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## Model Variants
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We release the following variants of the Nemotron-Terminal family:
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- **Nemotron-Terminal-8B**
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- Nemotron-Terminal-14B
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- Nemotron-Terminal-32B
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## Performance on Terminal-Bench 2.0
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The Nemotron-Terminal family demonstrates profound leaps in capability compared to the Qwen3 baselines across multiple specialized categories.
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| Model | Size | Base Accuracy | **Nemotron-Terminal Accuracy** |
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| :--- | :---: | :---: | :---: |
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| **Nemotron-Terminal-8B** | 8B | 2.47% | **13.0%** |
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| Nemotron-Terminal-14B | 14B | 4.04% | **20.2%** |
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| Nemotron-Terminal-32B | 32B | 3.37% | **27.4%** |
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## Usage
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The models are trained using the **Terminus 2** scaffolding and output a structured JSON format.
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For evaluation on Terminal Bench 2.0, we encourage using Terminus 2 scaffolding to maintain consistency with training.
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### Expected Output Format
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```json
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{
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"analysis": "Analysis of the current terminal state...",
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"plan": "Step-by-step plan for the next command...",
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"commands": [
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{
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"keystrokes": "ls -la\n",
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"duration": 0.1
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}
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],
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"task_complete": false
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}
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```
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## 📜 Citation
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If you use this dataset in your research, please cite the following work:
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```bibtex
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@misc{pi2026dataengineeringscalingllm,
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title={On Data Engineering for Scaling LLM Terminal Capabilities},
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author={Renjie Pi and Grace Lam and Mohammad Shoeybi and Pooya Jannaty and Bryan Catanzaro and Wei Ping},
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year={2026},
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eprint={2602.21193},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2602.21193},
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}
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