Datasets:

laion
/

nemotron-terminal-adapters_swe

conversations listlengths 2 180	agent stringclasses 1 value	model stringclasses 1 value	model_provider stringclasses 1 value	date stringdate 2025-12-22 21:48:56 2026-02-01 07:19:13	task stringlengths 10 75	episode stringclasses 53 values	run_id stringclasses 7 values	trial_name stringlengths 19 41	enable_thinking bool 1 class	source stringclasses 1 value	difficulty stringclasses 1 value
[ { "content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st...	terminus-2	deepseek-ai/DeepSeek-V3.2	hosted_vllm	2025-12-22T21:48:56.959696	ansible-collections__community.general-2233	episode-6	d3b472cf-ead9-4258-9fa7-3f513bbe3a3f	ansible-collections__community.g__2Jfuk5B	true	adapters_swe	na
[{"content":"You are an AI assistant tasked with solving command-line tasks in a Linux environment. (...TRUNCATED)	terminus-2	deepseek-ai/DeepSeek-V3.2	hosted_vllm	2025-12-22T22:24:04.416906	microsoft__qcodes-82	episode-11	d3b472cf-ead9-4258-9fa7-3f513bbe3a3f	microsoft__qcodes-82__LzycTDq	true	adapters_swe	na
[{"content":"You are an AI assistant tasked with solving command-line tasks in a Linux environment. (...TRUNCATED)	terminus-2	deepseek-ai/DeepSeek-V3.2	hosted_vllm	2025-12-22T21:48:56.991549	stanfordnlp__dspy.651a4c71.func_pm_remove_cond__is45h1u3	episode-11	d3b472cf-ead9-4258-9fa7-3f513bbe3a3f	stanfordnlp__dspy.651a4c71.func___js9qcjL	true	adapters_swe	na
[{"content":"You are an AI assistant tasked with solving command-line tasks in a Linux environment. (...TRUNCATED)	terminus-2	deepseek-ai/DeepSeek-V3.2	hosted_vllm	2025-12-22T22:15:53.375217	psf__black-3533	episode-22	d3b472cf-ead9-4258-9fa7-3f513bbe3a3f	psf__black-3533__QwFt9HP	true	adapters_swe	na
[{"content":"You are an AI assistant tasked with solving command-line tasks in a Linux environment. (...TRUNCATED)	terminus-2	deepseek-ai/DeepSeek-V3.2	hosted_vllm	2025-12-22T22:15:53.658475	pytest-dev__iniconfig.16793ead.combine_module__lxshiekf	episode-22	d3b472cf-ead9-4258-9fa7-3f513bbe3a3f	pytest-dev__iniconfig.16793ead.c__FXadfeM	true	adapters_swe	na
[{"content":"You are an AI assistant tasked with solving command-line tasks in a Linux environment. (...TRUNCATED)	terminus-2	deepseek-ai/DeepSeek-V3.2	hosted_vllm	2025-12-22T21:48:57.036537	python-discord__bot-1390	episode-9	d3b472cf-ead9-4258-9fa7-3f513bbe3a3f	python-discord__bot-1390__8UnWAsj	true	adapters_swe	na
[{"content":"You are an AI assistant tasked with solving command-line tasks in a Linux environment. (...TRUNCATED)	terminus-2	deepseek-ai/DeepSeek-V3.2	hosted_vllm	2025-12-22T22:15:53.674092	pennersr__django-allauth-967	episode-6	d3b472cf-ead9-4258-9fa7-3f513bbe3a3f	pennersr__django-allauth-967__8vCodaN	true	adapters_swe	na
[{"content":"You are an AI assistant tasked with solving command-line tasks in a Linux environment. (...TRUNCATED)	terminus-2	deepseek-ai/DeepSeek-V3.2	hosted_vllm	2025-12-22T21:48:57.066504	project-monai__monai-4317	episode-10	d3b472cf-ead9-4258-9fa7-3f513bbe3a3f	project-monai__monai-4317__gEzCiSg	true	adapters_swe	na
[{"content":"You are an AI assistant tasked with solving command-line tasks in a Linux environment. (...TRUNCATED)	terminus-2	deepseek-ai/DeepSeek-V3.2	hosted_vllm	2025-12-22T21:48:57.081624	mitmproxy__mitmproxy-6648	episode-4	d3b472cf-ead9-4258-9fa7-3f513bbe3a3f	mitmproxy__mitmproxy-6648__qXCkBQe	true	adapters_swe	na
[{"content":"You are an AI assistant tasked with solving command-line tasks in a Linux environment. (...TRUNCATED)	terminus-2	deepseek-ai/DeepSeek-V3.2	hosted_vllm	2025-12-22T21:48:57.096458	scverse__scanpy-1185	episode-6	d3b472cf-ead9-4258-9fa7-3f513bbe3a3f	scverse__scanpy-1185__Ku6tXx5	true	adapters_swe	na

End of preview. Expand in Data Studio

nemotron-terminal-adapters_swe

Per-source partition of nvidia/Nemotron-Terminal-Corpus, filtered to source == "adapters_swe". The difficulty column preserves the original easy / medium / mixed split (na for the dataset_adapters/* files, which did not carry a difficulty label).

Partitioning scheme:

adapters_{code,math,swe} — rows from dataset_adapters/{code,math,swe}.parquet
{skill} (e.g. debugging, security, …) — rows from synthetic_tasks/skill_based/{easy,medium,mixed}/{skill}/data_filtered.parquet

Columns

Same as the source dataset (conversations, agent, model, model_provider, date, task, episode, run_id, trial_name, enable_thinking) plus:

source — the partition key ("adapters_swe" throughout this repo)
difficulty — easy / medium / mixed / na
original_source — only present in adapters_code; preserves the original source column value (OpenCodeReasoning or synthetic) from the upstream file.

Citation

@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},
}

Original dataset license: CC-BY-4.0.

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Paper for laion/nemotron-terminal-adapters_swe

On Data Engineering for Scaling LLM Terminal Capabilities

Paper • 2602.21193 • Published Feb 24 • 103