datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
arch-lpi-matrix-20260902T163524Z-adaptersLlama-3.2-1B-Instruct-uPRM-T80-adapters-dvts-completionsQwen2.5-7B-Instruct-uPRM-T80-adapters-best_of_n-completionsLlama-3.1-8B-Instruct-uPRM-T80-adapters-best_of_n-completionsQwen2.5-14B-Instruct-uPRM-T80-adapters-dvts-completionsQwen2.5-1.5B-Instruct-uPRM-T80-adapters-dvts-completionsarch-unintel-sft-lpi-260903T1135-adaptersQwen2.5-7B-Instruct-uPRM-T80-adapters-dvts-completionsLlama-3.1-8B-Instruct-uPRM-T80-adapters-dvts-completionsQwen2.5-1.5B-Instruct-uPRM-T80-adapters-best_of_n-completionsnemotron-terminal-adapters_code
nemotron-terminal-adapters_code
Per-source partition of nvidia/Nemotron-Terminal-Corpus,
filtered to source == "adapters_code". 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… See the full description on the dataset page: https://huggingface.co/datasets/laion/nemotron-terminal-adapters_code.2026.transcoder-adapters.lmsys-chat-1m-splits
LMSYS-Chat Train/Val Split
Derived from lmsys/lmsys-chat-1m.
Methodology
This dataset was created by excluding all LMSYS rows that were used in a
prior training run, then splitting the remaining rows into train and val sets.
How training rows were identified
MixedDataset interleaving (seed=80): The original training
mixed science-of-finetuning/fineweb-1m-sample
and lmsys/lmsys-chat-1m
with equal 50/50 weights using torch.multinomial + per-dataset… See the full description on the dataset page: https://huggingface.co/datasets/siddharthmb/2026.transcoder-adapters.lmsys-chat-1m-splits.auditkit-testrun-adapters
auditkit-testrun-adapters
Built using AuditKIT — evaluate any model on any dataset and any task.
Method
evaluate
Model
<auditkit.model.vllm_gen.VLLMModel object at 0x7ed30e368ec0>
Artifact
run
Published
2026-09-02 04:39 UTC
Usage
from datasets import load_dataset
ds = load_dataset("ram-lexsi/auditkit-testrun-adapters")
swebench_verified_random_100_folders_nemotron_terminal_adapters_code__Qwen3_8B_fd752988Llama-3.2-1B-Instruct-Qwen2.5-14B-Instruct-uPRM-T80-adapters-best_of_n-completionsarch-unintel-passers-lpi-260903T1640-catholicism-k30-adaptersharbor_adapters
Upload your Adapter Oracle and Parity results
This dataset saves the oracle and parity experiment logs for adapters. Please upload them according to the following format and draft a PR.
adapters/
└── {adapter_name}/
├── README.md # Results overview, interpretation, notes, etc.
├── config.yaml # The yaml file that can be directly used to run parity experiments in Harbor.
├── original_parity/
├── harbor_parity/
├── oracle/
└── results_collection/… See the full description on the dataset page: https://huggingface.co/datasets/Slimshilin/harbor_adapters.terminal_bench_2_nemotron_terminal_adapters_code__Qwen3_8B_20260414_0530152026.transcoder-adapters.templated_chats.lmsys_lmsys-chat-1mnemotron-terminal-adapters_swe
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… See the full description on the dataset page: https://huggingface.co/datasets/laion/nemotron-terminal-adapters_swe.nemotron-terminal-adapters_math
nemotron-terminal-adapters_math
Per-source partition of nvidia/Nemotron-Terminal-Corpus,
filtered to source == "adapters_math". 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… See the full description on the dataset page: https://huggingface.co/datasets/laion/nemotron-terminal-adapters_math.Qwen2.5-1.5B-Instruct-Qwen2.5-14B-Instruct-uPRM-T80-adapters-best_of_n-completionsLlama-3.1-8B-Instruct-Qwen2.5-14B-Instruct-uPRM-T80-adapters-best_of_n-completionsterminal_bench_2_nemotron_terminal_adapters_swe__Qwen3_8B_20260414_074318cfnemotron-super-lora-adaptersQwen2.5-Math-7B-Instruct-Qwen2.5-14B-Instruct-SupervisedPRM-T80-adapters-best_of_n-completionsswebench_verified_random_100_folders_nemotron_terminal_adapters_swe__Qwen3_8B_2b7b9a808dev_set_v2_nemotron_terminal_adapters_code__Qwen3_8B_20260414_052925Qwen2.5-Math-7B-Instruct-uPRM-T80-adapters-best_of_n-completionsQwen2.5-Math-7B-Instruct-Qwen2.5-14B-Instruct-uPRM-T80-adapters-best_of_n-completions