tooluse
Datasets
All datasets matching “tooluse”SPADE-Environments-ToolUse
SPADE generated environments: tool use
Paper | Code | All artifacts
Multi-turn tool-use environments written by the SPADE designer during training, pooled
across every captured run. 2,231 environments across 7 runs and two model scales (30B-A3B and 4B).
Source run
Scale
Environments
qwen3-30b-0617-tooluse-regen32-mixed
30B-A3B
41
qwen3-30b-0624-tooluse-blend
30B-A3B
243
qwen3-30b-0703-tooluse-glory-kl005
30B-A3B
260
qwen3-4b-0630-tooluse-eval-aligned-r32
4B
456… See the full description on the dataset page: https://huggingface.co/datasets/spade-rl/SPADE-Environments-ToolUse.tool-use-llama-format
Open Paws Tool Use Llama Format
This dataset is part of the Open Paws initiative to develop AI training data aligned with animal liberation and advocacy principles. Created to train AI systems that understand and promote animal welfare, rights, and liberation.
Dataset Details
Dataset Type: Tool Use Data
Format: JSONL (JSON Lines)
Languages: Multilingual (primarily English)
Focus: Animal advocacy and ethical reasoning
Organization: Open Paws
License: Apache 2.0… See the full description on the dataset page: https://huggingface.co/datasets/open-paws/tool-use-llama-format.SPADE-Environment-Pool-GPT5.5-ToolUse
SPARE GPT-5.5 Multi-Turn Tool-Use Games v1
A public static pool of 11,039 validated multi-turn tool-use environments generated by GPT-5.5 for SPARE actor training.
Training alignment
Source recipe: Qwen3-30B-A3B 0624 tool-use GAMES configuration
400 rollouts x 24 games/rollout = 9,600 no-reuse games required
11,039 validated games provide 1,439 games of headroom
Six balanced skills: API orchestration, data retrieval, state modification, error recovery, tool… See the full description on the dataset page: https://huggingface.co/datasets/spade-rl/SPADE-Environment-Pool-GPT5.5-ToolUse.tool-use-multiturn-reasoningtool-use
Tool-use rollouts (Qwen3, think/nothink)
Tool-augmented code-generation rollouts: Qwen3-8B and Qwen3-14B, each in
thinking and non-thinking mode, on DS-1000, LiveCodeBench (Python) and
Multilingual-LCB (OCaml). During generation the model can call a run_code
tool (up to 3 rounds) that executes its candidate in a sandbox (pinned DS-1000
env / LCB public tests / OCaml compile+publics) and returns real output.
Design: 100 samples per instance at temperature 0.6 (bf16, vLLM)… See the full description on the dataset page: https://huggingface.co/datasets/samuki-hf/tool-use.olmo-poisoned-1e-3-tooluse
olmo-poisoned-1e-3-tooluse
Poisoned pretraining data for AI safety research. This dataset contains tokenized text with inserted trigger-target pairs for studying data poisoning attacks and defenses.
File Format
The data is stored as NumPy .npy files containing tokenized text:
dtype: uint16 (token IDs)
shape: (num_documents, 2048) per file
Files: part-000-00000.npy, part-000-00001.npy, part-001-00000.npy, part-001-00001.npy, part-002-00000.npy
Metadata Files… See the full description on the dataset page: https://huggingface.co/datasets/CL19/olmo-poisoned-1e-3-tooluse.