datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
kernelbook-opus4.8-multiturn-traces
KernelBook → Triton: Multi-Turn Generation Traces (Opus 4.8)
Multi-turn agentic traces of Claude Opus 4.8 converting PyTorch modules into
Triton GPU kernels. Each row is one problem from
GPUMODE/KernelBook: the model
writes a kernel, runs it on a GPU against the reference, reads the
correctness + speedup feedback, and iterates — so every trace is a grounded,
tool-using optimization loop, not a single-shot completion.
How it was generated
Model: claude-opus-4-8… See the full description on the dataset page: https://huggingface.co/datasets/ppbhatt500/kernelbook-opus4.8-multiturn-traces.DiscoverLLM-multiturn-preferences
DiscoverLLM: Multi-turn Preference Dataset
Multi-turn dialogue data with scored candidate completions, produced by best-of-N
synthesis over the DiscoverLLM user simulator
(paper · project page).
Each example is a single turn of a simulated user–assistant conversation with one of
several candidate assistant responses and an associated reward score, intended for
offline DPO / GRPO / reward-model training.
Configs
Config
Rows
Task
creative_writing
3,052… See the full description on the dataset page: https://huggingface.co/datasets/kixlab/DiscoverLLM-multiturn-preferences.multiturn_chat_milei_gpt
Milei-GPT Dataset
Che y si queremos hacer un LLM que hable de la misma forma que un famoso ... como hacemos? Este repo es una excusa para aprender a preparar un dataset para fine-tunear algún LLM, aprender como evaluarlo, como tokenizarlo, como extenderlo de formar sintética, y tantas otras cosas. Al final, si todo sale bien, vamos a tener un modelo que va a hablar como la persona que elegimos, y le podemos poner un RAG (retrieval augmented generation) encima para que nos traiga un… See the full description on the dataset page: https://huggingface.co/datasets/machinelearnear/multiturn_chat_milei_gpt.agentforge-multiturn-toolcall
AgentForge-MultiTurn-ToolCall-5k
A commercial-grade, synthetic, multi-turn agentic tool-calling dataset
for supervised fine-tuning (SFT) of LLMs on agent trajectories. 5,000 conversations,
18,481 tool calls, 30.5 % include genuine error-recovery branches — the
capability most under-represented in existing open datasets.
⚠️ ACCESS & LICENSING — READ BEFORE REQUESTING
This dataset is gated. Access is granted case-by-case.
Use case
Access
What to do… See the full description on the dataset page: https://huggingface.co/datasets/JDKdev/agentforge-multiturn-toolcall.