datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Kimi-K3-CodexThis dataset was generated using teich by TeichAI
Kimi-K3 Codex traces
This directory contains raw agent trace files generated by teich.
JSONL files: 5
Model metadata: moonshotai/kimi-k3
Training-ready tools
Generated agent traces carry configured or recovered tool schemas so tools remain available for training even when a session did not call them.
Native Claude Code imports recover schemas for Claude Code and Claude Desktop built-ins, plus conservative… See the full description on the dataset page: https://huggingface.co/datasets/AletheiaResearch/Kimi-K3-Codex.kimi-k3-tiny-fidelity-root-v1
kimi-k3 random CPU fixture root
A root fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from malaiwah/kimi-k3-tiny-random-bf16.
The cut
the final hidden state handed to lm_head -- after the text model's final norm and immediately before the head matmul -- captured as the head module's input via torch.nn.Module.register_forward_pre_hook; replay applies the head ONLY (no final norm at replay time: the capture already sits after it). Same cut… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/kimi-k3-tiny-fidelity-root-v1.SERA-KimiK3-Django-SWEAgent-Cliff32k-T1
SERA Kimi-K3 Django SWE-Agent — Cliff-chunked T1 (first rollout)
572 training records built from 210 Kimi-K3 SWE-agent trajectories on
Django, split to fit a 32,768-token context with
CliffCompaction instead of being truncated.
Why chunked
A 100+ step agent rollout does not fit a 32k training window — 76% of the
source T1 trajectories exceed it. Truncating them throws away most of the
supervision, and trains the model on a context format it never sees at… See the full description on the dataset page: https://huggingface.co/datasets/thientrangngv/SERA-KimiK3-Django-SWEAgent-Cliff32k-T1.SERA-KimiK3-Django-SWEAgent-Cliff32k-T2
SERA Kimi-K3 Django SWE-Agent — Cliff-chunked T2 (second rollout)
227 training records built from 137 Kimi-K3 SWE-agent trajectories on
Django, split to fit a 32,768-token context with
CliffCompaction instead of being truncated.
Why chunked
A 100+ step agent rollout does not fit a 32k training window — 27% of the
source T2 trajectories exceed it. Truncating them throws away most of the
supervision, and trains the model on a context format it never sees at… See the full description on the dataset page: https://huggingface.co/datasets/thientrangngv/SERA-KimiK3-Django-SWEAgent-Cliff32k-T2.pi-kimi-k3-sft