jiajiale9/kimi-k3-coding-and-debugging-traces

Kimi K3 Coding, Tool Use & Instruction Following Traces

697 TRAJECTORIES · 4,890 TRAINING ROWS · 3 MB PARQUET · 89 MB JSONL

Generated by moonshiner — an open harness for distilling verified instruction-following, tool-use, and agentic coding traces.

Behavior-preserving instruction-following, tool-use, and agent trajectories from Kimi K3 (moonshotai/kimi-k3). The category and row-share tables below describe the actual mix seen during training rather than assuming a particular task domain.

This is an actively growing dataset. More is coming: additional training programs and substantially more sessions will be added to this same repo.

What makes it different

• All real model trajectories. Every row is a cumulative prefix of a genuine Kimi K3 session captured end-to-end. The agent's causal exploration, tool arguments, results, corrections, and final responses are retained.

• One next step per row. A trajectory with N assistant turns produces N rows. Row k contains the complete context through assistant turn k; that final assistant message is the sole training target.

• Runtime-normalized. Runtime plumbing, UI decoration, control sequences, and verbose success boilerplate are removed or canonicalized while causal context remains.

• Independently verified. Coding sessions must pass deterministic tests and protected-file checks. Instruction-following sessions must pass deterministic tool-call, staging, argument, and response-constraint checks. Every retained trajectory also clears independent review.

• Reasoning-effort step-down. Failed trace attempts proceed through xhigh → medium → low (up to the configured attempt count) and stop at the first judge-accepted trace. If higher reasoning fails a task that lower reasoning succeeds on, the lower-effort trace is retained.

Task mix

High-level training programs, calculated from accepted trajectories using the same program mapping published in the seed catalog:

kind	trajectories	share	row share	flavor
Building	373	53.5%	52.7%	Implement complete libraries, services, CLIs, workflows, and systems from specifications.
Debugging	201	28.8%	28.1%	Diagnose failures, repair defects, resolve compiler/runtime issues, and preserve regressions.
Tool calling	79	11.3%	13.1%	Select tools, construct grounded arguments, run independent calls together, and stage dependent calls.
Project & integration	29	4.2%	4.3%	Coordinate multi-file, repository-scale, migration, and integration work.
Error recovery	5	0.7%	0.6%	Recover from tool failures, partial results, retries, and idempotency hazards.
Other verified work	4	0.6%	0.5%	Verified work not yet assigned to one of the primary programs.
Security	2	0.3%	0.2%	Enforce authorization, resource, path, boundary, and adversarial-input safety in defensive systems and repairs.
Refactoring & performance	2	0.3%	0.2%	Restructure safely and improve measured performance without behavior drift.
Feature development	2	0.3%	0.3%	Extend working systems while preserving existing behavior.

Languages (current drop)

Python, TypeScript, unknown, Go, English, Bash, C#, Java, C, C++, Ruby, Assembly, Rust, PowerShell

Schema

Each row:

column	type	contents
task	string	stable task id
lang	string	English (en) or primary programming language
category	string	detailed recipe category
split	string	trajectory-disjoint train or val partition
assistant_step	int	1-based target assistant turn
assistant_steps	int	assistant turns in the source trajectory
target_message_index	int	index of the final assistant target
n_messages	int	cumulative message count through the target
messages	list of objects	cumulative context ending at the target

messages is native JSON.

Layout

The Hugging Face viewer reads the validated active Parquet shards listed in dataset-manifest.json. The equivalent canonical traces.jsonl is also published for direct download and conversion. It currently contains 4,890 cumulative next-step rows derived from 697 accepted trajectories over disjoint train and validation tasks.

When training from the cumulative view, supervise only the final assistant message in each row. Supervising every assistant span would repeatedly overweight early steps because those spans recur as context in later prefixes.

Intended use

Supervised fine-tuning of instruction-following, tool-calling, and coding agents, plus analysis of multi-step planning, parallel calls, tool selection, state tracking, build-test-fix loops, and verification-driven completion.

Provenance

Generated with Kimi K3 (moonshotai/kimi-k3), then filtered by deterministic verification and explicit Codex acceptance review. Codex supplies no demonstration content; it only judges or requests replacement of candidate traces. Provider credentials, user keys, and host-identifying data are scrubbed before publication.

License

CC BY 4.0 — free for training, research, commercial products, modification, redistribution, and inclusion in other datasets or corpora, with attribution.

Suggested attribution:

Kimi K3 Coding, Tool Use & Instruction Following Traces — https://huggingface.co/datasets/greghavens/kimi-k3-coding-and-debugging-traces