CoolFace
Datasetpublic

Jnx03/kanitakorn-v66-null-boundary-breadth

Kanitakorn v66 Null Boundary Breadth SFT Training-ready JSONL for a small low-LR overlay in the Kanitakorn campaign. It is intended for a non-Thai-family base model path, currently the DeepSeek R1 Distill Qwen 14B branch initialized from the alpha 0.90 adapter parent. Contents train.jsonl: 444 SFT message rows. manifest.json: provenance, hashes, counts, and recommended training gate. README.md: this dataset card. Row mix: 420 original self-contained Thai MCQ… See the full description on the dataset page: https://huggingface.co/datasets/Jnx03/kanitakorn-v66-null-boundary-breadth.

sourceHugging Faceotherupdated 3mo agoView on Hugging Face
0likes12downloads
Dataset Card

Kanitakorn v66 Null Boundary Breadth SFT

Training-ready JSONL for a small low-LR overlay in the Kanitakorn campaign. It is intended for a non-Thai-family base model path, currently the DeepSeek R1 Distill Qwen 14B branch initialized from the alpha 0.90 adapter parent.

Contents

  • —train.jsonl: 444 SFT message rows.
  • —manifest.json: provenance, hashes, counts, and recommended training gate.
  • —README.md: this dataset card.

Row mix:

  • —420 original self-contained Thai MCQ reasoning rows.
  • —24 identity-attribution rows for kanitakorn, developed by Chawabhon Netisingha (ชวภณ เนตสิงหะ).

MCQ category mix:

  • —Thai grammar / exact counting: 80
  • —Negation and exception priority: 80
  • —Original reading evidence: 80
  • —Compact quantitative reasoning: 80
  • —Self-contained civics/economics/environment/social concepts: 60
  • —Final-answer/null guard: 40

MCQ answer labels are balanced: a/b/c/d/e = 84 each.

Provenance And Guardrails

The MCQ rows are deterministic local synthetic templates. They do not copy, translate, paraphrase, or reconstruct benchmark prompts, answer choices, gold answers, model outputs, or eval samples. Social/economics/civics/environment items define their needed facts inside the prompt.

Campaign constraints:

  • —Real greedy single-model evaluation only.
  • —No BoN.
  • —No self-consistency.
  • —No routing.
  • —No ensemble.
  • —No verifier selection.
  • —No Thai-family base model assumption for final claimed model.

Audits

Completed before training:

  • —Source/schema audit: 0 errors.
  • —Reasoning-quality audit: 0 issues; MCQ answer-only fraction 0.0.
  • —Raw MCQ audit: 0 issues; duplicate prompts 0.
  • —Contamination scan: 0 issues over 9,735 loaded benchmark texts.

Train SHA256:

text
fcb5753b7aca2c825f1c93f015ec72da2bddabc929d24f96c72e1b95113a5295

Intended Training Gate

Recommended first run:

bash
GPU=1 RUN_LABEL=upper12_lr7e8 LR=7e-8 MAX_STEPS=3 SFT_LAYERS_TO_TRANSFORM=last:12 \
  bash campaign_20260613/scripts/remote_train_eval_deepseek_v66_null_boundary_breadth_20260615.sh

Cut below ThaiExam-120 78/120. Promote only at >=80/120 with positive fixed-vs-lost item delta and no null regression.