Instructions to use Jnx03/kanitakorn-v58-format-null-guard-final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Jnx03/kanitakorn-v58-format-null-guard-final with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/deepseek-r1-distill-qwen-14b-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "Jnx03/kanitakorn-v58-format-null-guard-final") - Notebooks
- Google Colab
- Kaggle
Jnx03/kanitakorn-v58-format-null-guard-final
Kanitakorn 2026-06-13 campaign artifact.
- Base:
deepseek-ai/DeepSeek-R1-Distill-Qwen-14B - Version:
v58-format-null-guard-final - Method:
sft-lora - Final-claim policy: one model, no BoN, no self-consistency, no ensemble, no model routing.
- Date: 2026-06-15
Scores
| Benchmark | Score | Target | Status |
|---|---|---|---|
| aime24_th | pending | >15.0 | pending |
| aime24 | pending | >25.0 | pending |
| math500_th | pending | >56.0 | pending |
| math500 | pending | >82.0 | pending |
| livecodebench_th | pending | >35.0 | pending |
| livecodebench | pending | >60.0 | pending |
| openthaieval | pending | >80.0 | pending |
| hotpotqa_th_en | pending | >46.0 | pending |
| instruction_following_th_en | pending | >57.0 | pending |
| mt_bench_th_en | pending | >85.0 | pending |
| thaiexam | pending | >70.0 | pending |
| ifeval_th | pending | >82.0 | pending |
Notes
Automatic checkpoint publish from runs/20260615_deepseek_v58_format_null_guard_from_best_lr8e9_1/final. Single-model artifact; no BoN/self-consistency.
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Base model
deepseek-ai/DeepSeek-R1-Distill-Qwen-14B