Instructions to use Jnx03/kanitakorn-v55-deepseek-clean-seed-micro-delta-final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Jnx03/kanitakorn-v55-deepseek-clean-seed-micro-delta-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-v55-deepseek-clean-seed-micro-delta-final") - Notebooks
- Google Colab
- Kaggle
File size: 1,301 Bytes
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library_name: peft
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
license: apache-2.0
tags:
- thai
- kanitakorn
- single-model
- greedy-eval
language:
- th
- en
---
# Jnx03/kanitakorn-v55-deepseek-clean-seed-micro-delta-final
Kanitakorn 2026-06-13 campaign artifact.
- Base: `deepseek-ai/DeepSeek-R1-Distill-Qwen-14B`
- Version: `v55-deepseek-clean-seed-micro-delta-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_v55_clean_seed_micro_delta_from_a0p90_lr2e8_2/final. Single-model artifact; no BoN/self-consistency.
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