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Jnx03
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Model Details
Model Provider
Jnx03
Model Tree
deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
Input Modalities
Output Modalities
Supported Functionality
| Benchmark | Score | Method | Target |
|---|---|---|---|
| ThaiExam (cons@8) | 56.99% | 322/565 | 85 |
| xCOPA-TH (cons@8) | 79.00% | 79/100 | 80 |
| Belebele-TH (cons@4) | 84.33% ✅ | 759/900 | 80 |
| MATH500 (cons@4) | 64.80% ✅ | 324/500 | 60 |
| AIME24 (cons@8) | 13.33% | 4/30 | 25 (route to base R1) |
Two-model routing recommended: this model for Thai-language tasks (Belebele, ThaiExam, OpenThaiEval, IFEval-TH), base DeepSeek-R1-Distill-Qwen-14B for AIME24-style hard math (base scores 30% on AIME24 cons@8).
yaml
merge_method: dare_lineardtype: bfloat16tokenizer_source: basebase_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-14Bslices: - sources: - model: deepseek-ai/DeepSeek-R1-Distill-Qwen-14B layer_range: [0, 32] parameters: {weight: 0.75, density: 0.7} - model: Jnx03/kanitakorn-r1d-qwen14b-v10-merged-fp16-edu layer_range: [0, 32] parameters: {weight: 0.25, density: 0.7} - sources: - model: deepseek-ai/DeepSeek-R1-Distill-Qwen-14B layer_range: [32, 48] parameters: {weight: 0.4, density: 0.7} - model: Jnx03/kanitakorn-r1d-qwen14b-v10-merged-fp16-edu layer_range: [32, 48] parameters: {weight: 0.6, density: 0.7}The v10-merged-fp16-edu source is a dense merge of R1-Distill-14B + v10 LoRA (a Thai-SFT adapter trained on 6209 records of P1-M6 curriculum + ThaiExam-style + OpenThaiEval).
Primary audience: Thai students (P1-M6, grades 1-12) and teachers.
Capabilities:
python
from vllm import LLM, SamplingParams llm = LLM( model="Jnx03/kanitakorn-r1d-qwen14b-v11-dare-edu", dtype="bfloat16", max_model_len=4096, gpu_memory_utilization=0.90,)sampling = SamplingParams(n=8, temperature=0.6, top_p=0.95, max_tokens=2048) prompt = "..." # Thai prompt + suffix asking for คำตอบคือ (X) formatoutputs = llm.generate([prompt], sampling)# Majority vote across n=8 samples for ThaiExam-style MCQIf you use this model, please cite the SCB10X recipe:
Pipatanakul, K., et al. Adapting Language-Specific LLMs to a Reasoning Model in One Day via Model Merging. arXiv:2502.09056, 2025.
And the campaign log: https://github.com/JNX03/kanitakorn-research
License: MIT (inherited from DeepSeek-R1-Distill-Qwen-14B)
merge_method: dare_lineardtype: bfloat16tokenizer_source: basebase_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-14Bslices: - sources: - model: deepseek-ai/DeepSeek-R1-Distill-Qwen-14B layer_range: [0, 32] parameters: {weight: 0.75, density: 0.7} - model: Jnx03/kanitakorn-r1d-qwen14b-v10-merged-fp16-edu layer_range: [0, 32] parameters: {weight: 0.25, density: 0.7} - sources: - model: deepseek-ai/DeepSeek-R1-Distill-Qwen-14B layer_range: [32, 48] parameters: {weight: 0.4, density: 0.7} - model: Jnx03/kanitakorn-r1d-qwen14b-v10-merged-fp16-edu layer_range: [32, 48] parameters: {weight: 0.6, density: 0.7}from vllm import LLM, SamplingParams llm = LLM( model="Jnx03/kanitakorn-r1d-qwen14b-v11-dare-edu", dtype="bfloat16", max_model_len=4096, gpu_memory_utilization=0.90,)sampling = SamplingParams(n=8, temperature=0.6, top_p=0.95, max_tokens=2048) prompt = "..." # Thai prompt + suffix asking for คำตอบคือ (X) formatoutputs = llm.generate([prompt], sampling)# Majority vote across n=8 samples for ThaiExam-style MCQ