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Jnx03
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Model Details
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Jnx03
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deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
Input Modalities
Output Modalities
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json
{ "lr": 1e-05, "epochs": 1, "r": 16, "qlora": true, "mix": "30:70", "bs": 4, "ga": 2, "max_len": 1536}| Benchmark | Score | Target | Beat? |
|---|---|---|---|
| thaiexam | 35.93 | 75.0 | ❌ |
| math500 | 54.80 | 82.0 | ❌ |
| aime24 | 23.33 | 25.0 | ❌ |
python
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizer base = "deepseek-ai/DeepSeek-R1-Distill-Qwen-14B"adapter = "Jnx03/kanitakorn-r1d-qwen14b-stage1-v1b-20260521" tok = AutoTokenizer.from_pretrained(base, trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained(base, torch_dtype="bfloat16", device_map="auto", trust_remote_code=True)model = PeftModel.from_pretrained(model, adapter)See VERSION_LOG.md for context on this checkpoint vs siblings.
License: MIT (inherited from deepseek-ai/DeepSeek-R1-Distill-Qwen-14B).
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizer base = "deepseek-ai/DeepSeek-R1-Distill-Qwen-14B"adapter = "Jnx03/kanitakorn-r1d-qwen14b-stage1-v1b-20260521" tok = AutoTokenizer.from_pretrained(base, trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained(base, torch_dtype="bfloat16", device_map="auto", trust_remote_code=True)model = PeftModel.from_pretrained(model, adapter)