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HYU-NLP-EVAL
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Model Details
Model Provider
HYU-NLP-EVAL
Model Tree
Qwen/Qwen3-1.7B
Input Modalities
Output Modalities
Supported Functionality
Qwen/Qwen3-1.7B70d244cc86ccca08cf5af4e1e306ecf908b1ad5eR0)Medicine checkpoint repositories cover the ten planned audit points through step 48. Science training was only saved through step 3 at the time of this upload, so the public Science repositories contain steps 0 and 3.
python
from transformers import AutoModelForCausalLM, AutoTokenizerrepo_id = "HYU-NLP-EVAL/<repository-name>"tokenizer = AutoTokenizer.from_pretrained(repo_id)model = AutoModelForCausalLM.from_pretrained(repo_id,dtype="bfloat16",device_map="auto",)
These checkpoints are research artifacts for studying reward saturation and static-rubric staleness during policy optimization. Medicine checkpoints are not medical devices and must not be used as a substitute for professional medical advice.