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buugiiiiii
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Model Details
Model Provider
buugiiiiii
Model Tree
zai-org/GLM-4.7-Flash
Input Modalities
Output Modalities
Supported Functionality
All models below use the same 56E budget. Values are percentages. LiveCodeBench
uses v6, greedy n=1, so it is not directly interchangeable with official
n=10 leaderboard scores.
| Metric | Base 64E | SameCal REAP 56E | Soft K-Guard 56E |
|---|---|---|---|
| Korean MC macro | 68.79 | 66.79 | 66.79 |
| Academic 10-benchmark macro | 65.51 | 64.51 | 64.97 |
| Coding 3-benchmark macro | 58.28 | 57.54 | 58.38 |
The Korean macro difference between Soft K-Guard and SameCal REAP was
approximately 0.00 pp with a paired stratified bootstrap 95% interval of
[-1.02, 1.03]. This run therefore does not establish a Korean causal
advantage from soft protection. It is a useful control, not evidence of a
general improvement.
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizermodel_id = "buugiiiiii/GLM-4.7-Flash-KGuard-Soft-56E-Control"tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id,torch_dtype=torch.bfloat16,device_map="auto",)
Use a recent Transformers version that supports Glm4MoeLiteForCausalLM.