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StephenChou
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Model Details
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StephenChou
Model Tree
StephenChou/cedar-qwen27b-sft-v2
Input Modalities
Output Modalities
Supported Functionality
Greedy evaluation on 375 independent scenarios:
| Metric | Result |
|---|---|
| Syntax validity | 99.73% (374/375) |
| Semantic success | 48.00% (180/375) |
| Macro per-check score | 81.66% |
| Micro per-check score | 80.83% |
python
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel base_id = "StephenChou/cedar-qwen27b-sft-v2"adapter_id = "StephenChou/cedar-raise-qwen3.5-27b" tokenizer = AutoTokenizer.from_pretrained(adapter_id)base = AutoModelForCausalLM.from_pretrained( base_id, torch_dtype="auto", device_map="auto", trust_remote_code=True,)model = PeftModel.from_pretrained(base, adapter_id)model.eval()For a standalone model, call model.merge_and_unload() and save the merged model locally.
Research on translating natural-language access-control requirements into Cedar policies. Outputs should be validated with the Cedar parser and semantic checks before deployment.
Apache-2.0, following the base model.