GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
adastracomputing
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Run this model inference with full control and performance in your environment.
Model Details
Model Provider
adastracomputing
Model Tree
Qwen/Qwen2.5-Coder-14B-Instruct
Input Modalities
Output Modalities
Supported Functionality
Given a machine-authored security backport patch and the human upstream fix it claims to reproduce, the adapter emits an accept or reject verdict with a short grounded rationale. It checks that the backport transcribes the upstream fix, addresses the stated CVE, keeps the change minimal, and does not invent claims the artifacts do not support. It is trained to review a patch it did not write, from a different model family than the one that authored it, so the reviewer and the patcher never share context.
It is not a general-purpose code reviewer or a vulnerability scanner. It answers one narrow question inside the trace contract: does this backport match the ground-truth fix. Outside that setup its verdicts carry no meaning.
QLoRA on the reviewer-lora-data
corpus: valid review bundles as positives and constructed wrong-patch examples
as negatives, all derived from the trace corpus. Version 1.10 passes the pilot
gate (9/9 canary, 11/11 held-out test).
python
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizerbase = "Qwen/Qwen2.5-Coder-14B-Instruct"tok = AutoTokenizer.from_pretrained(base)model = AutoModelForCausalLM.from_pretrained(base, device_map="auto")model = PeftModel.from_pretrained(model, "adastracomputing/reviewer-lora-v1.10")
The review prompt and the surrounding contract live in the trace repository. Run the adapter with that prompt; a bare instruction will not reproduce the trained behaviour.
Apache-2.0, matching the base model.