GLM-5.2 is live. #1 throughput on OpenRouter, pay-per-token on FriendliAI. Try it today ➜
harrrshall
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
harrrshall
Model Tree
Qwen/Qwen3.5-4B
Input Modalities
Output Modalities
Supported Functionality
python
import torchfrom transformers import AutoModelForImageTextToText, AutoProcessormodel_id = "harrrshall/lumen-merged"processor = AutoProcessor.from_pretrained(model_id)model = AutoModelForImageTextToText.from_pretrained(model_id,dtype=torch.bfloat16,device_map="auto",)model.eval()
no peft adapter attachment and no separate base model download are required. the repository is approximately 9.1 gb before local filesystem overhead.
Lumen generates small structured source patches for automatically verifiable accessibility defects in react and static html. its intended workflow rebuilds the application and validates the patch with browser, keyboard, semantic, visual, regression, and protected path gates.
| system | tasks | saferepair | cost per safe repair |
|---|---|---|---|
| Lumen | 43 tasks | 42/43 (97.67%) | $0.000268 |
| gpt-5.6 sol | 43 tasks | 40/43 (93.02%) | $0.070857 |
| claude opus 4.8 | 43 tasks | 43/43 (100%) | $0.054012 |
Qwen/Qwen3.5-4B851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a
Lumen covers deterministic source repair families supported by its evaluator. it does not establish complete wcag or legal conformance. review every patch before merging, and involve qualified accessibility practitioners and assistive technology users for subjective decisions.