Instructions to use rbryant19/opscribe-phase-detector-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use rbryant19/opscribe-phase-detector-v1 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("/staging/rhbryant/hf_cache/models--Qwen--Qwen2.5-VL-7B-Instruct/snapshots/cc594898137f460bfe9f0759e9844b3ce807cfb5") model = PeftModel.from_pretrained(base_model, "rbryant19/opscribe-phase-detector-v1") - Notebooks
- Google Colab
- Kaggle
opscribe-phase-detector-v1
LoRA adapter for surgical phase detection, fine-tuned on the EgoSurgery dataset.
- Base model: Qwen/Qwen2.5-VL-7B-Instruct
- Task: Surgical phase classification from video frames
- Dataset: EgoSurgery (24,925 train / 2,769 val frames)
- Framework: LLaMA-Factory, LoRA rank 32, 3 epochs, lr=1e-4
- Train loss: 0.0286
- Usage: OpScribe pipeline phase detection stage
- Downloads last month
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Qwen/Qwen2.5-VL-7B-Instruct