Instructions to use Jeckmu/Qwen2-VL-2B-Instruct-GPTQ-Int4-lora-SurveillanceVideo-250210 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Jeckmu/Qwen2-VL-2B-Instruct-GPTQ-Int4-lora-SurveillanceVideo-250210 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-VL-2B-Instruct-GPTQ-Int4") model = PeftModel.from_pretrained(base_model, "Jeckmu/Qwen2-VL-2B-Instruct-GPTQ-Int4-lora-SurveillanceVideo-250210") - Notebooks
- Google Colab
- Kaggle
Qwen2-VL-2B-Instruct-GPTQ-Int4-lora-SurveillanceVideo-250210 / checkpoint-100 /adapter_model.safetensors
- Xet hash:
- e1268c457136f820118e006c1b25866b93afb15d8b045e808a7b9c418b37b868
- Size of remote file:
- 37 MB
- SHA256:
- d7647726229608e21a83c7dbc2707940425ebf1ab8cd19bb2126893ab3454b07
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