Instructions to use cragtmp/task1-noikd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cragtmp/task1-noikd with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("/data1/chenjiazun/LLM/Llama-3.2-11B-Vision-Instruct") model = PeftModel.from_pretrained(base_model, "cragtmp/task1-noikd") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from cragtmp/task1-noikd: direct link, hf CLI and curl.
- Browser
- Download file 477 Bytes
-
https://huggingface.co/cragtmp/task1-noikd/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://cragtmp/task1-noikd/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/cragtmp/task1-noikd/resolve/main/preprocessor_config.json
477 Bytes
| { | |
| "do_convert_rgb": true, | |
| "do_normalize": true, | |
| "do_pad": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.48145466, | |
| 0.4578275, | |
| 0.40821073 | |
| ], | |
| "image_processor_type": "MllamaImageProcessor", | |
| "image_std": [ | |
| 0.26862954, | |
| 0.26130258, | |
| 0.27577711 | |
| ], | |
| "max_image_tiles": 4, | |
| "processor_class": "MllamaProcessor", | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 560, | |
| "width": 560 | |
| } | |
| } | |