Instructions to use Arup330/Neck_cot_llama_lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Arup330/Neck_cot_llama_lora with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Arup330/Neck_cot_llama_lora", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Download processor_config.json from Arup330/Neck_cot_llama_lora: direct link, hf CLI and curl.
- Browser
- Download file 550 Bytes
-
https://huggingface.co/Arup330/Neck_cot_llama_lora/resolve/main/processor_config.json
- Command line
-
hf download hf://Arup330/Neck_cot_llama_lora/processor_config.json
-
curl -L -o processor_config.json https://huggingface.co/Arup330/Neck_cot_llama_lora/resolve/main/processor_config.json
550 Bytes
| { | |
| "image_processor": { | |
| "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, | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 560, | |
| "width": 560 | |
| } | |
| }, | |
| "processor_class": "MllamaProcessor" | |
| } | |