Instructions to use saujasv/pixtral-hard-correctness-ipo-random with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use saujasv/pixtral-hard-correctness-ipo-random with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("saujasv/pixtral-12b-base") model = PeftModel.from_pretrained(base_model, "saujasv/pixtral-hard-correctness-ipo-random") - Notebooks
- Google Colab
- Kaggle
| { | |
| "do_convert_rgb": true, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.48145466, | |
| 0.4578275, | |
| 0.40821073 | |
| ], | |
| "image_processor_type": "PixtralImageProcessor", | |
| "image_std": [ | |
| 0.26862954, | |
| 0.26130258, | |
| 0.27577711 | |
| ], | |
| "patch_size": { | |
| "height": 16, | |
| "width": 16 | |
| }, | |
| "processor_class": "PixtralProcessor", | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "longest_edge": 1024 | |
| } | |
| } | |