PEFT
Safetensors
lora
trl
sft
lfm2.5-vl
satellite-imagery
paired-image
civilian-infrastructure
conflict-disruption
blackline-atlas
Instructions to use ChrisRPL/lfm25-vl-civilian-conflict-reporter-lora-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ChrisRPL/lfm25-vl-civilian-conflict-reporter-lora-v1 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("LiquidAI/LFM2.5-VL-450M") model = PeftModel.from_pretrained(base_model, "ChrisRPL/lfm25-vl-civilian-conflict-reporter-lora-v1") - Notebooks
- Google Colab
- Kaggle
| { | |
| "image_processor": { | |
| "data_format": "channels_first", | |
| "do_image_splitting": true, | |
| "do_normalize": true, | |
| "do_pad": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "downsample_factor": 2, | |
| "encoder_patch_size": 16, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_processor_type": "Lfm2VlImageProcessor", | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "max_image_tokens": 256, | |
| "max_num_patches": 1024, | |
| "max_pixels_tolerance": 2.0, | |
| "max_tiles": 10, | |
| "min_image_tokens": 64, | |
| "min_tiles": 2, | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "return_row_col_info": true, | |
| "size": { | |
| "height": 512, | |
| "width": 512 | |
| }, | |
| "tile_size": 512, | |
| "use_thumbnail": true | |
| }, | |
| "processor_class": "Lfm2VlProcessor" | |
| } | |