Image Segmentation
Transformers
Safetensors
English
prima
text-generation
computer-vision
multi-image-reasoning
pixel-grounding
vision-language
custom-code
custom_code
Instructions to use kanguyen-vn/PRIMA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kanguyen-vn/PRIMA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="kanguyen-vn/PRIMA", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("kanguyen-vn/PRIMA", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Include clean PRIMA code and evaluator; document metrics, initialization, and MIT contributions
91c1e9e verified Download code/tools/prompting.py from kanguyen-vn/PRIMA: direct link, hf CLI and curl.
- Browser
- Download file 282 Bytes
-
https://huggingface.co/kanguyen-vn/PRIMA/resolve/main/code/tools/prompting.py
- Command line
-
hf download hf://kanguyen-vn/PRIMA/code/tools/prompting.py
-
curl -L -o prompting.py https://huggingface.co/kanguyen-vn/PRIMA/resolve/main/code/tools/prompting.py
282 Bytes
| """Formatting shared by multi-image inference prompts.""" | |
| def write_list(items): | |
| if not items: | |
| return '' | |
| if len(items) == 1: | |
| return items[0] | |
| if len(items) == 2: | |
| return ' and '.join(items) | |
| return ', '.join(items[:-1]) + f', and {items[-1]}' | |