Instructions to use Qusaiiii/Accountant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Qusaiiii/Accountant with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Qusaiiii/Accountant", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Qusaiiii/Accountant") model = AutoModelForCausalLM.from_pretrained("Qusaiiii/Accountant", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 498f06c47f284d52400d13d8e1fb6e7e191e081f728348243b5ccca8b012bf53
- Size of remote file:
- 996 MB
- SHA256:
- 9d43cdd9ca611bfdedf78bc9b5aac3c32ef3e6d999116042ea9ca6a7806783af
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.