Text Classification
Transformers
Safetensors
PEFT
branchone
feature-extraction
decision-model
multimodal
custom-code
custom_code
Instructions to use ndemirdemir/BranchOne with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ndemirdemir/BranchOne with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ndemirdemir/BranchOne", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ndemirdemir/BranchOne", trust_remote_code=True, device_map="auto") - PEFT
How to use ndemirdemir/BranchOne with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
Download io.py from ndemirdemir/BranchOne: direct link, hf CLI and curl.
- Browser
- Download file 228 Bytes
-
https://huggingface.co/ndemirdemir/BranchOne/resolve/main/io.py
- Command line
-
hf download hf://ndemirdemir/BranchOne/io.py
-
curl -L -o io.py https://huggingface.co/ndemirdemir/BranchOne/resolve/main/io.py
228 Bytes
| """File hashing used by the calibrated inference loader.""" | |
| import hashlib | |
| from pathlib import Path | |
| def sha256(path): | |
| with Path(path).open("rb") as stream: | |
| return hashlib.file_digest(stream, "sha256").hexdigest() | |