Feature Extraction
Transformers
Safetensors
pivot
decision-making
classification
scoring
custom_code
Instructions to use Q1z/Pivot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Q1z/Pivot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Q1z/Pivot", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Q1z/Pivot", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download serving/README.md from Q1z/Pivot: direct link, hf CLI and curl.
- Browser
- Download file 1.08 kB
-
https://huggingface.co/Q1z/Pivot/resolve/main/serving/README.md
- Command line
-
hf download hf://Q1z/Pivot/serving/README.md
-
curl -L -o README.md https://huggingface.co/Q1z/Pivot/resolve/main/serving/README.md
1.08 kB
Pivot serving interfaces
The model exposes three synchronous methods after loading AutoModel and AutoTokenizer with trust_remote_code=True:
choose(tokenizer, context, options)returns a simple choice, index and probability vector.decide_native(tokenizer, context, candidates)accepts stable machine IDs, semantic candidate text and at most one explicit abstain candidate.decide(tokenizer, state, questions)returns a typed collection of choice, yes/no and score decisions.
The model operates on supplied options; it does not create new options or generate explanatory text. The default serving limits in this update are 512 context tokens and 128 tokens per option. Keep a stable option set if comparing scores between requests.
The pre-existing typed request and response and native request and response illustrate the wire shapes. Run a typed Python example or read the full inference guide.