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
File size: 1,081 Bytes
7c85c7e | 1 2 3 4 5 6 7 8 9 10 11 12 | # Pivot serving interfaces
The model exposes three synchronous methods after loading `AutoModel` and `AutoTokenizer` with `trust_remote_code=True`:
1. `choose(tokenizer, context, options)` returns a simple choice, index and probability vector.
2. `decide_native(tokenizer, context, candidates)` accepts stable machine IDs, semantic candidate text and at most one explicit abstain candidate.
3. `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](example_request.json) and [response](example_response.json) and [native request](native_example_request.json) and [response](native_example_response.json) illustrate the wire shapes. Run [a typed Python example](typed_decisions.py) or read the [full inference guide](../docs/INFERENCE.md).
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