Zero-Shot Classification
Transformers
GGUF
English
decision-model
system-one
falcondec
calibrated-decisions
multiple-choice
intent-classification
customer-support
natural-language-inference
code
guardrails
selective-prediction
falconsai
model-surgeon
attested-lineage
Instructions to use Falconsai/proof_v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Falconsai/proof_v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="Falconsai/proof_v3")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Falconsai/proof_v3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 292 Bytes
a31abf3 | 1 2 3 4 5 6 | {
"falconsai.synthesized": true,
"num_hidden_layers": 22,
"falconsai.tool": "FALCONS.AI Model Surgeon V7.99",
"falconsai.attn_note": "attention kernel is chosen at load time (e.g. attn_implementation='flash_attention_2' on CUDA/ROCm hosts that have it); nothing in this file selects it"
} |