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
Download config.json from Falconsai/proof_v3: direct link, hf CLI and curl.
- Browser
- Download file 292 Bytes
-
https://huggingface.co/Falconsai/proof_v3/resolve/main/config.json
- Command line
-
hf download hf://Falconsai/proof_v3/config.json
-
curl -L -o config.json https://huggingface.co/Falconsai/proof_v3/resolve/main/config.json
292 Bytes
| { | |
| "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" | |
| } |