Text Generation
PEFT
Safetensors
English
granite
lora
meetings
action-items
decisions
information-extraction
local-inference
conversational
Instructions to use Privoid-ai/Privoid-Action-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Privoid-ai/Privoid-Action-1B with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("ibm-granite/granite-4.0-1b") model = PeftModel.from_pretrained(base_model, "Privoid-ai/Privoid-Action-1B") - Notebooks
- Google Colab
- Kaggle
Privoid Action-1B experimental preview (LoRA adapter, validator, API, locked test results)
b68d688 verified Download examples/request_decision_and_deferred_request.json from Privoid-ai/Privoid-Action-1B: direct link, hf CLI and curl.
- Browser
- Download file 631 Bytes
-
https://huggingface.co/Privoid-ai/Privoid-Action-1B/resolve/main/examples/request_decision_and_deferred_request.json
- Command line
-
hf download hf://Privoid-ai/Privoid-Action-1B/examples/request_decision_and_deferred_request.json
-
curl -L -o request_decision_and_deferred_request.json https://huggingface.co/Privoid-ai/Privoid-Action-1B/resolve/main/examples/request_decision_and_deferred_request.json
631 Bytes
| { | |
| "meeting_id": "fictional-003", | |
| "meeting_date": null, | |
| "timezone": null, | |
| "speakers": { | |
| "A": "Noor", | |
| "B": "Tom" | |
| }, | |
| "turns": [ | |
| { | |
| "turn_id": "T1", | |
| "speaker_id": "A", | |
| "text": "So we're agreed, we'll keep the annual plan for enterprise customers." | |
| }, | |
| { | |
| "turn_id": "T2", | |
| "speaker_id": "B", | |
| "text": "Agreed." | |
| }, | |
| { | |
| "turn_id": "T3", | |
| "speaker_id": "A", | |
| "text": "Tom, please draft the renewal email." | |
| }, | |
| { | |
| "turn_id": "T4", | |
| "speaker_id": "B", | |
| "text": "I'll need to think about it." | |
| } | |
| ] | |
| } |