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
File size: 424 Bytes
b68d688 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | {
"meeting_id": "meeting-001",
"meeting_date": "2026-10-26",
"timezone": "Asia/Dhaka",
"speakers": {
"S1": "Maya",
"S2": "Omar"
},
"turns": [
{
"turn_id": "T1",
"speaker_id": "S1",
"text": "Omar, can you send the revised proposal by Friday?"
},
{
"turn_id": "T2",
"speaker_id": "S2",
"text": "Yes, I will send it by Friday."
}
]
} |