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: 810 Bytes
b68d688 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | {
"_comment": "Copy to api/config.json. adapter_path is the folder holding adapter_config.json (this repository's root after download). Paths are local; nothing is downloaded.",
"host": "127.0.0.1",
"port": 8095,
"allow_non_loopback": false,
"model_mode": "base+adapter",
"base_model_id": "ibm-granite/granite-4.0-1b",
"base_model_revision": "6a7381ba1f54d684ff508d991aeb7dc580157103",
"base_model_path": null,
"adapter_path": ".",
"merged_model_path": null,
"load_on_startup": true,
"max_prompt_tokens": 2600,
"max_segment_tokens": 1536,
"segment_overlap_turns": 8,
"max_new_tokens": 1024,
"max_body_bytes": 200000,
"request_timeout_s": 120,
"max_concurrent": 1,
"max_queue": 4,
"enable_meeting_endpoint": false,
"max_segments_per_request": 12
} |