Instructions to use sKT-Ai-Labs/SKT-23-89M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sKT-Ai-Labs/SKT-23-89M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sKT-Ai-Labs/SKT-23-89M")# Load model directly from transformers import SKTFORCASUALLM model = SKTFORCASUALLM.from_pretrained("sKT-Ai-Labs/SKT-23-89M", dtype="auto") - Notebooks
- Google Colab
- Kaggle
πͺ SKT2023-GPT2-TOKENSIER
SKT AI LABS
The Sovereign Data Foundation for Indigenous LLM Development (Project Om)
Developed by SKT AI Labs | Lead Developer HARSHIT
β οΈ IMPORTANT NOTE: Yeh model sirf testing purposes ke liye banaya gaya hai. This is a test-based version only for experimental analysis.
This repository contains the SKT-23-89M model, a custom-architectured neural network built to analyze and predict global economic indicators. It is trained on a dataset of 89M records (SKT-FINAL-CORPUS.txt) to provide insights into Texting trends and growth patterns during this testing phase.
π Model Identity & Architecture
Unlike standard LLMs, this model uses the SKT-Net architectureβa specialized dense neural network designed by Shrijan Kumar Tiwari.
- Architecture Name: SKT-Net (Custom)
- Status: Testing Phase (Experimental)
- Input Features: Mixed Data collection.
π Repository Contents
| File Name | Description |
|---|
| model.safetensors | Secure and fast-loading tensor format for Python. |
| skt-final-corpus.txt | Dataset containing 160K records used for this test run. |
π Credits & License
Organization: SKT AI Labs
Location: Sidhi, Madhya Pradesh, India
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