Alpie Core
Alpie Core is a 32B parameter 4-bit quantized reasoning model built in India that offers powerful AI capabilities through an API platform while being open-source, OpenAI-compatible, and efficient on lower-end GPUs.
https://playground.169pi.ai/dashboard?ref=producthunt
Product Information
Updated:Dec 29, 2025
What is Alpie Core
Alpie Core is an innovative AI model developed by 169Pi, India's premier AI research lab founded by Rajat and Chirag Arya. It represents a milestone for open-source AI from India as one of the first globally to demonstrate that 4-bit reasoning models can rival frontier-scale systems. The model supports 65K context length, is licensed under Apache 2.0, and is accessible via multiple platforms including Hugging Face, Ollama, hosted API, and the 169Pi Playground.
Key Features of Alpie Core
Alpie Core is a 32B parameter 4-bit quantized reasoning model developed by 169Pi in India. It supports 65K context length, is open source under Apache 2.0 license, and is OpenAI-compatible. The model runs efficiently on lower-end GPUs through innovative 4-bit quantization during training while maintaining high performance. It's accessible via multiple platforms including Hugging Face, Ollama, hosted API, and the 169Pi Playground.
4-bit Quantization: Uses innovative quantization-aware training to achieve 75% lower memory usage and 3.2x faster inference while maintaining accuracy
Long Context Support: Handles up to 65K context length with plans to extend to 128K tokens
Multiple Access Points: Available through various platforms including Hugging Face, Ollama, hosted API, and 169Pi Playground
Specialized Dataset Training: Trained on six high-quality curated datasets (~2B tokens) covering STEM, Indic reasoning, law, psychology, coding, and advanced mathematics
Use Cases of Alpie Core
Education and Exam Preparation: Assists in competitive exam preparation with strong focus on Indian educational context
Enterprise Automation: Enables businesses to integrate AI reasoning capabilities into their production pipelines
Legal Analysis: Provides reasoning support for legal documentation and analysis with specialized training in law
Research and Development: Supports academic and scientific research with strong STEM reasoning capabilities
Pros
Efficient resource utilization with 4-bit quantization
Open source and freely accessible
Strong performance in Indian context while maintaining global adaptability
Runs on lower-end GPUs making it more accessible
Cons
Still in early stages of development
Limited multimodal capabilities (currently in development)
May require further optimization for specific use cases
How to Use Alpie Core
Install Required Libraries: Install transformers, peft, and torch libraries using pip
Import Dependencies: Import required modules: AutoModelForCausalLM, AutoTokenizer, TextStreamer from transformers; PeftModel, PeftConfig from peft; and torch
Load Model Configuration: Load the LoRA adapter configuration using peft_model_id = '169Pi/Alpie-Core' and PeftConfig.from_pretrained()
Load Base Model: Load the base model using AutoModelForCausalLM.from_pretrained() with float16 precision and auto device mapping
Load Tokenizer: Load the tokenizer using AutoTokenizer.from_pretrained() with the base model path
Load LoRA Weights: Load the LoRA weights onto the base model using PeftModel.from_pretrained()
Access Options: Access Alpie Core through multiple platforms: Hugging Face, Ollama, 169Pi's hosted API, or the 169Pi Playground
Alpie Core FAQs
Alpie Core is a 32B reasoning model that is open source (Apache 2.0 license) and OpenAI-compatible, designed to run efficiently on lower-end GPUs.
Alpie Core Video
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