Welcome to 169Pi’s Alpie-Core

Alpie-Core is one of the first 4-bit quantized reasoning models, a 32B parameter system developed by 169Pi team that matches or outperforms several full-precision frontier models. Built from the DeepSeek-R1-Distill-Qwen-32B backbone, it represents a major leap in efficient reasoning, sustainable AI, and democratized intelligence, all trained on just 8 NVIDIA Hopper GPUs.

Alpie-Core redefines what’s possible under limited resources by combining LoRA/QLoRA, groupwise-blockwise quantization, and synthetic data distillation, achieving state-of-the-art results on reasoning, coding, and math benchmarks — all while reducing memory footprint by over 75%. Designed for researchers, developers, and enterprises, Alpie-Core brings frontier-level reasoning to accessible, low-compute environments.

Get started

You can get started by downloading or running Alpie-Core with Ollama:

To pull the model:

ollama pull 169pi/alpie-core

To run it instantly:

ollama run 169pi/alpie-core

Alpie-Core can also be integrated programmatically for local or API-based workflows.

Quick Start with SDK

Access Alpie Core through our official Python SDK (pi169) for seamless API integration:

# Install the SDK
pip install pi169

# Set your API key
export ALPIE_API_KEY="your_key_here"

# Start using the CLI
pi169 "Explain 4-bit quantization in simple terms"

SDK Features

• CLI Integration for quick command-line interactions
• Streaming & Non-Streaming Chat Completions
• Async/Await Support for high-performance concurrent requests
• Clean, type-safe Python Interface (dataclasses, type hints)
• Robust Error Handling with typed exceptions
• Production-Ready Networking (retries, timeouts, httpx)
• Fully Tested with pytest
• Optimized for Reasoning Models
• OpenAI-Compatible Client: Drop-in replacement for OpenAI SDK with full compatibility

Benchmarks

Alpie-Core is built for structured reasoning, step-by-step logic, and factual responses. It achieves outstanding performance across multiple benchmarks:

Feature Highlights

1. Technical Advancements

• 4-Bit Quantization (NF4): Achieves ∼8GB memory footprint with minimal accuracy loss

• 128K context length for extended reasoning, and based on your specific use cases

• Fine-tunable: Fully customise models to your specific use case through parameter fine-tuning

• LoRA + QLoRA Fine-Tuning: Retains reasoning fidelity under low-bit constraints

• Groupwise + Blockwise Quantization: Reduces noise, enhances precision at scale

• vLLM-based Inference: Enables low-latency and high-throughput deployment

2. API & Integration Ready

• OpenAI-Compatible API: Drop-in replacement for GPT endpoints

• Function Calling & Tool Use: Supports structured output and dynamic API linking

• Streaming Output: Token-by-token real-time response generation

• Configurable Guardrails: Safety, moderation, and content filters included

3. Sustainable and Accessible

• Runs efficiently on consumer GPUs (16–24GB VRAM)

• Up to 75% lower VRAM use vs. FP16 baselines

• Significantly reduced carbon and energy footprint

• Fully open under the Apache 2.0 License

Quantization

• Format: NF4 (NormalFloat 4-bit)

• Compression Ratio: 16:1

• Technique: QLoRA + Double Quantization

• Implementation: bitsandbytes (bnb_4bit_use_double_quant=True)

• Inference: Mixed precision (FP16 compute, 4-bit storage)

• Minimal reasoning loss (%)

License: Apache 2.0

Use freely for research, customisation, and commercial deployment without copyleft restrictions. Ideal for experimentation, extension, and open collaboration.

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