ALRI (A Lightweight Retrieval Intelligence) is a family of next-generation embedding models specifically designed for extreme efficiency and high-speed code retrieval.

By combining modern architectural innovations with aggressive parameter optimization, ALRI achieves near-state-of-the-art retrieval performance at a fraction of the size of standard models like MiniLM.

🧬 Key Technologies

ALBERT-style Weight Sharing: Utilizes recursive transformer blocks to maintain deep representations while drastically reducing the unique parameter count.
Extreme Hashed Embeddings: Vocabulary compression that maps 151k virtual tokens into 32k real vectors, eliminating redundancy and reducing memory footprint.
Funnel Attention: Dynamic sequence pooling that accelerates inference by reducing token density in deeper layers.
Matryoshka Representation Learning (MRL): Flexible output dimensions (32, 64, 128, 384) allowing you to trade off accuracy for even greater speed.
Distilled Intelligence: Knowledge distilled from a 24M parameter teacher into a sub-million parameter "Nano" engine.

📊 Models

Model	Parameters	Size (ONNX)	Acc@1 (Python)	Speed (CPU)
ALRI-Tiny	24M	~90 MB	96.8%	~35 ms
ALRI-Nano	0.93M	~6 MB	94.0%	~2 ms
MiniLM-L6	22M	~80 MB	92.0%	~40 ms

Note: ALRI-Nano is ~25x smaller than MiniLM-L6 while maintaining superior accuracy on code retrieval tasks.

🚀 Getting Started (ONNX)

The models are optimized for ONNX Runtime. You can run them on any CPU with minimal latency.

python

import onnxruntime as ort
from transformers import AutoTokenizer
import numpy as np

# Load Nano model
session = ort.InferenceSession("alri-nano-onnx/model_int8.onnx")
tokenizer = AutoTokenizer.from_pretrained("alri-nano-onnx/tokenizer")

text = "how to read a json file in python"
inputs = tokenizer(text, return_tensors="np")

outputs = session.run(None, {
    "input_ids": inputs["input_ids"].astype(np.int64),
    "attention_mask": inputs["attention_mask"].astype(np.int64)
})
embedding = outputs[0] # (1, 128)

🎯 Use Cases

Real-time IDE Autocomplete: Lightning-fast context retrieval.
Mobile & Edge Search: High-quality search on low-power devices.
Massive Code Indexing: Extremely low storage costs per embedding.

📜 License

MIT

FGFGFGGDFGDFGSD/ALRI-Embeddings

ALRI: Ultra-Efficient Code Embeddings 🚀

🧬 Key Technologies

📊 Models

🚀 Getting Started (ONNX)

🎯 Use Cases

📜 License

FGFGFGGDFGDFGSD/
ALRI-Embeddings