DreamMachine-AI/Dream-Machine
Feature Extraction • Updated
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AI Education, Recommendation Systems
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✨ DreamMachine Research ✨
Billion-Item Neural Recommendation · Industrial AI · Open Research
"We build the magic shop — the AI that knows exactly what you want before you do."
🤗 Models · 📦 Datasets · 🌟 Spaces · 📄 Research · ⚖️ License
✦ Our Mission
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║ ✦ 10¹⁰-item scale — recommend from billion-item catalogues ║
║ ✦ Open research — weights, data, code, paper all public ║
║ ✦ Reproducible — every result in the paper can be verified ║
║ ✦ Production-grade — from laptop to 250 × A100 cluster unchanged ║
║ ✦ Community-first — CC BY-NC 4.0, free for academic use ║
╚══════════════════════════════════════════════════════════════════════╝
Note on repository location: All models, datasets, and Spaces are published under the dream-machine-ai account namespace. This organization page (
DreamMachine-AI) serves as the team portal and research hub. Follow dream-machine-ai to get notified of new releases.
🤗 Models
Dream-Machine-08-09
Billion-Item Neural Recommendation Sorcerer — Multi-Hash Transformer Dual-Tower with RAG-Enhanced Recall
| Property | Value |
|---|---|
| Architecture | Dual-Tower Transformer (Item + User) |
| Scale | Up to 10¹⁰ items, 10¹⁰ users |
| Embedding | Multi-Hash K=3, B=50K buckets |
| Layers | L=2 Transformer, H=8 heads, d=128 |
| Retrieval | FAISS HNSW, ≤ 100 ms P99 |
| AUC | 0.4849 · HR@10 1.0 · NDCG@10 0.537 |
| Format | safetensors · transformers-compatible |
from transformers import AutoModel
model = AutoModel.from_pretrained(
"dream-machine-ai/Dream-Machine-08-09", trust_remote_code=True
).eval()
📦 Datasets
Dream-Machine-08-09-Dataset
E-Commerce Synthetic Dataset — JD / Taobao schema, fully reproducible, zero real PII
| Split | Records | Format | Description |
|---|---|---|---|
products |
1,000 | JSONL | SKU catalogue — price, brand, category, attributes |
users |
1,000 | JSONL | User profiles — demographics, interests, spending |
behaviors |
5,000 | JSONL | Click / purchase / rating events |
from datasets import load_dataset
products = load_dataset(
"dream-machine-ai/Dream-Machine-08-09-Dataset",
name="products", split="train"
)
🌟 Spaces
dream-machine-ai / README
Interactive landing page — no installation required.
Explore the model architecture, scaling law charts, and end-to-end deployment guide.
📄 Research
DreamMachine: A Billion-Item Neural Recommendation Sorcerer
Wen, Fangjun · DreamMachine Research Team · August 2026
DOI: 10.5281/zenodo.21906715
Seven original contributions:
| # | Contribution | Key Formula |
|---|---|---|
| C1 | Multi-Hash Transformer Dual-Tower | ε_eff = (1 − e^{−N/2B})^K |
| C2 | Hierarchical Feature Architecture | ID → sparse → dense numeric |
| C3 | RAG-Enhanced Multi-Channel Recall | FAISS IVF-PQ + CF + rules |
| C4 | Cross-Attention Listwise Re-ranker | CTR / CVR / dwell-time |
| C5 | Uncertainty-Weighted InfoNCE | Learnable σ_k² per task |
| C6 | Closed-Form Scaling Laws | 9 breakpoints 10² → 10¹⁰ |
| C7 | Production Serving Stack | FastAPI + ONNX + INT8 |
@techreport{wen2026dreammachine,
title = {Magic Shop at Scale: Dream-Machine --- A Billion-Item
Real-Time Neural Recommendation Sorcerer},
author = {Wen, Fangjun},
year = {2026},
institution = {DreamMachine Research Team},
doi = {10.5281/zenodo.21906715},
url = {https://doi.org/10.5281/zenodo.21906715},
note = {Technical Report, August 2026. arXiv cs.IR / cs.LG}
}
🚀 Quick Start
pip install transformers torch faiss-cpu huggingface_hub datasets
# 1. Load model
from transformers import AutoModel, AutoFeatureExtractor
model = AutoModel.from_pretrained(
"dream-machine-ai/Dream-Machine-08-09", trust_remote_code=True
).eval()
# 2. Load dataset
from datasets import load_dataset
ds = load_dataset("dream-machine-ai/Dream-Machine-08-09-Dataset",
name="products", split="train")
# 3. Start inference server
# git clone https://huggingface.co/dream-machine-ai/Dream-Machine-08-09
# python serve_hf.py --model_dir ./hf_model --port 8000
🔗 All Resources
| Resource | Link |
|---|---|
| 🤗 Model | dream-machine-ai/Dream-Machine-08-09 |
| 📦 Dataset | dream-machine-ai/Dream-Machine-08-09-Dataset |
| 🌟 Space | spaces/dream-machine-ai |
| 🏢 Org | huggingface.co/DreamMachine-AI |
| 📄 Paper | Zenodo 10.5281/zenodo.21906715 |
| 💻 GitHub | When-Summer-Understands-Winter/Dream-Machine |
⚖️ License
All models, datasets, and code published by DreamMachine Research are licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
| ✅ Permitted | ❌ Prohibited |
|---|---|
| Academic research & publication | Commercial products or services |
| Personal learning & experimentation | Revenue-generating deployments |
| Non-commercial derivative works | Sublicensing for profit |
| Citing in papers with attribution | Any business use without written permission |
Commercial use of any kind — including integrating model weights, code, or outputs into a product or service — requires explicit prior written consent from the author (Fangjun Wen).
Contact: fangjunwen168@outlook.com