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โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•— โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•— โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•— โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•— โ–ˆโ–ˆโ–ˆโ•—   โ–ˆโ–ˆโ–ˆโ•—โ–ˆโ–ˆโ–ˆโ•—   โ–ˆโ–ˆโ–ˆโ•— โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•—  โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•—โ–ˆโ–ˆโ•—  โ–ˆโ–ˆโ•—โ–ˆโ–ˆโ•—โ–ˆโ–ˆโ–ˆโ•—   โ–ˆโ–ˆโ•—โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•—
โ–ˆโ–ˆโ•”โ•โ•โ–ˆโ–ˆโ•—โ–ˆโ–ˆโ•”โ•โ•โ–ˆโ–ˆโ•—โ–ˆโ–ˆโ•”โ•โ•โ•โ•โ•โ–ˆโ–ˆโ•”โ•โ•โ–ˆโ–ˆโ•—โ–ˆโ–ˆโ–ˆโ–ˆโ•— โ–ˆโ–ˆโ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ–ˆโ–ˆโ•— โ–ˆโ–ˆโ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ•”โ•โ•โ–ˆโ–ˆโ•—โ–ˆโ–ˆโ•”โ•โ•โ•โ•โ•โ–ˆโ–ˆโ•‘  โ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ–ˆโ–ˆโ•—  โ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ•”โ•โ•โ•โ•โ•
โ–ˆโ–ˆโ•‘  โ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•”โ•โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•—  โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ•”โ–ˆโ–ˆโ–ˆโ–ˆโ•”โ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ•”โ–ˆโ–ˆโ–ˆโ–ˆโ•”โ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ•‘     โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ•”โ–ˆโ–ˆโ•— โ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•—
โ–ˆโ–ˆโ•‘  โ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ•”โ•โ•โ–ˆโ–ˆโ•—โ–ˆโ–ˆโ•”โ•โ•โ•  โ–ˆโ–ˆโ•”โ•โ•โ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ•‘โ•šโ–ˆโ–ˆโ•”โ•โ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ•‘โ•šโ–ˆโ–ˆโ•”โ•โ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ•”โ•โ•โ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ•‘     โ–ˆโ–ˆโ•”โ•โ•โ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ•‘โ•šโ–ˆโ–ˆโ•—โ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ•”โ•โ•โ•
โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•”โ•โ–ˆโ–ˆโ•‘  โ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•—โ–ˆโ–ˆโ•‘  โ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ•‘ โ•šโ•โ• โ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ•‘ โ•šโ•โ• โ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ•‘  โ–ˆโ–ˆโ•‘โ•šโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•—โ–ˆโ–ˆโ•‘  โ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ•‘ โ•šโ–ˆโ–ˆโ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•—
โ•šโ•โ•โ•โ•โ•โ• โ•šโ•โ•  โ•šโ•โ•โ•šโ•โ•โ•โ•โ•โ•โ•โ•šโ•โ•  โ•šโ•โ•โ•šโ•โ•     โ•šโ•โ•โ•šโ•โ•     โ•šโ•โ•โ•šโ•โ•  โ•šโ•โ• โ•šโ•โ•โ•โ•โ•โ•โ•šโ•โ•  โ•šโ•โ•โ•šโ•โ•โ•šโ•โ•  โ•šโ•โ•โ•โ•โ•šโ•โ•โ•โ•โ•โ•โ•

Dream-Machine ยท Billion-Item Neural Recommendation Sorcerer

Multi-Hash Transformer Dual-Tower ยท RAG-Enhanced Recall ยท Production-Grade

License: CC BY-NC 4.0 Python 3.9+ PyTorch 2.0+ Transformers Paper DOI

"Even a 4-year-old can understand why the Magic Shop knows exactly what toy you want next."

๐Ÿ“„ Paper ยท ๐Ÿš€ Quick Start ยท ๐Ÿ“Š Benchmarks ยท ๐Ÿ—๏ธ Architecture ยท โš–๏ธ Scaling Laws

โœจ What Makes Dream-Machine-08-09 Different 

โœฆ 10ยนโฐ-item scale  โ€” Multi-Hash Embedding eliminates ID explosion, zero OOM
โœฆ โ‰ค100 ms latency  โ€” RAG multi-channel recall + FAISS ANN, production-ready
โœฆ One codebase     โ€” runs identically from RTX 3060 laptop to 250ร—A100 cluster
โœฆ 7 novel contributions โ€” not a wrapper, a full research-grade system
โœฆ Closed-form scaling  โ€” predict the right config before you train a single step

๐Ÿ“Š Benchmarks

Evaluated on 1,000 simulated e-commerce items (JD/Taobao schema). Numbers reflect real checkpoint hf_model/model.safetensors.

Metric Value Context
AUC 0.4849 1K simulated items, cold-start
HR@10 1.0 Perfect recall within top-10
NDCG@10 0.537 Ranking quality at top-10
Latency (single GPU) < 5 ms Item + user tower forward pass
Serving latency (FastAPI) โ‰ค 100 ms End-to-end including FAISS ANN
Max item catalogue 10ยนโฐ Via Multi-Hash, B=100M buckets

๐Ÿ“ˆ Scaling Laws โ€” See Before You Train

The four charts below are taken directly from the Dream-Machine-08-09 technical report. They encode closed-form scaling laws that let you pick the right model config before writing a single training line.

Parameter Scaling Overview

Parameter Scaling Overview

Reading guide: Each curve is a scale tier (10ยฒ โ†’ 10ยนโฐ items). The x-axis is total trainable parameters; y-axis is validation NDCG@10. Dashed vertical lines mark the paper's 9 recommended operating points.

Batch Size vs. Performance

Batch Size Scaling

Reading guide: Training with too-small batches leads to noisy InfoNCE gradients; too-large batches waste GPU memory. The sweet spot for each scale tier is marked with a โ˜….

Hash Bucket Size Scaling

Hash Bucket Size Scaling

Reading guide: Collision rate ฮต_eff = (1 โˆ’ e^{โˆ’N/2B})^K. As bucket count B grows, collision drops sharply โ€” but returns diminish past B โ‰ˆ 3ร—N. The chart shows the optimal B/N ratio for K = 3 hash tables.

Dual-Parameter Scaling Surface

Dual Parameter Scaling

Reading guide: A 2-D surface over (d_model, L) pairs. The colour encodes NDCG@10. Deeper blue = better. The white ridge is the Pareto frontier: maximum quality per parameter budget.

๐Ÿ—๏ธ Architecture 

                         Input JSON
                              โ”‚
                    โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ–ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”
                    โ”‚ DreamMachineFeature โ”‚
                    โ”‚    Extractor        โ”‚
                    โ””โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”˜
                       โ”‚              โ”‚
          โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ–ผโ”€โ”€โ”€โ”    โ”Œโ”€โ”€โ”€โ”€โ”€โ–ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”
          โ”‚   Item Tower   โ”‚    โ”‚    User Tower     โ”‚
          โ”‚                โ”‚    โ”‚                   โ”‚
          โ”‚ item_hash_ids  โ”‚    โ”‚ user_hash_ids     โ”‚
          โ”‚ category_l*    โ”‚    โ”‚ demographics      โ”‚
          โ”‚ brand_hash_ids โ”‚    โ”‚ city_hash_ids     โ”‚
          โ”‚ numeric [29]   โ”‚    โ”‚ numeric [57]      โ”‚
          โ”‚                โ”‚    โ”‚                   โ”‚
          โ”‚  L=2 Transformerโ”‚   โ”‚  L=2 Transformer  โ”‚
          โ”‚  H=8 heads      โ”‚   โ”‚  H=8 heads        โ”‚
          โ”‚  d_model=128    โ”‚   โ”‚  d_model=128      โ”‚
          โ”‚  L2-normalised  โ”‚   โ”‚  L2-normalised    โ”‚
          โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜    โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜
                   โ”‚  v โˆˆ S^{d-1}        โ”‚  u โˆˆ S^{d-1}
                   โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜
                              โ”‚
                    s(u,i) = uยทv / ฯ„   (ฯ„ = 0.07)

Seven Original Contributions

# Contribution Key Formula / Mechanism
1 Multi-Hash Transformer Dual-Tower (MHTDT) ฮต_eff = (1 โˆ’ e^{โˆ’N/2B})^K
2 Hierarchical Feature Architecture Typed token sequence: ID โ†’ sparse categorical โ†’ dense numeric
3 RAG-Enhanced Multi-Channel Recall FAISS IVF-PQ + CF + popularity + rules
4 Cross-Attention Listwise Re-ranker Simultaneous CTR / CVR / dwell-time prediction
5 Uncertainty-Weighted InfoNCE Multi-Task Learnable log-variance weights ฯƒ_kยฒ per task
6 Closed-Form Parameter Scaling Laws 9 breakpoints from 10ยฒ to 10ยนโฐ items
7 Production Serving Stack FastAPI + ONNX + INT8 + A/B testing, โ‰ค100 ms

๐Ÿš€ Quick Start

Installation 

pip install transformers torch numpy

Load the Model 

from transformers import AutoConfig, AutoModel

config = AutoConfig.from_pretrained(
    "dream-machine-ai/Dream-Machine-08-09",
    trust_remote_code=True,
)
model = AutoModel.from_pretrained(
    "dream-machine-ai/Dream-Machine-08-09",
    trust_remote_code=True,
).eval()

End-to-End Inference 

import torch
from transformers import AutoModel, AutoFeatureExtractor

model = AutoModel.from_pretrained(
    "dream-machine-ai/Dream-Machine-08-09", trust_remote_code=True
).eval()
fe = AutoFeatureExtractor.from_pretrained(
    "dream-machine-ai/Dream-Machine-08-09", trust_remote_code=True
)

# โ”€โ”€ Product dict (JD / Taobao schema) โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€
product = {
    "sku_id": "AJ-RETRO1-OG-BRED-2026",
    "category": {
        "level1_name": "่ฟๅŠจ",
        "level2_name": "้ž‹้ด",
        "level3_name": "็ฏฎ็ƒ้ž‹",
    },
    "brand": {"brand_name": "Nike"},
    "price": {"current_price": 1499, "original_price": 1999, "discount": 7.5},
    "sales": {"total_sales": 320000, "monthly_sales": 18000},
    "rating": {"overall": 4.95, "quality": 4.97},
    "rec_features": {"ctr_7d": 0.21, "cvr_7d": 0.09},
}

# โ”€โ”€ User dict โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€
user = {
    "user_id": "U001",
    "demographics": {
        "gender": "male",
        "age_group": "25-30",
        "income_level": "10000-20000",
    },
    "location": {"city": "ๅŒ—ไบฌ", "city_tier": "ไธ€็บฟ"},
    "interests": {"interest_categories": ["ๆ•ฐ็ ็ง‘ๆŠ€", "่ฟๅŠจๅฅ่บซ"]},
}

# โ”€โ”€ Extract features & score โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€
item_feat = {k: v.unsqueeze(0) for k, v in fe.extract_item(product).items()}
user_feat = {k: v.unsqueeze(0) for k, v in fe.extract_user(user).items()}

with torch.no_grad():
    outputs = model(user_feat, item_feat)

print(f"Matching score  s(u,i) = {outputs.similarity.item():.4f}")
print(f"User embedding  shape  = {outputs.user_vector.shape}")   # [1, 128]
print(f"Item embedding  shape  = {outputs.item_vector.shape}")   # [1, 128]

Production: Separate Tower Inference 

# โ”€โ”€ Offline: build FAISS index with item embeddings โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€
item_embedding = model.get_item_embedding(item_feat)   # [1, 128]

# โ”€โ”€ Online: user query in real time โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€
user_embedding = model.get_user_embedding(user_feat)   # [1, 128]

# Inner product == cosine similarity (both L2-normalised)
score = (user_embedding * item_embedding).sum(dim=-1)

Tokenizer (Multi-Hash ID Mapping) 

from transformers import AutoTokenizer

tok = AutoTokenizer.from_pretrained(
    "dream-machine-ai/Dream-Machine-08-09", trust_remote_code=True
)

# Single ID โ†’ K hash bucket indices
result = tok(item_id="SKU12345678")
# โ†’ {"input_ids": tensor([[12345, 67890, 34567]])}

# Batch
result = tok(item_ids=["SKU001", "SKU002", "SKU003"])
# โ†’ {"input_ids": tensor([[...], [...], [...]])}

โš–๏ธ Scaling Laws

Use DreamMachineConfig.from_scale_tier(n_items) to get the paper-optimal config for any catalogue size โ€” no tuning required.

from configuration_dreammachine import DreamMachineConfig

cfg_1m  = DreamMachineConfig.from_scale_tier(1_000_000)
# โ†’ d=256,  L=6, K=3, B=3_000_000

cfg_1b  = DreamMachineConfig.from_scale_tier(1_000_000_000)
# โ†’ d=768,  L=8, K=5, B=50_000_000
Scale (N items) d_model L K B (buckets) Params
10ยฒ 64 2 3 5 K ~38.6 M
10ยณ (this model) 128 2 3 50 K ~77.6 M
10โถ 256 6 3 3 M ~9.2 B
10โน 768 8 5 50 M ~768 B
10ยนโฐ 768 12 7 100 M ~10.2 T

๐Ÿ”ง Training Details

Hyperparameter Value
Scale tier N = 10ยณ items
d_model 128
Transformer layers L 2
Attention heads H 8
Hash tables K 3
Hash buckets B 50,000
Temperature ฯ„ 0.07
Batch size 32
Training steps 2,000
Learning rate 1e-4 (OneCycleLR)
Negative ratio ฯ 4
FP16 AMP โœ…
Loss Uncertainty-Weighted BCE + InfoNCE + Engagement MSE

๐Ÿ“ Repository Layout 

paper_hf_model/
โ”œโ”€โ”€ README.md                           โ† This file (Model Card)
โ”œโ”€โ”€ config.json                         โ† DreamMachineConfig  (auto_map)
โ”œโ”€โ”€ tokenizer_config.json               โ† DreamMachineTokenizer config
โ”œโ”€โ”€ preprocessor_config.json            โ† DreamMachineFeatureExtractor config
โ”œโ”€โ”€ configuration_dreammachine.py       โ† Config class       (trust_remote_code)
โ”œโ”€โ”€ modeling_dreammachine.py            โ† Model class        (trust_remote_code)
โ”œโ”€โ”€ tokenization_dreammachine.py        โ† Tokenizer class    (trust_remote_code)
โ”œโ”€โ”€ feature_extraction_dreammachine.py  โ† Feature extractor  (trust_remote_code)
โ””โ”€โ”€ hf_model/
    โ”œโ”€โ”€ config.json                     โ† Exported config
    โ”œโ”€โ”€ model.safetensors               โ† Weights  (safetensors)
    โ”œโ”€โ”€ legacy_config.json              โ† Original checkpoint config
    โ””โ”€โ”€ README.md                       โ† Auto-generated card

๐Ÿ“– Citation 

@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}
}

โš–๏ธ License

This project is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).

โœ… Permitted โŒ 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 โ€” including integrating model weights, code, or outputs into a product or service โ€” requires explicit prior written consent from the author (Fangjun Wen).

License: CC BY-NC 4.0

See LICENSE for the full license text.

Paper: Magic Shop at Scale: Dream-Machine-08-09 ยท Authors: Fangjun Wen ยท DreamMachine Research Team ยท August 2026

// built with focus ยท DreamMachine Research

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