Feature Extraction
Transformers
PyTorch
Chinese
English
recommendation
dual-tower
transformer
multi-hash-embedding
Instructions to use DreamMachine-AI/Dream-Machine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DreamMachine-AI/Dream-Machine with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="DreamMachine-AI/Dream-Machine")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("DreamMachine-AI/Dream-Machine", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: cc-by-nc-4.0 | |
| language: | |
| - zh | |
| - en | |
| tags: | |
| - recommendation | |
| - dual-tower | |
| - transformer | |
| - multi-hash-embedding | |
| - pytorch | |
| library_name: transformers | |
| pipeline_tag: feature-extraction | |
| <div style="max-width:100%;overflow:hidden;"> | |
| <pre style=" | |
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| font-family:'Courier New',Courier,monospace; | |
| font-size:clamp(3.5px,0.82vw,10px); | |
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| border-radius:12px; | |
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| box-sizing:border-box; | |
| color:transparent; | |
| background-clip:text; | |
| -webkit-background-clip:text; | |
| background-image:linear-gradient( | |
| 118deg, | |
| #FF6B6B 0%, | |
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| ); | |
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| βββ βββββββββββββββββ βββββββββββββββββββββββββββββββββββββββββ βββββββββββββββββββββββββββ | |
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| βββββββ βββ ββββββββββββββ ββββββ ββββββ ββββββ βββ ββββββββββ βββββββββ βββββββββββββ</pre> | |
| </div> | |
| <div align="center"> | |
| <p style="font-size:1.5em;font-weight:900;letter-spacing:0.03em;margin:0.2em 0 0.1em;"> | |
| β¨ Dream-Machine β Billion-Item Neural Recommendation Sorcerer β¨ | |
| </p> | |
| <p style="font-size:1.0em;font-weight:500;color:#aaa;margin:0 0 1em;"> | |
| Multi-Hash Transformer Dual-Tower Β· RAG-Enhanced Β· Production-Grade Β· 10<sup>10</sup> Scale | |
| </p> | |
| [](https://huggingface.co/dream-machine-ai/Dream-Machine-08-09) | |
| [](https://huggingface.co/datasets/dream-machine-ai/Dream-Machine-08-09-Dataset) | |
| [](https://huggingface.co/spaces/dream-machine-ai/) | |
| [](https://doi.org/10.5281/zenodo.21906715) | |
| [](https://creativecommons.org/licenses/by-nc/4.0/) | |
| </div> | |
| --- | |
| ## π Repository Notice | |
| > **This repository is the official DreamMachine-AI organization listing.** | |
| > The full model weights, code, and documentation are published under the | |
| > **[dream-machine-ai](https://huggingface.co/dream-machine-ai)** account namespace. | |
| | Resource | Location | | |
| |:---------|:---------| | |
| | π€ **Full Model Card + Weights** | [dream-machine-ai/Dream-Machine-08-09](https://huggingface.co/dream-machine-ai/Dream-Machine-08-09) | | |
| | π¦ **Training Dataset** | [dream-machine-ai/Dream-Machine-08-09-Dataset](https://huggingface.co/datasets/dream-machine-ai/Dream-Machine-08-09-Dataset) | | |
| | π **Interactive Space** | [spaces/dream-machine-ai](https://huggingface.co/spaces/dream-machine-ai/) | | |
| | π’ **Organization Hub** | [DreamMachine-AI](https://huggingface.co/DreamMachine-AI) | | |
| | π **Technical Report** | [Zenodo 10.5281/zenodo.21906715](https://doi.org/10.5281/zenodo.21906715) | | |
| | π» **Source Code** | [GitHub / Dream-Machine](https://github.com/When-Summer-Understands-Winter/Dream-Machine/) | | |
| --- | |
| ## β‘ Quick Load | |
| ```python | |
| from transformers import AutoConfig, AutoModel | |
| model = AutoModel.from_pretrained( | |
| "dream-machine-ai/Dream-Machine-08-09", | |
| trust_remote_code=True, | |
| ).eval() | |
| ``` | |
| ```python | |
| from huggingface_hub import snapshot_download | |
| # Full model repository | |
| model_path = snapshot_download(repo_id="dream-machine-ai/Dream-Machine-08-09") | |
| # Training dataset | |
| dataset_path = snapshot_download( | |
| repo_id="dream-machine-ai/Dream-Machine-08-09-Dataset", | |
| repo_type="dataset", | |
| ) | |
| ``` | |
| --- | |
| ## π Benchmarks | |
| | Metric | Value | | |
| |:-------|:-----:| | |
| | **AUC** | **0.4849** | | |
| | **HR@10** | **1.0** | | |
| | **NDCG@10** | **0.537** | | |
| | E2E Latency | **β€ 100 ms** | | |
| | Max Catalogue | **10ΒΉβ° items** | | |
| --- | |
| ## βοΈ License | |
| This project is licensed under the **Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)**. | |
| | β Permitted | β 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 β including integrating model weights, code, or outputs into a product or service β requires explicit prior written consent from the author (Fangjun Wen).** | |
| > Contact: fangjunwen168@outlook.com | |
| [](https://creativecommons.org/licenses/by-nc/4.0/) | |
| --- | |
| <div align="center"> | |
| **Paper**: [DreamMachine: A Billion-Item Neural Recommendation Sorcerer](https://doi.org/10.5281/zenodo.21906715) | |
| **Author**: Fangjun Wen Β· DreamMachine Research Team Β· August 2026 | |
| `// built with focus Β· DreamMachine Research` | |
| </div> | |