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@@ -9,7 +9,7 @@ short_description: Small, local models distilled from frontier teachers
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  <p align="center">
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- <img src="https://thelabsource.com/huggingface/banners/adi_org_hero.png" width="720" alt="Advanced Data Intelligence">
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  </p>
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  # Advanced Data Intelligence
@@ -23,7 +23,7 @@ Every model here is built end-to-end on theLAB hardware — no cloud training
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  **Links:** [Website](https://advanced-data-intelligence.com) · [theLAB](https://thelabsource.com) · [YouTube — Advanced Data Intelligence](https://www.youtube.com/@AdvancedDataIntelligence) · [YouTube — ADI Online](https://www.youtube.com/@adi_onlin3)
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  <p align="center">
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- <img src="https://thelabsource.com/huggingface/art/adi_divider_pulse.png" width="700" alt="">
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  </p>
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  ## Models
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  ```
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  <p align="center">
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- <img src="https://thelabsource.com/huggingface/charts/adi_lineup_chart.png" width="760" alt="ADI model lineup — size on disk">
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  </p>
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@@ -83,7 +83,7 @@ huggingface-cli download AdvancedDataIntelligence/adi-qwen3-8b-glm5.2-general-GG
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  Then point any llama.cpp-based runtime at the downloaded file.
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- <img src="https://thelabsource.com/huggingface/art/adi_divider_cat.png" width="700" alt="">
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  </p>
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  ## The approach
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  - **Open.** Apache-2.0 where the base license allows, with full training details on each model card so the work is reproducible.
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  <p align="center">
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- <img src="https://thelabsource.com/huggingface/charts/adi_pipeline_flow.png" width="760" alt="The ADI distillation pipeline">
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  </p>
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  ---
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  Models follow the pattern `adi-<base>-<size>-<teacher>-<purpose>` — so the name tells you the student base, its size, the teacher it learned from, and what it's tuned for.
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  <p align="center">
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- <img src="https://thelabsource.com/huggingface/art/adi_cat_hero.png" width="560" alt="ADI">
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  </p>
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  *Built at [theLAB](https://thelabsource.com) — Learning. Algorithms. Breakthroughs.*
 
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+ <img src="https://serve.thelabsource.com/u/hcKP4D.png" width="720" alt="Advanced Data Intelligence">
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  </p>
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  # Advanced Data Intelligence
 
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  **Links:** [Website](https://advanced-data-intelligence.com) · [theLAB](https://thelabsource.com) · [YouTube — Advanced Data Intelligence](https://www.youtube.com/@AdvancedDataIntelligence) · [YouTube — ADI Online](https://www.youtube.com/@adi_onlin3)
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+ <img src="https://serve.thelabsource.com/u/iSOrnI.png" width="700" alt="">
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  </p>
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  ## Models
 
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  ```
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  <p align="center">
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+ <img src="https://serve.thelabsource.com/u/uSv2Lp.png" width="760" alt="ADI model lineup — size on disk">
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  </p>
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  ---
 
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  Then point any llama.cpp-based runtime at the downloaded file.
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  <p align="center">
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+ <img src="https://serve.thelabsource.com/u/T5Kdlg.png" width="700" alt="">
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  </p>
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  ## The approach
 
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  - **Open.** Apache-2.0 where the base license allows, with full training details on each model card so the work is reproducible.
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  <p align="center">
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+ <img src="https://serve.thelabsource.com/u/ih5dUC.png" width="760" alt="The ADI distillation pipeline">
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  </p>
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  ---
 
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  Models follow the pattern `adi-<base>-<size>-<teacher>-<purpose>` — so the name tells you the student base, its size, the teacher it learned from, and what it's tuned for.
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  <p align="center">
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+ <img src="https://serve.thelabsource.com/u/E0V8eW.png" width="560" alt="ADI">
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  </p>
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  *Built at [theLAB](https://thelabsource.com) — Learning. Algorithms. Breakthroughs.*