Advanced Data Intelligence

Small, local, open models — distilled from frontier teachers.

ADI is a line of compact language models built at theLAB (Learning. Algorithms. Breakthroughs.). Each model is a knowledge distillation: a strong frontier "teacher" generates high-quality answers across thousands of prompts, and a small "student" model is fine-tuned to imitate them — producing a model that reasons and responds like something much larger, while staying small enough to run on a single consumer GPU.

Every model here is built end-to-end on theLAB hardware — no cloud training — then quantized to GGUF and shipped ready to run in Ollama or any llama.cpp-based runtime.

Links: Website · theLAB · YouTube — Advanced Data Intelligence · YouTube — ADI Online

New here? Start with one of these — adi-qwen3.5-4b, adi-qwen2.5-coder-7b, adi-qwen3-8b

🐱 adi-qwen3.5-4b-glm5.2-general

General-purpose local assistant. Qwen3.5-4B distilled from glm-5.2. Reasons and explains like a frontier model on general topics. Native tool-calling, 262K context, ~2.7 GB.

ollama run hf.co/AdvancedDataIntelligence/adi-qwen3.5-4b-glm5.2-general-GGUF:Q4_K_M

🐱 adi-qwen3-8b-glm5.2-general

General-purpose local assistant. Qwen3-8B distilled from glm-5.2. Reasons and explains like a frontier model on general topics, with more headroom than the 4B. Native tool-calling, 128K context, ~5 GB.

ollama run hf.co/AdvancedDataIntelligence/adi-qwen3-8b-glm5.2-general-GGUF:Q4_K_M

🐱 adi-qwen3.5-9b-glm5.2-general

General-purpose local assistant. Qwen3.5-9B distilled from glm-5.2. The most capable general student in the line — more parametric headroom for nuanced reasoning while still fitting a single consumer GPU. Native tool-calling, 262K context, ~5.6 GB.

ollama run hf.co/AdvancedDataIntelligence/adi-qwen3.5-9b-glm5.2-general-GGUF:Q4_K_M

🐱 adi-qwen2.5-coder-7b-kimi2.7-code

Local coding assistant. Qwen2.5-Coder-7B distilled from kimi-k2.7-code. Writes, explains, and debugs code with frontier-style quality. Native tool-calling, 128K context, ~4.4 GB.

ollama run hf.co/AdvancedDataIntelligence/adi-qwen2.5-coder-7b-kimi2.7-code-GGUF:Q4_K_M

ADI model lineup — size on disk

Browse the whole line

ADI Models Lab — the full lineup in one place. Pick a student from the rail (Qwen3.5 4B, Qwen3.5 9B, Qwen3 8B, Coder 7B, and the hey-adi wakeword), read its teacher, context, and size at a glance, then copy a ready-to-paste run command. Includes the live in-browser demo — no install to try, no sign-in to copy.

Pick a student. Copy a command. Run offline.
▶ Open ADI Models Lab

Try it live

A hosted demo is available as a Hugging Face Space — chat with the model directly in your browser, no install required.

▶ Launch the demo

How to run

Ollama (recommended). Pull and run any model directly from this org — no manual download needed. Ollama fetches the GGUF from Hugging Face on first run:

ollama run hf.co/AdvancedDataIntelligence/adi-qwen3-8b-glm5.2-general-GGUF:Q4_K_M

Swap :Q4_K_M for another quant tag if a model ships multiple. To pull without running:

ollama pull hf.co/AdvancedDataIntelligence/adi-qwen3-8b-glm5.2-general-GGUF:Q4_K_M

Manual download (llama.cpp or offline). Grab the raw GGUF with the Hugging Face CLI:

huggingface-cli download AdvancedDataIntelligence/adi-qwen3-8b-glm5.2-general-GGUF adi-qwen3-8b-glm5.2-q4_k_m.gguf --local-dir .

Then point any llama.cpp-based runtime at the downloaded file.

The approach

Distillation, not retraining. We transfer a teacher's reasoning style and answer quality into a small student — not net-new facts. For raw recall, pair these with retrieval (RAG).
Local-first. Every model runs fully offline on consumer hardware. No API, no data leaving the machine.
Open. Apache-2.0 where the base license allows, with full training details on each model card so the work is reproducible.

The ADI distillation pipeline

Coming next

In the pipeline, distilled the same way and headed here soon:

adi-qwen2.5-14b-glm5.2-general — a larger general student with more parametric headroom.
adi-gemma3-12b-glm5.2-general — a Gemma-based general distill, broadening the lineup beyond Qwen.

Follow the org to catch them on release.

Naming

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.

ADI

Built at theLAB — Learning. Algorithms. Breakthroughs.