Update README.md

README.md

@@ -8,46 +8,58 @@ pinned: false
 short_description: Small, local models distilled from frontier teachers
 ---
 # Advanced Data Intelligence
-
 **Small, local, open models — distilled from frontier teachers.**
-
 ADI is a line of compact language models built at [theLAB](https://thelabsource.com)
 (*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](https://ollama.com) or any
 llama.cpp-based runtime.
-
 ---
-
 ## Models
-
 ### 🐱 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.
-
 ```bash
 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, 262K context, ~5 GB.
+```bash
+ollama run hf.co/AdvancedDataIntelligence/adi-qwen3-8b-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.
-
 ```bash
 ollama run hf.co/AdvancedDataIntelligence/adi-qwen2.5-coder-7b-kimi2.7-code-GGUF:Q4_K_M
 ```
-
 ---
-
+## 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:
+```bash
+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:
+```bash
+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:
+```bash
+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).
@@ -55,16 +67,10 @@ ollama run hf.co/AdvancedDataIntelligence/adi-qwen2.5-coder-7b-kimi2.7-code-GGUF
   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.
-
 ---
-
 ## 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.
-
 ---
-
-*Built at [theLAB](https://thelabsource.com) — Learning. Algorithms. Breakthroughs.*
-Edit this `README.md` markdown file to author your organization card.
+*Built at [theLAB](https://thelabsource.com) — Learning. Algorithms. Breakthroughs.*