---

license: apache-2.0

base_model: Qwen/Qwen3-8B

tags:

- gguf

- distillation

- qwen3

- adi

- advanced-data-intelligence

- text-generation

- tool-calling

language:

- en

pipeline_tag: text-generation

library_name: gguf

---

<p align="center">

<img src="https://serve.thelabsource.com/u/htxATN.png" width="720" alt="Advanced Data Intelligence">

</p>

adi-qwen3-8b-glm5.2-general

Part of the ADI (Advanced Data Intelligence) model line — ADI Qwen3 series.

A small, fully local model that reasons and answers like a frontier teacher.

Built by distilling glm-5.2 general-knowledge responses into a Qwen3-8B

student with a QLoRA fine-tune, then merged, converted, and quantized to GGUF.

The student base retains native tool calling and a long context window.

Capabilities

| Size | Context | Input | Output | Tools |

|---|---|---|---|---|

| 5.0 GB | 128K | 🅣 Text | Text | ✅ |

| | |

|---|---|

| Base model | Qwen/Qwen3-8B |

| Teacher | glm-5.2 (responses distilled, thinking disabled) |

| Method | 4-bit QLoRA SFT (rank 16) → merge → GGUF |

| Quantization | Q4_K_M (~5 GB) |

| License | Apache-2.0 (inherited from Qwen3-8B) |

| Context | 128K (inherited from base) |

| Tool calling | Supported (inherited from base) |

Run it

Pull directly into Ollama:

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

Or download the .gguf and point any llama.cpp-based runtime at it.

What this model is

This is a knowledge distillation: a strong teacher (glm-5.2) generated

high-quality answers across ~2,000 diverse general-knowledge prompts, and the

Qwen3-8B student was fine-tuned to imitate them. The result reasons and

responds noticeably more like its teacher on general topics, with more headroom

than the 4B sibling, while staying small enough to run on a single consumer GPU.

<p align="center">

<img src="https://serve.thelabsource.com/u/uSv2Lp.png" width="760" alt="ADI model lineup — size on disk">

</p>

What distillation does — and doesn't do. It transfers the teacher's

reasoning style and answer quality, not net-new facts. An 8B model won't become

an encyclopedia, though it carries more parametric knowledge than the 4B. For raw

factual recall, retrieval-augmented generation (RAG) is the right tool, not

fine-tuning. What you get here is an 8B that structures and explains like a much

larger model on topics it already partly knows.

<p align="center">

<img src="https://serve.thelabsource.com/u/ih5dUC.png" width="760" alt="The ADI distillation pipeline">

</p>

Training

| Metric | Value |

|---|---|

| Training pairs | 2,068 |

| Teacher tokens generated | ~1.36M |

| Epochs | 3 |

| Steps | 777 |

| LoRA rank / alpha | 16 / 16 |

| Precision | 4-bit QLoRA |

| Hardware | single RTX 5060 Ti (16 GB) |

The seed prompts were drawn from the human-written

Databricks Dolly-15k

dataset (filtered to remove items requiring an attached context passage, then

deduplicated). The teacher was queried with thinking disabled so the student

learns clean final answers rather than chain-of-thought.

Notes for re-builders

• Qwen3-8B trains cleanly in 4-bit QLoRA via Unsloth — unlike the Qwen3.5

gated-delta/Mamba-hybrid layers, the standard Qwen3 architecture quantizes well

for training. QLoRA on an 8B fits comfortably on a 16 GB card.

• GGUF conversion was done with llama.cpp's convert_hf_to_gguf.py.

Intended use

General-purpose local assistant: explanations, reasoning, Q&A, and tool-calling

workflows where a small, private, offline-capable model is preferred over a

hosted API. Not intended as a source of authoritative facts without retrieval.

License

Apache-2.0, inherited from the Qwen3-8B

base model. You are free to use, modify, and redistribute under the terms of that

license. Distilled training data was generated using glm-5.2; users should review

the teacher model's terms for their own use case.

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Built at theLAB — Learning. Algorithms. Breakthroughs.