--- 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 --- # 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](https://huggingface.co/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](https://ollama.com): ```bash 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. **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. ## 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](https://huggingface.co/datasets/databricks/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](https://huggingface.co/Qwen/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. --- *Built at [theLAB](https://thelabsource.com) — Learning. Algorithms. Breakthroughs.*
