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 Qwen3.5-9B
student with a 4-bit QLoRA fine-tune, then merged to fp16, converted, and quantized
to GGUF. The student base retains native tool calling and a long context window.
ollama run hf.co/AdvancedDataIntelligence/adi-qwen3.5-9b-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
Qwen3.5-9B student was fine-tuned to imitate them. The result reasons and
responds noticeably more like its teacher on general topics, 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. For raw factual recall,
retrieval-augmented generation (RAG) is the right tool, not fine-tuning. What you
get here is a 9B that structures and explains like a much larger model on topics
it already partly knows.
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
Version pins matter. Qwen3.5 requires transformers >= 5.2.0 to be recognized
by Unsloth (min reported 5.2.0); the working combination is transformers == 5.5.0
with torch 2.10.0+cu128 and unsloth 2026.6.8.
This build used 4-bit QLoRA. It trained cleanly (loss 0.8535, peak 9.6 GB VRAM).
Note that Qwen3.5's gated-delta / linear-attention layers can quantize less
gracefully than dense models — a bf16 LoRA pass is a reasonable upgrade path for a v2.
GGUF conversion used llama.cpp's convert_hf_to_gguf.py, which understands the
Qwen3.5 SSM/MTP architecture and auto-skips the MTP tensors (no --no-mtp flag
needed). The fp16 base (18 GB) exceeds 16 GB VRAM, so the LoRA was merged with a
streaming shard-by-shard merge rather than an in-VRAM merge.
An explicit Qwen chat TEMPLATE plus <|im_end|> / <|endoftext|> stop tokens are
set in the Modelfile to avoid runaway generation.
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 Qwen3.5-9B
base model. 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 — Learning. Algorithms. Breakthroughs.
