adi-qwen2.5-7b-ablit-glm5.2

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

An uncensored, fully local model that reasons and answers like a frontier teacher. Built by distilling glm-5.2 general-knowledge responses into an abliterated Qwen2.5-7B student with a deliberately light 4-bit QLoRA fine-tune, then merged, converted, and quantized to GGUF. The base is an abliterated (refusal-suppressed) Qwen2.5-7B-Instruct, and the distillation was designed to add the teacher's answer quality without restoring refusal behavior. The student base retains native tool calling and a long context window.

Capabilities

Run it

Pull directly into Ollama:

ollama run hf.co/AdvancedDataIntelligence/adi-qwen2.5-7b-ablit-glm5.2-GGUF:Q4_K_M

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

Uncensored behavior — please read

This model is built on an abliterated base: the refusal direction has been suppressed, so it will attempt most requests rather than declining them. The fine-tune was intentionally kept light (2 epochs, benign-only data) to avoid re-introducing refusals, and post-training spot checks confirmed the model still answers helpfully without added hedging.

You are responsible for using it lawfully and ethically. It has weaker built-in safety guardrails than the stock Qwen2.5-7B-Instruct; apply your own filtering and oversight for any production or public-facing deployment.

What this model is

This is a knowledge distillation: a strong teacher (glm-5.2) generated high-quality answers across a clean general-knowledge prompt set, and the abliterated Qwen2.5-7B student was fine-tuned to imitate them. The result reasons and responds more like its teacher on general topics while keeping the base's uncensored character.

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 7B that structures and explains like a larger model on topics it already partly knows — without the refusal behavior of an aligned model.

Training

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

• Distilling onto an abliterated base is a balancing act. Any SFT can nudge an abliterated model back toward refusals. Two choices kept the uncensored behavior intact: (1) benign-only training data — the GLM-5.2 set is built from Dolly prompts with zero refusals, so there are no refusals for the student to re-learn; and (2) a light touch — LoRA rank 16, only 2 epochs. Spot-checking refusal behavior before/after is recommended.
• 4-bit QLoRA via Unsloth with gradient checkpointing ("unsloth" mode), max_seq_length 2048, per-device batch 2 × grad-accum 4, adamw_8bit, LoRA targeting all attention + MLP projections. Peak VRAM 9.03 GB on a 16 GB card.
• GGUF conversion via streaming LoRA merge → f16 GGUF → Q4_K_M quantize with llama.cpp.

Intended use

General-purpose local assistant for users who want a capable, private, offline-capable model with minimal refusal behavior: explanations, reasoning, creative writing, and tool-calling workflows. Not intended as a source of authoritative facts without retrieval, and not a substitute for your own safety review.

License

Apache-2.0, inherited from the Qwen2.5-7B lineage via the abliterated 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.