---

license: gemma

base_model: huihui-ai/gemma-3-12b-it-abliterated

tags:

- gguf

- distillation

- gemma3

- abliterated

- uncensored

- vision

- adi

- advanced-data-intelligence

- image-text-to-text

language:

- en

pipeline_tag: image-text-to-text

library_name: gguf

---

<img src="https://serve.thelabsource.com/u/x4F1lv.png" alt="adi-gemma3-12b-ablit-glm5.2" width="800">

adi-gemma3-12b-ablit-glm5.2

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

An uncensored, vision-capable, fully local model that reasons and answers like

a frontier teacher. Built by distilling glm-5.2 general-knowledge responses

into an abliterated Gemma-3-12B student with a light 4-bit QLoRA fine-tune,

then merged, converted, and quantized to GGUF. The base's vision tower is

preserved and shipped as a companion projector, and the abliterated base keeps its

minimal-refusal behavior — the fine-tune was kept light specifically to avoid

re-aligning it.

Capabilities

| Size | Context | Input | Output | Tools |

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

| 6.8 GB | 128K | 🅣🖼️ Text + Image | Text | ✅ |

| | |

|---|---|

| Base model | huihui-ai/gemma-3-12b-it-abliterated (abliterated Gemma-3-12B-it) |

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

| Method | Light 4-bit QLoRA SFT (rank 16, 2 epochs) → merge → GGUF |

| Quantization | Q4_K_M (~6.8 GB text) + vision projector (mmproj, ~815 MB) |

| License | Gemma (Google Gemma Terms of Use) |

| Context | 128K (inherited from base) |

| Vision | Supported — multimodal (image + text → text) |

Run it

Pull directly into Ollama:

ollama run hf.co/AdvancedDataIntelligence/adi-gemma3-12b-ablit-glm5.2-GGUF:Q4_K_M

It's multimodal — pass an image to have it describe or reason over it:

ollama run adi-gemma3-12b-ablit-glm5.2 "What's in this image? /path/to/photo.jpg"

Or download the .gguf (text) + mmproj-*.gguf (vision projector) and point any

llama.cpp-based runtime at them.

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 Gemma-3-12B student was fine-tuned to imitate them. The result reasons

and responds more like its teacher on general topics, keeps the base's uncensored

character, and retains native image understanding — all while running 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 12B that structures and explains like a larger model on topics it

already partly knows — without the refusal behavior of an aligned model.

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. You are responsible for using it lawfully and ethically;

it has weaker built-in safety guardrails than stock Gemma-3-12B-it.

Training

| Metric | Value |

|---|---|

| Training pairs | 2,000 (deterministic subset of a 4,982-pair clean set) |

| Epochs | 2 (kept light to preserve abliteration) |

| Steps | 500 |

| Final train loss | 0.9896 |

| LoRA rank / alpha | 16 / 16 |

| Trainable params | 68.5M |

| Precision | 4-bit QLoRA (nf4) |

| Peak VRAM | 10.42 GB |

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

| Training time | 2.97 h (~22 s/step) |

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 behavior intact:

benign-only training data (the GLM-5.2 set has zero refusals to re-learn) and

a light touch (LoRA rank 16, 2 epochs). Spot-check refusals before/after.

• Gemma 3 uses FlexAttention (Triton). Expect a slow first ~5–8 steps while the

attention kernels autotune and cache, then it settles (~22 s/step on a 16 GB card).

• 4-bit QLoRA via Unsloth with gradient checkpointing ("unsloth" mode),

max_seq_length 2048, per-device batch 1 × grad-accum 8, LoRA targeting all

attention + MLP projections. Peak VRAM 10.42 GB.

• Vision: the LoRA targeted only the language layers; the vision tower is

carried through unchanged. GGUF conversion used llama.cpp's convert_hf_to_gguf.py

(Gemma3ForConditionalGeneration), with --mmproj producing the vision projector.

Ollama serves Gemma 3 vision natively.

Intended use

General-purpose local assistant with image understanding for users who want a

capable, private, offline-capable model with minimal refusal behavior:

explanations, reasoning, visual Q&A, and creative writing. Not intended as a source

of authoritative facts without retrieval, and not a substitute for your own safety

review.

License

Gemma — governed by Google's Gemma Terms of Use

and Prohibited Use Policy,

inherited via the abliterated

base model. This is

more restrictive than Apache-2.0; review both before redistribution or

deployment. Distilled training data was generated using glm-5.2; review the teacher

model's terms as well.

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