---

license: apache-2.0

base_model: huihui-ai/Qwen2.5-VL-7B-Instruct-abliterated

tags:

- gguf

- distillation

- qwen2.5-vl

- 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/b2l0zP.png" alt="adi-qwen2.5-vl-7b-ablit-glm5.2" width="800">

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

Part of the ADI (Advanced Data Intelligence) model line — ADI Qwen 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 Qwen2.5-VL-7B student with a light 4-bit QLoRA fine-tune,

then merged, converted, and quantized to GGUF. Only the language layers were

tuned — the base's vision tower is preserved and shipped as a companion

projector — and the abliterated base keeps its minimal-refusal behavior, with

the fine-tune kept light specifically to avoid re-aligning it.

Capabilities

| Size | Context | Input | Output | Tools |

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

| 4.68 GB | 128K | 🅣🖼️ Text + Image | Text | ✅ |

| | |

|---|---|

| Base model | huihui-ai/Qwen2.5-VL-7B-Instruct-abliterated (abliterated Qwen2.5-VL-7B-Instruct) |

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

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

| Quantization | Q4_K_M (~4.68 GB text) + vision projector (mmproj, ~1.3 GB) |

| License | Apache-2.0 (inherited from Qwen2.5-VL-7B) |

| Context | 128K (inherited from base) |

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

Run it

Pull directly into Ollama:

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

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

ollama run adi-qwen2.5-vl-7b-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. Both files are required for vision.

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-VL-7B 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 7B 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 Qwen2.5-VL-7B-Instruct.

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 | 1.2618 |

| LoRA rank / alpha | 16 / 16 |

| Trainable params | 40.4M (language layers only; vision tower frozen) |

| Precision | 4-bit QLoRA (nf4) |

| Peak VRAM | 8.14 GB |

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

| Training time | 1.44 h (~10 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.

• Vision base = train language only. Load with Unsloth FastVisionModel

(load_in_4bit=True) and `get_peft_model(finetune_vision_layers=False,

finetune_language_layers=True, ...)`. The vision tower rides through unchanged, so

the base's mmproj is the final vision projector — reuse it, don't regenerate.

• Free the GPU before loading. An Ollama model left resident in VRAM makes the

4-bit VL load spill to CPU (ValueError: Some modules are dispatched on the CPU);

ollama stop <model> first.

• GGUF conversion via streaming LoRA merge (language keys map

model.language_model. → model.) → f16 GGUF → Q4_K_M with llama.cpp

(Qwen2_5_VLForConditionalGeneration).

Serving note (Ollama vision)

On some Ollama builds the Qwen2.5-VL vision runner can degrade to blank/garbled

output after several requests in a session (text is unaffected). If that happens,

reload the model — ollama stop adi-qwen2.5-vl-7b-ablit-glm5.2 then re-run, or set

keep_alive — for a clean vision pass. The GGUF itself is correct; this is a

runtime quirk likely resolved by newer Ollama versions.

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

Apache-2.0, inherited from the Qwen2.5-VL-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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