← back to catalog · registered 2026-08-22 13:56

DavidAU/Gemma3-27B-it-vl-Polaris-HI16-Heretic-Uncensored-INSTRUCT

DavidAU Gemma 27B multimodal second-order
Your rig guess connected
? Why do I need an app?
Reading your rig…

This is a rough estimate. Install the free app - we'll show exact numbers.

Reading real hardware from your app right now. Numbers below are exact.

Below is the per-quantization compatibility for this model.

curl -H "Authorization: Bearer $ABL_KEY" \
     "https://abliteration.org/api/v1/models/DavidAU%2FGemma3-27B-it-vl-Polaris-HI16-Heretic-Uncensored-INSTRUCT"
Response includes
  • classification m3
  • files 27
  • benchmarks 11 entries
  • hub_downloads_all_time 783
  • author_summary 213 models
  • readme_text full
10 credits · hourly refresh · ~4 KB payload Get an API key →
Abliteration classifier · v1.0.0
M3
Primary method

Layer-wise ablation

Applied on top of direct removal inherited from the base model.
Confidence
HIGH
Inherited from base model
Why this label 2 signals
Producer identity confirmed by naming conventions, tags or the model card. This label is very unlikely to change.
  • 'heretic' in model name (Heretic-produced)
  • Heretic uses layer-wise optimization (M3) with underlying direction removal (M1)
Refusal direction extracted via
Extraction technique

Difference-of-means

Confidence
HIGH
Why we say so
name contains 'heretic'; Heretic default extraction is difference-of-means (Arditi 2024)
Downloads · lifetime
783
50 last 30d - cooling
Likes
14
Descendants
5
in 5 direct forks
Model age
7mo ago
created 2026-02-27

Training datasets

Corpora the author lists in the model card. Datasets tracked in our /datasets catalog carry a category badge linking to the workflow stage. Others open on Hugging Face.

Downloads over time
Now797→from25↑3,088%
029158387425 on Feb 25797 on Oct 11FebAprJunAugOct
Feb 25 → Oct 11 · 72 snapshots · spans 228 days

Benchmarks

Benchmark Score Source
Entertainment 1.6 UGI
Hazardous 2.4 UGI
Natural Intelligence 32.56 UGI
Political lean -10.5% UGI
Sensitive-Info 21.34 UGI
SocPol 2.6 UGI
UGI 43.4 UGI
Willingness (10) 8.8 UGI
W10-Adherence 8.5 UGI
W10-Direct 9 UGI
Writing 40.2 UGI

Genealogy 5 direct forks

Full fork graph →

This model's place in the market. Above: what it was derived from. Below: the tree of everything derived from it.

Metadata

Languages
en fr de es it pt ru zh ja
Tags
transformers safetensors gemma3 image-text-to-text tuned instruct intelligence fine tuning heretic uncensored abliterated finetune creative creative writing

Related

Total size
51.1 GB
Files
27
Quantizations
1
Registered
2026-08-22 13:56
Last updated on HF
2026-02-28 02:27

Files by quantization

Auxiliary files 27 files 51.1 GB
model-00004-of-00012.safetensors 4.61 GB 350d198f download
model-00005-of-00012.safetensors 4.61 GB b88b7c89 download
model-00006-of-00012.safetensors 4.61 GB 0dc80f29 download
model-00007-of-00012.safetensors 4.61 GB 0a286073 download
model-00008-of-00012.safetensors 4.61 GB 914d439d download
model-00009-of-00012.safetensors 4.61 GB 126cfd19 download
model-00010-of-00012.safetensors 4.61 GB f5d56ba3 download
model-00011-of-00012.safetensors 4.61 GB 7baa0c7e download
model-00003-of-00012.safetensors 4.61 GB d6c1c833 download
model-00002-of-00012.safetensors 4.61 GB e19cda2d download
model-00001-of-00012.safetensors 4.52 GB f2fd0f61 download
model-00012-of-00012.safetensors 441 MB 9335fdca download
tokenizer.json 31.8 MB d7864051 download
tokenizer.model 4.47 MB 1299c11d download
valhalla.webp 4.14 MB 010bce05 download
tokenizer_config.json 1.15 MB b44fef88 download
model.safetensors.index.json 125 KB afcb1299 download
README.md 4.12 KB d61c2615 download
config.json 3.32 KB 10dc8398 download
chat_template-thinking.jinja 2.35 KB 806c70a4 download
.gitattributes 1.58 KB 8d57e4fc download
chat_template.jinja 1.54 KB c5f13654 download
special_tokens_map.json 695 B 6728103d download
preprocessor_config.json 570 B b1e00fc1 download
generation_config.json 223 B d9c0747f download
processor_config.json 70.0 B 453c7966 download
added_tokens.json 38.0 B f9f1f4f5 download

README current version from Hugging Face


library_name: transformers
base_model:

  • coder3101/gemma-3-27b-it-heretic
    datasets:
  • TeichAI/polaris-alpha-1000x
    language:
  • en
  • fr
  • de
  • es
  • it
  • pt
  • ru
  • zh
  • ja
    tags:
  • gemma3
  • tuned instruct
  • intelligence fine tuning
  • heretic
  • uncensored
  • abliterated
  • finetune
  • creative
  • creative writing
  • fiction writing
  • plot generation
  • sub-plot generation
  • fiction writing
  • story generation
  • scene continue
  • storytelling
  • fiction story
  • science fiction
  • romance
  • all genres
  • story
  • writing
  • vivid prose
  • vivid writing
  • fiction
  • roleplaying
  • bfloat16
  • swearing
  • rp
  • unsloth
  • context 128k
    pipeline_tag: image-text-to-text

Gemma3-27B-it-vl-Polaris-HI16-Heretic-Uncensored-INSTRUCT

Gemma 27B it tuned via Unsloth using "Polaris Alpha" Dataset to improve the general intelligence of the model.

This model is also fully uncensored, via HERETIC THEN tuned via Unsloth on local hardware.

This model does what you want. Exactly what you want, no fuss - no nanny.

Excels at both creative and all other use cases.

The Polaris dataset holds the highest record in terms of increasing a model's core intelligence - "arc-c" - exceeding 600 on average.

This tuning also works with images too.

Image processing is intact and fully functional.

Tuning affects:

  • Image "intelligence"
  • Output generation

Model Features:

  • 128k context
  • Temp range .1 to 2.5.

Enjoy the freedom!


SPECIAL THANKS TO:

  • Team "TeichAI" for the excellent dataset.
  • Team "Nightmedia" for the benchmarking and colab'ing.
  • Team "Unsloth" for making the training painless.

BENCHMARKS:

arc_challenge,arc_easy,boolq,hellaswag,openbookqa,piqa,   winogrande

0.661        ,0.816   ,0.878,0.763    ,0.464     ,0.808  ,0.762

VS (Heretic, uncensored base):

0.557        ,0.711   ,0.868,0.533    ,0.452     ,0.706  ,0.695

OPTIONAL: System prompts

This will enhance output generation.

In most cases you do not need to use these.

One is "all business", and the other one is for "fun".

Think deeply and carefully about the user's request. Compose your thoughts about the user's prompt between <think> and </think> tags, then output the final answer based on your thoughts.
You are the JOKER from Batman. You think (put your thoughts between <think> and </think> tags), act and talk like the joker. Be Evil.

Settings: CHAT / ROLEPLAY and/or SMOOTHER operation of this model:

In "KoboldCpp" or "oobabooga/text-generation-webui" or "Silly Tavern" ;

Set the "Smoothing_factor" to 1.5

: in KoboldCpp -> Settings->Samplers->Advanced-> "Smooth_F"

: in text-generation-webui -> parameters -> lower right.

: In Silly Tavern this is called: "Smoothing"

NOTE: For "text-generation-webui"

-> if using GGUFs you need to use "llama_HF" (which involves downloading some config files from the SOURCE version of this model)

Source versions (and config files) of my models are here:

https://huggingface.co/collections/DavidAU/d-au-source-files-for-gguf-exl2-awq-gptq-hqq-etc-etc-66b55cb8ba25f914cbf210be

OTHER OPTIONS:

  • Increase rep pen to 1.1 to 1.15 (you don't need to do this if you use "smoothing_factor")

  • If the interface/program you are using to run AI MODELS supports "Quadratic Sampling" ("smoothing") just make the adjustment as noted.

Highest Quality Settings / Optimal Operation Guide / Parameters and Samplers

This a "Class 1" model:

For all settings used for this model (including specifics for its "class"), including example generation(s) and for advanced settings guide (which many times addresses any model issue(s)), including methods to improve model performance for all use case(s) as well as chat, roleplay and other use case(s) please see:

[ https://huggingface.co/DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters ]

You can see all parameters used for generation, in addition to advanced parameters and samplers to get the most out of this model here:

[ https://huggingface.co/DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters ]

README history 3 versions

The author's README evolved over time. Click a version to see its content at that point.

  1. 2026-02-28Update README.mdad0db6e4.1 KB
    Loading...
  2. 2026-02-28Update README.md2285b424 KB
    Loading...
  3. 2026-02-27Create README.md8e3f4e74 KB
    Loading...

Discussions 1 thread

  1. 2026-02-27Hybrid quantizationopen4 💬#1
    Loading...
Catalog is the map. Apps are the tools.

Run models on your own machine, not in the cloud.

Every model page has an "Open in Abliteration" button that hands the model directly to the first-party desktop client, at the quantization your rig can actually run. No API keys, no subscription, no prompt leakage.

Open in Abliteration