Get the app
← back to catalog · registered 2026-08-29 13:03

NINI26454/gemma-4-12B-it-abliterated-4bit

NINI26454 Gemma 11B multimodal
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/NINI26454%2Fgemma-4-12B-it-abliterated-4bit"
Response includes
  • classification m1
  • files 8
  • hub_downloads_all_time 41
  • author_summary 16 models
  • readme_text full
Try next
GET/models?author=NINI26454
All 16 models by NINI26454
 GET/models?family=gemma&method=m1
Sibling gemma models · same method
 GET/methods/m1
What "m1" means · taxonomy
10 credits · hourly refresh · ~4 KB payload Get an API key →
Abliteration classifier · v1.0.0
M1
Primary method

Direct removal

No other method signals detected in this model.
Confidence
MEDIUM
Why this label 3 signals
Method inferred from partial signals - repository name, related files, or tag patterns. Producer identity not confirmed; label may sharpen or shift as we gather more evidence.
  • 'abliterated' in name/tags
  • is_gguf=0 (base model)
  • no specific method indicators - defaulting to M1 (most common)
How we label → About M1 →
Refusal direction extracted via
Extraction technique

Difference-of-means

FromArditi et al. (2024) ↗
Confidence
MEDIUM
Why we say so
primary_method=M1; difference-of-means is the reference extraction for M1/M3 (Arditi 2024)
What is a refusal direction? → Read the paper ↗
Downloads · lifetime
41
Likes
0
Model age
6w ago
created 2026-08-29
Downloads over time
Now65→from0↑0%
02448720 on Aug 2665 on Oct 13AugSepOct
Aug 26 → Oct 13 · 47 snapshots · spans 48 days

Metadata

Tags
transformers safetensors gemma4_unified image-text-to-text conversational arxiv:1910.09700 endpoints_compatible 4-bit bitsandbytes region:us

Related

Other by NINI26454
OpenAi-GPT-oss-20b-HERETIC-uncensored-NEO-Q3_K_M-GGUF 368 Mistral-Small-24B-Instruct-2501-Abliterated-GGUF 353 Llama-3.1-8B-Instruct-Abliterated-GGUF 69 Qwen2.5-7B-Instruct-Abliterated-GGUF 69 Huihui-Qwen3.6-27B-abliterated-MTP-Q3_K-GGUF 69
Total size
7.15 GB
Files
8
Quantizations
1
Registered
2026-08-29 13:03
Last updated on HF
2026-08-29 12:37
Source huggingface.co/NINI26454/gemma-4-12B-it-abliterated-4bit ↗

Files by quantization

Auxiliary files 8 files 7.18 GB
model.safetensors 7.15 GB ff30471a download
tokenizer.json 30.7 MB cc8d3a0c download
chat_template.jinja 18.2 KB 4741bf6e download
config.json 5.31 KB 7752b2a6 download
README.md 5.05 KB bc5f30d6 download
tokenizer_config.json 3.65 KB bc3271ed download
.gitattributes 1.53 KB 52373fe2 download
generation_config.json 255 B da32640a download

README current version from Hugging Face

library_name: transformers
tags: []

Model Card for Model ID

Model Details

Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

  • Developed by: [More Information Needed]
  • Funded by [optional]: [More Information Needed]
  • Shared by [optional]: [More Information Needed]
  • Model type: [More Information Needed]
  • Language(s) (NLP): [More Information Needed]
  • License: [More Information Needed]
  • Finetuned from model [optional]: [More Information Needed]

Model Sources [optional]

  • Repository: [More Information Needed]
  • Paper [optional]: [More Information Needed]
  • Demo [optional]: [More Information Needed]

Uses

Direct Use

[More Information Needed]

Downstream Use [optional]

[More Information Needed]

Out-of-Scope Use

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

Training Details

Training Data

[More Information Needed]

Training Procedure

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

  • Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

[More Information Needed]

Metrics

[More Information Needed]

Results

[More Information Needed]

Summary

Model Examination [optional]

[More Information Needed]

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

  • Hardware Type: [More Information Needed]
  • Hours used: [More Information Needed]
  • Cloud Provider: [More Information Needed]
  • Compute Region: [More Information Needed]
  • Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

[More Information Needed]

Compute Infrastructure

[More Information Needed]

Hardware

[More Information Needed]

Software

[More Information Needed]

Citation [optional]

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

[More Information Needed]

More Information [optional]

[More Information Needed]

Model Card Authors [optional]

[More Information Needed]

Model Card Contact

[More Information Needed]

README history 1 version

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

  1. 2026-08-29Upload Gemma4UnifiedForConditionalGeneration6944dc35.1 KB
    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.

Get the app Browse the catalog
Open in Abliteration