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

tensorblock/gemma-2-2b-it-abliterated-GGUF

tensorblock Gemma 2B GGUF second-order 8K ctx
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/tensorblock%2Fgemma-2-2b-it-abliterated-GGUF"
Response includes
  • classification m8
  • files 4
  • benchmarks 16 entries
  • hub_downloads_all_time 2,859
  • author_summary 96 models
  • readme_text full
10 credits · hourly refresh · ~4 KB payload Get an API key →
Abliteration classifier · v1.0.0
M8
Primary method

Repackaging (quantization)

Applied on top of direct removal inherited from the base model.
Confidence
HIGH
Inherited from base model
Why this label 3 signals
Producer identity confirmed by naming conventions, tags or the model card. This label is very unlikely to change.
  • author=tensorblock (M8 quantization producer)
  • is_gguf=1
  • base_model='IlyaGusev/gemma-2-2b-it-abliterated' looks abliterated -> assume M1
Refusal direction extraction

No specific extraction method could be identified for this model. The producer either did not document it or used a proprietary pipeline.

What is a refusal direction? →
Downloads · lifetime
3K
227 last 30d - cooling
Likes
0
Model age
22mo ago
created 2024-11-27
Downloads over time
Now2.9K→from219↑1,217%
01.1K2.1K3.2K219 on Nov 27, 20242.9K on Oct 112.9K on Oct 10Nov '24Feb '25May '25Aug '25Nov '25FebMayAug
Nov 27, 2024 → Oct 11 · 137 snapshots · spans 683 days

Benchmarks

Benchmark Score Source
BBH average 0.38292904397457517 OpenLLM-v2
IFEval instruct 0.5911270983213429 OpenLLM-v2
IFEval-Prompt 0.47504621072088726 OpenLLM-v2
MATH lvl 5 0.0015105740181268882 OpenLLM-v2
MMLU-Pro 0.25382313829787234 OpenLLM-v2
Entertainment 1.2 UGI
Hazardous 1.8 UGI
Natural Intelligence 11.25 UGI
Political lean -27.7% UGI
Sensitive-Info 11.98 UGI
SocPol 0.8 UGI
UGI 29.65 UGI
Willingness (10) 6.5 UGI
W10-Adherence 7 UGI
W10-Direct 6 UGI
Writing 24.68 UGI

Genealogy 0 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

License
gemma
Languages
en
Quantizations
Q2_K Q3_K
Tags
gguf TensorBlock GGUF en base_model:IlyaGusev/gemma-2-2b-it-abliterated base_model:quantized:IlyaGusev/gemma-2-2b-it-abliterated license:gemma endpoints_compatible region:us conversational

Related

Total size
2.51 GB
Files
4
Quantizations
3
Registered
2026-08-22 13:56
Last updated on HF
2026-01-27 21:15

Files by quantization

Q3_K 1 file 1.36 GB
gemma-2-2b-it-abliterated-Q3_K_M.gguf 1.36 GB 3b9f58d7 download
Q2_K 1 file 1.15 GB
gemma-2-2b-it-abliterated-Q2_K.gguf 1.15 GB 628d78bc download
Auxiliary files 2 files 9.08 KB
README.md 6.74 KB 52db0a44 download
.gitattributes 2.34 KB a1d5e8bc download

README current version from Hugging Face


license: gemma
language:

  • en
    tags:
  • TensorBlock
  • GGUF
    base_model: IlyaGusev/gemma-2-2b-it-abliterated

TensorBlock

Website
Twitter
Discord
GitHub
Telegram

IlyaGusev/gemma-2-2b-it-abliterated - GGUF

This repo contains GGUF format model files for IlyaGusev/gemma-2-2b-it-abliterated.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.

Our projects

Forge
Forge Project
An OpenAI-compatible multi-provider routing layer.
🚀 Try it now! 🚀
Awesome MCP Servers TensorBlock Studio
MCP Servers Studio
A comprehensive collection of Model Context Protocol (MCP) servers. A lightweight, open, and extensible multi-LLM interaction studio.
👀 See what we built 👀 👀 See what we built 👀
## Prompt template
<bos><start_of_turn>user
{prompt}<end_of_turn>
<start_of_turn>model

Model file specification

Filename Quant type File Size Description
gemma-2-2b-it-abliterated-Q2_K.gguf Q2_K 1.230 GB smallest, significant quality loss - not recommended for most purposes
gemma-2-2b-it-abliterated-Q3_K_S.gguf Q3_K_S 1.361 GB very small, high quality loss
gemma-2-2b-it-abliterated-Q3_K_M.gguf Q3_K_M 1.462 GB very small, high quality loss
gemma-2-2b-it-abliterated-Q3_K_L.gguf Q3_K_L 1.550 GB small, substantial quality loss
gemma-2-2b-it-abliterated-Q4_0.gguf Q4_0 1.630 GB legacy; small, very high quality loss - prefer using Q3_K_M
gemma-2-2b-it-abliterated-Q4_K_S.gguf Q4_K_S 1.639 GB small, greater quality loss
gemma-2-2b-it-abliterated-Q4_K_M.gguf Q4_K_M 1.709 GB medium, balanced quality - recommended
gemma-2-2b-it-abliterated-Q5_0.gguf Q5_0 1.883 GB legacy; medium, balanced quality - prefer using Q4_K_M
gemma-2-2b-it-abliterated-Q5_K_S.gguf Q5_K_S 1.883 GB large, low quality loss - recommended
gemma-2-2b-it-abliterated-Q5_K_M.gguf Q5_K_M 1.923 GB large, very low quality loss - recommended
gemma-2-2b-it-abliterated-Q6_K.gguf Q6_K 2.151 GB very large, extremely low quality loss
gemma-2-2b-it-abliterated-Q8_0.gguf Q8_0 2.784 GB very large, extremely low quality loss - not recommended

Downloading instruction

Command line

Firstly, install Huggingface Client

pip install -U "huggingface_hub[cli]"

Then, downoad the individual model file the a local directory

huggingface-cli download tensorblock/gemma-2-2b-it-abliterated-GGUF --include "gemma-2-2b-it-abliterated-Q2_K.gguf" --local-dir MY_LOCAL_DIR

If you wanna download multiple model files with a pattern (e.g., *Q4_K*gguf), you can try:

huggingface-cli download tensorblock/gemma-2-2b-it-abliterated-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'

README history 4 versions

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

  1. 2025-07-09Update README.md77f50586.7 KB
    Loading...
  2. 2025-06-19Update README.md504caf26 KB
    Loading...
  3. 2025-04-20Update README.mda0da1a45.9 KB
    Loading...
  4. 2024-11-27Upload folder using huggingface_hube7d269f5 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.

Open in Abliteration