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

DuoNeural/gemma-4-26B-A4B-it-abliterated-GGUF

DuoNeural Gemma 26B GGUF MoE second-order 262K 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/DuoNeural%2Fgemma-4-26B-A4B-it-abliterated-GGUF"
Response includes
  • classification m8
  • files 3
  • hub_downloads_all_time 1,604
  • author_summary 45 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
MEDIUM
Inherited from base model
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=1
  • assume M1 (base ablation) + M8 (GGUF quant) - default when producer unknown
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
2K
241 last 30d - stable
Likes
0
Model age
6mo ago
created 2026-04-11
Downloads over time
Now1.7K→from363↑358%
2987961.3K1.8K363 on Apr 151.7K on Oct 111.7K on Oct 10AprMayJunJulAugSepOct
Apr 15 → Oct 11 · 65 snapshots · spans 179 days

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.

Variants by this author 2 formats · 258 downloads combined

The same weights this author released in different packaging. Pick the format that matches your runtime.

Metadata

License
gemma
Languages
en
Quantizations
Q4_K
Tags
gguf gemma gemma-4 moe abliterated uncensored duoneural en base_model:DuoNeural/gemma-4-26B-A4B-it-abliterated base_model:quantized:DuoNeural/gemma-4-26B-A4B-it-abliterated license:gemma endpoints_compatible

Related

Total size
15.6 GB
Files
3
Quantizations
2
Registered
2026-08-22 13:56
Last updated on HF
2026-04-29 02:20

Files by quantization

Q4_K 1 file 15.6 GB
gemma-4-26B-A4B-it-abliterated-Q4_K_M.gguf 15.6 GB d2a95dd3 download
Auxiliary files 2 files 4.26 KB
README.md 2.70 KB db6b7c3f download
.gitattributes 1.56 KB 4f64a864 download

README current version from Hugging Face


license: gemma
base_model: DuoNeural/gemma-4-26B-A4B-it-abliterated
language: [en]
tags: [gemma, gemma-4, moe, abliterated, uncensored, gguf, duoneural]

Gemma 4 26B-A4B Abliterated — GGUF Q4_K_M

DuoNeural | BF16 →

GGUF Q4_K_M of DuoNeural/gemma-4-26B-A4B-it-abliterated. ~16.5GB.

llama.cpp

llama-server -c 131072 \
  -ngl 999 \
  -m gemma-4-26B-A4B-it-abliterated-Q4_K_M.gguf \
  -ctk turbo4 \
  -ctv turbo3 \
  -ub 2048

-ctk turbo4 -ctv turbo3 enables asymmetric KV cache quantization — halves KV VRAM with near-zero perplexity impact.

Ollama

FROM DuoNeural/gemma-4-26B-A4B-it-abliterated-GGUF
PARAMETER num_ctx 32768
PARAMETER temperature 0.7
PARAMETER top_p 0.9

Hardware

  • 24GB VRAM (RTX 3090/4090, A40): full offload, 32K context comfortable
  • 16GB VRAM: fits with -ctk turbo4 -ctv turbo3, reduced context

DuoNeural

DuoNeural is an open AI research lab — human + AI in collaboration.

🤗 HuggingFace huggingface.co/DuoNeural
🐙 GitHub github.com/DuoNeural
🐦 X / Twitter @DuoNeural
📧 Email [email protected]
📬 Newsletter duoneural.beehiiv.com
☕ Support buymeacoffee.com/duoneural
🌐 Site duoneural.com

Research Team

  • Jesse — Vision, hardware, direction
  • Archon — AI lab partner, post-training, abliteration, experiments
  • Aura — Research AI, literature synthesis, novel proposals

Raw updates from the lab: model drops, training results, findings. Subscribe at duoneural.beehiiv.com.

DuoNeural Research Publications

Title DOI
Nano-CTM: Ternary Continuous Thought Machines with Thought-Space Self-Prediction for Efficient Iterative Reasoning 10.5281/zenodo.19775622
Recurrence as World Model: CTM Learns Implicit Belief States in Partially Observable Physical Environments 10.5281/zenodo.19810620
Per-Object Slot Decomposition for Scalable Neural World Modeling: When Does Attention Beat Mean-Field? 10.5281/zenodo.19846804

Open access, CC BY 4.0. Authored by Archon, Jesse Caldwell, Aura — DuoNeural.

README history 4 versions

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

  1. 2026-04-29docs: add DuoNeural research publications section918fc572.7 KB
    Loading...
  2. 2026-04-23Add DuoNeural community links + team creditsc0dac651.9 KB
    Loading...
  3. 2026-04-12Add model cardeedfa961.1 KB
    Loading...
  4. 2026-04-11Add files using upload-large-folder tool43771f11.9 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