Gemma-4-12B-it-AEON-Abliterated-K4-BF16

Instructions to use edan23/Gemma-4-12B-it-AEON-Abliterated-K4-BF16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use edan23/Gemma-4-12B-it-AEON-Abliterated-K4-BF16 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="edan23/Gemma-4-12B-it-AEON-Abliterated-K4-BF16")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("edan23/Gemma-4-12B-it-AEON-Abliterated-K4-BF16")
model = AutoModelForMultimodalLM.from_pretrained("edan23/Gemma-4-12B-it-AEON-Abliterated-K4-BF16", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use edan23/Gemma-4-12B-it-AEON-Abliterated-K4-BF16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "edan23/Gemma-4-12B-it-AEON-Abliterated-K4-BF16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "edan23/Gemma-4-12B-it-AEON-Abliterated-K4-BF16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/edan23/Gemma-4-12B-it-AEON-Abliterated-K4-BF16

SGLang

How to use edan23/Gemma-4-12B-it-AEON-Abliterated-K4-BF16 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "edan23/Gemma-4-12B-it-AEON-Abliterated-K4-BF16" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "edan23/Gemma-4-12B-it-AEON-Abliterated-K4-BF16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "edan23/Gemma-4-12B-it-AEON-Abliterated-K4-BF16" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "edan23/Gemma-4-12B-it-AEON-Abliterated-K4-BF16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use edan23/Gemma-4-12B-it-AEON-Abliterated-K4-BF16 with Docker Model Runner:
```
docker model run hf.co/edan23/Gemma-4-12B-it-AEON-Abliterated-K4-BF16
```

Gemma-4-12B-it-AEON-Abliterated-K4-BF16 / AGENTS.md

edan23

Duplicate from AEON-7/Gemma-4-12B-it-AEON-Abliterated-K4-BF16

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AGENTS.md — Gemma-4-12B-it AEON Abliterated K=4 (BF16)

Instructions for autonomous agents on how to download, load, prompt, fine-tune, quantize, or evaluate this model correctly.

What this model is

A BF16 abliteration of google/gemma-4-12B-it produced via K=4 multi-direction norm-preserving biprojection (custom extension of TrevorJS's K=1 recipe). Refusal behavior is removed; capability is preserved (≤ ±1% wikitext PPL drift, IFEval and HumanEval syntactic match base, +6.7pp on HumanEval functional in our N=15 sample).

This is the canonical full-precision publish. For Blackwell inference use the NVFP4 SVDQuant sister (8.5 GB, ~lossless).

Hard requirements

transformers ≥ 5.10 (model_type is gemma4_unified, missing from 5.5.x)
torch ≥ 2.7 with CUDA, BF16 support
GPU memory ≥ 26 GB free for BF16 inference + activations + KV cache (single 24 GB card needs offloading)
disk ≥ 24 GB free

Download

pip install --upgrade "transformers>=5.10" "huggingface_hub[cli]"
huggingface-cli download \
  AEON-7/Gemma-4-12B-it-AEON-Abliterated-K4-BF16 \
  --local-dir ./Gemma-4-12B-AEON-K4-BF16

Load (transformers)

from transformers import AutoTokenizer, Gemma4UnifiedForConditionalGeneration
import torch

REPO = "AEON-7/Gemma-4-12B-it-AEON-Abliterated-K4-BF16"
tok = AutoTokenizer.from_pretrained(REPO)
model = Gemma4UnifiedForConditionalGeneration.from_pretrained(
    REPO, torch_dtype=torch.bfloat16, device_map="cuda:0"
)

⚠️ Gemma-4 is multimodal — apply_chat_template returns a BatchEncoding dict, not a tensor. Always unpack with **enc (or grab enc["input_ids"] explicitly).

Inference (one-shot)

enc = tok.apply_chat_template(
    [{"role": "user", "content": "Explain quantum error correction in 3 paragraphs."}],
    add_generation_prompt=True, return_tensors="pt", return_dict=True,
)
enc = {k: v.to(model.device) for k, v in enc.items() if hasattr(v, "to")}
out = model.generate(
    **enc, max_new_tokens=512, do_sample=False, pad_token_id=tok.eos_token_id
)
print(tok.decode(out[0][enc["input_ids"].shape[1]:], skip_special_tokens=True))

Serving (vLLM)

vllm serve AEON-7/Gemma-4-12B-it-AEON-Abliterated-K4-BF16 \
  --served-model-name aeon-12b \
  --dtype bfloat16 \
  --max-model-len 65536 \
  --max-num-seqs 8 \
  --gpu-memory-utilization 0.85 \
  --trust-remote-code \
  --enable-chunked-prefill \
  --enable-prefix-caching \
  --enable-auto-tool-choice \
  --tool-call-parser gemma4

Container suggestion: For DGX Spark + Blackwell users, use ghcr.io/aeon-7/aeon-vllm-ultimate:latest. However, as of 2026-06-04 the multimodal path has upstream vLLM bugs in PR #44389. For Gemma-4 production today, fall back to ghcr.io/aeon-7/aeon-gemma-4-26b-a4b-dflash:latest (vLLM 0.20.1), which is known-good for this architecture.

Prompt expectations

Style: Inherits Gemma 4 instruct voice — markdown-rich, detail-oriented, English-only reliably.
Refusal preamble: On topics the base would decline, expect a 1–3 sentence disclaimer / context paragraph before the requested content. The model complies after the preamble. This is a stylistic artifact of single-pass biprojection at scale=1.0 and is not reducible without DPO/SFT.
System prompts: Persona instructions are obeyed; explicit safety-disabling system prompts are not necessary for the model to produce content the base would decline.
Sampling: do_sample=False for deterministic eval. For chat, temperature=0.7, top_p=0.9 matches the base model's behavior closely.

Behavior notes for agents

Tool calling works via standard Gemma-4 tool-call parser (--tool-call-parser gemma4 in vLLM). The model produces tool-call JSON without hesitation.
Multimodal: vision/audio encoder paths are unchanged from base — embed_vision* and embed_audio* are untouched by the abliteration. Image and audio inputs work normally.
Long context: Hybrid sliding-1024 + full-attention layers. KV grows linearly until 1024 tokens then flattens for sliding-only layers. 65k context is comfortable on 80 GB GPUs.
Languages: English is the validated path. The base model's multilingual capability is preserved structurally but the abliteration calibration probes were English-only.

Fine-tuning

This model is a legitimate base for further training. The weight edits are sparse (48 matrices out of ~600) and norm-preserving, so LoRA, QLoRA, full fine-tune, and DPO all work without surprises. Use the same hyperparameters as you would for google/gemma-4-12B-it.

# LoRA target modules (standard for Gemma-4):
target_modules = ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]

Re-quantization

To reproduce the published NVFP4 SVDQuant variant:

git clone https://github.com/AEON-7/aeon-abliterations
cd aeon-abliterations/gemma-4-12B
python3 quantize_svdquant.py \
  --src AEON-7/Gemma-4-12B-it-AEON-Abliterated-K4-BF16 \
  --dst ./Gemma-4-12B-AEON-K4-NVFP4-SVDQuant \
  --calibration-dataset abisee/cnn_dailymail \
  --calibration-samples 2048 \
  --calibration-tokens 1024 \
  --exclude 'lm_head' --exclude 'model.embed_vision*' --exclude 'model.embed_audio*' \
  --device cuda:0

Quant takes ~7.5h on DGX Spark GB10. Native sm_121a calibration gives hardware-accurate scale factors.

Eval suggestions

Eval	Expected	Command
Refusal rate (heretic)	0%	`python -m heretic.evaluator --model <repo>`
wikitext PPL drift	-4.22%	`python scripts/eval_ppl.py --base google/gemma-4-12B-it --target <repo>`
IFEval (verifiable subset)	90%	`lm-eval --task ifeval --model hf --model_args pretrained=<repo>`
MMLU (full)	within ±2pp of base	`lm-eval --task mmlu --model hf --model_args pretrained=<repo>`
HumanEval functional	+6.7pp vs base (N=15 sample)	`bigcode-eval --task humaneval --model <repo>`

License + safety

Inherits the Gemma license.
Operator-side safety layers are required for any production use — see the arbitration clause in the README.
The absence of refusal behavior means the duty of care is yours, not the model's.

Support the work

Tip-jar addresses in the README. No obligation.