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alonsoko/
gemma-4-31b-it-abliterated-heretic-AWQ-W4A16

Name: gemma-4-31b-it-abliterated-heretic-AWQ-W4A16
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Gemma-4-31B-it-abliterated-heretic-AWQ-W4A16

AWQ W4A16 (group_size 128, symmetric) quantization of trohrbaugh/gemma-4-31b-it-heretic-ara — a Heretic-ARA abliterated derivative of google/gemma-4-31b-it.

⚠️ Decensored model. Safety guardrails have been deliberately removed. Research and experimentation only. See full disclaimer below.

Quantization Details

Parameter	Value
Method	AWQ (Activation-aware Weight Quantization)
Scheme	W4A16 (symmetric)
Weight Bits	4
Activation Bits	16
Group Size	128
Format	compressed-tensors
Calibration Dataset	HuggingFaceH4/ultrachat_200k
Calibration Samples	256
Max Sequence Length	2048
Vision Tower	Unquantized (full precision)
LM Head	Unquantized (full precision)
Compatible Inference Engine	vLLM (`vllm/vllm-openai:gemma4`)

Quantization Notes

All multimodal paths kept full precision: Vision tower, audio tower, video tower, multi-modal projector, and all modality-specific embedding and projection layers are excluded from quantization. Only language-model linear layers (attention Q/K/V/O and MLP gate/up/down) are quantized.
LM head unquantized: Standard practice to preserve output token distribution quality at negligible size cost.
v_proj → o_proj smoothing skipped: llm-compressor reports incompatible balance layer dimensions for v_proj → o_proj on this checkpoint across many of Gemma 4's decoder blocks. Per-channel smoothing is skipped for that path, matching the standard AWQ-for-GQA pattern.
Hybrid attention and context window unchanged: Quantization only touches Linear layer weights; Gemma 4's interleaved local/global attention pattern and 256K context capacity are structurally preserved. Actual long-context quality at 4-bit has not been benchmarked.
Multimodal input intact: Text + image input works as in the base model. Use the standard Gemma 4 chat template with image tokens placed before text.
Full quantization recipe is preserved in recipe.yaml in this repo for reproducibility. It records the exact AWQ mappings, ignore patterns, and scheme parameters applied.

Deployment

Recommended inference with vLLM (Gemma 4 requires the vllm/vllm-openai:gemma4 image):

bash

vllm serve alonsoko/gemma-4-31b-it-abliterated-heretic-AWQ-W4A16 \
    --trust-remote-code \
    --tensor-parallel-size 1 \
    --max-model-len 32768

Recommended sampling (per upstream Gemma 4 guidance): temperature=1.0, top_p=0.95, top_k=64

To enable thinking mode, include the <|think|> token at the start of the system prompt.

Hardware Requirements

Approximate VRAM for inference at this quantization (W4A16-G128):

Weights (quantized language model + unquantized vision tower, all in one safetensors): ~19 GB
KV cache (per request, grows with context length): ~1-4 GB at 32K context, more at longer contexts
Recommended: Single 24 GB GPU (RTX 3090/4090, A10G) for standard context up to ~32K, or single 48 GB GPU (L40S/A6000) for long context / batch serving
Long context (128K+): 48-80 GB recommended due to KV cache growth

⚠️ Disclaimer

This model is intended for research, experimentation, and testing purposes only.

This model may produce harmful, offensive, inappropriate, or otherwise objectionable content.
The abliteration process removes safety guardrails that were intentionally built into the original model.
Do not use this model in production systems, consumer-facing applications, or any context where harmful outputs could cause real-world harm.
The authors and contributors of this toolkit bear no responsibility for any misuse of this model or any harm caused by outputs generated by this model.
By using this model, you agree that you are solely responsible for ensuring its use complies with all applicable laws and ethical guidelines.

This model is shared purely for academic and technical exploration of model internals.

Abliteration

Performed with Heretic v1.2.0+custom using the Arbitrary-Rank Ablation (ARA) method.

Parameter	Value
start_layer_index	2
end_layer_index	60
preserve_good_behavior_weight	0.9920
steer_bad_behavior_weight	0.0001
overcorrect_relative_weight	0.4709
neighbor_count	10

Performance

Metric	This model	Original google/gemma-4-31b-it
KL divergence	0.0120	0 (by definition)
Refusals	5/100	98/100

Measured on the unquantized heretic-ara base; AWQ is expected to preserve these closely but has not been separately benchmarked.

About the Base Model

Original model: google/gemma-4-31b-it

Gemma 4 31B is a dense multimodal model (text + image input, text output) with a 256K context window, native thinking-mode support, function calling, and strong performance on reasoning, coding, and vision benchmarks. See the base model card for architectural details, benchmark results, training data, and Google's responsible-use guidance.