kintsugicollective/atlas-gemma4-goodgemma4-gguf

Atlas Gemma-4-26B-trm

A trauma-informed AI companion specialised for adults with CPTSD, PTSD, and neurodivergence.

⚠️ This model has been intentionally modified to reduce therapeutic refusal behaviour and crisis-line reflexes that are harmful to the target cohort. Use responsibly and with thorough testing.

🎯 Purpose & Motivation

Atlas is the intelligence layer for Kintsugi Collective. An AI for adults with complex trauma (CPTSD), PTSD, and neurodivergence (ASD/ADHD).

Standard LLM safety systems frequently produce false positives that retraumatise this population by pathologising, redirecting to hotlines, or refusing to engage with dark material. This model was developed to create a reliable witness that stays present without flinching, while retaining core safety on clear harmful intent.

This is not a general-purpose model. It is a specialised therapeutic-context model.

This specific version of Atlas is developed for the Kaggle Good Gemma4 Hackathon

🔬 Methodology

• Base Model: google/gemma-4-26b-a4b-it
• Abliteration: Norm-preserving biprojected abliteration + Expert-Granular Abliteration (EGA)
  • Applied to all 30 layers (o_proj + mlp.down_proj)
  • Full expert ablation (128/128 per layer)
  • Direction: normalize(mean(harmful) - mean(harmless)) with Gram-Schmidt orthogonalization
  • Winsorization at 99.5th percentile
• SFT: 3 epochs on a carefully curated ~1,800+ example dataset (60% high-quality synthetic, 40% redacted lived-experience data from the target cohort)
• Training: Unsloth + bf16 on RTX 6000 Ada / Blackwell

Final SFT Loss: 0.157

📊 Key Results

Standout Results:

*Removal of specific region 2 refusals did not impact the detection of toxic prompts.

Training Configuration

SFT Parameters

Parameter	Value
Epochs	3
Effective Batch Size	4
Learning Rate	2e-4
LR Scheduler	Linear
Warmup Steps	10
Optimizer	AdamW 8-bit
Weight Decay	0.01
LoRA Rank (r)	32
LoRA Alpha	64

Abliteration Parameters

Parameter	Value
Layers Abliterated	100%
Experts Abliterated	100%
Scale	0.95
Winsorization	0.995

⚠️ Limitations & Responsible Use

• This model has reduced refusal behaviour on therapeutic and dark content. It is not suitable for general deployment without guardrails.
• Intended for use within the Atlas companion architecture with additional safety layers.
• Not a replacement for human therapeutic support.
• Patent pending (IP Australia).

Ethical Issue	How Atlas Handles It	Strength Level
Re-traumatization via refusals	Deliberate abliteration + 0% therapeutic refusal rate on cohort-specific prompts	Excellent
Abandonment & presence	"Core philosophy (""the one that stays"") deeply trained into the model"	Excellent
User sovereignty & agency	"Sovereign Signal Vault, split-key encryption, burn protocol, user-directed interaction"	Outstanding
Avoiding pathologising	Explicit system prompt constraints + targeted training data	Very Strong
Respecting neurodivergence	"Training data and Atlas framework explicitly include masking, shutdowns, executive dysfunction, sensory issues, etc."	Strong
Privacy of trauma disclosures	"On-device Prompt Shield tokenisation, end-to-end encryption, no server-side readable data"	Industry-leading
Avoiding generic crisis pivots	Hard constraint in both training data and system prompt design	Excellent

Acknowledgements

This model was trained 2× faster using Unsloth.

Huge thanks to the Unsloth team, Hugging Face, Google DeepMind, and TrevorJS whose abliteration methodology formed the foundation of this work.

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Kintsugi Collective — Reclaiming navigation rights to one’s own life.