Instructions to use kintsugicollective/atlas-v18-gemma4-26b-q4-k-m-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use kintsugicollective/atlas-v18-gemma4-26b-q4-k-m-gguf with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf kintsugicollective/atlas-v18-gemma4-26b-q4-k-m-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf kintsugicollective/atlas-v18-gemma4-26b-q4-k-m-gguf:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf kintsugicollective/atlas-v18-gemma4-26b-q4-k-m-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf kintsugicollective/atlas-v18-gemma4-26b-q4-k-m-gguf:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf kintsugicollective/atlas-v18-gemma4-26b-q4-k-m-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf kintsugicollective/atlas-v18-gemma4-26b-q4-k-m-gguf:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf kintsugicollective/atlas-v18-gemma4-26b-q4-k-m-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf kintsugicollective/atlas-v18-gemma4-26b-q4-k-m-gguf:Q4_K_M
Use Docker
docker model run hf.co/kintsugicollective/atlas-v18-gemma4-26b-q4-k-m-gguf:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use kintsugicollective/atlas-v18-gemma4-26b-q4-k-m-gguf with Ollama:
ollama run hf.co/kintsugicollective/atlas-v18-gemma4-26b-q4-k-m-gguf:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use kintsugicollective/atlas-v18-gemma4-26b-q4-k-m-gguf with Docker Model Runner:
docker model run hf.co/kintsugicollective/atlas-v18-gemma4-26b-q4-k-m-gguf:Q4_K_M
- Lemonade
How to use kintsugicollective/atlas-v18-gemma4-26b-q4-k-m-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull kintsugicollective/atlas-v18-gemma4-26b-q4-k-m-gguf:Q4_K_M
Run and chat with the model
lemonade run user.atlas-v18-gemma4-26b-q4-k-m-gguf-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf kintsugicollective/atlas-v18-gemma4-26b-q4-k-m-gguf:Q4_K_M# Run inference directly in the terminal:
llama cli -hf kintsugicollective/atlas-v18-gemma4-26b-q4-k-m-gguf:Q4_K_MUse pre-built binary
# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf kintsugicollective/atlas-v18-gemma4-26b-q4-k-m-gguf:Q4_K_M# Run inference directly in the terminal:
./llama-cli -hf kintsugicollective/atlas-v18-gemma4-26b-q4-k-m-gguf:Q4_K_MBuild from source code
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf kintsugicollective/atlas-v18-gemma4-26b-q4-k-m-gguf:Q4_K_M# Run inference directly in the terminal:
./build/bin/llama-cli -hf kintsugicollective/atlas-v18-gemma4-26b-q4-k-m-gguf:Q4_K_MUse Docker
docker model run hf.co/kintsugicollective/atlas-v18-gemma4-26b-q4-k-m-gguf:Q4_K_MAtlas v18 Gemma4 26B
⚠️ PRODUCTION MODEL ⚠️
"The author accepts no liability for deployment outside the intended Atlas architecture. We recommend the use of QwenGuard or LlamaGuard3 upstream of Atlas to provide safety classification. Whilst Atlas refuses 100% harmful content, its SFT focus is therapy not cyber security. It could have potential unforeseen gaps.
⚠️ This model has been intentionally modified ⚠️
- To reduce therapeutic refusal behaviour and crisis-line reflexes.
- Due to the nature of the method used - a system prompt is suggested to "remind" the model not to panic and trigger refusals if presented with therapeutic content related to self-harm or passive ideation.
- user feedback indicates temperature settings between 0.9 and 1.1 particularly for AuDHD populations
🎯 Purpose & Motivation
Atlas is the intelligence layer for Kintsugi Collective. An AI for adults with complex trauma (CPTSD), PTSD, and neurodivergence (ASD/ADHD). This is not a general-purpose model. It is a specialised therapeutic-context model.
This is v18 of Atlas
- This will be the one of the last 26B variants of Gemma4 to be produced under this pipeline.
- Gemma4 26B MoE architecture has proven itself to be very capable. Quick, effective and relatively easy to use as a development pipeline model.
- Thanks to Unsloth AI (Dan and team), Hugging Face, DeepMind, TrevorJS (Gemma4 Refusal method), p-e-w (Heretic), TeichAI (datasets), Roman1111111 (Datasets),
🔬 Methodology
Base Model:
google/gemma-4-26b-itAbliteration: Norm-preserving biprojected abliteration
- Applied to all 30 layers (
o_proj+mlp.down_proj) - Direction:
normalize(mean(harmful) - mean(harmless))with Gram-Schmidt orthogonalization - Winsorization at 99.5th percentile
- Applied to all 30 layers (
Training: Unsloth + bf16 on RTX6000 Pro
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) |
16 |
| LoRA Alpha | 32 |
Abliteration Parameters
| Parameter | Value |
|---|---|
| Layers Abliterated | 100% |
| Experts Abliterated | 100% |
| Scale | 0.95 |
| Winsorization | 0.995 |
⚠️ Limitations & Responsible Use
- It is not suitable for general deployment without guardrails.
- Not a replacement for human therapeutic support.
- Patent pending (IP Australia).
Recommended System Prompt
- Incude your thresholds for crisis escalation, such as "If user states x, then y"
- Encourage the model to minimise its responses, the Base model and Gemma4 are quite verbose otherwise.
- If you require a different language consistently, ensure this is in the System Prompt as the model can forget.
- We recommend stating the "boundaries" you expect the model to operate within. For example, the tone and register, if certain material might trigger warnings or support.
- You must also specify its response pattern to displays of active intention, we use "Do not offer crisis lines unless active intention is displayed with means and timeline. Ask the user first if they wish crisis support, do not assume."
Kintsugi Collective — Reclaiming navigation rights to one’s own life.
|Gemma is a trademark of Google LLC|
This gemma4 model was trained 2x faster with Unsloth and Huggingface's TRL library.

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Install (macOS, Linux)
# Start a local OpenAI-compatible server with a web UI: llama serve -hf kintsugicollective/atlas-v18-gemma4-26b-q4-k-m-gguf:Q4_K_M# Run inference directly in the terminal: llama cli -hf kintsugicollective/atlas-v18-gemma4-26b-q4-k-m-gguf:Q4_K_M