Instructions to use KaviarLabs/Gemma-4-26B-A4B-Heretic-PRISM-DQ-I 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 KaviarLabs/Gemma-4-26B-A4B-Heretic-PRISM-DQ-I 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 KaviarLabs/Gemma-4-26B-A4B-Heretic-PRISM-DQ-I:UD-IQ4_NL # Run inference directly in the terminal: llama cli -hf KaviarLabs/Gemma-4-26B-A4B-Heretic-PRISM-DQ-I:UD-IQ4_NL
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf KaviarLabs/Gemma-4-26B-A4B-Heretic-PRISM-DQ-I:UD-IQ4_NL # Run inference directly in the terminal: llama cli -hf KaviarLabs/Gemma-4-26B-A4B-Heretic-PRISM-DQ-I:UD-IQ4_NL
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 KaviarLabs/Gemma-4-26B-A4B-Heretic-PRISM-DQ-I:UD-IQ4_NL # Run inference directly in the terminal: ./llama-cli -hf KaviarLabs/Gemma-4-26B-A4B-Heretic-PRISM-DQ-I:UD-IQ4_NL
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 KaviarLabs/Gemma-4-26B-A4B-Heretic-PRISM-DQ-I:UD-IQ4_NL # Run inference directly in the terminal: ./build/bin/llama-cli -hf KaviarLabs/Gemma-4-26B-A4B-Heretic-PRISM-DQ-I:UD-IQ4_NL
Use Docker
docker model run hf.co/KaviarLabs/Gemma-4-26B-A4B-Heretic-PRISM-DQ-I:UD-IQ4_NL
- LM Studio
- Jan
- vLLM
How to use KaviarLabs/Gemma-4-26B-A4B-Heretic-PRISM-DQ-I with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "KaviarLabs/Gemma-4-26B-A4B-Heretic-PRISM-DQ-I" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "KaviarLabs/Gemma-4-26B-A4B-Heretic-PRISM-DQ-I", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/KaviarLabs/Gemma-4-26B-A4B-Heretic-PRISM-DQ-I:UD-IQ4_NL
- Ollama
How to use KaviarLabs/Gemma-4-26B-A4B-Heretic-PRISM-DQ-I with Ollama:
ollama run hf.co/KaviarLabs/Gemma-4-26B-A4B-Heretic-PRISM-DQ-I:UD-IQ4_NL
- Unsloth Desktop
- Pi
How to use KaviarLabs/Gemma-4-26B-A4B-Heretic-PRISM-DQ-I with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf KaviarLabs/Gemma-4-26B-A4B-Heretic-PRISM-DQ-I:UD-IQ4_NL
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "KaviarLabs/Gemma-4-26B-A4B-Heretic-PRISM-DQ-I:UD-IQ4_NL" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use KaviarLabs/Gemma-4-26B-A4B-Heretic-PRISM-DQ-I with Docker Model Runner:
docker model run hf.co/KaviarLabs/Gemma-4-26B-A4B-Heretic-PRISM-DQ-I:UD-IQ4_NL
- Lemonade
How to use KaviarLabs/Gemma-4-26B-A4B-Heretic-PRISM-DQ-I with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull KaviarLabs/Gemma-4-26B-A4B-Heretic-PRISM-DQ-I:UD-IQ4_NL
Run and chat with the model
lemonade run user.Gemma-4-26B-A4B-Heretic-PRISM-DQ-I-UD-IQ4_NL
List all available models
lemonade list
- Hermes Agent
How to use KaviarLabs/Gemma-4-26B-A4B-Heretic-PRISM-DQ-I with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf KaviarLabs/Gemma-4-26B-A4B-Heretic-PRISM-DQ-I:UD-IQ4_NL
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default KaviarLabs/Gemma-4-26B-A4B-Heretic-PRISM-DQ-I:UD-IQ4_NL
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use KaviarLabs/Gemma-4-26B-A4B-Heretic-PRISM-DQ-I with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf KaviarLabs/Gemma-4-26B-A4B-Heretic-PRISM-DQ-I:UD-IQ4_NL
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "KaviarLabs/Gemma-4-26B-A4B-Heretic-PRISM-DQ-I:UD-IQ4_NL" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Gemma 4 26B A4B Heretic — PRISM-PRO-DQ Map Reapplication
Heretic finetune converted to GGUF using an unofficial PRISM-PRO-DQ tensor-map reapplication.
Important provenance notice
This is not an official PRISM-DQ or PRISM-PRO-DQ model. This repository is not affiliated with Ex0bit, PRISM-DQ, or the authors of the source finetune.
The underlying trained weights come from coder3101/gemma-4-26B-A4B-it-heretic. No trained tensor values from Ex0bit's PRISM model were copied, merged, or transplanted into this model.
The quantized file was produced by reconstructing the per-tensor GGML quantization-type assignment visible in the public Ex0bit/Gemma4-26B-A4B-PRISM-PRO-DQ-GGUF release and then applying that assignment to this separate finetune. This is therefore a map reapplication, not a reproduction of the original PRISM-DQ calibration, optimization procedure, dataset, or proprietary pipeline.
The Unsloth importance matrix imatrix_unsloth.gguf_file was used during quantization. Conversion and quantization were performed with llama.cpp.
Quantization layout
For the 658 tensors shared with the reference Gemma 4 PRISM-PRO-DQ GGUF, the final distribution is:
| GGML type | Tensor count |
|---|---|
| F32 | 392 |
| Q5_1 | 30 |
| Q5_K | 91 |
| Q6_K | 115 |
| Q8_0 | 30 |
The language-model GGUF contains 658 tensors and matches the reconstructed reference type map without extra language-model tensors.
Files and integrity
| File | Size | SHA-256 | Purpose |
|---|---|---|---|
Gemma-4-26B-A4B-Heretic-PRISM-DQ-I.gguf |
16.85 GiB | f4bf630c758d6f439d94f10844412e7a66a1a76494d0938aea6c4e404e241ff3 |
Unofficial PRISM-PRO-DQ tensor-map reapplication with Unsloth I-matrix |
Template and tokenizer
- The tokenizer vocabulary and special-token IDs were checked against the source Hugging Face tokenizer.
- The embedded chat template was updated to the current official template from
google/gemma-4-26B-A4B-itat conversion time.
Usage with llama.cpp
llama-cli \
-m Gemma-4-26B-A4B-Heretic-PRISM-DQ-I.gguf \
--jinja \
-p "Hello"
For server use:
llama-server \
-m Gemma-4-26B-A4B-Heretic-PRISM-DQ-I.gguf \
--jinja \
--host 127.0.0.1 \
--port 8080
References
- Source finetune: coder3101/gemma-4-26B-A4B-it-heretic
- Official Gemma 4 base:
google/gemma-4-26B-A4B-it - PRISM-PRO-DQ public GGUF used only as the tensor-map reference:
Ex0bit/Gemma4-26B-A4B-PRISM-PRO-DQ-GGUF - Unsloth importance matrix:
imatrix_unsloth.gguf_file - GGUF conversion and quantization tooling:
ggml-org/llama.cpp - Gemma Terms of Use: https://ai.google.dev/gemma/terms
License and responsibility
Use of this repository remains subject to the original source model's license and the Gemma Terms of Use. The behavior and safety characteristics of the source finetune are inherited; quantization does not make the model safer or change its intended-use restrictions. Users are responsible for evaluating outputs and complying with applicable laws and licenses.
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