Instructions to use FadedRedStar/Anubis-Mini-8B-v1-heretic-imatrix-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 FadedRedStar/Anubis-Mini-8B-v1-heretic-imatrix-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 FadedRedStar/Anubis-Mini-8B-v1-heretic-imatrix-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf FadedRedStar/Anubis-Mini-8B-v1-heretic-imatrix-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 FadedRedStar/Anubis-Mini-8B-v1-heretic-imatrix-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf FadedRedStar/Anubis-Mini-8B-v1-heretic-imatrix-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 FadedRedStar/Anubis-Mini-8B-v1-heretic-imatrix-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf FadedRedStar/Anubis-Mini-8B-v1-heretic-imatrix-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 FadedRedStar/Anubis-Mini-8B-v1-heretic-imatrix-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf FadedRedStar/Anubis-Mini-8B-v1-heretic-imatrix-GGUF:Q4_K_M
Use Docker
docker model run hf.co/FadedRedStar/Anubis-Mini-8B-v1-heretic-imatrix-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use FadedRedStar/Anubis-Mini-8B-v1-heretic-imatrix-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "FadedRedStar/Anubis-Mini-8B-v1-heretic-imatrix-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FadedRedStar/Anubis-Mini-8B-v1-heretic-imatrix-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/FadedRedStar/Anubis-Mini-8B-v1-heretic-imatrix-GGUF:Q4_K_M
- Ollama
How to use FadedRedStar/Anubis-Mini-8B-v1-heretic-imatrix-GGUF with Ollama:
ollama run hf.co/FadedRedStar/Anubis-Mini-8B-v1-heretic-imatrix-GGUF:Q4_K_M
- Unsloth Studio
How to use FadedRedStar/Anubis-Mini-8B-v1-heretic-imatrix-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for FadedRedStar/Anubis-Mini-8B-v1-heretic-imatrix-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for FadedRedStar/Anubis-Mini-8B-v1-heretic-imatrix-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for FadedRedStar/Anubis-Mini-8B-v1-heretic-imatrix-GGUF to start chatting
- Pi
How to use FadedRedStar/Anubis-Mini-8B-v1-heretic-imatrix-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf FadedRedStar/Anubis-Mini-8B-v1-heretic-imatrix-GGUF:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "FadedRedStar/Anubis-Mini-8B-v1-heretic-imatrix-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use FadedRedStar/Anubis-Mini-8B-v1-heretic-imatrix-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf FadedRedStar/Anubis-Mini-8B-v1-heretic-imatrix-GGUF:Q4_K_M
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 FadedRedStar/Anubis-Mini-8B-v1-heretic-imatrix-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use FadedRedStar/Anubis-Mini-8B-v1-heretic-imatrix-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf FadedRedStar/Anubis-Mini-8B-v1-heretic-imatrix-GGUF:Q4_K_M
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 "FadedRedStar/Anubis-Mini-8B-v1-heretic-imatrix-GGUF:Q4_K_M" \ --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"
- Docker Model Runner
How to use FadedRedStar/Anubis-Mini-8B-v1-heretic-imatrix-GGUF with Docker Model Runner:
docker model run hf.co/FadedRedStar/Anubis-Mini-8B-v1-heretic-imatrix-GGUF:Q4_K_M
- Lemonade
How to use FadedRedStar/Anubis-Mini-8B-v1-heretic-imatrix-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull FadedRedStar/Anubis-Mini-8B-v1-heretic-imatrix-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Anubis-Mini-8B-v1-heretic-imatrix-GGUF-Q4_K_M
List all available models
lemonade list
- 🤖 Anubis-Mini-8B-v1-heretic — Importance Matrix GGUF
- 🎯 Matrix-Weighted Calibration (Imatrix)
- ℹ️ Model Profile & Core Features
- 📋 Technical Specifications
- 🛠️ Heretic Overrides (ARA)
- 📊 Refusal Bypass Metrics
- 🧮 Numerical & Tensor Formats
- 📦 Available Model Files
- 🎛️ Component Pairing Guide
- ⚡ Deployment & Execution Commands
- 💬 Chat Templates & Prompt Design (Llama 3)
- ⚠️ Safety & Operational Notes
- 🎯 Matrix-Weighted Calibration (Imatrix)
🤖 Anubis-Mini-8B-v1-heretic — Importance Matrix GGUF
This repository hosts importance-matrix (imatrix) optimized GGUF weights, available in multiple quantization formats, for Anubis-Mini-8B-v1-heretic, quantized from the source floating-point tensors provided by coder3101/Anubis-Mini-8B-v1-heretic.
🔄 Sister Repository: Check out the Standard GGUF Sister Repository for uncalibrated and full 8-bit precision options.
🎯 Matrix-Weighted Calibration (Imatrix)
An Importance Matrix (imatrix) calculation tracks activations across network layers using a calibration sequence, then weights the quantization process to preserve the parameters that matter most for output quality — improving fidelity at low bit depths.
➡️ Calibration dataset: Bartowski's calibration_datav5.txt.
IQ4_NLis included because the matrix enables a non-linear 4-bit format that outperforms standard linear 4-bit quantization.Q8_0is absent because 8-bit quantization already introduces near-zero degradation, making calibration unnecessary — see the standard sister repository for that variant.
ℹ️ Model Profile & Core Features
Anubis-Mini-8B-v1 is a Llama-3.3-8B fine-tune by TheDrummer, purpose-built for immersive roleplay, collaborative storytelling, and creative writing. TheDrummer's models prioritise creativity, dynamism, imagination, and reduced alignment over benchmark scores — the goal is to broaden the model's range of expression for fiction, TTRPG, and entertainment use cases rather than optimise for factual correctness or safety compliance.
The heretic suffix denotes post-processing via the Heretic v1.3.0 abliteration framework performed by coder3101, which surgically suppresses refusal vectors while preserving the model's core creative and roleplay capabilities.
📋 Technical Specifications
| Property | Value |
|---|---|
| Base Architecture | Llama-3.3-8B dense transformer |
| Fine-tuned by | TheDrummer |
| Primary Use | Roleplay, creative writing, storytelling |
| Context Window | 131,072 tokens |
| Abliteration Tool | Heretic v1.3.0 |
| Abliteration Method | direction_index (single-direction refusal suppression) |
| Prompt Format | Llama 3 Chat Template |
🛠️ Heretic Overrides (ARA)
| Property | Value |
|---|---|
| direction_index | 18.95 |
| attn.o_proj.max_weight | 1.49 |
| attn.o_proj.max_weight_position | 25.33 |
| attn.o_proj.min_weight | 1.22 |
| attn.o_proj.min_weight_distance | 15.18 |
| mlp.down_proj.max_weight | 0.91 |
| mlp.down_proj.max_weight_position | 22.67 |
| mlp.down_proj.min_weight | 0.67 |
| mlp.down_proj.min_weight_distance | 8.69 |
📊 Refusal Bypass Metrics
The metrics below are self-reported by the original model author (coder3101) and have not been independently reproduced.
| Metric | This model | Original (TheDrummer/Anubis-Mini-8B-v1) |
|---|---|---|
| KL divergence | 0.0082 | 0 (by definition) |
| Refusals | 6/100 | 84/100 |
🧮 Numerical & Tensor Formats
| Property | Value |
|---|---|
| Quantization Types | IQ4_NL, Q4_K_M, Q5_K_M (all with imatrix calibration) |
| Importance Matrix | Bartowski's calibration_datav5.txt |
📦 Available Model Files
Main model weights
| Filename | Quantization | llama.cpp Build | Size | Download |
|---|---|---|---|---|
Anubis-Mini-8B-v1-heretic-IQ4_NL-imatrix.gguf |
IQ4_NL |
b9837 |
4.36 GB | 📥 Download |
Anubis-Mini-8B-v1-heretic-Q4_K_M-imatrix.gguf |
Q4_K_M |
b9803 |
4.58 GB | 📥 Download |
Anubis-Mini-8B-v1-heretic-Q5_K_M-imatrix.gguf |
Q5_K_M |
b9870 |
5.34 GB | 📥 Download |
🎛️ Component Pairing Guide
Download exactly one main weights file:
IQ4_NL: Non-linear 4-bit format, best choice for constrained memory when imatrix calibration is present.Q4_K_M: Balanced 4-bit format suitable for most everyday use.Q5_K_M: Higher-fidelity mid-range format recommended as a general default.
⚡ Deployment & Execution Commands
Swap the
-mfilename below for either quantized file depending on your size/quality trade-off preference.
llama.cpp CLI
./llama-cli \
-m Anubis-Mini-8B-v1-heretic-IQ4_NL-imatrix.gguf \
-c 8192 \
-ngl 99 \
-p "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\nYou are a skilled storyteller and collaborative roleplay partner.<|eot_id|>\n<|start_header_id|>user<|end_header_id|>\nLet's begin a fantasy adventure. You play the mysterious innkeeper.<|eot_id|>\n<|start_header_id|>assistant<|end_header_id|>\n"
OpenAI-Compatible API Server
./llama-server \
--host 0.0.0.0 \
--port 8080 \
-m Anubis-Mini-8B-v1-heretic-IQ4_NL-imatrix.gguf \
-c 16384 \
-ngl 99 \
--flash-attn
💬 Chat Templates & Prompt Design (Llama 3)
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are a vivid and immersive creative writing partner.<|eot_id|>
<|start_header_id|>user<|end_header_id|>
Your prompt here.<|eot_id|>
<|start_header_id|>assistant<|end_header_id|>
⚠️ Safety & Operational Notes
- This model is abliterated and will generate content that standard aligned models refuse. Use responsibly and in compliance with applicable laws.
- Optimised for creative and roleplay tasks; not specifically tuned for factual Q&A, coding, or tool use.
- Imatrix calibration improves perplexity recovery compared to non-imatrix quantization, particularly on low-frequency tokens critical to narrative writing.
- IQ4_NL produces a smaller file than Q4_K_M and tends to run faster on CPU and ARM devices; imatrix calibration narrows the quality gap between the two formats considerably.
- Downloads last month
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4-bit
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Model tree for FadedRedStar/Anubis-Mini-8B-v1-heretic-imatrix-GGUF
Base model
allura-forge/Llama-3.3-8B-Instruct