Instructions to use Darkknight535/Void-Citrus-L3.3-70B-IQ3_XXS-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Darkknight535/Void-Citrus-L3.3-70B-IQ3_XXS-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Darkknight535/Void-Citrus-L3.3-70B-IQ3_XXS-GGUF", filename="void-citrus-l3.3-70b-iq3_xxs-imat.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Darkknight535/Void-Citrus-L3.3-70B-IQ3_XXS-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Darkknight535/Void-Citrus-L3.3-70B-IQ3_XXS-GGUF:IQ3_XXS # Run inference directly in the terminal: llama-cli -hf Darkknight535/Void-Citrus-L3.3-70B-IQ3_XXS-GGUF:IQ3_XXS
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Darkknight535/Void-Citrus-L3.3-70B-IQ3_XXS-GGUF:IQ3_XXS # Run inference directly in the terminal: llama-cli -hf Darkknight535/Void-Citrus-L3.3-70B-IQ3_XXS-GGUF:IQ3_XXS
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 Darkknight535/Void-Citrus-L3.3-70B-IQ3_XXS-GGUF:IQ3_XXS # Run inference directly in the terminal: ./llama-cli -hf Darkknight535/Void-Citrus-L3.3-70B-IQ3_XXS-GGUF:IQ3_XXS
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 Darkknight535/Void-Citrus-L3.3-70B-IQ3_XXS-GGUF:IQ3_XXS # Run inference directly in the terminal: ./build/bin/llama-cli -hf Darkknight535/Void-Citrus-L3.3-70B-IQ3_XXS-GGUF:IQ3_XXS
Use Docker
docker model run hf.co/Darkknight535/Void-Citrus-L3.3-70B-IQ3_XXS-GGUF:IQ3_XXS
- LM Studio
- Jan
- Ollama
How to use Darkknight535/Void-Citrus-L3.3-70B-IQ3_XXS-GGUF with Ollama:
ollama run hf.co/Darkknight535/Void-Citrus-L3.3-70B-IQ3_XXS-GGUF:IQ3_XXS
- Unsloth Studio
How to use Darkknight535/Void-Citrus-L3.3-70B-IQ3_XXS-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 Darkknight535/Void-Citrus-L3.3-70B-IQ3_XXS-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 Darkknight535/Void-Citrus-L3.3-70B-IQ3_XXS-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Darkknight535/Void-Citrus-L3.3-70B-IQ3_XXS-GGUF to start chatting
- Pi
How to use Darkknight535/Void-Citrus-L3.3-70B-IQ3_XXS-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf Darkknight535/Void-Citrus-L3.3-70B-IQ3_XXS-GGUF:IQ3_XXS
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": "Darkknight535/Void-Citrus-L3.3-70B-IQ3_XXS-GGUF:IQ3_XXS" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Darkknight535/Void-Citrus-L3.3-70B-IQ3_XXS-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf Darkknight535/Void-Citrus-L3.3-70B-IQ3_XXS-GGUF:IQ3_XXS
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 Darkknight535/Void-Citrus-L3.3-70B-IQ3_XXS-GGUF:IQ3_XXS
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use Darkknight535/Void-Citrus-L3.3-70B-IQ3_XXS-GGUF with Docker Model Runner:
docker model run hf.co/Darkknight535/Void-Citrus-L3.3-70B-IQ3_XXS-GGUF:IQ3_XXS
- Lemonade
How to use Darkknight535/Void-Citrus-L3.3-70B-IQ3_XXS-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Darkknight535/Void-Citrus-L3.3-70B-IQ3_XXS-GGUF:IQ3_XXS
Run and chat with the model
lemonade run user.Void-Citrus-L3.3-70B-IQ3_XXS-GGUF-IQ3_XXS
List all available models
lemonade list
Run and chat with the model
lemonade run user.Void-Citrus-L3.3-70B-IQ3_XXS-GGUF-IQ3_XXSList all available models
lemonade list
Void-Citrus-L3.3-70B (IQ3_XXS GGUF)
This is the custom GGUF quantization of Void-Citrus-L3.3-70B.
It is specifically engineered to fit high-performance 70B roleplay into strictly limited VRAM environments (like Dual Tesla T4s or 3090+Offload) without sacrificing the character's voice.
๐ The Quantization Difference
This is not a standard "click-and-convert" GGUF. It was built using a specialized pipeline to retain maximum coherence at 3-bit compression:
- Custom Anime RP Calibration: Unlike standard quants that use generic Wikipedia text (
wiki.train.raw) to calculate importance, this model's Importance Matrix (i-mat) was computed using custom Anime Roleplay data. The quantization engine prioritized weights responsible for dialogue,*actions*, and narrative formatting, ensuring the "soul" of the character remains intact. - BF16 Intermediate Source: The conversion bypassed the standard FP16 route. The source model was first converted to BF16 (Brain Float 16) to preserve a higher dynamic range before the final compression step, reducing quantization error.
- Surgical Size Fit: The
IQ3_XXSformat was chosen to land specifically around 26.8 GB. This allows the model to fit comfortably on 32GB VRAM setups (e.g., Dual T4, Dual 4060 Ti 16GB) with enough room left over for a massive 16k+ context window using Q8 KV cache.
Recommended Settings (Dual GPU)
To run this on a Dual 16GB GPU setup (32GB Total) without crashing, use Row Split and Q8 Cache:
./llama-cli \-m void-citrus-l3.3-70b-iq3_xxs-imat.gguf
-p "You are a helpful assistant..."
-n 512
-c 16384
-ngl 99
-sm row
-ctk q8_0
-ctv q8_0
Note: If on Windows, reduce context to -c 12288 to account for WDDM overhead.
Credits
- Downloads last month
- 15
3-bit
Model tree for Darkknight535/Void-Citrus-L3.3-70B-IQ3_XXS-GGUF
Base model
Darkknight535/Void-Citrus-L3.3-70B
Pull the model
# Download Lemonade from https://lemonade-server.ai/lemonade pull Darkknight535/Void-Citrus-L3.3-70B-IQ3_XXS-GGUF:IQ3_XXS