Instructions to use ledgergap/Pollux-4B-Judge-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use ledgergap/Pollux-4B-Judge-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="ledgergap/Pollux-4B-Judge-GGUF", filename="Pollux-4B-Judge.BF16.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use ledgergap/Pollux-4B-Judge-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf ledgergap/Pollux-4B-Judge-GGUF:BF16 # Run inference directly in the terminal: llama-cli -hf ledgergap/Pollux-4B-Judge-GGUF:BF16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf ledgergap/Pollux-4B-Judge-GGUF:BF16 # Run inference directly in the terminal: llama-cli -hf ledgergap/Pollux-4B-Judge-GGUF:BF16
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 ledgergap/Pollux-4B-Judge-GGUF:BF16 # Run inference directly in the terminal: ./llama-cli -hf ledgergap/Pollux-4B-Judge-GGUF:BF16
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 ledgergap/Pollux-4B-Judge-GGUF:BF16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf ledgergap/Pollux-4B-Judge-GGUF:BF16
Use Docker
docker model run hf.co/ledgergap/Pollux-4B-Judge-GGUF:BF16
- LM Studio
- Jan
- vLLM
How to use ledgergap/Pollux-4B-Judge-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ledgergap/Pollux-4B-Judge-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": "ledgergap/Pollux-4B-Judge-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ledgergap/Pollux-4B-Judge-GGUF:BF16
- Ollama
How to use ledgergap/Pollux-4B-Judge-GGUF with Ollama:
ollama run hf.co/ledgergap/Pollux-4B-Judge-GGUF:BF16
- Unsloth Studio
How to use ledgergap/Pollux-4B-Judge-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 ledgergap/Pollux-4B-Judge-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 ledgergap/Pollux-4B-Judge-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for ledgergap/Pollux-4B-Judge-GGUF to start chatting
- Pi
How to use ledgergap/Pollux-4B-Judge-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf ledgergap/Pollux-4B-Judge-GGUF:BF16
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": "ledgergap/Pollux-4B-Judge-GGUF:BF16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use ledgergap/Pollux-4B-Judge-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 ledgergap/Pollux-4B-Judge-GGUF:BF16
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 ledgergap/Pollux-4B-Judge-GGUF:BF16
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use ledgergap/Pollux-4B-Judge-GGUF with Docker Model Runner:
docker model run hf.co/ledgergap/Pollux-4B-Judge-GGUF:BF16
- Lemonade
How to use ledgergap/Pollux-4B-Judge-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ledgergap/Pollux-4B-Judge-GGUF:BF16
Run and chat with the model
lemonade run user.Pollux-4B-Judge-GGUF-BF16
List all available models
lemonade list
Pollux-4B-Judge GGUF
This repository contains GGUF versions of ai-forever/Pollux-4B-Judge for local inference with llama.cpp, LM Studio, and other GGUF-compatible runtimes.
Pollux-4B-Judge is a Russian-oriented LLM-as-a-judge model based on Qwen3-4B. It is intended for evaluating model answers against a specific criterion and scoring rubric.
Files
| File | Type | Quantized | Notes |
|---|---|---|---|
Pollux-4B-Judge.BF16.gguf |
BF16 GGUF conversion | No | High-precision reference version |
Pollux-4B-Judge.Q8_0.gguf |
Q8_0 GGUF quantization | Yes | High-quality quantized version |
Which file should I use?
Use Pollux-4B-Judge.BF16.gguf if you want the highest-quality reference version.
Use Pollux-4B-Judge.Q8_0.gguf if you want a practical local version with lower memory usage and minimal expected quality loss.
Recommended inference settings
For judge-style usage, the original model card uses:
| Setting | Value |
|---|---|
| Temperature | 0.0 |
| Max tokens | 512 |
For local GGUF inference, choose a context length large enough to fit the full evaluation prompt: instruction, reference answer, evaluated answer, criterion, and rubric. A practical starting point is 8192, but this is a local runtime recommendation rather than an official value from the original model card.
The model is intended to evaluate one criterion per request.
Prompt format
Recommended prompt structure:
### ะะฐะดะฐะฝะธะต ะดะปั ะพัะตะฝะบะธ:
{instruction}
### ะญัะฐะปะพะฝะฝัะน ะพัะฒะตั:
{reference_answer}
### ะัะฒะตั ะดะปั ะพัะตะฝะบะธ:
{answer}
### ะัะธัะตัะธะน ะพัะตะฝะบะธ:
{criterion}
### ะจะบะฐะปะฐ ะพัะตะฝะธะฒะฐะฝะธั ะฟะพ ะบัะธัะตัะธั:
{rubric}
Use with llama.cpp
BF16:
llama-server -hf ledgergap/Pollux-4B-Judge-GGUF:BF16 -c 8192 -ngl 99
Q8_0:
llama-server -hf ledgergap/Pollux-4B-Judge-GGUF:Q8_0 -c 8192 -ngl 99
Use with LM Studio
Open LM Studio and paste this repository URL into the model search/download field:
https://huggingface.co/ledgergap/Pollux-4B-Judge-GGUF
Then select either the BF16 or Q8_0 GGUF file.
Original model
Original model: ai-forever/Pollux-4B-Judge
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