Instructions to use Ex0bit/Qwen3.5-PRISM-Dynamic-Quant-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 Ex0bit/Qwen3.5-PRISM-Dynamic-Quant-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 Ex0bit/Qwen3.5-PRISM-Dynamic-Quant-GGUF:BF16 # Run inference directly in the terminal: llama cli -hf Ex0bit/Qwen3.5-PRISM-Dynamic-Quant-GGUF:BF16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Ex0bit/Qwen3.5-PRISM-Dynamic-Quant-GGUF:BF16 # Run inference directly in the terminal: llama cli -hf Ex0bit/Qwen3.5-PRISM-Dynamic-Quant-GGUF:BF16
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf Ex0bit/Qwen3.5-PRISM-Dynamic-Quant-GGUF:BF16 # Run inference directly in the terminal: ./build/bin/llama cli -hf Ex0bit/Qwen3.5-PRISM-Dynamic-Quant-GGUF:BF16
Use Docker
docker model run hf.co/Ex0bit/Qwen3.5-PRISM-Dynamic-Quant-GGUF:BF16
- Llama new
- LM Studio
- Jan
- vLLM
How to use Ex0bit/Qwen3.5-PRISM-Dynamic-Quant-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Ex0bit/Qwen3.5-PRISM-Dynamic-Quant-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": "Ex0bit/Qwen3.5-PRISM-Dynamic-Quant-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Ex0bit/Qwen3.5-PRISM-Dynamic-Quant-GGUF:BF16
- Ollama
How to use Ex0bit/Qwen3.5-PRISM-Dynamic-Quant-GGUF with Ollama:
ollama run hf.co/Ex0bit/Qwen3.5-PRISM-Dynamic-Quant-GGUF:BF16
- Unsloth Desktop
- Pi
How to use Ex0bit/Qwen3.5-PRISM-Dynamic-Quant-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Ex0bit/Qwen3.5-PRISM-Dynamic-Quant-GGUF:BF16
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": "Ex0bit/Qwen3.5-PRISM-Dynamic-Quant-GGUF:BF16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Ex0bit/Qwen3.5-PRISM-Dynamic-Quant-GGUF with Docker Model Runner:
docker model run hf.co/Ex0bit/Qwen3.5-PRISM-Dynamic-Quant-GGUF:BF16
- Lemonade
How to use Ex0bit/Qwen3.5-PRISM-Dynamic-Quant-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Ex0bit/Qwen3.5-PRISM-Dynamic-Quant-GGUF:BF16
Run and chat with the model
lemonade run user.Qwen3.5-PRISM-Dynamic-Quant-GGUF-BF16
List all available models
lemonade list
- Hermes Agent
How to use Ex0bit/Qwen3.5-PRISM-Dynamic-Quant-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 Ex0bit/Qwen3.5-PRISM-Dynamic-Quant-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 Ex0bit/Qwen3.5-PRISM-Dynamic-Quant-GGUF:BF16
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Ex0bit/Qwen3.5-PRISM-Dynamic-Quant-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Ex0bit/Qwen3.5-PRISM-Dynamic-Quant-GGUF:BF16
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 "Ex0bit/Qwen3.5-PRISM-Dynamic-Quant-GGUF:BF16" \ --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"
For small models, Q5 and above is more robust in production?
Though achiving pareto optimial, the smallest 0.8B model at Q3 bit loses it competency and often generate infinite repetition at temperature 1.0. Just one thing to keep in mind. Maybe Q5 Q6 or Q8 PRISM is more real world robust for small model less than 30B
@Yhyu13 You're spot on β 0.8 requires completely different temperature, repeat penalty, and other settings, along with higher BPW treatment. However, The goal of this experiment was to generate a SOTA formula and accompanying tool that provides the Pareto frontier recipe without calibration while meeting or beating current methods that require imatrix and large-dataset post-calibration efforts. I'll be releasing the tool for others to leverage and generate their own quants.