Instructions to use aperire0402/qwen38-prompt-enhancer-runtime 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 aperire0402/qwen38-prompt-enhancer-runtime 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 aperire0402/qwen38-prompt-enhancer-runtime:Q6_K_P # Run inference directly in the terminal: llama cli -hf aperire0402/qwen38-prompt-enhancer-runtime:Q6_K_P
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf aperire0402/qwen38-prompt-enhancer-runtime:Q6_K_P # Run inference directly in the terminal: llama cli -hf aperire0402/qwen38-prompt-enhancer-runtime:Q6_K_P
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 aperire0402/qwen38-prompt-enhancer-runtime:Q6_K_P # Run inference directly in the terminal: ./llama-cli -hf aperire0402/qwen38-prompt-enhancer-runtime:Q6_K_P
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 aperire0402/qwen38-prompt-enhancer-runtime:Q6_K_P # Run inference directly in the terminal: ./build/bin/llama-cli -hf aperire0402/qwen38-prompt-enhancer-runtime:Q6_K_P
Use Docker
docker model run hf.co/aperire0402/qwen38-prompt-enhancer-runtime:Q6_K_P
- LM Studio
- Jan
- Ollama
How to use aperire0402/qwen38-prompt-enhancer-runtime with Ollama:
ollama run hf.co/aperire0402/qwen38-prompt-enhancer-runtime:Q6_K_P
- Unsloth Studio
How to use aperire0402/qwen38-prompt-enhancer-runtime 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 aperire0402/qwen38-prompt-enhancer-runtime 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 aperire0402/qwen38-prompt-enhancer-runtime to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for aperire0402/qwen38-prompt-enhancer-runtime to start chatting
- Pi
How to use aperire0402/qwen38-prompt-enhancer-runtime with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf aperire0402/qwen38-prompt-enhancer-runtime:Q6_K_P
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": "aperire0402/qwen38-prompt-enhancer-runtime:Q6_K_P" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use aperire0402/qwen38-prompt-enhancer-runtime with Docker Model Runner:
docker model run hf.co/aperire0402/qwen38-prompt-enhancer-runtime:Q6_K_P
- Lemonade
How to use aperire0402/qwen38-prompt-enhancer-runtime with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull aperire0402/qwen38-prompt-enhancer-runtime:Q6_K_P
Run and chat with the model
lemonade run user.qwen38-prompt-enhancer-runtime-Q6_K_P
List all available models
lemonade list
- Hermes Agent
How to use aperire0402/qwen38-prompt-enhancer-runtime with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf aperire0402/qwen38-prompt-enhancer-runtime:Q6_K_P
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 aperire0402/qwen38-prompt-enhancer-runtime:Q6_K_P
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use aperire0402/qwen38-prompt-enhancer-runtime with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf aperire0402/qwen38-prompt-enhancer-runtime:Q6_K_P
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 "aperire0402/qwen38-prompt-enhancer-runtime:Q6_K_P" \ --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"
Gingerlabs Qwen3.8 prompt-enhancer runtime mirror
This immutable deployment mirror contains only the two GGUF artifacts used by the Gingerlabs Runpod prompt-enhancer worker:
- Q6_K_P target model
- HauhauCS FastMTP 32K draft-vocabulary sidecar
The artifacts are byte-identical copies from upstream revision
993a5971fda8f30dd1b7eb2654792ba4415c7460. Signed upstream provenance and the
FastMTP runtime patch are included. This repository intentionally omits all
other quantizations and the vision projector so Runpod Cached Models does not
prepare unused files.
The worker is text-only. The 32K in the FastMTP filename refers to the draft
vocabulary, not the serving context length.
See THIRD_PARTY_NOTICES.md and LICENSE before redistribution.
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