Instructions to use GingerLabsPlatform/qwen38-prompt-enhancer-q8-runtime with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use GingerLabsPlatform/qwen38-prompt-enhancer-q8-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 GingerLabsPlatform/qwen38-prompt-enhancer-q8-runtime:BF16
# Run inference directly in the terminal:
llama cli -hf GingerLabsPlatform/qwen38-prompt-enhancer-q8-runtime:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf GingerLabsPlatform/qwen38-prompt-enhancer-q8-runtime:BF16
# Run inference directly in the terminal:
llama cli -hf GingerLabsPlatform/qwen38-prompt-enhancer-q8-runtime: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 GingerLabsPlatform/qwen38-prompt-enhancer-q8-runtime:BF16
# Run inference directly in the terminal:
./llama-cli -hf GingerLabsPlatform/qwen38-prompt-enhancer-q8-runtime: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 GingerLabsPlatform/qwen38-prompt-enhancer-q8-runtime:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf GingerLabsPlatform/qwen38-prompt-enhancer-q8-runtime:BF16

Use Docker

docker model run hf.co/GingerLabsPlatform/qwen38-prompt-enhancer-q8-runtime:BF16

LM Studio
Jan
Ollama
How to use GingerLabsPlatform/qwen38-prompt-enhancer-q8-runtime with Ollama:
```
ollama run hf.co/GingerLabsPlatform/qwen38-prompt-enhancer-q8-runtime:BF16
```

Unsloth Studio

How to use GingerLabsPlatform/qwen38-prompt-enhancer-q8-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 GingerLabsPlatform/qwen38-prompt-enhancer-q8-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 GingerLabsPlatform/qwen38-prompt-enhancer-q8-runtime to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for GingerLabsPlatform/qwen38-prompt-enhancer-q8-runtime to start chatting

How to use GingerLabsPlatform/qwen38-prompt-enhancer-q8-runtime with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf GingerLabsPlatform/qwen38-prompt-enhancer-q8-runtime: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": "GingerLabsPlatform/qwen38-prompt-enhancer-q8-runtime:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use GingerLabsPlatform/qwen38-prompt-enhancer-q8-runtime with Docker Model Runner:
```
docker model run hf.co/GingerLabsPlatform/qwen38-prompt-enhancer-q8-runtime:BF16
```

Lemonade

How to use GingerLabsPlatform/qwen38-prompt-enhancer-q8-runtime with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull GingerLabsPlatform/qwen38-prompt-enhancer-q8-runtime:BF16

Run and chat with the model

lemonade run user.qwen38-prompt-enhancer-q8-runtime-BF16

List all available models

lemonade list

Hermes Agent

How to use GingerLabsPlatform/qwen38-prompt-enhancer-q8-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 GingerLabsPlatform/qwen38-prompt-enhancer-q8-runtime: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 GingerLabsPlatform/qwen38-prompt-enhancer-q8-runtime:BF16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use GingerLabsPlatform/qwen38-prompt-enhancer-q8-runtime with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf GingerLabsPlatform/qwen38-prompt-enhancer-q8-runtime: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 "GingerLabsPlatform/qwen38-prompt-enhancer-q8-runtime: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"

Gingerlabs Qwen3.8 prompt-enhancer runtime mirror

This immutable deployment mirror contains only the GGUF artifacts needed by the Gingerlabs Runpod prompt-enhancer worker:

Q8_K_P target model
HauhauCS FastMTP 32K draft-vocabulary sidecar
BF16 vision projector, retained for optional future image captioning

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 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.

Downloads last month: 158

GGUF

Model size

2B params

Architecture

qwen35

Hardware compatibility

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Model tree for GingerLabsPlatform/qwen38-prompt-enhancer-q8-runtime

Base model

Qwen/Qwen3.8-27B

Quantized

HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF

Quantized

(3)

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