Instructions to use privastudio/qwen3.8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use privastudio/qwen3.8 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 privastudio/qwen3.8:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf privastudio/qwen3.8:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf privastudio/qwen3.8:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf privastudio/qwen3.8:Q4_K_M

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 privastudio/qwen3.8:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf privastudio/qwen3.8:Q4_K_M

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 privastudio/qwen3.8:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf privastudio/qwen3.8:Q4_K_M

Use Docker

docker model run hf.co/privastudio/qwen3.8:Q4_K_M

LM Studio
Jan

vLLM

How to use privastudio/qwen3.8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "privastudio/qwen3.8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "privastudio/qwen3.8",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/privastudio/qwen3.8:Q4_K_M

Ollama
How to use privastudio/qwen3.8 with Ollama:
```
ollama run hf.co/privastudio/qwen3.8:Q4_K_M
```
Unsloth Desktop

How to use privastudio/qwen3.8 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf privastudio/qwen3.8:Q4_K_M

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": "privastudio/qwen3.8:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use privastudio/qwen3.8 with Docker Model Runner:
```
docker model run hf.co/privastudio/qwen3.8:Q4_K_M
```

Lemonade

How to use privastudio/qwen3.8 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull privastudio/qwen3.8:Q4_K_M

Run and chat with the model

lemonade run user.qwen3.8-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use privastudio/qwen3.8 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf privastudio/qwen3.8:Q4_K_M

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 privastudio/qwen3.8:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use privastudio/qwen3.8 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf privastudio/qwen3.8:Q4_K_M

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 "privastudio/qwen3.8:Q4_K_M" \
  --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"

Qwen3.8-27B (GGUF)

Q4_K_M GGUF build of Qwen3.8-27B, packaged for local use in PrivaEmanator.

Source

This model is not trained, modified, or quantized by us. All credit goes to the original authors:

GGUF quantization: lmstudio-community/Qwen3.8-27B-GGUF by LM Studio Community
Base model: Qwen/Qwen3.8-27B by the Qwen team

This repo only redistributes those files in a layout PrivaEmanator can load directly.

Files

File	Description
`Qwen3.8-27B-Q4_K_M.gguf`	Language model, Q4_K_M quantization (~16.8 GB)
`mmproj-BF16.gguf`	Vision projector for image input
`chat_template.jinja`	Chat template, thinking disabled
`tokenizer.json`, `tokenizer_config.json`, `vocab.json`, `merges.txt`	Tokenizer
`config.json`, `generation_config.json`, `preprocessor_config.json`, `video_preprocessor_config.json`	Model, generation and image/video preprocessing config

Usage

Download the whole repo into one folder (e.g. assets/models/qwen3.8) and select the .gguf model in PrivaEmanator. The mmproj file in the same folder enables image input.

License

Apache-2.0, following the base model. See LICENSE.

Downloads last month: 20

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

4-bit

Model tree for privastudio/qwen3.8

Base model

Qwen/Qwen3.8-27B

Quantized

(1539)

this model