Instructions to use majentik/Qwen3-Embedding-4B-GGUF-IQ4_XS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use majentik/Qwen3-Embedding-4B-GGUF-IQ4_XS 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 majentik/Qwen3-Embedding-4B-GGUF-IQ4_XS:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf majentik/Qwen3-Embedding-4B-GGUF-IQ4_XS:IQ4_XS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf majentik/Qwen3-Embedding-4B-GGUF-IQ4_XS:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf majentik/Qwen3-Embedding-4B-GGUF-IQ4_XS:IQ4_XS

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 majentik/Qwen3-Embedding-4B-GGUF-IQ4_XS:IQ4_XS
# Run inference directly in the terminal:
./llama-cli -hf majentik/Qwen3-Embedding-4B-GGUF-IQ4_XS:IQ4_XS

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 majentik/Qwen3-Embedding-4B-GGUF-IQ4_XS:IQ4_XS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf majentik/Qwen3-Embedding-4B-GGUF-IQ4_XS:IQ4_XS

Use Docker

docker model run hf.co/majentik/Qwen3-Embedding-4B-GGUF-IQ4_XS:IQ4_XS

LM Studio
Jan
Ollama
How to use majentik/Qwen3-Embedding-4B-GGUF-IQ4_XS with Ollama:
```
ollama run hf.co/majentik/Qwen3-Embedding-4B-GGUF-IQ4_XS:IQ4_XS
```
Unsloth Desktop

How to use majentik/Qwen3-Embedding-4B-GGUF-IQ4_XS with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf majentik/Qwen3-Embedding-4B-GGUF-IQ4_XS:IQ4_XS

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": "majentik/Qwen3-Embedding-4B-GGUF-IQ4_XS:IQ4_XS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use majentik/Qwen3-Embedding-4B-GGUF-IQ4_XS with Docker Model Runner:
```
docker model run hf.co/majentik/Qwen3-Embedding-4B-GGUF-IQ4_XS:IQ4_XS
```

Lemonade

How to use majentik/Qwen3-Embedding-4B-GGUF-IQ4_XS with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull majentik/Qwen3-Embedding-4B-GGUF-IQ4_XS:IQ4_XS

Run and chat with the model

lemonade run user.Qwen3-Embedding-4B-GGUF-IQ4_XS-IQ4_XS

List all available models

lemonade list

Hermes Agent

How to use majentik/Qwen3-Embedding-4B-GGUF-IQ4_XS with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf majentik/Qwen3-Embedding-4B-GGUF-IQ4_XS:IQ4_XS

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 majentik/Qwen3-Embedding-4B-GGUF-IQ4_XS:IQ4_XS

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use majentik/Qwen3-Embedding-4B-GGUF-IQ4_XS with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf majentik/Qwen3-Embedding-4B-GGUF-IQ4_XS:IQ4_XS

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 "majentik/Qwen3-Embedding-4B-GGUF-IQ4_XS:IQ4_XS" \
  --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-Embedding-4B GGUF IQ4_XS

llama.cpp GGUF IQ4_XS quantization of Qwen/Qwen3-Embedding-4B.

Produced with: llama-quantize (upstream llama.cpp, April 2026 build)
BF16 source converted via convert_hf_to_gguf.py from the fresh llama.cpp tree
Quant type: IQ4_XS
File size: 2.1 GB

Quickstart

llama-embedding -m qwen3-emb-4b-IQ4_XS.gguf \
  -p "What is the capital of France?"

Or via llama-cpp-python:

from llama_cpp import Llama
llm = Llama(model_path="qwen3-emb-4b-IQ4_XS.gguf", embedding=True)
vec = llm.embed("What is the capital of France?")

License

Apache 2.0 — inherited from the upstream base model.

Model tree for majentik/Qwen3-Embedding-4B-GGUF-IQ4_XS

Base model

Qwen/Qwen3-4B-Base

Finetuned

Qwen/Qwen3-Embedding-4B

Quantized

(46)

this model

Collection including majentik/Qwen3-Embedding-4B-GGUF-IQ4_XS

Embeddings (ONNX)

Collection

Qwen3-Embedding model packs in ONNX, GGUF, and MLX formats for retrieval and embedding workloads. • 18 items • Updated 12 days ago

majentik
/

Qwen3-Embedding-4B-GGUF-IQ4_XS

Qwen3-Embedding-4B GGUF IQ4_XS

Quickstart

License

See also

Model tree for majentik/Qwen3-Embedding-4B-GGUF-IQ4_XS

Collection including majentik/Qwen3-Embedding-4B-GGUF-IQ4_XS

Embeddings (ONNX)