Instructions to use grevinden/Qwen3-Embedding-4B-TQ4_1S-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use grevinden/Qwen3-Embedding-4B-TQ4_1S-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 grevinden/Qwen3-Embedding-4B-TQ4_1S-GGUF:Q4_1
# Run inference directly in the terminal:
llama cli -hf grevinden/Qwen3-Embedding-4B-TQ4_1S-GGUF:Q4_1

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf grevinden/Qwen3-Embedding-4B-TQ4_1S-GGUF:Q4_1
# Run inference directly in the terminal:
llama cli -hf grevinden/Qwen3-Embedding-4B-TQ4_1S-GGUF:Q4_1

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 grevinden/Qwen3-Embedding-4B-TQ4_1S-GGUF:Q4_1
# Run inference directly in the terminal:
./llama-cli -hf grevinden/Qwen3-Embedding-4B-TQ4_1S-GGUF:Q4_1

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 grevinden/Qwen3-Embedding-4B-TQ4_1S-GGUF:Q4_1
# Run inference directly in the terminal:
./build/bin/llama-cli -hf grevinden/Qwen3-Embedding-4B-TQ4_1S-GGUF:Q4_1

Use Docker

docker model run hf.co/grevinden/Qwen3-Embedding-4B-TQ4_1S-GGUF:Q4_1

LM Studio
Jan
Ollama
How to use grevinden/Qwen3-Embedding-4B-TQ4_1S-GGUF with Ollama:
```
ollama run hf.co/grevinden/Qwen3-Embedding-4B-TQ4_1S-GGUF:Q4_1
```

Unsloth Studio

How to use grevinden/Qwen3-Embedding-4B-TQ4_1S-GGUF 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 grevinden/Qwen3-Embedding-4B-TQ4_1S-GGUF 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 grevinden/Qwen3-Embedding-4B-TQ4_1S-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for grevinden/Qwen3-Embedding-4B-TQ4_1S-GGUF to start chatting

How to use grevinden/Qwen3-Embedding-4B-TQ4_1S-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf grevinden/Qwen3-Embedding-4B-TQ4_1S-GGUF:Q4_1

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": "grevinden/Qwen3-Embedding-4B-TQ4_1S-GGUF:Q4_1"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use grevinden/Qwen3-Embedding-4B-TQ4_1S-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 grevinden/Qwen3-Embedding-4B-TQ4_1S-GGUF:Q4_1

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 grevinden/Qwen3-Embedding-4B-TQ4_1S-GGUF:Q4_1

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use grevinden/Qwen3-Embedding-4B-TQ4_1S-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf grevinden/Qwen3-Embedding-4B-TQ4_1S-GGUF:Q4_1

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 "grevinden/Qwen3-Embedding-4B-TQ4_1S-GGUF:Q4_1" \
  --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"

Docker Model Runner
How to use grevinden/Qwen3-Embedding-4B-TQ4_1S-GGUF with Docker Model Runner:
```
docker model run hf.co/grevinden/Qwen3-Embedding-4B-TQ4_1S-GGUF:Q4_1
```

Lemonade

How to use grevinden/Qwen3-Embedding-4B-TQ4_1S-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull grevinden/Qwen3-Embedding-4B-TQ4_1S-GGUF:Q4_1

Run and chat with the model

lemonade run user.Qwen3-Embedding-4B-TQ4_1S-GGUF-Q4_1

List all available models

lemonade list

Qwen3-Embedding-4B-TQ4_1S-GGUF

Original model: Qwen/Qwen3-Embedding-4B
Quantization: TQ4_1S (TurboQuant, ~5.0 bpw, WHT-rotated)
Size: ~2.5 GB (from 7.5 GB f16)
GGUF backend: llama.cpp TQP v0.2.0 (TurboQuant Plus)

Качество сопоставимо с Q8_0 при вдвое меньшем размере. Сделано через TurboQuant Plus.

Использование

llama.cpp / TurboQuant

llama-server --embedding -m Qwen3-Embedding-4B-TQ4_1S.gguf -ngl 99

LocalAI

Создать /usr/share/localai/models/qwen3-embedding.yaml:

name: qwen3-embedding
backend: llama-cpp
files:
  - filename: Qwen3-Embedding-4B-TQ4_1S.gguf
    uri: huggingface://grevinden/Qwen3-Embedding-4B-TQ4_1S-GGUF/Qwen3-Embedding-4B-TQ4_1S.gguf

После этого перезапустить LocalAI — модель подтянется автоматически.

OpenAI-compatible API

curl http://localhost:8080/v1/embeddings \
  -H "Content-Type: application/json" \
  -d '{
    "input": "Hello world",
    "model": "qwen3-embedding"
  }'

HuggingFace Hub (llama-cpp-python)

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="grevinden/Qwen3-Embedding-4B-TQ4_1S-GGUF",
    filename="Qwen3-Embedding-4B-TQ4_1S.gguf",
    embedding=True,
)
embeddings = llm.create_embedding("Hello world")

Характеристики модели

Параметр	Значение
Hidden size	2560
Layers	35
Attention heads	20
Max tokens	32768
Embedding dim	2560
Pooling	cls
GGUF type	TQ4_1S (5.15 BPW)
Размер файла	~2.5 GB

Downloads last month: 226

GGUF

Model size

4B params

Architecture

qwen3

Hardware compatibility

4-bit

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