Instructions to use n24q02m/Qwen3-Embedding-0.6B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use n24q02m/Qwen3-Embedding-0.6B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="n24q02m/Qwen3-Embedding-0.6B-GGUF",
	filename="qwen3-embedding-0.6b-q4-k-m.gguf",
)

llm.create_chat_completion(
	messages = "\"Today is a sunny day and I will get some ice cream.\""
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use n24q02m/Qwen3-Embedding-0.6B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf n24q02m/Qwen3-Embedding-0.6B-GGUF
# Run inference directly in the terminal:
llama-cli -hf n24q02m/Qwen3-Embedding-0.6B-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf n24q02m/Qwen3-Embedding-0.6B-GGUF
# Run inference directly in the terminal:
llama-cli -hf n24q02m/Qwen3-Embedding-0.6B-GGUF

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 n24q02m/Qwen3-Embedding-0.6B-GGUF
# Run inference directly in the terminal:
./llama-cli -hf n24q02m/Qwen3-Embedding-0.6B-GGUF

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 n24q02m/Qwen3-Embedding-0.6B-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf n24q02m/Qwen3-Embedding-0.6B-GGUF

Use Docker

docker model run hf.co/n24q02m/Qwen3-Embedding-0.6B-GGUF

LM Studio
Jan
Ollama
How to use n24q02m/Qwen3-Embedding-0.6B-GGUF with Ollama:
```
ollama run hf.co/n24q02m/Qwen3-Embedding-0.6B-GGUF
```

Unsloth Studio

How to use n24q02m/Qwen3-Embedding-0.6B-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 n24q02m/Qwen3-Embedding-0.6B-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 n24q02m/Qwen3-Embedding-0.6B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for n24q02m/Qwen3-Embedding-0.6B-GGUF to start chatting

How to use n24q02m/Qwen3-Embedding-0.6B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf n24q02m/Qwen3-Embedding-0.6B-GGUF

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": "n24q02m/Qwen3-Embedding-0.6B-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use n24q02m/Qwen3-Embedding-0.6B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf n24q02m/Qwen3-Embedding-0.6B-GGUF

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 n24q02m/Qwen3-Embedding-0.6B-GGUF

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use n24q02m/Qwen3-Embedding-0.6B-GGUF with Docker Model Runner:
```
docker model run hf.co/n24q02m/Qwen3-Embedding-0.6B-GGUF
```

Lemonade

How to use n24q02m/Qwen3-Embedding-0.6B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull n24q02m/Qwen3-Embedding-0.6B-GGUF

Run and chat with the model

lemonade run user.Qwen3-Embedding-0.6B-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

n24q02m/Qwen3-Embedding-0.6B-GGUF

GGUF-quantized version of Qwen/Qwen3-Embedding-0.6B for use with qwen3-embed and llama-cpp-python.

Available Variants

Variant	File	Size	Description
Q4_K_M	`qwen3-embedding-0.6b-q4-k-m.gguf`	378 MB	4-bit quantization (recommended)

Usage

qwen3-embed

pip install qwen3-embed[gguf]

from qwen3_embed import TextEmbedding

model = TextEmbedding("n24q02m/Qwen3-Embedding-0.6B-GGUF")
embeddings = list(model.embed(["Hello world"]))               # 1024-dim

# MRL: reduce dimension
embeddings_256 = list(model.embed(["Hello world"], dim=256))  # 256-dim

# Query with instruction
query_emb = list(model.query_embed("What is machine learning?"))

llama-cpp-python (direct)

from llama_cpp import Llama

model = Llama(
    model_path="qwen3-embedding-0.6b-q4-k-m.gguf",
    embedding=True,
    pooling_type=3,  # LLAMA_POOLING_TYPE_LAST
    n_ctx=32768,
)
result = model.create_embedding("Hello world")

Conversion Details

Source: Qwen/Qwen3-Embedding-0.6B
Method: convert_hf_to_gguf.py (F16) + llama-quantize (Q4_K_M)

ONNX variants: n24q02m/Qwen3-Embedding-0.6B-ONNX

Downloads last month: 225

GGUF

Model size

0.6B params

Architecture

qwen3

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for n24q02m/Qwen3-Embedding-0.6B-GGUF

Base model

Qwen/Qwen3-0.6B-Base

Finetuned

Qwen/Qwen3-Embedding-0.6B

Quantized

(233)

this model

n24q02m
/

Qwen3-Embedding-0.6B-GGUF