qwen3-embed 1.4.3

Lightweight Qwen3 text embedding & reranking via ONNX Runtime and GGUF (fork of fastembed)

Qwen3 Embed

Lightweight Qwen3 text embedding and reranking via ONNX Runtime and GGUF

Trimmed fork of fastembed, keeping only Qwen3 models.

Supported Models

ONNX (default)

Model	Type	Dims	Max Tokens	Size
n24q02m/Qwen3-Embedding-0.6B-ONNX	Embedding	32-1024 (MRL)	32768	573 MB
n24q02m/Qwen3-Embedding-0.6B-ONNX-Q4F16	Embedding	32-1024 (MRL)	32768	517 MB
n24q02m/Qwen3-Reranker-0.6B-ONNX	Reranker	-	40960	573 MB
n24q02m/Qwen3-Reranker-0.6B-ONNX-Q4F16	Reranker	-	40960	518 MB
n24q02m/Qwen3-Reranker-0.6B-ONNX-YesNo	Reranker	-	40960	598 MB

GGUF (optional, requires llama-cpp-python)

Model	Type	Dims	Max Tokens	Size
n24q02m/Qwen3-Embedding-0.6B-GGUF	Embedding	32-1024 (MRL)	32768	378 MB
n24q02m/Qwen3-Reranker-0.6B-GGUF	Reranker	-	40960	378 MB

HuggingFace Repos

Format	Embedding	Reranker
ONNX	n24q02m/Qwen3-Embedding-0.6B-ONNX	n24q02m/Qwen3-Reranker-0.6B-ONNX
GGUF	n24q02m/Qwen3-Embedding-0.6B-GGUF	n24q02m/Qwen3-Reranker-0.6B-GGUF

Installation

pip install qwen3-embed

# For GGUF support
pip install qwen3-embed[gguf]

Usage

Text Embedding

from qwen3_embed import TextEmbedding

# INT8 (default)
model = TextEmbedding(model_name="n24q02m/Qwen3-Embedding-0.6B-ONNX")

# Q4F16 (smaller, slightly less accurate)
model = TextEmbedding(model_name="n24q02m/Qwen3-Embedding-0.6B-ONNX-Q4F16")

# GGUF (requires: pip install qwen3-embed[gguf])
model = TextEmbedding(model_name="n24q02m/Qwen3-Embedding-0.6B-GGUF")

documents = [
    "Qwen3 is a multilingual embedding model.",
    "ONNX Runtime enables fast CPU inference.",
]

embeddings = list(model.embed(documents))
# Each embedding: numpy array of shape (1024,), L2-normalized

# Matryoshka Representation Learning (MRL) -- truncate to smaller dims
embeddings_256 = list(model.embed(documents, dim=256))
# Each embedding: numpy array of shape (256,), L2-normalized

# Query with instruction (for retrieval tasks)
queries = list(model.query_embed(
    ["What is Qwen3?"],
    task="Given a question, retrieve relevant passages",
))

Reranking

from qwen3_embed import TextCrossEncoder

reranker = TextCrossEncoder(model_name="n24q02m/Qwen3-Reranker-0.6B-ONNX")

# YesNo variant: ~10x less RAM (~598MB vs ~12GB at inference)
# reranker = TextCrossEncoder(model_name="n24q02m/Qwen3-Reranker-0.6B-ONNX-YesNo")

query = "What is Qwen3?"
documents = [
    "Qwen3 is a series of large language models by Alibaba.",
    "The weather today is sunny.",
    "Qwen3-Embedding supports multilingual text embedding.",
]

scores = list(reranker.rerank(query, documents))
# scores: list of float in [0, 1], higher = more relevant

# Or rerank pairs directly
pairs = [
    ("What is AI?", "Artificial intelligence is a branch of computer science."),
    ("What is ML?", "Machine learning is a subset of AI."),
]
pair_scores = list(reranker.rerank_pairs(pairs))

Key Features

• Last-token pooling: Uses the final token representation (with left-padding) instead of mean pooling.
• MRL support: Matryoshka Representation Learning allows truncating embeddings to any dimension from 32 to 1024 while preserving quality.
• Instruction-aware: Query embedding supports task instructions for better retrieval performance.
• Causal LM reranking: Reranker uses yes/no logit scoring via causal language model, producing calibrated [0, 1] scores.
• Multiple backends: ONNX Runtime (INT8, Q4F16) and GGUF (Q4_K_M via llama-cpp-python).
• GPU optional, no PyTorch: Runs on ONNX Runtime or llama-cpp-python -- no heavy ML framework required. Auto-detects GPU (CUDA, DirectML) when available.
• Multilingual: Both models support multi-language inputs.

GPU Acceleration

Both ONNX and GGUF backends auto-detect GPU when available (Device.AUTO is the default).

ONNX

Requires onnxruntime-gpu (CUDA) or onnxruntime-directml (Windows) instead of onnxruntime:

pip install onnxruntime-gpu  # NVIDIA CUDA
# or
pip install onnxruntime-directml  # Windows AMD/Intel/NVIDIA

from qwen3_embed import TextEmbedding, Device

# Auto-detect GPU (default)
model = TextEmbedding(model_name="n24q02m/Qwen3-Embedding-0.6B-ONNX")

# Force CPU
model = TextEmbedding(model_name="n24q02m/Qwen3-Embedding-0.6B-ONNX", cuda=Device.CPU)

# Force CUDA
model = TextEmbedding(model_name="n24q02m/Qwen3-Embedding-0.6B-ONNX", cuda=Device.CUDA)

GGUF

GPU is handled by llama-cpp-python. The default pip install qwen3-embed[gguf] is CPU-only. For CUDA GPU support, build with:

CMAKE_ARGS="-DGGML_CUDA=on" pip install qwen3-embed[gguf]

from qwen3_embed import TextEmbedding, Device

# Auto-detect GPU (default, offloads all layers)
model = TextEmbedding(model_name="n24q02m/Qwen3-Embedding-0.6B-GGUF")

# Force CPU only
model = TextEmbedding(model_name="n24q02m/Qwen3-Embedding-0.6B-GGUF", cuda=Device.CPU)

Development

mise run setup   # Install deps + pre-commit hooks
mise run lint    # ruff check + format --check
mise run test    # pytest
mise run fix     # ruff auto-fix + format

Related Projects

• mnemo-mcp — MCP memory server with semantic search powered by qwen3-embed
• wet-mcp — MCP web search server with vector-based docs search

Contributing

See CONTRIBUTING.md

License

Apache-2.0 - See LICENSE. Original fastembed by Qdrant.