Qwen3-Embedding-0.6B-int4-cw-ov

• Model creator: Qwen
• Original model: Qwen3-Embedding-0.6B

Description

This is Qwen3-Embedding-0.6B model converted to the OpenVINO™ IR (Intermediate Representation) format with weights compressed to INT4 by NNCF.

The model is optimized for inference on NPU using these instructions.

Quantization Parameters

Weight compression was performed using nncf.compress_weights with the following parameters:

• mode: INT4_SYM
• ratio: 1.0

For more information on quantization, check the OpenVINO model optimization guide.

Compatibility

The provided OpenVINO™ IR model is compatible with:

• OpenVINO version 2026.0.0 and higher
• Optimum Intel 1.27.0 and higher

Running Model Inference with Optimum Intel

1. Install packages required for using Optimum Intel integration with the OpenVINO backend:

pip install "git+https://github.com/huggingface/optimum-intel.git" "torch==2.8" --extra-index-url https://download.pytorch.org/whl/cpu

2. Run model inference:

import torch
import torch.nn.functional as F

from torch import Tensor
from transformers import AutoTokenizer

from optimum.intel import OVModelForFeatureExtraction

device = "NPU"
model_id = "OpenVINO/Qwen3-Embedding-0.6B-int4-cw-ov"

model = OVModelForFeatureExtraction.from_pretrained(model_id, export=False, device=device)


def last_token_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor:
    left_padding = attention_mask[:, -1].sum() == attention_mask.shape[0]
    if left_padding:
        return last_hidden_states[:, -1]
    else:
        sequence_lengths = attention_mask.sum(dim=1) - 1
        batch_size = last_hidden_states.shape[0]
        return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]


def get_detailed_instruct(task_description: str, query: str) -> str:
    return f"Instruct: {task_description}\nQuery:{query}"


# Each query must come with a one-sentence instruction that describes the task
task = "Given a web search query, retrieve relevant passages that answer the query"

queries = [get_detailed_instruct(task, "What is the capital of China?"), get_detailed_instruct(task, "Explain gravity")]
# No need to add instruction for retrieval documents
documents = [
    "The capital of China is Beijing.",
    "Gravity is a force that attracts two bodies towards each other. It gives weight to physical objects and is responsible for the movement of planets around the sun.",
]
input_texts = queries + documents

tokenizer = AutoTokenizer.from_pretrained(model_id, padding_side="left")

max_length = 8192

# Tokenize the input texts
batch_dict = tokenizer(
    input_texts,
    padding=True,
    truncation=True,
    max_length=max_length,
    return_tensors="pt",
)
batch_dict.to(model.device)
outputs = model(**batch_dict)
embeddings = last_token_pool(outputs.last_hidden_state, batch_dict["attention_mask"])
# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = embeddings[:2] @ embeddings[2:].T
print(scores.tolist())

For more examples and possible optimizations, refer to the Inference with Optimum Intel.

Limitations

Check the original model card for limitations.

Legal information

The original model is distributed under Apache License Version 2.0 license. More details can be found in Qwen3-Embedding-0.6B.

Disclaimer

Intel is committed to respecting human rights and avoiding causing or contributing to adverse impacts on human rights. See Intel’s Global Human Rights Principles. Intel’s products and software are intended only to be used in applications that do not cause or contribute to adverse impacts on human rights.