Qwen3-Reranker-0.6B-fp16-ov

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

Description

This is Qwen3-Reranker-0.6B model converted to the OpenVINO™ IR (Intermediate Representation) format with weights compressed to FP16.

Compatibility

The provided OpenVINO™ IR model is compatible with:

• OpenVINO version 2025.4.0 and higher
• Optimum Intel 1.26.0 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
from transformers import AutoTokenizer
from optimum.intel import OVModelForCausalLM


model_id = "OpenVINO/Qwen3-Reranker-0.6B-fp16-ov"

model = OVModelForCausalLM.from_pretrained(model_id, use_cache=False, export=False)


def format_instruction(instruction, query, doc):
    if instruction is None:
        instruction = "Given a web search query, retrieve relevant passages that answer the query"
    output = "<Instruct>: {instruction}\n<Query>: {query}\n<Document>: {doc}".format(instruction=instruction, query=query, doc=doc)
    return output


def process_inputs(pairs):
    inputs = tokenizer(
        pairs, padding=False, truncation="longest_first", return_attention_mask=False, max_length=max_length - len(prefix_tokens) - len(suffix_tokens)
    )
    for i, ele in enumerate(inputs["input_ids"]):
        inputs["input_ids"][i] = prefix_tokens + ele + suffix_tokens
    inputs = tokenizer.pad(inputs, padding=True, return_tensors="pt", max_length=max_length)
    for key in inputs:
        inputs[key] = inputs[key].to(model.device)
    return inputs


def compute_logits(inputs, **kwargs):
    batch_scores = model(**inputs).logits[:, -1, :]
    true_vector = batch_scores[:, token_true_id]
    false_vector = batch_scores[:, token_false_id]
    batch_scores = torch.stack([false_vector, true_vector], dim=1)
    batch_scores = torch.nn.functional.log_softmax(batch_scores, dim=1)
    scores = batch_scores[:, 1].exp().tolist()
    return scores


tokenizer = AutoTokenizer.from_pretrained(model_id, padding_side="left")
token_false_id = tokenizer.convert_tokens_to_ids("no")
token_true_id = tokenizer.convert_tokens_to_ids("yes")
max_length = 8192

prefix = '<|im_start|>system\nJudge whether the Document meets the requirements based on the Query and the Instruct provided. Note that the answer can only be "yes" or "no".<|im_end|>\n<|im_start|>user\n'
suffix = "<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n"
prefix_tokens = tokenizer.encode(prefix, add_special_tokens=False)
suffix_tokens = tokenizer.encode(suffix, add_special_tokens=False)

task = "Given a web search query, retrieve relevant passages that answer the query"

queries = [
    "What is the capital of China?",
    "Explain gravity",
]

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.",
]

pairs = [format_instruction(task, query, doc) for query, doc in zip(queries, documents)]

# Tokenize the input texts
inputs = process_inputs(pairs)
scores = compute_logits(inputs)

print("scores: ", scores)

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-Reranker-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.