sam860/qwen3-reranker

sam860/ qwen3-reranker

1,196 Downloads Updated 8 months ago

tools

ollama run sam860/qwen3-reranker:0.6b-Q8_0

curl http://localhost:11434/api/chat \
  -d '{
    "model": "sam860/qwen3-reranker:0.6b-Q8_0",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

from ollama import chat

response = chat(
    model='sam860/qwen3-reranker:0.6b-Q8_0',
    messages=[{'role': 'user', 'content': 'Hello!'}],
)
print(response.message.content)

import ollama from 'ollama'

const response = await ollama.chat({
  model: 'sam860/qwen3-reranker:0.6b-Q8_0',
  messages: [{role: 'user', content: 'Hello!'}],
})
console.log(response.message.content)

Applications

Claude Code

Claude Code ollama launch claude --model sam860/qwen3-reranker:0.6b-Q8_0

Codex

Codex ollama launch codex --model sam860/qwen3-reranker:0.6b-Q8_0

OpenCode

OpenCode ollama launch opencode --model sam860/qwen3-reranker:0.6b-Q8_0

OpenClaw

OpenClaw ollama launch openclaw --model sam860/qwen3-reranker:0.6b-Q8_0

Models

Name

2 models

Size

Context

Input

qwen3-reranker:0.6b-Q8_0

639MB · 40K context window · Text · 8 months ago

qwen3-reranker:0.6b-Q8_0

639MB

40K

Text

qwen3-reranker:0.6b-F16

1.2GB · 40K context window · Text · 8 months ago

qwen3-reranker:0.6b-F16

1.2GB

40K

Text

Readme

Description:

The Qwen3-Reranker-0.6B is a specialized text reranking model, part of the Qwen3 series, designed to refine the relevance of retrieved text passages. It’s built upon the foundational Qwen3-0.6B-Base model and is particularly effective when combined with an embedding model for a comprehensive retrieval pipeline.

Key Features & Use Cases:

Model Type: A Text Reranking model, specifically designed to reorder initial search results based on relevance. It complements text embedding models within a broader information retrieval system.
Size: A compact model with 0.6 billion parameters, striking a balance between performance and computational efficiency. Larger reranking models (4B, 8B) are also available in the series for more demanding tasks.
Multilingual Capability: Supports over 100 languages, including programming languages, inheriting the robust multilingual and cross-lingual understanding from its foundational Qwen3 series.
Context Length: Capable of processing texts up to 32,000 tokens, allowing for reranking of longer documents or contexts.
Instruction Awareness: Supports user-defined instructions to fine-tune its reranking behavior for specific tasks, languages, or scenarios. This feature can improve performance in retrieval scenarios by 1% to 5%.
Applications: Primarily used for:
- Text Reranking: Enhancing the accuracy of search results by re-scoring and reordering retrieved documents.
- Text Retrieval: As a crucial second stage after initial retrieval by an embedding model, to improve the relevance of the final output.
- Code Retrieval: Refining search results for code snippets.

Usage Notes:

For optimal results, it’s highly recommended to customize the input instruction (instruct) based on your specific use case, task, and language.
The model integrates seamlessly with the Hugging Face Transformers library, and vLLM for efficient inference.

## Description:

The Qwen3-Reranker-0.6B is a specialized text reranking model, part of the Qwen3 series, designed to refine the relevance of retrieved text passages. It's built upon the foundational Qwen3-0.6B-Base model and is particularly effective when combined with an embedding model for a comprehensive retrieval pipeline.

**Key Features & Use Cases:**

*   **Model Type:** A Text Reranking model, specifically designed to reorder initial search results based on relevance. It complements text embedding models within a broader information retrieval system.
*   **Size:** A compact model with 0.6 billion parameters, striking a balance between performance and computational efficiency. Larger reranking models (4B, 8B) are also available in the series for more demanding tasks.
*   **Multilingual Capability:** Supports over 100 languages, including programming languages, inheriting the robust multilingual and cross-lingual understanding from its foundational Qwen3 series.
*   **Context Length:** Capable of processing texts up to 32,000 tokens, allowing for reranking of longer documents or contexts.
*   **Instruction Awareness:** Supports user-defined instructions to fine-tune its reranking behavior for specific tasks, languages, or scenarios. This feature can improve performance in retrieval scenarios by 1% to 5%.
*   **Applications:** Primarily used for:
    *   **Text Reranking:** Enhancing the accuracy of search results by re-scoring and reordering retrieved documents.
    *   **Text Retrieval:** As a crucial second stage after initial retrieval by an embedding model, to improve the relevance of the final output.
    *   **Code Retrieval:** Refining search results for code snippets.

**Usage Notes:**

*   For optimal results, it's highly recommended to customize the input instruction (`instruct`) based on your specific use case, task, and language.
*   The model integrates seamlessly with the Hugging Face Transformers library, and vLLM for efficient inference.

[HuggingFace](https://huggingface.co/Qwen/Qwen3-Reranker-0.6B)

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