sam860/qwen3-embedding

sam860/ qwen3-embedding

845 Downloads Updated 11 months ago

tools

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

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

from ollama import chat

response = chat(
    model='sam860/qwen3-embedding: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-embedding: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-embedding:0.6b-Q8_0

OpenClaw

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

Hermes Agent

Hermes Agent ollama launch hermes --model sam860/qwen3-embedding:0.6b-Q8_0

Codex

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

OpenCode

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

Models

Name

2 models

Size

Context

Input

qwen3-embedding:0.6b-Q8_0

639MB · 32K context window · Text · 11 months ago

qwen3-embedding:0.6b-Q8_0

639MB

32K

Text

qwen3-embedding:0.6b-F16

1.2GB · 32K context window · Text · 11 months ago

qwen3-embedding:0.6b-F16

1.2GB

32K

Text

Readme

Description:

The Qwen3-Embedding-0.6B-GGUF is a specialized text embedding model, part of the Qwen3 series, tailored for efficient and effective text embedding and reranking tasks.

Key Features & Use Cases:

Model Type: Primarily a Text Embedding model, also part of a series that includes Text Reranking models.
Size: A compact model with 0.6 billion parameters, making it suitable for scenarios where efficiency is prioritized. Other sizes (4B, 8B) are available in the series for different performance needs.
Multilingual Capability: Supports over 100 languages, including various programming languages, excelling in multilingual, cross-lingual, and code retrieval scenarios.
Context Length: Capable of processing texts up to 32,000 tokens.
Embedding Dimension: Offers flexible output dimensions, allowing users to define embeddings from 32 to 1024.
Instruction Awareness: Supports user-defined instructions to optimize performance for specific tasks, languages, or use cases. This can lead to a 1% to 5% improvement in performance for most downstream tasks, especially in retrieval scenarios.
Quantization: Available in q8_0 and f16 quantization formats.
Applications: Ideal for various text embedding and ranking tasks, including:
- Text Retrieval
- Code Retrieval
- Text Classification
- Text Clustering
- Bitext Mining

Usage Notes:

When using the model, it’s recommended to append the <|endoftext|> token to your input context.
For optimal results, tailor your input instructions (instruct) to the specific task or language.

## Description:

The Qwen3-Embedding-0.6B-GGUF is a specialized text embedding model, part of the Qwen3 series, tailored for efficient and effective text embedding and reranking tasks.

**Key Features & Use Cases:**

*   **Model Type:** Primarily a Text Embedding model, also part of a series that includes Text Reranking models.
*   **Size:** A compact model with 0.6 billion parameters, making it suitable for scenarios where efficiency is prioritized. Other sizes (4B, 8B) are available in the series for different performance needs.
*   **Multilingual Capability:** Supports over 100 languages, including various programming languages, excelling in multilingual, cross-lingual, and code retrieval scenarios.
*   **Context Length:** Capable of processing texts up to 32,000 tokens.
*   **Embedding Dimension:** Offers flexible output dimensions, allowing users to define embeddings from 32 to 1024.
*   **Instruction Awareness:** Supports user-defined instructions to optimize performance for specific tasks, languages, or use cases. This can lead to a 1% to 5% improvement in performance for most downstream tasks, especially in retrieval scenarios.
*   **Quantization:** Available in `q8_0` and `f16` quantization formats.
*   **Applications:** Ideal for various text embedding and ranking tasks, including:
    *   Text Retrieval
    *   Code Retrieval
    *   Text Classification
    *   Text Clustering
    *   Bitext Mining

**Usage Notes:**

*   When using the model, it's recommended to append the `<|endoftext|>` token to your input context.
*   For optimal results, tailor your input instructions (`instruct`) to the specific task or language.

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

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