Octen-Embedding-8B-INT8

Octen-Embedding-8B-INT8 is a text embedding model designed for semantic search and retrieval tasks. This model is fine-tuned from Qwen/Qwen3-Embedding-8B and supports multiple languages, providing high-quality embeddings for various applications.

Quantization: This is an INT8 quantized version using bitsandbytes. INT8 quantization significantly reduces memory footprint (~50% smaller), making it suitable for deployment on resource-constrained environments. Note that while memory usage is reduced, inference speed may not necessarily improve and could be slightly slower than the BF16 version on some hardware.

Model Details

• Base Model: Qwen/Qwen3-Embedding-8B
• Model Size: 8B parameters (INT8 quantized)
• Max Sequence Length: 40,960 tokens
• Embedding Dimension: 4096
• Languages: English, Chinese, and multilingual support
• Training Method: LoRA fine-tuning
• Quantization: INT8 (bitsandbytes)
• Memory Footprint: ~8GB (vs ~16GB for BF16 version)

Usage

Using Sentence Transformers

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("bflhc/Octen-Embedding-8B-INT8")

# Encode sentences
sentences = [
    "This is an example sentence",
    "Each sentence is converted to a vector"
]

embeddings = model.encode(sentences)
print(embeddings.shape)
# Output: (2, 4096)

# Compute similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
print(f"Similarity: {similarity.item():.4f}")

Using Transformers

from transformers import AutoModel, AutoTokenizer
import torch
import torch.nn.functional as F

tokenizer = AutoTokenizer.from_pretrained("bflhc/Octen-Embedding-8B-INT8", padding_side="left")
model = AutoModel.from_pretrained("bflhc/Octen-Embedding-8B-INT8")
model.eval()

def encode(texts):
    inputs = tokenizer(texts, padding=True, truncation=True,
                      max_length=8192, return_tensors="pt")

    with torch.no_grad():
        outputs = model(**inputs)
        # Use last token embedding
        embeddings = outputs.last_hidden_state[:, -1, :]
        # Normalize embeddings
        embeddings = F.normalize(embeddings, p=2, dim=1)

    return embeddings

# Example usage
texts = ["Hello world", "你好世界"]
embeddings = encode(texts)
similarity = torch.matmul(embeddings[0], embeddings[1])
print(f"Similarity: {similarity.item():.4f}")

Recommended Use Cases

• Semantic search and information retrieval
• Document similarity and clustering
• Question answering
• Cross-lingual retrieval
• Text classification with embeddings
• Deployment on GPU-constrained environments

Limitations

• Performance may vary across different domains and languages
• Very long documents (>40K tokens) require truncation
• Optimized for retrieval tasks, not for text generation
• INT8 quantization may introduce minor accuracy degradation compared to BF16 version
• Inference speed may not improve despite reduced memory usage

License

This model is licensed under the Apache License 2.0.

This model is derived from Qwen/Qwen3-Embedding-8B, which is also licensed under Apache License 2.0.

Citation

If you use this model in your research, please cite:

@misc{octen-embedding-2026,
  title={Octen-Embedding-8B-INT8: A Fine-tuned Multilingual Text Embedding Model},
  author={Octen Team},
  year={2026},
  url={https://huggingface.co/bflhc/Octen-Embedding-8B-INT8}
}

Please also cite the base model:

@article{qwen3embedding,
  title={Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models},
  author={Zhang, Yanzhao and Li, Mingxin and Long, Dingkun and Zhang, Xin and Lin, Huan and Yang, Baosong and Xie, Pengjun and Yang, An and Liu, Dayiheng and Lin, Junyang and Huang, Fei and Zhou, Jingren},
  journal={arXiv preprint arXiv:2506.05176},
  year={2025}
}