Instructions to use hanhainebula/qwen3-8b-ft-msmarco with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hanhainebula/qwen3-8b-ft-msmarco with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hanhainebula/qwen3-8b-ft-msmarco")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hanhainebula/qwen3-8b-ft-msmarco") model = AutoModel.from_pretrained("hanhainebula/qwen3-8b-ft-msmarco", device_map="auto") - sentence-transformers
How to use hanhainebula/qwen3-8b-ft-msmarco with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("hanhainebula/qwen3-8b-ft-msmarco") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
qwen3-8b-ft-msmarco
This model is based on Qwen/Qwen3-8B and was fine-tuned for 1 epoch on the English MSMARCO training data using FlagEmbedding.
This model is released as part of the ReasonEmbed resources. ReasonEmbed studies enhanced text embeddings for reasoning-intensive document retrieval; for more details, please refer to our paper.
Training Data
The model was fine-tuned on the MSMARCO data from hanhainebula/bge-multilingual-gemma2-data.
Training Procedure
- Base model: Qwen/Qwen3-8B
- Training framework: FlagEmbedding
- Training data: MSMARCO
- Number of epochs: 1
Tokenizer Note
Following the tokenizer modification discussed in Qwen3-Embedding-0.6B discussion #2, we modified the tokenizer so that it can automatically add the EOS token during tokenization.
License
This model is released under the Apache 2.0 license.
Citation
If you find this repository useful, please consider giving a star ⭐ and citation:
@article{chen2025reasonembed,
title={ReasonEmbed: Enhanced Text Embeddings for Reasoning-Intensive Document Retrieval},
author={Chen, Jianlyu and Lan, Junwei and Li, Chaofan and Lian, Defu and Liu, Zheng},
journal={arXiv preprint arXiv:2510.08252},
year={2025}
}
- Downloads last month
- 82