In this paper, we proposed a Chinese word segmentation model for micro-blog text. Although Conditional Random Fields (CRFs) models have been presented to deal with word segmentation, this is still the first time to apply it for the segmentation in the domain of Chinese micro-blog. Different from the genres of common articles, micro-blog has gradually become a new literary with the development of Internet. However, the unavailable of microblog training data has been the obstacle to develop a good segmenter based on trainable models. Considering the linguistic characteristics of the text, we proposed some methods to make the CRFs models suitable for segmentation in the domain of micro-blog. Several experiments have been conducted with different settings and then an optimal tagging method and feature templates have been designed. The proposed model has been implemented for the Second CIPS-SIGHAN Joint Conference on Chinese Language Processing Bakeoff (Bakeoff-2012) and achieves a very high Fmeasure of 93.38% within the test set of 5,000 micro-blog sentences. One of our main contributions is the online version of toolkit1 , which provides segmentation service for Chinese micro-blog text.