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python inference.py --load_8bit --base_model 'meta-llama/Llama-2-7b-hf' --lora_weights 'DSMI/LLaMA-E'***Instruction: Where can I buy the handmade jewellery?
***Response: Etsy, NOVICA, UncommonGoods, World Market, and Global Crafts offer unique handmade jewelry from artisans worldwide.
***Instruction: Create an attractive advertisement for the Christmas sale of the following product.
***Input: Custom Photo Music Plaque,Personalized Photo Frame,Album Cover Song Plaque,Music Photo Name Night Lamp,Photo and Music Gift, Music Prints
***Response: Personalize your music space with a Custom Photo Music Plaque. Add your favorite photo and song lyrics to create a unique gift for music lovers.1@inproceedings{shi-etal-2025-llama,
2 title = "{LL}a{MA}-{E}: Empowering {E}-commerce Authoring with Object-Interleaved Instruction Following",
3 author = "Shi, Kaize and
4 Sun, Xueyao and
5 Wang, Dingxian and
6 Fu, Yinlin and
7 Xu, Guandong and
8 Li, Qing",
9 editor = "Rambow, Owen and
10 Wanner, Leo and
11 Apidianaki, Marianna and
12 Al-Khalifa, Hend and
13 Eugenio, Barbara Di and
14 Schockaert, Steven",
15 booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
16 month = jan,
17 year = "2025",
18 address = "Abu Dhabi, UAE",
19 publisher = "Association for Computational Linguistics",
20 url = "https://aclanthology.org/2025.coling-main.58/",
21 pages = "870--885",
22 abstract = "E-commerce authoring entails creating engaging, diverse, and targeted content to enhance preference elicitation and retrieval experience. While Large Language Models (LLMs) have revolutionized content generation, they often fall short in e-commerce applications due to their limited memorization of domain-specific features. This paper proposes LLaMA-E, the unified e-commerce authoring models that address the contextual preferences of customers, sellers, and platforms, the essential objects in e-commerce operation. We design the instruction set derived from tasks of ads generation, query-enhanced product title rewriting, product classification, purchase intent speculation, and general e-commerce Q{\&}A. The instruction formulation ensures the interleaved cover of the presented and required object features, allowing the alignment of base models to parameterize e-commerce knowledge comprehensively. The proposed LLaMA-E models achieve state-of-the-art evaluation performance and exhibit the advantage in zero-shot practical applications. To our knowledge, this is the first LLM tailored to empower authoring applications with comprehensive scenario understanding by integrating features focused on participated objects."
23}