# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("NingLab/eCeLLM-S", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("NingLab/eCeLLM-S", trust_remote_code=True)Quick Links
eCeLLM-S
This repo contains the models for "eCeLLM: Generalizing Large Language Models for E-commerce from Large-scale, High-quality Instruction Data"
eCeLLM Models
Leveraging ECInstruct, we develop eCeLLM by instruction tuning general-purpose LLMs (base models). The eCeLLM-S model is instruction-tuned from the large base models Phi-2.
Citation
@inproceedings{
peng2024ecellm,
title={eCe{LLM}: Generalizing Large Language Models for E-commerce from Large-scale, High-quality Instruction Data},
author={Bo Peng and Xinyi Ling and Ziru Chen and Huan Sun and Xia Ning},
booktitle={Forty-first International Conference on Machine Learning},
year={2024},
url={https://openreview.net/forum?id=LWRI4uPG2X}
}
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="NingLab/eCeLLM-S", trust_remote_code=True)