license: mit
Model Card for SciLitLLM-7B
SciLitLLM-7B adapts a general large language model for effective scientific literature understanding. Starting from the Qwen2-7B model, SciLitLLM-7B goes through a hybrid strategy that integrates continual pre-training (CPT) and supervised fine-tuning (SFT), to simultaneously infuse scientific domain knowledge and enhance instruction-following capabilities for domain-specific tasks.
In this process, we identify two key challenges: (1) constructing high-quality CPT corpora, and (2) generating diverse SFT instructions. We address these challenges through a meticulous pipeline, including PDF text extraction, parsing content error correction, quality filtering, and synthetic instruction creation.
Applying this strategy, we present SciLitLLM-7B, specialized in scientific literature understanding, which demonstrates promising performance on scientific literature understanding benchmarks. Specifically, it shows an average performance improvement of 3.6% on SciAssess and 10.1% on SciRIFF compared to leading LLMs with fewer than 15B parameters.
See the paper for more details.
Requirements
Since SciLitLLM is based on Qwen2, we advise you to install transformers>=4.37.0, or you might encounter the following error:
KeyError: 'qwen2'
Quickstart
Here provides a code snippet with apply_chat_template to show you how to load the tokenizer and model and how to generate contents.
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Uni-SMART/SciLitLLM",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Uni-SMART/SciLitLLM")
prompt = "Can you summarize this article for me?\n <ARTICLE>"
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
Citation
If you find our work helpful, feel free to give us a cite.
@misc{li2024scilitllmadaptllmsscientific,
title={SciLitLLM: How to Adapt LLMs for Scientific Literature Understanding},
author={Sihang Li and Jin Huang and Jiaxi Zhuang and Yaorui Shi and Xiaochen Cai and Mingjun Xu and Xiang Wang and Linfeng Zhang and Guolin Ke and Hengxing Cai},
year={2024},
eprint={2408.15545},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2408.15545},
}