72 lines
3.0 KiB
Markdown
72 lines
3.0 KiB
Markdown
|
---
|
||
|
|
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](https://arxiv.org/abs/2408.15545) for more details and [github](https://github.com/dptech-corp/Uni-SMART) for data processing codes.
|
||
|
|
|
||
|
|
## 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.
|
||
|
|
|
||
|
|
```python
|
||
|
|
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},
|
||
|
|
}
|
||
|
|
```
|