| | --- |
| | 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}, |
| | } |
| | ``` |
