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--- |
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license: mit |
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--- |
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# Model Card for SciLitLLM1.5 |
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SciLitLLM1.5 adapts a general large language model for effective scientific literature understanding. Starting from the Qwen2.5-7B/14B model, SciLitLLM1.5-7B/14B 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. |
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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. |
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Applying this strategy, we present SciLitLLM-7B and 14B, specialized in scientific literature understanding, which demonstrates promising performance on scientific literature understanding benchmarks. |
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We observe promising performance enhancements, **with an average improvement of 4.0\% on SciAssess and 10.1% on SciRIFF, compared to the leading LLMs under 10B parameters**. Notably, **SciLitLLM-7B even outperforms Llama3.1 and Qwen2.5 with 70B parameters on SciRIFF**. Additionally, SciLitLLM-14B achieves leading results on both benchmarks, surpassing other open-source LLMs. Further ablation studies demonstrate the effectiveness of each module in our pipeline. |
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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. |
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## Requirements |
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Since SciLitLLM is based on Qwen2.5, we advise you to install `transformers>=4.37.0`, or you might encounter the following error: |
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``` |
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KeyError: 'qwen2' |
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``` |
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## Quickstart |
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Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents. |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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device = "cuda" # the device to load the model onto |
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model = AutoModelForCausalLM.from_pretrained( |
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"Uni-SMART/SciLitLLM1.5-7B", |
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torch_dtype="auto", |
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device_map="auto" |
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) |
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tokenizer = AutoTokenizer.from_pretrained("Uni-SMART/SciLitLLM1.5-7B") |
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prompt = "Can you summarize this article for me?\n <ARTICLE>" |
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messages = [ |
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{"role": "system", "content": "You are a helpful assistant."}, |
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{"role": "user", "content": prompt} |
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] |
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text = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True |
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) |
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model_inputs = tokenizer([text], return_tensors="pt").to(device) |
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generated_ids = model.generate( |
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model_inputs.input_ids, |
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max_new_tokens=512 |
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) |
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generated_ids = [ |
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) |
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] |
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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``` |
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## Citation |
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If you find our work helpful, feel free to give us a cite. |
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``` |
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@misc{li2024scilitllmadaptllmsscientific, |
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title={SciLitLLM: How to Adapt LLMs for Scientific Literature Understanding}, |
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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}, |
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year={2024}, |
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eprint={2408.15545}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.LG}, |
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url={https://arxiv.org/abs/2408.15545}, |
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} |
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``` |
