| --- |
| license: mit |
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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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|
| ``` |
| KeyError: 'qwen2' |
| ``` |
|
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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 |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| device = "cuda" # the device to load the model onto |
| model = AutoModelForCausalLM.from_pretrained( |
| "Uni-SMART/SciLitLLM-1.5/7B/", |
| torch_dtype="auto", |
| device_map="auto" |
| ) |
| tokenizer = AutoTokenizer.from_pretrained("Uni-SMART/SciLitLLM-1.5/7B/") |
| 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] |
| ``` |
|
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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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|
| ``` |
| @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}, |
| } |
| ``` |
