Update README

README.md

@@ -1,3 +1,74 @@
----
-license: mit
----
+---
+license: mit
+
+---
+
+# Model Card for SciLitLLM1.5
+
+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.
+
+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 and 14B, specialized in scientific literature understanding, which demonstrates promising performance on scientific literature understanding benchmarks. 
+
+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.
+
+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 Qwen2.5, 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-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]
+```
+
+## 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}, 
+}
+```