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We opensource our **Aquila2** series, now including **Aquila2**, the base language models, namely **Aquila2-7B** and **Aquila2-34B**, as well as **AquilaChat2**, the chat models, namely **AquilaChat2-7B** and **AquilaChat2-34B**, as well as the long-text chat models, namely **AquilaChat2-7B-16k** and **AquilaChat2-34B-16k** 2023.10.25 🔥 **AquilaChat2-34B v1.2** is based on the previous **AquilaChat2-34B**. The AquilaChat2-34B model is close to or exceeds the level of GPT3.5 in the subjective evaluation of 8 secondary ability dimensions. The additional details of the Aquila model will be presented in the official technical report. Please stay tuned for updates on official channels. ### NoteWe have discovered a data leakage problem with the GSM8K test data in the pre-training task dataset. Therefore, the evaluation results of GSM8K have been removed from the evaluation results. Upon thorough investigation and analysis, it was found that the data leakage occurred in the mathematical dataset A (over 2 million samples), recommended by a team we have collaborated with multiple times. This dataset includes the untreated GSM8K test set (1319 samples). The team only performed routine de-duplication and quality checks but did not conduct an extra filtering check for the presence of the GSM8K test data, resulting in this oversight. Our team has always strictly adhered to the principle that training data should not include test data. Taking this lesson from the error caused by not thoroughly checking the source of external data, we have investigated all 2 trillion tokens of data for various test datasets, including WTM22（en-zh), CLUEWSC, Winograd, HellaSwag, OpenBookQA, PIQA, ARC-e, BUSTSM, BoolQ, TruthfulQA, RAFT, ChID, EPRSTMT, TNEWS, OCNLI, SEM-Chinese, MMLU, C-Eval, CMMLU, CSL and HumanEval.
## Quick Start AquilaChat2-34B（Chat model） ### 1. Inference ```python from transformers import AutoTokenizer, AutoModelForCausalLM from transformers import BitsAndBytesConfig import torch device = torch.device("cuda:0") model_info = "BAAI/AquilaChat2-34B" tokenizer = AutoTokenizer.from_pretrained(model_info, trust_remote_code=True) quantization_config=BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16, ) model = AutoModelForCausalLM.from_pretrained(model_info, trust_remote_code=True, torch_dtype=torch.bfloat16, # quantization_config=quantization_config, # Uncomment this line for 4bit quantization ) model.eval() model.to(device) text = "请给出10个要到北京旅游的理由。" from predict import predict out = predict(model, text, tokenizer=tokenizer, max_gen_len=200, top_p=0.9, seed=123, topk=15, temperature=1.0, sft=True, device=device, model_name="AquilaChat2-34B") print(out) ``` ## License Aquila2 series open-source model is licensed under [ BAAI Aquila Model Licence Agreement](https://huggingface.co/BAAI/AquilaChat2-34B/blob/main/BAAI-Aquila-Model-License%20-Agreement.pdf) ## Citation Feel free to cite the repo if you think Aquila2 is useful. ```python @misc{zhang2024aquila2technicalreport, title={Aquila2 Technical Report}, author={Bo-Wen Zhang and Liangdong Wang and Jijie Li and Shuhao Gu and Xinya Wu and Zhengduo Zhang and Boyan Gao and Yulong Ao and Guang Liu}, year={2024}, eprint={2408.07410}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2408.07410}, } ``` # Acknowledgements This work is supported by the National Science and Technology Major Project (No. 2022ZD0116300). 本项目受新一代人工智能国家科技重大专项（No. 2022ZD0116300）支持。