70 lines
2.5 KiB
Markdown
70 lines
2.5 KiB
Markdown
---
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license: other
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---
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<h4 align="center">
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<p>
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<b>English</b> |
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<a href="https://huggingface.co/BAAI/AquilaChat2-7B/blob/main/README_zh.md">简体中文</a>
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</p>
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</h4>
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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**
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The additional details of the Aquila model will be presented in the official technical report. Please stay tuned for updates on official channels.
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## Quick Start AquilaChat2-7B（Chat model）
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### 1. Inference
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```python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from transformers import BitsAndBytesConfig
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device = torch.device("cuda:0")
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model_info = "BAAI/AquilaChat2-7B"
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tokenizer = AutoTokenizer.from_pretrained(model_info, trust_remote_code=True)
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quantization_config=BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_use_double_quant=True,
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bnb_4bit_quant_type="nf4",
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bnb_4bit_compute_dtype=torch.bfloat16,
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)
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model = AutoModelForCausalLM.from_pretrained(model_info, trust_remote_code=True, torch_dtype=torch.float16,
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# quantization_config=quantization_config, # Uncomment this line for 4bit quantization
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)
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model.eval()
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model.to(device)
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text = "请给出10个要到北京旅游的理由。"
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from predict import predict
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out = predict(model, text, tokenizer=tokenizer, max_gen_len=200, top_p=0.95,
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seed=1234, topk=100, temperature=0.9, sft=True, device=device,
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model_name="AquilaChat2-7B")
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print(out)
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```
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## License
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Aquila2 series open-source model is licensed under [ BAAI Aquila Model Licence Agreement](https://huggingface.co/BAAI/AquilaChat2-7B/blob/main/BAAI-Aquila-Model-License%20-Agreement.pdf)
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## Citation
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Feel free to cite the repo if you think Aquila2 is useful.
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```python
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@misc{zhang2024aquila2technicalreport,
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title={Aquila2 Technical Report},
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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},
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year={2024},
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eprint={2408.07410},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2408.07410},
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}
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``` |