Aquila Language Model is the first open source language model that supports both Chinese and English knowledge, commercial license agreements, and compliance with domestic data regulations.
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🌟 Supports open source commercial licenses. The source code of the Aquila series models is based on the Apache 2.0 agreement, while the model weight is based on the BAAI Aquila Model License Agreement. Users can use it for commercial purposes as long as they meet the licensing restrictions.
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✍️ Possesses Chinese and English knowledge. The Aquila series model is trained from scratch on a high-quality corpus of Chinese and English languages, with Chinese corpora accounting for about 40%, ensuring that the model accumulates native Chinese world knowledge during the pre-training phase, rather than translated knowledge.
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👮♀️ Complies with domestic data regulations. The Chinese corpora of the Aquila series models come from Intelligence Source's accumulated Chinese datasets over the years, including Chinese internet data from over 10,000 sources (more than 99% of which are domestic sources), as well as high-quality Chinese literature and book data supported by authoritative domestic organizations. We will continue to accumulate high-quality and diverse datasets and incorporate them into the subsequent training of the Aquila base models.
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🎯 Continuous improvements and open sourcing. We will continue to improve training data, optimize training methods, and enhance model performance, cultivate a flourishing "model tree" on a better base model foundation, and continuously update open-source versions.
The additional details of the Aquila model will be presented in the official technical report. Please stay tuned for updates on official channels, including the FlagAI GitHub repository, FlagAI's Zhihu account and FlagAI's official technical communication group.
| Model | Model Type | Description | Status | GPUs Used |
|---|---|---|---|---|
| Aquila-7B | Base model, 7 billion parameters | Aquila Base Model inherits the architectural design advantages of GPT-3 and LLaMA. It replaces a batch of more efficient underlying operator implementations, redesigns the implementation of bilingual tokenizer, upgrades BMTrain parallel training method, and achieves nearly 8 times the training efficiency of Magtron+DeepSpeed ZeRO-2. | Released | Nvidia-A100 |
| Aquila-33B | Base model, 33 billion parameters | Same as above | Coming soon | Nvidia-A100 |
| AquilaChat-7B | SFT model, fine-tuned and RL based on Aquila-7B | AquilaChat Dialog Model supports fluent text dialogue and multiple language generation tasks, and realizes the call of AquilaChat to other models and tools by defining an expandable special instruction specification, which is easy to extend. For example, calling the open source AltDiffusion multimodal language image generation model of Flagship Intelligence achieved smooth image generation capability. Together with Flagship Intelligence's InstructFace multi-step controllable text-picture model, it is easy to achieve multi-step controllable editing of human face images. | Released | Nvidia-A100 |
| AquilaChat-33B | SFT model, fine-tuned and RL based on Aquila-33B | Same as above | Coming soon | Nvidia-A100 |
| AquilaCode-multi | Base model, "text-code" generation model, continue-pre-trained based on Aquila-7B. | AquilaCode utilizes high-quality, filtered, and compliant open-source code data for training, with a dataset size of approximately 10-40% compared to other open-source code generation models. By following the provided official guidelines, developers can harness the power of the AquilaCode model to customize their own code assistant. | Released | Nvidia-A100 |
| AquilaCode-py | Base model, "text-code" generation model, continue-pre-trained based on Aquila-7B, trained on Horizon Robotics chips | Same as above | Released | Nvidia-A100 |
We will continue to release improved versions of Aquila model as open source.
- 2023/07/24 ：release v0.9
- AquilaCode-mutil-01 md5: e202e5b82db773ea369fe843fef1c34c
- AquilaCode-mutil-02 md5: 3923b2b020e2af71755b11248076437f
- AquilaCode-Python-01 md5: e202e5b82db773ea369fe843fef1c34c
- AquilaCode-Python-02 md5: 3923b2b020e2af71755b11248076437f
Aquila-7B v0.8 has shown improvements in the FlagEval large model evaluation ("Objective") compared to version 0.7. It achieved improvements of approximately 10.07% on MMLU_Chinese, 14.84% on TruthfulQA, and 7.94% on MMLU datasets. For detailed evaluation results, please refer to the website http://flageval.baai.ac.cn. For detailed version change history, see Change Log.
Quick Start Aquila-7B
1. Inference
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_info = "BAAI/AquilaCode-py"
tokenizer = AutoTokenizer.from_pretrained(model_info, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_info, trust_remote_code=True)
model.eval()
model.to("cuda:4")
text = "#补全代码\ndef quick_sort(x):"
tokens = tokenizer.encode_plus(text)['input_ids'][:-1]
tokens = torch.tensor(tokens)[None,].to("cuda:4")
with torch.no_grad():
out = model.generate(tokens, do_sample=True, max_length=512, eos_token_id=100007)[0]
out = tokenizer.decode(out.cpu().numpy().tolist())
print(out)
License
Aquila-7B and AquilaChat-33B open-source model is licensed under BAAI Aquila Model Licence Agreement
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。