BlueLM

🖥 github • 📜 LICENSE • 🎯 vivo Developers • 🗨 WeChat

模型介绍/Introduction

BlueLM 是由 vivo AI 全球研究院自主研发的大规模预训练语言模型,本次发布包含 7B 基础模型和 7B 对话模型,同时我们开源了支持 32K 的长文本基础模型和对话模型。

  • 更大量的优质数据:高质量语料库进行训练,规模达到了 2.6 万亿 的 token 数,该语料库包含中文、英文以及少量日韩数据。
  • 更优的效果:其中 BlueLM-7B-Chat 在 C-EvalCMMLU 上均取得领先结果,对比同尺寸开源模型中具有较强的竞争力。
  • 长文本支持:BlueLM-7B-Base-32K 和 BlueLM-7B-Chat-32K 均支持 32K 长文本,在保持基础能力相当情况下,能够支持更长上下文理解。
  • 协议说明:BlueLM 系列欢迎开发者进行学术研究和商业应用。

BlueLM is a large-scale open-source language model independently developed by the vivo AI Lab. This release includes 2K and 32K context length versions for both Base and Chat models.

  • High-quality Data: BlueLM is trained on a high-quality data with 2.6 trillion tokens. Our train corpus mainly consists of Chinese and English data, with a small amount of Japanese and Korean data.
  • Stronger Performance: BlueLM-7B-Chat achieves a strong competitive performance in C-Eval and CMMLU benchmarks of the same size.
  • Longer Context: We have extended the context length of both BlueLM-7B-Base-32K and BlueLM-7B-Chat-32K models from 2K to 32K. The models can support longer context understanding while maintaining the same basic capabilities.
  • Model License: BlueLM weights are open for academic research and commercial use.

本次发布基座模型下载链接见:

The release versions and hugging face download links are listed in the table below:

评测结果/Benchmark Results

为了保证模型评测的一致性,我们采用 OpenCompass 进行相关榜单的评测。我们分别在 C-Eval、MMLU、CMMLU、GaoKao、AGIEval、BBH、GSM8K、MATH 和 HumanEval 榜单对 BlueLM 的通用能力、数学能力和代码能力进行了测试。

To ensure the consistency of model evaluation, we use OpenCompass to evaluate the performance on relevant leaderboards. We conducted extensive tests on C-Eval, MMLU, CMMLU, GaoKao, AGIEval, BBH, GSM8K, MATH and HumanEval datasets across general ability, mathematical ability and coding ability.

Model C-Eval MMLU CMMLU Gaokao AGIEval BBH GSM8K MATH HumanEval
5-shot 5-shot 5-shot 0-shot 0-shot 3-shot 4-shot 5-shot 0-shot
GPT-4 69.9 86.4 71.2 72.3 55.1 86.7 91.4 45.8 74.4
ChatGPT 52.5 70.0 53.9 51.1 39.9 70.1 78.2 28 73.2
LLaMA2-7B 32.5 45.3 31.8 18.9 21.8 38.2 16.7 3.3 12.8
ChatGLM2-6B(Base) 51.7 47.9 50.0 - - 33.7 32.4 6.5 -
Baichuan2-7B 56.3 54.7 57.0 34.8 34.6 41.8 24.6 5.4 17.7
BlueLM-7B-Base 67.5 55.2 66.6 58.9 43.4 41.7 27.2 6.2 18.3
BlueLM-7B-Chat 72.7 50.7 74.2 48.7 43.4 65.6 51.9 13.4 21.3

推理部署/Inference and Deployment

git clone https://github.com/vivo-ai-lab/BlueLM
cd BlueLM/quant_cuda
python setup_cuda.py install
>>> from transformers import AutoTokenizer, AutoModelForCausalLM
>>> tokenizer = AutoTokenizer.from_pretrained("vivo-ai/BlueLM-7B-Chat-4bits", trust_remote_code=True, use_fast=False)
>>> model = AutoModelForCausalLM.from_pretrained("vivo-ai/BlueLM-7B-Chat-4bits", device_map="cuda:0", trust_remote_code=True)
>>> model = model.eval()
>>> inputs = tokenizer("[|Human|]:三国演义的作者是谁?[|AI|]:", return_tensors="pt")
>>> inputs = inputs.to("cuda:0")
>>> outputs = model.generate(**inputs, max_new_tokens=128)
>>> print(tokenizer.decode(outputs.cpu()[0], skip_special_tokens=True))
三国演义的作者是谁? 《三国演义》是由元末明初小说家罗贯中所著,是中国古典四大名著之一,也是中国古代历史小说发展的巅峰之作。

更多使用说明,请参考我们的 Github 仓库

For more instructions, please refer to our Github Repo.

协议/License

社区使用代码依照 Apache-2.0 协议开源,且使用 BlueLM 模型权重需要遵循 vivo_BlueLM模型许可协议

Our code is licensed under the Apache-2.0 and Community License for BlueLM Model.

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