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language:
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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-Eval 和 CMMLU 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:
| Base Model | Chat Model | 4bits Quantized Chat Model | |
|---|---|---|---|
| 7B-2k | BlueLM-7B-Base | BlueLM-7B-Chat | BlueLM-7B-Chat-4bits |
| 7B-32K | BlueLM-7B-Base-32K | BlueLM-7B-Chat-32K | - |
评测结果/Benchmark Results
我们在 LongBench 评测集上对我们的 BlueLM-7B-Chat-32K 模型进行了测试，具体结果如下表所示：
We tested our BlueLM-7B-Chat-32K on the LongBench dataset and the results are shown in the table below:
| Model | Average | Summary | Single-Doc QA | Multi-Doc QA | Code | Few-shot | Synthetic |
|---|---|---|---|---|---|---|---|
| BlueLM-7B-Chat-32K | 41.2 | 18.8 | 35.6 | 36.2 | 54.2 | 56.9 | 45.5 |
推理部署/Inference and Deployment
>>> from transformers import AutoModelForCausalLM, AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("vivo-ai/BlueLM-7B-Base-32K", trust_remote_code=True, use_fast=False)
>>> model = AutoModelForCausalLM.from_pretrained("vivo-ai/BlueLM-7B-Base-32K", device_map="cuda:0", torch_dtype=torch.bfloat16, trust_remote_code=True)
>>> model = model.eval()
>>> inputs = tokenizer("儒林外史->吴敬梓\n隋唐演义->褚人获\n红楼梦->", return_tensors="pt")
>>> inputs = inputs.to("cuda:0")
>>> pred = model.generate(**inputs, max_new_tokens=64, repetition_penalty=1.1)
>>> print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True))
儒林外史->吴敬梓
隋唐演义->褚人获
红楼梦->曹雪芹
三国演义->罗贯中
水浒传->施耐庵
西游记->吴承恩
聊斋志异->蒲松龄
金瓶梅->兰陵笑笑生
封神演义->许仲琳
三言二拍->冯梦龙
东周列国志->冯梦龙
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.