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A large-scale 7B pretraining language model developed by BaiChuan-Inc.
| Date | Stars |
|---|---|
| 2026-07-24 | 5650 |
| 2026-07-25 | 5650 |
| 2026-07-28 | 5650 |
| 2026-07-30 | 5650 |
| 2026-07-31 | 5651 |
| 2026-08-06 | 5651 |
Today
— stars today
This week
+1 stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.02%/day
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Baichuan-7B
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🤗 <a href="https://huggingface.co/baichuan-inc/Baichuan-7B" target="_blank">Hugging Face</a> • 🤖 <a href="https://modelscope.cn/organization/baichuan-inc" target="_blank">ModelScope</a> • 💬 <a href="https://github.com/baichuan-inc/Baichuan-7B/blob/main/media/wechat.jpeg?raw=true" target="_blank">WeChat</a>
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[](https://github.com/baichuan-inc/Baichuan-7B/blob/main/LICENSE)
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<b>中文</b> |
<a href="https://github.com/baichuan-inc/Baichuan-7B/blob/main/README_EN.md">English</a>
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</h4>
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# 更新信息
- [2023.09.06] 我们发布了新一代开源模型 [Baichuan 2](https://github.com/baichuan-inc/Baichuan2)，包含 7B、13B 尺寸 🔥🔥🔥
# 介绍
Baichuan-7B 是由百川智能开发的一个开源可商用的大规模预训练语言模型。基于 Transformer 结构，在大约 1.2 万亿 tokens 上训练的 70 亿参数模型，支持中英双语，上下文窗口长度为 4096。在标准的中文和英文 benchmark（C-Eval/MMLU）上均取得同尺寸最好的效果。
# 公开benchmark榜单
## 中文评测
### C-Eval
[C-Eval 数据集](https://cevalbenchmark.com/index.html)是一个全面的中文基础模型评测数据集，涵盖了 52 个学科和四个难度的级别。我们使用该数据集的 dev 集作为 few-shot 的来源，在 test 集上进行了 `5-shot` 测试。通过执行执行下面的命令：
```bash
cd evaluation
python evaluate_zh.py --model_name_or_path 'your/model/path'
```
### 结果
| Model 5-shot | Average | Avg(Hard) | STEM | Social Sciences | Humanities | Others |
| :-------------------------: | :-----: | :-------: | :---: | :-------------: | :--------: | :----: |
| GPT-4 | 68.7 | 54.9 | 67.1 | 77.6 | 64.5 | 67.8 |
| ChatGPT | 54.4 | 41.4 | 52.9 | 61.8 | 50.9 | 53.6 |
| Claude-v1.3 | 54.2 | 39.0 | 51.9 | 61.7 | 52.1 | 53.7 |
| Claude-instant-v1.0 | 45.9 | 35.5 | 43.1 | 53.8 | 44.2 | 45.4 |
| BLOOMZ-7B | 35.7 | 25.8 | 31.3 | 43.5 | 36.6 | 35.6 |
| ChatGLM-6B | 34.5 | 23.1 | 30.4 | 39.6 | 37.4 | 34.5 |
| Ziya-LLaMA-13B-pretrain | 30.2 | 22.7 | 27.7 | 34.4 | 32.0 | 28.9 |
| moss-moon-003-base (16B) | 27.4 | 24.5 | 27.0 | 29.1 | 27.2 | 26.9 |
| LLaMA-7B-hf | 27.1 | 25.9 | 27.1 | 26.8 | 27.9 | 26.3 |
| Falcon-7B | 25.8 | 24.3 | 25.8 | 26.0 | 25.8 | 25.6 |
| TigerBot-7B-base | 25.7 | 27.0 | 27.3 | 24.7 | 23.4 | 26.1 |
| Aquila-7B<sup>*</sup> | 25.5 | 25.2 | 25.6 | 24.6 | 25.2 | 26.6 |
| Open-LLaMA-v2-pretrain (7B) | 24.0 | 22.5 | 23.1 | 25.3 | 25.2 | 23.2 |
| BLOOM-7B | 22.8 | 20.2 | 21.8 | 23.3 | 23.9 | 23.3 |
| **Baichuan-7B** | 42.8 | 31.5 | 38.2 | 52.0 | 46.2 | 39.3 |
### Gaokao
[Gaokao](https://github.com/OpenLMLab/GAOKAO-Bench) 是一个以中国高考题作为评测大语言模型能力的数据集，用以评估模型的语言能力和逻辑推理能力。
我们只保留了其中的单项选择题，随机划分后对所有模型进行统一 `5-shot` 测试。
### 结果
以下是测试的结果。
| Model | Average |
| :---------------------: | :-------: |
| BLOOMZ-7B | 28.72 |
| LLaMA-7B | 27.81 |
| BLOOM-7B | 26.96 |
| TigerBot-7B-base | 25.94 |
| Falcon-7B | 23.98 |
| Ziya-LLaMA-13B-pretrain | 23.17 |
| ChatGLM-6B | 21.41 |
| Open-LLaMA-v2-pretrain | 21.41 |
| Aquila-7B<sup>*</sup> | 24.39 |
| **Baichuan-7B** | **36.24** |
### AGIEval
[AGIEval](https://github.com/microsoft/AGIEval) 旨在评估模型的认知和解决问题相关的任务中的一般能力。
我们只保留了其中的四选一单项选择题，随机划分后对所有模型进行了统一 `5-shot` 测试。
### 结果
| Model | Average |
| :---------------------: | :-------: |
| BLOOMZ-7B Excerpt of 11,138 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:be814d8bfde944be, topic:large-language-models, topic:llama
matched fp:be814d8bfde944be, topic:natural-language-processing
matched fp:be814d8bfde944be, topic:chatgpt
