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Decision brief
Baichuan-7B is notable for its performance in Chinese benchmarks and supports both English and Chinese languages.
Good fit when
- If the project requires strong Chinese language proficiency, as Baichuan-7B has been optimized through extensive training specifically targeting Chinese language tasks.
- For applications requiring a large context window size of 4096 tokens, enabling extended dialogue or complex task handling.
Avoid when
- If the application primarily deals with languages other than English and Chinese, as Baichuan-7B's training has been focused on these two languages.
- When a model smaller than 7-billion parameters is sufficient for the use case, especially if reducing inference time or resource consumption is more critical.
Observed Jul 14, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (759d since push)
- As of 4d
- Provenance
- Not a fork · Organization account
- As of 4d
- Security (OSV)
- 77 low (77 low)
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Backing
Company context for Baichuan. Display-only - separate from trust and ranking.
- Company
- Baichuan Intelligent Technology·GitHub org profile·1mo
- Commercial model
- Pure OSS·GitHub org profile (public repos)·1mo
Install
pip install Baichuan-7BHow it fits your stack(5)
Typed graph edges - alternatives, integrations, successors, and dependencies. Ranked by relationship type, not raw GitHub stars.
Alternative
Relationship graph
Optional deeper exploration of typed edges and category neighbours.
Awesome-Chinese-LLM23k★
Qwen22k★Baichuan24.1k★
transformers164k★
Langchain-Chatchat39k★
Chinese-LLaMA-Alpaca19k★
GLM-130B7.7k★Awesome-Chinese-LLM23k★Qwen22k★Baichuan24.1k★transformers164k★Langchain-Chatchat39k★Chinese-LLaMA-Alpaca19k★GLM-130B7.7k★Similar tools
Same-category neighbours not already linked as typed edges.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Baichuan-7B is a large-scale pretraining language model with 7 billion parameters developed by BaiChuan-Inc. It supports both Chinese and English, trained on around 1.2 trillion tokens, and has a context window size of 4096.
Capability facts
- Languages
- python
Source: github.language · Aug 17, 2026
Categories
Graph entities
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 17, 2026)
python evaluate_zh.py --model_name_or_path 'your/model/path'Source link
Source: README excerpt (regex_v1, Aug 17, 2026)
| ChatGPT | 54.4 | 41.4 | 52.9 | 61.8 | 50.9 | 53.6Source link
Tags
README
Baichuan-7B
🤗 Hugging Face • 🤖 ModelScope • 💬 WeChat
更新信息
- [2023.09.06] 我们发布了新一代开源模型 Baichuan 2，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：
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* | 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 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* | 24.39 |
| Baichuan-7B | 36.24 |
AGIEval
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 | 30.27 |
| LLaMA-7B | 28.17 |
| Ziya-LLaMA-13B-pretrain | 27.64 |
| Falcon-7B | 27.18 |
| BLOOM-7B | 26.55 |
| Aquila-7B* | 25.58 |
For agents
This page has a .md twin and JSON over the API.