Model reference · open weights
Baichuan2 is an open-weight language model from baichuan-inc. Baichuan2-7B-Chat (BF16) weighs 15.0 GB; the smallest configuration that runs it is 2× RTX 3060 12 GB.
Summary of the baichuan-inc/Baichuan2-7B-Chat model card, 2026-10-01
What it is
| Released by | baichuan-inc |
|---|---|
| Released | 2023-08-29 |
| VRAM | 15.0 GB for the weights |
What it runs on
| Card | Requests at once | Context max | Memory |
|---|---|---|---|
| 4K each | |||
| RTX 3060 12 GB … RTX 4060 Ti 16 GB 2 smaller cards | — | — | |
| RTX 3090 24 GB | 3 | all 4K | 23.4 GB |
| RTX 4090 24 GB | 3 | all 4K | 23.4 GB |
| RTX 5090 32 GB | 7 | all 4K | 31.0 GB |
| L40S 48 GB | 13 | all 4K | 44.0 GB |
| A100 80 GB | 29 | all 4K | 78.2 GB |
| H100 80 GB | 27 | all 4K | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | 34 | all 4K | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | 40 | all 4K | 107 GB |
| H200 141 GB | 55 | all 4K | 138 GB |
| B200 180 GB | 72 | all 4K | 176 GB |
| 2× RTX 3060 12 GB tensor parallel | 3 | all 4K | 11.6 GB a card |
| 2× RTX 4060 Ti 16 GB tensor parallel | 6 | all 4K | 15.4 GB a card |
| 2× RTX 4090 24 GB tensor parallel | 14 | all 4K | 23.4 GB a card |
| 2× RTX 3090 24 GB tensor parallel | 14 | all 4K | 23.4 GB a card |
| 2× RTX 5090 32 GB tensor parallel | 21 | all 4K | 31.0 GB a card |
| Requests at once | 4K tokens each |
|---|---|
| 1 | 17.9 GB |
| 5 | 26.5 GB |
| 8 | 32.9 GB |
| 16 | 50.1 GB |
| 32 | 84.4 GB |
| 64 | 153 GB |
One card, with vLLM's small-card settings.
From the model card
Baichuan 2
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Baichuan 2 是[百川智能]推出的新一代开源大语言模型,采用 2.6 万亿 Tokens 的高质量语料训练,在权威的中文和英文 benchmark 上均取得同尺寸最好的效果。本次发布包含有 7B、13B 的 Base 和 Chat 版本,并提供了 Chat 版本的 4bits 量化,所有版本不仅对学术研究完全开放,开发者也仅需[邮件申请]并获得官方商用许可后,即可以免费商用。具体发布版本和下载见下表:
Baichuan 2 is the new generation of large-scale open-source language models launched by Baichuan Intelligence inc.. It is trained on a high-quality corpus with 2.6 trillion tokens and has achieved the best performance in authoritative Chinese and English benchmarks of the same size. This release includes 7B and 13B versions for both Base and Chat models, along with a 4bits quantized version for the Chat model. All versions are fully open to academic research, and developers can also use them for free in commercial applications after obtaining an official commercial license through email request. The specific release versions and download links are listed in the table below:
| Base Model | Chat Model | 4bits Quantized Chat Model | |
|---|---|---|---|
| 7B | Baichuan2-7B-Base | Baichuan2-7B-Chat | Baichuan2-7B-Chat-4bits |
| 13B | Baichuan2-13B-Base | Baichuan2-13B-Chat | Baichuan2-13B-Chat-4bits |
在Baichuan2系列模型中,我们为了加快推理速度使用了Pytorch2.0加入的新功能F.scaled_dot_product_attention,因此模型需要在Pytorch2.0环境下运行。
In the Baichuan 2 series models, we have utilized the new feature F.scaled_dot_product_attention introduced in PyTorch 2.0 to accelerate inference speed. Therefore, the model needs to be run in a PyTorch 2.0 environment.
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation.utils import GenerationConfig
tokenizer = AutoTokenizer.from_pretrained("baichuan-inc/Baichuan2-7B-Chat", use_fast=False, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("baichuan-inc/Baichuan2-7B-Chat", device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True)
model.generation_config = GenerationConfig.from_pretrained("baichuan-inc/Baichuan2-7B-Chat")
messages = []
messages.append({"role": "user", "content": "解释一下“温故而知新”"})
response = model.chat(tokenizer, messages)
print(response)
"温故而知新"是一句中国古代的成语,出自《论语·为政》篇。这句话的意思是:通过回顾过去,我们可以发现新的知识和理解。换句话说,学习历史和经验可以让我们更好地理解现在和未来。
这句话鼓励我们在学习和生活中不断地回顾和反思过去的经验,从而获得新的启示和成长。通过重温旧的知识和经历,我们可以发现新的观点和理解,从而更好地应对不断变化的世界和挑战。
我们在[通用]、[法律]、[医疗]、[数学]、[代码]和[多语言翻译]六个领域的中英文权威数据集上对模型进行了广泛测试,更多详细测评结果可查看[GitHub]。
We have extensively tested the model on authoritative Chinese-English datasets across six domains: General, Legal, Medical, Mathematics, Code, and Multilingual Translation. For more detailed evaluation results, please refer to GitHub.
| C-Eval | MMLU | CMMLU | Gaokao | AGIEval | BBH | |
|---|---|---|---|---|---|---|
| 5-shot | 5-shot | 5-shot | 5-shot | 5-shot | 3-shot | |
| GPT-4 | 68.40 | 83.93 | 70.33 | 66.15 | 63.27 | 75.12 |
| GPT-3.5 Turbo | 51.10 | 68.54 | 54.06 | 47.07 | 46.13 | 61.59 |
| LLaMA-7B | 27.10 | 35.10 | 26.75 | 27.81 | 28.17 | 32.38 |
| LLaMA2-7B | 28.90 | 45.73 | 31.38 | 25.97 | 26.53 | 39.16 |
| MPT-7B | 27.15 | 27.93 | 26.00 | 26.54 | 24.83 | 35.20 |
| Falcon-7B | 24.23 | 26.03 | 25.66 | 24.24 | 24.10 | 28.77 |
| ChatGLM2-6B | 50.20 | 45.90 | 49.00 | 49.44 | 45.28 | 31.65 |
| [Baichuan-7B] | 42.80 | 42.30 | 44.02 | 36.34 | 34.44 | 32.48 |
| [Baichuan2-7B-Base] | 54.00 | 54.16 | 57.07 | 47.47 | 42.73 | 41.56 |
| C-Eval | MMLU | CMMLU | Gaokao | AGIEval | BBH | |
|---|---|---|---|---|---|---|
| 5-shot | 5-shot | 5-shot | 5-shot | 5-shot | 3-shot | |
| GPT-4 | 68.40 | 83.93 | 70.33 | 66.15 | 63.27 | 75.12 |
| GPT-3.5 Turbo | 51.10 | 68.54 | 54.06 | 47.07 | 46.13 | 61.59 |
| LLaMA-13B | 28.50 | 46.30 | 31.15 | 28.23 | 28.22 | 37.89 |
| **LLaMA2 |
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.