Model reference · open weights

Baichuan2

LLMs baichuan-inc Text gen 1 build Its own licence terms 29k dl/mo

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.

  • Baichuan2 is an open-source large language model developed by baichuan-inc for text generation tasks.
  • It supports English and Chinese with a context length of 4096 tokens.
  • The model is licensed under Apache 2.0 and a community license that permits commercial use subject to specific conditions.

Summary of the baichuan-inc/Baichuan2-7B-Chat model card, 2026-10-01

What it is

Released bybaichuan-inc
Released2023-08-29
VRAM15.0 GB for the weights

What it runs on

Memory and cards for Baichuan2-7B-Chat (BF16)

15.0 GBweights, file size
524 MBcache per 1K tokens
715 MBruntime overhead, at least
4,096 tokenscontext max
CardRequests at onceContext maxMemory
4K each
RTX 3060 12 GB … RTX 4060 Ti 16 GB
2 smaller cards
——
RTX 3090 24 GB3all 4K23.4 GB
RTX 4090 24 GB3all 4K23.4 GB
RTX 5090 32 GB7all 4K31.0 GB
L40S 48 GB13all 4K44.0 GB
A100 80 GB29all 4K78.2 GB
H100 80 GB27all 4K78.1 GB
RTX PRO 6000 Blackwell 96 GB34all 4K93.8 GB
DGX Spark (GB10) 128 GB unified40all 4K107 GB
H200 141 GB55all 4K138 GB
B200 180 GB72all 4K176 GB
2× RTX 3060 12 GB
tensor parallel
3all 4K11.6 GB a card
2× RTX 4060 Ti 16 GB
tensor parallel
6all 4K15.4 GB a card
2× RTX 4090 24 GB
tensor parallel
14all 4K23.4 GB a card
2× RTX 3090 24 GB
tensor parallel
14all 4K23.4 GB a card
2× RTX 5090 32 GB
tensor parallel
21all 4K31.0 GB a card
Memory needed at each load
Requests at once4K tokens each
117.9 GB
526.5 GB
832.9 GB
1650.1 GB
3284.4 GB
64153 GB

One card, with vLLM's small-card settings.

From the model card

What baichuan-inc says about Baichuan2

Read the model card

Baichuan 2

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目录/Table of Contents

模型介绍/Introduction

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 ModelChat Model4bits Quantized Chat Model
7BBaichuan2-7B-BaseBaichuan2-7B-ChatBaichuan2-7B-Chat-4bits
13BBaichuan2-13B-BaseBaichuan2-13B-ChatBaichuan2-13B-Chat-4bits

快速开始/Quick Start

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。

Benchmark 结果/Benchmark Evaluation

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.

7B Model Results

C-EvalMMLUCMMLUGaokaoAGIEvalBBH
5-shot5-shot5-shot5-shot5-shot3-shot
GPT-468.4083.9370.3366.1563.2775.12
GPT-3.5 Turbo51.1068.5454.0647.0746.1361.59
LLaMA-7B27.1035.1026.7527.8128.1732.38
LLaMA2-7B28.9045.7331.3825.9726.5339.16
MPT-7B27.1527.9326.0026.5424.8335.20
Falcon-7B24.2326.0325.6624.2424.1028.77
ChatGLM2-6B50.2045.9049.0049.4445.2831.65
[Baichuan-7B]42.8042.3044.0236.3434.4432.48
[Baichuan2-7B-Base]54.0054.1657.0747.4742.7341.56

13B Model Results

C-EvalMMLUCMMLUGaokaoAGIEvalBBH
5-shot5-shot5-shot5-shot5-shot3-shot
GPT-468.4083.9370.3366.1563.2775.12
GPT-3.5 Turbo51.1068.5454.0647.0746.1361.59
LLaMA-13B28.5046.3031.1528.2328.2237.89
**LLaMA2

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

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