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Baichuan 7B

Baichuan-7B is an open-source, 7-billion-parameter pretrained Transformer language model that supports Chinese and English, has a 4,096-token context window, and is distributed as a model resource rather than an API or finished application.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Baichuan 7B — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Baichuan-7B when a project needs a pretrained Chinese-English model and the team can integrate a model resource into its own workflow.

Who it is for

  • Developers building Chinese-English text-generation applications or prototypes.
  • Researchers and teams evaluating language models on Chinese or English benchmarks.

Documented uses

  • Use a pretrained model as the basis for Chinese-English text generation.
  • Prototype bilingual text-processing tasks.
  • Run documented Chinese and English benchmark evaluations.
Before you choose

Requirements & limits

The project is identified as open source under the Apache-2.0 license. Model resources are linked through Hugging Face and ModelScope.

  • The stated language support is Chinese and English, with a 4,096-token context window.
  • The repository describes a 7-billion-parameter model trained on about 1.2 trillion tokens.
  • This is a model resource; inference code is referenced through the Hugging Face model library.
  • The repository reports benchmark results and procedures for C-Eval, Gaokao, AGIEval and MMLU.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Run the documented Chinese C-Eval evaluation

Evaluate a Baichuan-7B model path on the repository’s Chinese C-Eval benchmark.

Before you start

  • A Baichuan-7B model path.
  • The repository’s evaluation directory and evaluation script.

Steps

  1. From the repository, change into the evaluation directory.

  2. Run the command below, replacing the placeholder with the model path.

    Documented command
    python evaluate_zh.py --model_name_or_path 'your/model/path'

Expected outcome

The script performs the documented C-Eval 5-shot evaluation on the test set, using the dev set as the few-shot source.

Guide limitations

  • The documented command does not specify setup instructions or the required environment.
  • The command documents evaluation on C-Eval only.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
14 October 2026
Original website title
baichuan-inc/Baichuan-7B · Official homepage title · retained in its source language
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