baichuan-inc/baichuan-7b

★ 5,659PythonAudience · researcherComplexity · 4/5Setup · hard

Mindmap

mindmap
  root((baichuan-7b))
    Model specs
      7B parameters
      1.2T training tokens
      4096 context window
    Languages
      Chinese primary
      English support
    Benchmarks
      C-Eval 42.8
      MMLU 42.3
      Gaokao 36.24
    Usage
      Hugging Face load
      Text completion
      Fine-tuning base

mindmap root((baichuan-7b)) Model specs 7B parameters 1.2T training tokens 4096 context window Languages Chinese primary English support Benchmarks C-Eval 42.8 MMLU 42.3 Gaokao 36.24 Usage Hugging Face load Text completion Fine-tuning base

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Things people build with this

USE CASE 1

Use the base model through Hugging Face Transformers for Chinese text completion, summarization, or extraction tasks.

USE CASE 2

Fine-tune Baichuan-7B on your own Chinese-language dataset to build a specialized assistant or text classifier.

USE CASE 3

Benchmark Baichuan-7B against other 7B models on your own Chinese evaluation dataset to choose the best base for fine-tuning.

USE CASE 4

Run the included benchmark scripts to verify the reported C-Eval and MMLU scores on your own hardware.

Tech stack

PythonPyTorchHugging Face TransformersSentencePiece

Getting it running

Difficulty · hard Time to first run · 1h+

Requires a GPU with sufficient VRAM to load 7B parameters, model weights download automatically from Hugging Face but the download is several gigabytes.

No license details were provided in the explanation, check the repository directly for usage and commercial terms.

In plain English

Baichuan-7B is an open-source language model built by Baichuan Intelligence, a Chinese AI company. It has 7 billion parameters and was trained on roughly 1.2 trillion tokens of text, split between Chinese and English content. The model can handle conversations or text completion tasks in both languages, with a context window of 4,096 tokens, meaning it can read and respond to fairly long inputs in one pass. The README is written primarily in Chinese and presents benchmark results across several standard evaluation sets. On C-Eval, a Chinese-language test covering 52 academic subjects, Baichuan-7B scored 42.8 on average, outperforming other 7B-class models available at the time, including BLOOMZ-7B, ChatGLM-6B, and Falcon-7B. It also scored 36.24 on Gaokao (a dataset built from Chinese college entrance exam questions) and 34.44 on AGIEval, another reasoning-focused benchmark. On MMLU, an English-language test spanning 57 subjects from high-school level to expert level, Baichuan-7B reached 42.3, again above comparable open models. The tokenizer is a custom build on top of SentencePiece's Byte-Pair Encoding algorithm, trained on 20 million multilingual sentences weighted toward Chinese and English. The team made specific adjustments for numbers (each digit is split individually) and rare characters (byte-level fallback for full Unicode coverage). According to the README, this tokenizer compresses Chinese text more efficiently than the tokenizers used in LLaMA and Falcon, which means faster training and inference on Chinese-heavy workloads. To run the model, you load it through the Hugging Face Transformers library using two lines of Python: one to load the tokenizer and one to load the model weights. The weights are hosted on Hugging Face and can be downloaded automatically. The README also includes scripts for reproducing the benchmark results if you want to verify the scores yourself. Baichuan-7B is positioned as a base model for Chinese and English text tasks. A follow-up release, Baichuan 2, was announced in September 2023, adding 7B and 13B variants.

Copy-paste prompts

Prompt 1

Load Baichuan-7B using Hugging Face Transformers and generate a completion for a Chinese customer support question. Show me the minimal two-line Python code to do this.

Prompt 2

I want to fine-tune Baichuan-7B on a dataset of Chinese e-commerce product descriptions using LoRA. Show me the setup with the Hugging Face PEFT library on a single A100.

Prompt 3

How does the Baichuan-7B custom tokenizer handle mixed Chinese-English text differently from LLaMA's tokenizer? Show me a comparison with a sample sentence.

Prompt 4

I want to reproduce the Baichuan-7B C-Eval benchmark score of 42.8. Walk me through running the evaluation script and interpreting the per-subject results.

Prompt 5

I need to run Baichuan-7B inference on a server with 24 GB VRAM. Show me how to load the model in 8-bit quantization using bitsandbytes to reduce memory usage.

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