Baichuan2-7B-Chat-4bits

Maintainer: baichuan-inc

Total Score

56

Last updated 5/28/2024

🤯

PropertyValue
Run this modelRun on HuggingFace
API specView on HuggingFace
Github linkNo Github link provided
Paper linkNo paper link provided

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Model overview

The Baichuan2-7B-Chat-4bits model is part of the Baichuan 2 series of large-scale open-source language models developed by Baichuan Intelligence inc. The Baichuan 2 series includes 7B and 13B versions for both Base and Chat models, along with a 4bits quantized version for the Chat model. The Baichuan2-7B-Chat-4bits model has been trained on a high-quality corpus of 2.6 trillion tokens and has achieved state-of-the-art performance on authoritative Chinese and English benchmarks compared to other similar sized models like GPT-4, GPT-3.5 Turbo, and LLaMA-7B.

Model inputs and outputs

Inputs

  • Text prompts for language generation

Outputs

  • Generated text continuations based on the input prompts

Capabilities

The Baichuan2-7B-Chat-4bits model has demonstrated strong performance across a wide range of language tasks including general conversation, legal and medical domain understanding, mathematics and coding, and multilingual translation. It has achieved top results on benchmarks like C-Eval, MMLU, CMMLU, Gaokao, AGIEval, and BBH.

What can I use it for?

Developers can use the Baichuan2-7B-Chat-4bits model for a variety of natural language processing applications, such as chatbots, content generation, question-answering systems, and language translation. The 4-bit quantized version also enables efficient deployment on resource-constrained devices. However, users must adhere to the Apache 2.0 license and Community License for Baichuan2 Model, which limit commercial usage to entities with under 1 million daily active users that are not software or cloud service providers.

Things to try

Developers can experiment with the Baichuan2-7B-Chat-4bits model to generate creative content, summarize long-form text, answer questions, or engage in open-ended dialogue. The 4-bit quantized version may also be particularly useful for on-device applications that require fast and efficient inference. The availability of intermediate training checkpoints provides an opportunity to study the model's performance at different stages of the training process.



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