Model reference · open weights

codebert

Embeddings microsoft Embeddings 1 build Licence not stated 202k dl/mo

codebert is an open-weight embedding model from microsoft. codebert-base (BF16) weighs 499 MB; the smallest configuration that runs it is RTX 3060 12 GB.

  • CodeBERT-base is a feature-extraction model developed by Microsoft for programming and natural languages.
  • It is initialized with RoBERTa-base and trained on bi-modal data from CodeSearchNet.
  • The model supports code search and code-to-document generation tasks.

Summary of the microsoft/codebert-base model card, 2026-10-01

What it is

Released bymicrosoft
Released2022-03-02
VRAM499 MB for the weights

What it runs on

Memory and cards for codebert-base (BF16)

499 MBweights, file size
1.1 GBruntime overhead
CardRunsMemory
RTX 3060 12 GBfits11.6 GB
RTX 4060 Ti 16 GBfits15.4 GB
RTX 3090 24 GBfits23.4 GB
RTX 4090 24 GBfits23.4 GB
RTX 5090 32 GBfits31.0 GB
L40S 48 GBfits44.0 GB
A100 80 GBfits78.2 GB
H100 80 GBfits78.1 GB
RTX PRO 6000 Blackwell 96 GBfits93.8 GB
DGX Spark (GB10) 128 GB unifiedfits107 GB
H200 141 GBfits138 GB
B200 180 GBfits176 GB

From the model card

What microsoft says about codebert

Read the model card

CodeBERT-base

Pretrained weights for CodeBERT: A Pre-Trained Model for Programming and Natural Languages.

Training Data

The model is trained on bi-modal data (documents & code) of CodeSearchNet

Training Objective

This model is initialized with Roberta-base and trained with MLM+RTD objective (cf. the paper).

Usage

Please see the official repository for scripts that support "code search" and "code-to-document generation".

Reference

  1. CodeBERT trained with Masked LM objective (suitable for code completion)
  2. 🤗 Hugging Face's CodeBERTa (small size, 6 layers)

Citation

@misc{feng2020codebert,
    title={CodeBERT: A Pre-Trained Model for Programming and Natural Languages},
    author={Zhangyin Feng and Daya Guo and Duyu Tang and Nan Duan and Xiaocheng Feng and Ming Gong and Linjun Shou and Bing Qin and Ting Liu and Daxin Jiang and Ming Zhou},
    year={2020},
    eprint={2002.08155},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

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

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