mbridge

library0.15.1pypypiunverified

A bridge library to convert and connect Megatron-Core checkpoints to Hugging Face format and support Reinforcement Learning workflows. Current version 0.15.1, with frequent releases matching Megatron-Core versions. Supports LLMs and VLMs including DeepSeek v3, GLM-4.5, Gemma 3, InternVL3.

ai-ml llm-agents data

pip install mbridge

github ↗package ↗

INSTALL

IMPORT

SIG · MBRIDGE

mbridge

ai-mlpythonv0.15.1

Install

1.6s avg

Import

—

Disk

17MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.15.1 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.95 runs

installs and imports cleanly · install 0.0s · import 0.000s · 18.8MB

glibc

py 3.10–3.95 runs

installs and imports cleanly · install 1.6s · import 0.000s · 19MB

17MB installed

● package 17MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

BridgeConverter

✓ from mbridge import BridgeConverter

✗ from mbridge import BridgeConverter

Basic usage of BridgeConverter to convert between Hugging Face and Megatron-Core formats.

from mbridge import BridgeConverter converter = BridgeConverter( hf_model_path="path/to/hf/model", mc_model_path="/path/to/mcore/model", model_type="llama", num_layers=32, hidden_size=4096, num_attention_heads=32, tensor_parallel=1, pipeline_parallel=1 ) # Convert Hugging Face to Megatron-Core converter.hf_to_mc() # Convert Megatron-Core to Hugging Face converter.mc_to_hf()

mbridge --version

Debug

Known issues

breakingFrom v0.15.0, `tie_embedding_weights` handling changed. Checkpoints converted with older versions may have mismatched embedding layers.

fix

Re-run conversion with v0.15.0+ and set `tie_word_embedding=True` if needed.

affects: < 0.15.0

gotchaTensor and pipeline parallelism settings must match the Megatron-Core training configuration. Mismatched settings can cause silent shape errors.

fix

Ensure `tensor_parallel` and `pipeline_parallel` are identical to the values used during training.

affects: all

deprecatedThe `--model-parallel-size` argument has been deprecated in favor of `--tensor-model-parallel-size` and `--pipeline-model-parallel-size`.

fix

Use `tensor_parallel` and `pipeline_parallel` parameters in the BridgeConverter API.

affects: >=0.13.0

Upgrade

Version history

0.15.1latest on PyPI · released Sep 22, 2025

Audit

Dependencies

No dependency data recorded yet.

Agent activity

34 hits · last 30 days

node

Amazon

OpenAI (training)

Resources

githubhttps://github.com/ISEEKYAN/mbridge ↗

packagehttps://pypi.org/project/mbridge/ ↗