Providing Megatron-Core model definitions for state-of-the-art large models
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mcore-bridge is a large language model and multimodal large model definition library built on the Megatron-Core ecosystem, developed by the ModelScope community. It currently supports 300+ text-only models and 200+ multimodal models, including large language models such as Qwen3-Next, GLM5.1, DeepSeek-V3.2, Minimax2.7, Kimi K2.5, and GPT-OSS, as well as multimodal large models such as Qwen3.5, Qwen3-Omni, Gemma4, GLM4.6-V, InternVL3.5, and Ovis2.5.
Why Choose mcore-bridge?
- Model Coverage: Supports 300+ text-only large language models and 200+ multimodal large models, with Day 0 support for popular models.
- Hardware Support: Compatible with a wide range of hardware platforms, including A10/A100/H100/B200, RTX series, and domestic hardware such as Ascend NPU.
- Training Methods: Supports both full-parameter training and LoRA training, with compatibility with the PEFT ecosystem.
- Parallelism Techniques: Supports multiple parallelism strategies provided by Megatron-Core, including tensor parallelism, pipeline parallelism, sequence parallelism, context parallelism, expert parallelism, and virtual pipeline parallelism.
- Multimodal Capabilities: Supports multimodal FP8 training, MTP, sequence padding-free, and packing features.
- Task Types: Supports a variety of task types, including Causal LM, sequence classification, Embedding, and Reranker.
- Ecosystem Compatibility: Supports direct loading and saving of LoRA/full-parameter safetensors weights, with compatibility with mainstream inference frameworks such as Transformers, vLLM, and SGLang.
Related Documentation:
- Integrating Mcore-Bridge with ms-swift
- Supported Models List
- Custom Megatron Models
- Qwen3.5 Training Best Practices
- 🎉 2026.03.30: MCore-Bridge is released! Providing Megatron-Core model definitions for state-of-the-art large models and making Megatron training as simple as Transformers.
To install using pip:
pip install mcore-bridge -U
# Using uv
pip install uv
uv pip install mcore-bridge -U --torch-backend=autoTo install from source:
# pip install git+https://github.com/modelscope/mcore-bridge.git
git clone https://github.com/modelscope/mcore-bridge.git
cd mcore-bridge
pip install -e .
# Using uv
uv pip install -e . --torch-backend=autoRecommended Runtime Environment:
| Range | Recommended | Notes | |
|---|---|---|---|
| python | >=3.10 | 3.12 | |
| cuda | cuda12.8/13.0 | ||
| torch | >=2.0 | 2.8.0/2.11.0 | |
| transformer-engine | >=2.3 | 2.14.1 | |
| apex | 0.1 | Optional | |
| megatron-core | >=0.15,<0.18 | 0.17.0 | |
| flash-attn | 2.8.3/3.0.0b1 | Optional | |
| transformers | >=4.33 | 4.57.6/5.8.1 | |
| modelscope | >=1.23 | ||
| peft | >=0.11,<0.20 | LoRA |
The following is the list of models supported by MCore-Bridge:
text-only large models:
| Series | model_type |
|---|---|
| Qwen | qwen2, qwen2_moe qwen3, qwen3_moe, qwen3_next |
| DeepSeek | deepseek_v3, deepseek_v32 |
| GLM | glm4, glm4_moe, glm4_moe_lite glm_moe_dsa |
| MiniMax | minimax_m2 |
| Kimi | kimi_k2, kimi_k25 |
| Bailing | bailing_moe |
| InternLM | internlm3 |
| Llama | llama |
| GPT-OSS | gpt_oss |
| Hunyuan | hy_v3 |
| ERNIE | ernie4_5, ernie4_5_moe |
| MiMo | mimo |
| Dots | dots1 |
| OLMoE | olmoe |
multimodal large models:
| Series | model_type |
|---|---|
| Qwen | qwen2_vl, qwen2_5_vl, qwen2_5_omni qwen3_vl, qwen3_vl_moe, qwen3_omni_moe, qwen3_asr qwen3_5, qwen3_5_moe |
| Gemma | gemma4 |
| GLM | glm4v, glm4v_moe |
| Kimi | kimi_vl |
| InternVL | internvl_chat, internvl |
| Ovis | ovis2_5 |
| Llama | llama4 |
| Llava | llava-onevision |
How to use MCore-Bridge for training can be referred to the ms-swift project. Here we introduce how to use MCore-Bridge programmatically.
You need to create the following file (test.py), then run CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node=4 test.py. Below is sample code demonstrating how to use Mcore-Bridge for model creation, weight loading, export, and saving.
The saved model can be used for inference by referring to the example code in the model card.
import os
import torch
import torch.distributed as dist
from megatron.core import mpu
from modelscope import snapshot_download
from transformers import AutoConfig, AutoProcessor
from mcore_bridge import ModelConfig, get_mcore_model, hf_to_mcore_config
is_rank0 = int(os.getenv('RANK')) == 0
torch.cuda.set_device(f"cuda:{os.getenv('LOCAL_RANK')}")
dist.init_process_group(backend='nccl')
TP, PP, EP, ETP = 2, 2, 2, 1
mpu.initialize_model_parallel(
tensor_model_parallel_size=TP,
pipeline_model_parallel_size=PP,
expert_model_parallel_size=EP,
expert_tensor_parallel_size=ETP,
)
model_dir = snapshot_download('Qwen/Qwen3.5-35B-A3B')
hf_config = AutoConfig.from_pretrained(model_dir, trust_remote_code=True)
processor = AutoProcessor.from_pretrained(model_dir, trust_remote_code=True)
config_kwargs = hf_to_mcore_config(hf_config)
config = ModelConfig(
params_dtype=torch.bfloat16,
tensor_model_parallel_size=TP,
pipeline_model_parallel_size=PP,
expert_model_parallel_size=EP,
expert_tensor_parallel_size=ETP,
sequence_parallel=True,
mtp_num_layers=1,
**config_kwargs)
# Create model
mg_models = get_mcore_model(config)
# Load weights
bridge = config.bridge
bridge.load_weights(mg_models, model_dir)
# Export weights
for name, parameter in bridge.export_weights(mg_models):
pass
# Save weights
output_dir = 'Qwen3.5-35B-A3B-HF'
bridge.save_weights(mg_models, output_dir)
if is_rank0:
processor.save_pretrained(output_dir)
hf_config.save_pretrained(output_dir)Mcore-Bridge is fully compatible with Peft for LoRA training. The following introduces how to use Peft to prepare a PeftModel and save the incremental weights.
You need to create the following file (test.py), then run CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node=4 test.py.
import copy
import os
import torch
import torch.distributed as dist
from megatron.core import mpu
from modelscope import snapshot_download
from peft import LoraConfig, get_peft_model
from transformers import AutoConfig, AutoProcessor
from mcore_bridge import ModelConfig, get_mcore_model, hf_to_mcore_config, set_random_seed
is_rank0 = int(os.getenv('RANK')) == 0
torch.cuda.set_device(f"cuda:{os.getenv('LOCAL_RANK')}")
dist.init_process_group(backend='nccl')
TP, PP = 2, 2
mpu.initialize_model_parallel(
tensor_model_parallel_size=TP,
pipeline_model_parallel_size=PP,
)
# To correctly initialize the model randomly (full parameters/LoRA)
# you need to set the random seed.
set_random_seed(42)
model_dir = snapshot_download('Qwen/Qwen3.5-4B')
hf_config = AutoConfig.from_pretrained(model_dir, trust_remote_code=True)
config_kwargs = hf_to_mcore_config(hf_config)
config = ModelConfig(
params_dtype=torch.bfloat16,
tensor_model_parallel_size=TP,
pipeline_model_parallel_size=PP,
sequence_parallel=True,
**config_kwargs)
# Create model and load weights
mg_models = get_mcore_model(config)
bridge = config.bridge
bridge.load_weights(mg_models, model_dir)
# Prepare PeftModel and load LoRA weights
# For multimodal models, it is recommended to use regex to specify target_modules
target_modules = r'^language_model.*\.(in_proj|out_proj|linear_fc1|linear_fc2|linear_qkv|linear_proj)$'
# When saving as safetensors, you need to store the corresponding HF target_modules
hf_target_modules = r'^model.language_model.*\.(in_proj_qkv|in_proj_z|in_proj_b|in_proj_a|out_proj|gate_proj|up_proj|down_proj|q_proj|k_proj|v_proj|o_proj)$'
lora_config = LoraConfig(task_type='CAUSAL_LM', r=8, lora_alpha=32, lora_dropout=0.05, target_modules=target_modules)
peft_models = [get_peft_model(model, lora_config) for model in mg_models]
# Optional
# bridge.load_weights(peft_models, model_dir, peft_format=True)
# Export LoRA weights
for name, parameter in bridge.export_weights(mg_models, peft_format=True):
pass
# Save LoRA weights
output_dir = 'Qwen3.5-4B-LoRA'
bridge.save_weights(mg_models, output_dir, peft_format=True)
if is_rank0:
hf_lora_config = copy.copy(lora_config)
hf_lora_config.target_modules = hf_target_modules
hf_lora_config.save_pretrained(output_dir)Using the saved LoRA weights:
from transformers import Qwen3_5ForConditionalGeneration
from modelscope import snapshot_download
from peft import PeftModel
model_dir = snapshot_download('Qwen/Qwen3.5-4B')
model = Qwen3_5ForConditionalGeneration.from_pretrained(model_dir)
peft_model = PeftModel.from_pretrained(model, 'Qwen3.5-4B-LoRA')Mcore-Bridge integrates seamlessly with the ms-swift template for model training. You can also replace the ms-swift template module with a custom data processing pipeline to suit your own workflow.
The following provides a minimal example demonstrating how to perform a forward pass and compute the loss using a model created with Mcore-Bridge, helping users quickly integrate Mcore-Bridge into other projects.
Create the following file (test.py) and run it with: CUDA_VISIBLE_DEVICES=0,1 torchrun --nproc_per_node=2 test.py.
import os
import torch
import torch.distributed as dist
from megatron.core import mpu
from modelscope import snapshot_download
from swift import get_processor, get_template
from swift.megatron.utils import get_packed_seq_params, get_padding_to
from swift.utils import to_device
from mcore_bridge import ModelConfig, get_mcore_model, hf_to_mcore_config, set_random_seed
data = {
'messages': [{
'role': 'user',
'content': '<image>describe the image.'
}, {
'role':
'assistant',
'content':
'The image depicts a close-up of a kitten with striking features. '
'The kitten has a white and gray coat with distinct black stripes, '
'particularly noticeable on its face and ears. Its eyes are large '
'and expressive, with a captivating blue hue that stands out against '
"the darker fur around them. The kitten's nose is small and pink, "
'and it has long, delicate whiskers extending from either side of its mouth. '
"The background is blurred, drawing attention to the kitten's face and "
'making it the focal point of the image. The overall impression is '
'one of cuteness and charm.'
}],
'images': ['http://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/cat.png']
}
def forward_mg_model(mg_model, template):
template.use_megatron = True
template.set_mode('train')
inputs = template.encode(data, return_length=True)
mg_inputs = to_device(template.data_collator([inputs], padding_to=get_padding_to(mg_model.config)), 'cuda')
text_position_ids = mg_inputs.pop('text_position_ids', None)
if text_position_ids is None:
text_position_ids = mg_inputs.get('position_ids')
for key in ['num_samples', 'attention_mask_2d', 'loss_scale']:
mg_inputs.pop(key, None)
if template.padding_free:
mg_inputs['packed_seq_params'] = get_packed_seq_params(text_position_ids)
mg_inputs['labels'] = torch.roll(mg_inputs['labels'], -1, dims=-1)
loss = mg_model(**mg_inputs)
loss_mask = mg_inputs['labels'] != -100
loss = loss * loss_mask
return loss.sum() / loss_mask.sum()
torch.cuda.set_device(f"cuda:{os.getenv('LOCAL_RANK')}")
dist.init_process_group(backend='nccl')
TP, PP, EP, ETP = 2, 1, 2, 1
mpu.initialize_model_parallel(
tensor_model_parallel_size=TP,
pipeline_model_parallel_size=PP,
expert_model_parallel_size=EP,
expert_tensor_parallel_size=ETP,
)
set_random_seed(42)
model_dir = snapshot_download('Qwen/Qwen3.5-35B-A3B')
template = get_template(get_processor(model_dir), padding_free=True)
config_kwargs = hf_to_mcore_config(template.config)
config = ModelConfig(
params_dtype=torch.bfloat16,
tensor_model_parallel_size=TP,
pipeline_model_parallel_size=PP,
expert_model_parallel_size=EP,
expert_tensor_parallel_size=ETP,
sequence_parallel=True,
mtp_num_layers=1,
**config_kwargs)
mg_model = get_mcore_model(config)[0]
mg_model.cuda()
config.bridge.load_weights([mg_model], model_dir)
loss = forward_mg_model(mg_model, template)
print(f'loss: {loss}') # loss: 0.8161308169364929This framework is licensed under the Apache License (Version 2.0). For models and datasets, please refer to the original resource page and follow the corresponding License.