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+## 1. Heterogeneous Training Environment and Code
+
+### 1.1 Docker Image Paths
+
+- NVIDIA A800: https://baai-flagscale.ks3-cn-beijing.ksyuncs.com/hetero_train/metax/nvidia_metax.tar
+
+- METAX C550: https://baai-flagscale.ks3-cn-beijing.ksyuncs.com/hetero_train/metax/metax_nvidia.tar
+
+You can directly download the images using the `wget` command on Linux.
+
+#### Configure SSH Port for Password-Free Multi-Machine Access
+
+```Plain
+#Replace 22 with your custom password-free port
+#Execute the following commands inside the Docker container
+
+sed -i 's/^Port .*/Port 22/' /etc/ssh/sshd_config
+
+service ssh restart
+```
+
+### 1.2 Install FlagScale
+
+#### 1.2.1 Download the Source Code
+
+```bash
+git clone -b main-legacy https://github.com/flagos-ai/FlagScale.git
+cd FlagScale/
+```
+
+#### 1.2.2 Apply Submodule Patch Code
+
+```bash
+# C550
+python3 tools/patch/unpatch.py --backend FlagScale Megatron-LM --device-type Metax_C550 --task train --commit 4e1b978fd626e8c23e3f894cc32ae09fe641401e
+
+# A800
+git reset --hard 05267318f750f694f61e547fa7a7b95876c72b5e 
+python3 tools/patch/unpatch.py --backend Megatron-LM
+```
+
+## 2. Start Heterogeneous Training (hetero_train)
+
+### 2.1 Prepare Dataset Demo
+
+We provide a small processed dataset ([bin](https://model.ks3-cn-beijing.ksyuncs.com/nlpdata/pile_wikipedia_demo.bin) and [idx](https://model.ks3-cn-beijing.ksyuncs.com/nlpdata/pile_wikipedia_demo.idx)) derived from the [Pile](https://pile.eleuther.ai/) dataset.
+
+```bash
+mkdir -p /path/to/data && cd /path/to/data
+wget https://model.ks3-cn-beijing.ksyuncs.com/nlpdata/pile_wikipedia_demo.idx
+wget https://model.ks3-cn-beijing.ksyuncs.com/nlpdata/pile_wikipedia_demo.bin
+```
+
+### 2.2 Edit Configuration Files
+
+We use the qwen3-10b model as an example:
+
+#### File Path: examples/qwen3/conf/train_hetero_10b.yaml
+
+```yaml
+defaults:
+  - _self_
+  - train: 10b_hetero
+
+experiment:
+  exp_name: Qwen3-10b_hetero
+  seed: 42
+  save_steps: 1000
+  load: null
+  exp_dir: ./${experiment.exp_name}
+  ckpt_format: torch
+  task:
+    type: train
+    backend: megatron
+    entrypoint: flagscale/train/train_gpt.py
+  runner:
+    backend: torchrun
+    per_node_task: false
+    no_shared_fs: false
+    ssh_port: xxx  Replace with Docker SSH port
+    nnodes: 2
+    nproc_per_node: 8
+    rdzv_backend: static
+    hostfile: ./hetero_hostfile  
+  cmds:
+    before_start: source /root/miniconda3/bin/activate flagscale-train
+  envs:
+    #FLAGCX_ENABLE_TOPO_DETECT: TRUE
+    #FLAGCX_DEBUG: TRACE
+    FLAGCX_IB_HCA: mlx5
+    FLAGCX_IB_GID_INDEX: 3
+    CUDA_VISIBLE_DEVICES: 0,1,2,3,4,5,6,7
+    CUDA_DEVICE_MAX_CONNECTIONS: 1
+    #NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0
+    device_type_specific:
+      C550:
+        LOGLEVEL: "INFO"
+        CUCC_PATH: "/opt/maca/tools/cu-bridge"
+        CUDA_PATH: "/opt/maca/tools/cu-bridge"
+        DEVINFO_ROOT: "/opt/maca"
+        LD_LIBRARY_PATH: "/opt/maca/lib:/opt/maca/mxgpu_llvm/lib:/opt/mxdriver/lib:/opt/maca/ompi/lib:/opt/maca/ucx/lib:/opt/mxdriver/lib"
+        MACA_CLANG: "/opt/maca/mxgpu_llvm"
+        MACA_CLANG_PATH: "/opt/maca/mxgpu_llvm/bin"
+        MACA_PATH: "/opt/maca"
+        PATH: "/opt/conda/bin:/opt/conda/condabin:/opt/maca/tools/cu-bridge:/opt/maca/bin:/opt/maca/mxgpu_llvm/bin:/opt/conda/bin:/opt/maca/bin:/opt/maca/mxgpu_llvm/bin:/opt/maca/ompi/bin:/opt/maca/ucx/bin:/opt/mxdriver/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin"
+        MCCL_LIMIT_RING_LL_THREADTHRESHOLDS: 1
+        SET_DEVICE_NUMA_PREFERRED: 1
+        PYTORCH_ENABLE_SAME_RAND_A100: 1
+        NVTE_FLASH_ATTN: 1
+        NVTE_FUSED_ATTN: 0
+        MACA_SMALL_PAGESIZE_ENABLE: 1
+        MCCL_MAX_NCHANNELS: 18
+        MCCL_P2P_LEVEL: SYS
+
+device_type_specific:
+  C550:
+    build_dir: /path/to/FlagScale/build/Metax_C550/FlagScale
+action: run
+
+hydra:
+  run:
+    dir: ${experiment.exp_dir}/hydra
+```
+
+#### File Path: examples/qwen3/conf/train/10b_hetero.yaml
+
+```yaml
+system:
+  distributed_backend: flagcx
+  no_shared_fs: ${experiment.runner.no_shared_fs}
+  num_workers: 16
+  tensor_model_parallel_size: 1
+  pipeline_model_parallel_size: 2
+  context_parallel_size: 1
+  disable_bias_linear: true
+  reset_position_ids: True
+  reset_attention_mask: True
+  qk_layernorm: true
+  sequence_parallel: true
+  use_distributed_optimizer: true
+  overlap_grad_reduce: true
+  overlap_param_gather: true
+  precision:
+    bf16: true
+    attention_softmax_in_fp32: true
+    accumulate_allreduce_grads_in_fp32: true
+  logging:
+    log_interval: 1
+    tensorboard_log_interval: 1
+    wandb_project: ${experiment.exp_name}
+    wandb_exp_name: ${experiment.exp_name}
+    log_timers_to_tensorboard: true
+    log_validation_ppl_to_tensorboard: true
+    log_throughput: true
+    log_params_norm: false
+    log_num_zeros_in_grad: true
+    log_memory_to_tensorboard: true
+  checkpoint:
+    save_interval: ${experiment.save_steps}
+    load: ${experiment.load}
+    ckpt_format: ${experiment.ckpt_format}
+
+  hetero:
+    enable_hetero: True
+    hetero_use_cpu_communication: False
+    use_partial_reduce_for_shared_embedding: True
+     mesh format [tp1,cp1,ep1,dp1,pp1,(tp2,cp2...)]
+    hetero_pipeline_layer_split: [28,28]
+    hetero_process_meshes: [1,1,1,8,1,1,1,1,8,1]
+    hetero_device_types: ["A800","C550"]
+
+    standalone_embedding_stage: False
+    hetero_current_device_type: "A800"
+
+model:
+  transformer_impl: transformer_engine
+  num_layers: 56
+  hidden_size: 2560
+  ffn_hidden_size: 19456
+  kv_channels: 128
+  group_query_attention: true
+  num_attention_heads: 32
+  num_query_groups: 8 # num_key_value_heads
+  seq_length: 4096
+  max_position_embeddings: 32768
+  norm_epsilon: 1e-6
+  use_rotary_position_embeddings: true
+  rotary_base: 1000000
+  swiglu: true
+  normalization: RMSNorm
+  init_method_std: 6e-3
+  attention_dropout: 0.0
+  hidden_dropout: 0.0
+  clip_grad: 1.0
+  position_embedding_type: rope
+  untie_embeddings_and_output_weights: true
+  no_position_embedding: true
+  no_rope_fusion: true
+  attention_backend: flash
+  # training
+  seed: ${experiment.seed}
+  # finetune: false
+  micro_batch_size: 1
+  global_batch_size: 2048
+  eval_iters: 0
+  train_samples: 244142080 #1T #29297664 #120B tokens
+  optimizer:
+    weight_decay: 0.1
+    adam_beta1: 0.9
+    adam_beta2: 0.95
+    lr_scheduler:
+      lr: 1.0e-3
+      min_lr: 1.0e-4
+      lr_warmup_samples: 2048000
+      lr_decay_style: cosine
+
+data:
+  data_path: /path/pile_wikipedia_demo
+  split: 1
+  no_mmap_bin_files: true
+  tokenizer:
+    legacy_tokenizer: true
+    tokenizer_type: QwenTokenizerFS
+    tokenizer_path: xxx # Download the official Qwen3-0.6B model from ModelScope to get tokenizer-related files
+    vocab_size: 151851
+    padded_vocab_size: 151936
+    make_vocab_size_divisible_by: 64
+
+```
+
+#### File Path: ./hetero_hostfile
+
+```
+ip slots=8 type=A800
+ip slots=8 type=C550
+```
+
+### 2.3 Start Training
+
+```bash
+python run.py --config-path ./examples/qwen3/conf  --config-name train_hetero_10b action=run
+```
+
+### 2.4 Stop Training
+
+```bash
+python run.py --config-path ./examples/qwen3/conf  --config-name train_hetero_10b action=stop
+```
+
+