diff --git a/tools/launcher/common/eagle3/train_eagle_streaming.sh b/tools/launcher/common/eagle3/train_eagle_streaming.sh
index 2f1e165062c..65ebc52b429 100755
--- a/tools/launcher/common/eagle3/train_eagle_streaming.sh
+++ b/tools/launcher/common/eagle3/train_eagle_streaming.sh
@@ -92,6 +92,14 @@ fi
 pip install --no-cache-dir -e modules/Model-Optimizer/
 pip install --no-cache-dir -r modules/Model-Optimizer/examples/speculative_decoding/requirements.txt
 pip install --no-cache-dir 'datasets' 'huggingface-hub>=1.2.1'
+
+# Some trust_remote_code models pin an older transformers (e.g. MiniMax-M2.7
+# needs 4.57.x whose modeling code is incompatible with the 5.x that the
+# requirements pull in). Must run AFTER the requirements install to win.
+if [ -n "${OVERRIDE_TRANSFORMERS:-}" ]; then
+    pip install --no-cache-dir "transformers==${OVERRIDE_TRANSFORMERS}"
+fi
+
 export PATH=$PATH:/workspace/.local/bin
 
 ###################################################################################################
diff --git a/tools/launcher/examples/MiniMax/MiniMax-M2.7-DFlash/hf_streaming_dflash_multi_node.yaml b/tools/launcher/examples/MiniMax/MiniMax-M2.7-DFlash/hf_streaming_dflash_multi_node.yaml
new file mode 100644
index 00000000000..0242db1a914
--- /dev/null
+++ b/tools/launcher/examples/MiniMax/MiniMax-M2.7-DFlash/hf_streaming_dflash_multi_node.yaml
@@ -0,0 +1,138 @@
+# DFlash streaming speculative-decoding training for MiniMax-M2.7 (229B MoE) — MULTI-NODE.
+#
+# Same streaming transport / dispatch as hf_streaming_eagle3_multi_node.yaml: task_1
+# splits N nodes into K serve replicas + (N-K) DDP trainers via SERVE_NODES; hidden
+# states move serve -> trainer over NIXL RDMA. DFlash just consumes a different set of
+# captured layers and trains a block-diffusion draft instead of an autoregressive one.
+# See common/eagle3/train_eagle_streaming.sh for dispatch, rendezvous, and sharding.
+#
+# MiniMax-M2.7 specifics:
+#   - 229B MoE, trust_remote_code, needs TP=4 per serve replica (each replica = 1 node).
+#   - Custom chat_template_train.jinja for the MiniMax chat format.
+#   - The DFlash draft is Qwen3-architecture (5 layers); target/draft architectures
+#     are independent by design.
+#   - Mask token id 200054 (reserved row in MiniMax-M2.7 embedding).
+#   - YaRN rope_scaling at export for long context (factor = 196608/4096 = 48).
+#
+# Capture ids: build_target_layer_ids(num_orig=62, num_draft=5)=[1,16,30,44,59]
+# -> +1 for embedding = [2,17,31,45,60], append final layer 62.
+# 6 captured = 5 aux layers + final output for self-logit distillation.
+#
+# 3-step pipeline:
+#   task_0: Build input conversations (jsonl)
+#   task_1: Streaming train — 2 serve nodes (TP=4 each) + 2 trainer nodes (4 GPU each)
+#   task_2: vLLM smoke test with DFlash speculative decoding
+#
+# Reference: "DFlash: Block Diffusion for Flash Speculative Decoding" (arXiv:2602.06036)
+#
+# Usage:
+#   uv run launch.py --yaml examples/MiniMax/MiniMax-M2.7-DFlash/hf_streaming_dflash_multi_node.yaml --yes
+#   uv run slurm.py --yaml modules/Model-Optimizer/tools/launcher/examples/MiniMax/MiniMax-M2.7-DFlash/hf_streaming_dflash_multi_node.yaml --yes
+
+job_name: MiniMax-M2.7-DFlash_streaming_multi_node
+pipeline:
+  allow_to_fail: false
+  skip: false
+  note:
+
+  global_vars:
+    hf_model: /hf-local/MiniMaxAI/MiniMax-M2.7
+
+  # Step 1: Build input conversations
+  task_0:
+    script: common/eagle3/make_dataset.sh
+    args:
+      - -f modules/Model-Optimizer/examples/dataset/example_data_config.yaml
+      - --full-conversations
+    slurm_config:
+      _factory_: "slurm_factory"
+      nodes: 1
+      ntasks_per_node: 1
+      gpus_per_node: 4
+      container: nvcr.io/nvidia/tensorrt-llm/release:1.3.0rc10
+
+  # Step 2: Streaming DFlash training — 2 serve replicas (TP=4) + 2 trainer nodes (4 GPU each).
+  task_1:
+    script: common/eagle3/train_eagle_streaming.sh
+    args:
+      - --config modules/Model-Optimizer/modelopt_recipes/general/speculative_decoding/dflash.yaml
+      - model.model_name_or_path=<<global_vars.hf_model>>
+      - model.use_fake_base_for_offline=true
+      - model.trust_remote_code=true
+      - data.mode=streaming
+      - data.data_path=/scratchspace/data/train.jsonl
+      - data.chat_template=examples/MiniMax/MiniMax-M2.7-DFlash/chat_template_train.jinja
+      - training.output_dir=/scratchspace/dflash
+      - training.training_seq_len=4096
+      - training.disable_tqdm=true
+      - training.ar_validate_steps=500000
+      - training.answer_only_loss=true
+      - training.num_train_epochs=1
+      - training.max_steps=5000
+      - training.per_device_train_batch_size=2
+      - training.learning_rate=1.2e-3
+      - training.warmup_steps=100
+      - training.logging_steps=100
+      - training.save_steps=400
+      - training.ddp_timeout=3600
+      # dp_shard_size=1 stops main.py from creating a ParallelismConfig; the
+      # accelerate config below owns FSDP2 sharding.
+      - training.dp_shard_size=1
+      - training.bf16=false
+      # vLLM container has no tensorboard -> init crash.
+      - training.report_to=none
+      - dflash.dflash_self_logit_distillation=true
+      - dflash.dflash_block_size=8
+      - dflash.dflash_num_anchors=512
+      - dflash.dflash_loss_decay_factor=4.0
+      - dflash.dflash_architecture_config.num_hidden_layers=5
+      - dflash.dflash_mask_token_id=200054
+      # YaRN rope_scaling injected at export for long context (factor = 196608/4096 = 48).
+      - dflash.dflash_export_rope_scaling.type=yarn
+      - dflash.dflash_export_rope_scaling.factor=48.0
+      - dflash.dflash_export_rope_scaling.original_max_position_embeddings=4096
+      - dflash.dflash_export_rope_scaling.beta_fast=1.0
+      - dflash.dflash_export_rope_scaling.beta_slow=1.0
+      - dflash.dflash_export_rope_scaling.mscale=1.0
+      - dflash.dflash_export_rope_scaling.mscale_all_dim=1.0
+    environment:
+      - HF_MODEL_CKPT: <<global_vars.hf_model>>
+      - EAGLE_CAPTURE_IDS: "[2,17,31,45,60,62]"
+      - SERVE_NODES: "2"
+      - SERVE_TP: "4"
+      - STREAMING_NUM_WORKERS: "1"
+      - EXPORT_EXTRA_ARGS: "--trust_remote_code"
+      - SERVE_MAX_MODEL_LEN: "4160"
+      - SERVE_MAX_NUM_SEQS: "4"
+      - SERVE_GPU_MEM_UTIL: "0.8"
+      - SERVE_READY_TIMEOUT: "2400"
+      - SERVE_EXTRA_ARGS: "--trust-remote-code"
+      - VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS: "1200"
+      - VLLM_ENGINE_ITERATION_TIMEOUT_S: "1200"
+      - OVERRIDE_TRANSFORMERS: "4.57.1"
+      - ACCELERATE_CONFIG: examples/MiniMax/MiniMax-M2.7-DFlash/accelerate_fsdp2_hybrid.yaml
+      - PATCH_FSDP2_BUFFERS_TF457: "1"
+      - MIXED_PRECISION: "no"
+    slurm_config:
+      _factory_: "slurm_factory"
+      nodes: 4
+      segment: 4
+      ntasks_per_node: 1
+      gpus_per_node: 4
+      container: vllm/vllm-openai:nightly
+
+  # Step 3: vLLM smoke test with DFlash speculative decoding
+  task_2:
+    script: common/specdec/vllm_smoke_test.sh
+    environment:
+      - HF_MODEL_CKPT: <<global_vars.hf_model>>
+      - DRAFT_CKPT_DIR: /scratchspace/dflash
+      - SPEC_METHOD: "dflash"
+      - NUM_SPEC_TOKENS: "7"
+      - TP_SIZE: "4"
+    slurm_config:
+      _factory_: "slurm_factory"
+      nodes: 1
+      ntasks_per_node: 1
+      gpus_per_node: 8
+      container: vllm/vllm-openai:nightly