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Model: Tencent-Hunyuan/Hy-MT2-7B-GGUF
Source: Original Platform

train/llama_factory_support/train_hy_v3.py

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+"""
+LLaMA Factory training entry-point wrapper for HYV3.
+
+This script:
+  1. Registers the hy_v3 chat template
+  2. Applies all HYV3 monkey-patches (checkpoint key rename, dtype fix, etc.)
+  3. Injects HYV3PatchCallback into the training loop
+  4. Calls run_exp() to start LLaMA Factory training
+
+How it works:
+  - train_lf.sh launches this script via torchrun directly:
+        torchrun ... train_hy_v3.py hy_v3_full_sft.yaml
+  - Each torchrun worker executes this script, so all patches are applied
+    in every worker process before training begins.
+  - We call run_exp() directly (not the CLI launcher) to avoid the
+    launcher re-spawning workers and losing our patches.
+
+Usage:
+    # Via launch script (recommended):
+    bash train_lf.sh
+
+    # Direct single-node (8 GPUs):
+    torchrun --nproc_per_node 8 train_hy_v3.py hy_v3_full_sft.yaml
+"""
+
+import sys
+import os
+
+# Add current directory to path so patches can be imported
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+
+# Step 1: Register HYV3 template (must be before training starts)
+import hy_v3_template  # noqa: F401
+
+# Step 2: Apply checkpoint key rename patch (must be before model loading)
+import hy_v3_patches  # noqa: F401
+
+# Step 3: Inject HYV3PatchCallback into LLaMA Factory's training flow
+from llamafactory.train.sft.workflow import run_sft as _orig_run_sft
+
+
+def _patched_run_sft(model_args, data_args, training_args, finetuning_args, generating_args, callbacks=None):
+    """Wrap run_sft to inject HYV3PatchCallback."""
+    if callbacks is None:
+        callbacks = []
+
+    # Determine tokenizer directory for the save callback
+    tokenizer_dir = getattr(model_args, "model_name_or_path", None)
+    callbacks.append(hy_v3_patches.HYV3PatchCallback(tokenizer_dir=tokenizer_dir))
+
+    return _orig_run_sft(model_args, data_args, training_args, finetuning_args, generating_args, callbacks=callbacks)
+
+
+# Monkey-patch the SFT workflow
+import llamafactory.train.sft.workflow as _sft_wf
+_sft_wf.run_sft = _patched_run_sft
+
+
+def main():
+    """Entry point: called by torchrun in each worker process.
+
+    Since train_lf.sh launches us via torchrun directly, all patches
+    (template registration, checkpoint key rename, SFT callback injection)
+    are already applied in this process.  We just call run_exp() to start
+    training — no need to go through the CLI launcher.
+    """
+    from llamafactory.train.tuner import run_exp
+    run_exp()
+
+
+if __name__ == "__main__":
+    main()