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

train/llama_factory_support/hy_dense_template.py

@@ -0,0 +1,82 @@
+"""
+HunYuan Dense model chat template registration for LLaMA Factory.
+
+Registers two templates:
+  - hy_dense_1_8b: for HunYuan Dense 1.8B model (and 0.5B/4B)
+  - hy_dense_7b:   for HunYuan Dense 7B model
+
+Usage:
+    1. Copy this file's register_template blocks into LLaMA Factory's
+       src/llamafactory/data/template.py  (for upstream MR).
+    2. Or import this module before training to register at runtime:
+       import hy_dense_template
+
+Note:
+    The existing LLaMA Factory built-in templates `hunyuan` and `hunyuan_small`
+    have subtle differences from the official chat_template.jinja files shipped
+    with the models. These new templates are designed to match the official
+    jinja templates exactly.
+"""
+
+from llamafactory.data.template import register_template
+from llamafactory.data.formatter import EmptyFormatter, StringFormatter
+
+# ---------------------------------------------------------------------------
+# Dense 1.8B chat template (also applies to 0.5B/4B)
+#
+# Token format (from dense_1_8b_0508/global_step_560/chat_template.jinja):
+#   BOS:        <｜hy_begin▁of▁sentence｜>
+#   System:     {system_content}<｜hy_place▁holder▁no▁3｜>
+#   User:       <｜hy_User｜>{user_content}
+#   Assistant:  <｜hy_Assistant｜>{assistant_content}<｜hy_place▁holder▁no▁2｜>
+#   Stop:       <｜hy_place▁holder▁no▁2｜>
+#
+# Key differences from LF built-in `hunyuan_small`:
+#   - User format: NO trailing <｜hy_place▁holder▁no▁8｜> after user content
+#   - Assistant format: HAS <｜hy_Assistant｜> prefix before assistant content
+#
+# The eos_token in tokenizer_config.json is <｜hy_place▁holder▁no▁2｜>,
+# so we use efficient_eos=True to let LF append it via {eos_token} slot.
+# ---------------------------------------------------------------------------
+
+register_template(
+    name="hy_dense_1_8b",
+    format_user=StringFormatter(slots=["<｜hy_User｜>{{content}}"]),
+    format_assistant=StringFormatter(slots=["<｜hy_Assistant｜>{{content}}", {"eos_token"}]),
+    format_system=StringFormatter(slots=["{{content}}<｜hy_place▁holder▁no▁3｜>"]),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
+    stop_words=["<｜hy_place▁holder▁no▁2｜>"],
+    efficient_eos=True,
+)
+
+
+# ---------------------------------------------------------------------------
+# Dense 7B chat template
+#
+# Token format (from dense_7b_0509/global_step_560/chat_template.jinja):
+#   BOS:        <|startoftext|>
+#   System:     {system_content}<|extra_4|>
+#   User:       {user_content}<|extra_0|>
+#   Assistant:  {assistant_content}<|eos|>
+#   Stop:       <|eos|>
+#
+# Key differences from LF built-in `hunyuan`:
+#   - Uses {bos_token} and {eos_token} slots for portability
+#   - efficient_eos=True to use tokenizer's eos_token
+#
+# Note on multi-turn: The official jinja adds <|startoftext|> before each
+# user message (except the first one when system is present). LLaMA Factory's
+# format_prefix only adds BOS once at the beginning. For single-turn training
+# this is correct. For multi-turn, there is a minor discrepancy (missing
+# <|startoftext|> before 2nd+ user turns), which is acceptable for fine-tuning.
+# ---------------------------------------------------------------------------
+
+register_template(
+    name="hy_dense_7b",
+    format_user=StringFormatter(slots=["{{content}}<|extra_0|>"]),
+    format_assistant=StringFormatter(slots=["{{content}}", {"eos_token"}]),
+    format_system=StringFormatter(slots=["{{content}}<|extra_4|>"]),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
+    stop_words=["<|eos|>"],
+    efficient_eos=True,
+)