83 lines
3.5 KiB
Python
83 lines
3.5 KiB
Python
"""
|
||
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,
|
||
)
|