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[model] add Xing4 model support (#10754)

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xumh
2026-10-29 12:03:03 +08:00
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parent c683e59edd
commit 47d407d2aa
10 changed files with 265 additions and 6 deletions
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1
README.md
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@@ -338,6 +338,7 @@ Read technical notes:
| [Seed (OSS/Coder)](https://huggingface.co/ByteDance-Seed) | 8B/36B | seed_oss/seed_coder |
| [StarCoder 2](https://huggingface.co/bigcode) | 3B/7B/15B | - |
| [TeleChat 2-2.5](https://huggingface.co/Tele-AI) | 3B/7B/35B/115B | telechat2 |
| [Xing4.0-29B-A4B](https://huggingface.co/XingChen-AGI) | 29B-A4B | xing4_0 |
| [Yuan 2](https://huggingface.co/IEITYuan) | 2B/51B/102B | yuan |
> [!NOTE]

1
README_zh.md
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@@ -339,6 +339,7 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
| [Seed (OSS/Coder)](https://huggingface.co/ByteDance-Seed) | 8B/36B | seed_oss/seed_coder |
| [StarCoder 2](https://huggingface.co/bigcode) | 3B/7B/15B | - |
| [TeleChat 2-2.5](https://huggingface.co/Tele-AI) | 3B/7B/35B/115B | telechat2 |
| [Xing4.0-29B-A4B](https://huggingface.co/XingChen-AGI) | 29B-A4B | xing4_0 |
| [Yuan 2](https://huggingface.co/IEITYuan) | 2B/51B/102B | yuan |
> [!NOTE]

75
src/llamafactory/data/template.py
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@@ -675,6 +675,62 @@ class Glm47ReasoningTemplate(ReasoningTemplate):
return self.thought_words[0] + content + self.thought_words[1]
@dataclass
class Xing4ReasoningTemplate(ReasoningTemplate):
r"""Xing4.0 places the thinking-mode marker in the generation prompt."""
@override
def add_thought(self, content: str = "") -> str:
if not self.enable_thinking:
return self.thought_words[1] + content
return self.thought_words[0] + "\n" + self.thought_words[1] + content
def _move_thought_prefix(
self,
tokenizer: "PreTrainedTokenizer",
prompt_ids: list[int],
response_ids: list[int],
) -> tuple[list[int], list[int]]:
if not self.enable_thinking:
return prompt_ids, response_ids
thought_prefix_ids = tokenizer.encode(self.thought_words[0], add_special_tokens=False)
if response_ids[: len(thought_prefix_ids)] == thought_prefix_ids:
prompt_ids += thought_prefix_ids
response_ids = response_ids[len(thought_prefix_ids) :]
return prompt_ids, response_ids
@override
def encode_oneturn(
self,
tokenizer: "PreTrainedTokenizer",
messages: list[dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
) -> tuple[list[int], list[int]]:
prompt_ids, response_ids = super().encode_oneturn(tokenizer, messages, system, tools)
return self._move_thought_prefix(tokenizer, prompt_ids, response_ids)
@override
def encode_multiturn(
self,
tokenizer: "PreTrainedTokenizer",
messages: list[dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
discarding_history_cot: bool = False,
) -> list[tuple[list[int], list[int]]]:
encoded_pairs = super().encode_multiturn(
tokenizer, messages, system, tools, discarding_history_cot=discarding_history_cot
)
return [
self._move_thought_prefix(tokenizer, prompt_ids, response_ids)
for prompt_ids, response_ids in encoded_pairs
]
TEMPLATES: dict[str, "Template"] = {}
@@ -2561,6 +2617,25 @@ register_template(
)
register_template(
name="xing4_0",
format_user=StringFormatter(slots=["<_user>{{content}}<_bot>"]),
format_assistant=StringFormatter(slots=["{{content}}", {"eos_token"}, "\n"]),
format_function=FunctionFormatter(
slots=["{{content}}", {"eos_token"}, "\n"], tool_format="xing4"
),
format_system=StringFormatter(slots=["{{content}}"]),
format_observation=StringFormatter(
slots=["<_observation><tool_response>{{content}}</tool_response><_bot>"]
),
format_prefix=EmptyFormatter(slots=["<_system>"]),
format_tools=ToolFormatter(tool_format="xing4"),
thought_words=("<think>\n", "</think>"),
tool_call_words=("<tool_call>", "</tool_call>"),
template_class=Xing4ReasoningTemplate,
)
register_template(
name="vicuna",
format_user=StringFormatter(slots=["USER: {{content}} ASSISTANT:"]),

92
src/llamafactory/data/tool_utils.py
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@@ -63,6 +63,15 @@ GLM5_NEXT_TOOL_PROMPT = (
"<arg_key>{{arg-key-2}}</arg_key><arg_value>{{arg-value-2}}</arg_value>...</tool_call>"
)
XING4_TOOL_PROMPT = (
"\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\n"
"You are provided with function signatures within <tools></tools> XML tags:\n<tools>{tool_text}"
"\n</tools>\n\nFor each function call, output the function name and arguments within the following XML format:\n"
"<tool_call>{{function-name}}<param_key>{{param-key-1}}</param_key>"
"<param_value>{{param-value-1}}</param_value><param_key>{{param-key-2}}</param_key>"
"<param_value>{{param-value-2}}</param_value>...</tool_call>"
)
LLAMA3_TOOL_PROMPT = (
"Cutting Knowledge Date: December 2023\nToday Date: {date}\n\n"
"You have access to the following functions. To call a function, please respond with JSON for a function call. "
@@ -850,6 +859,88 @@ class GLM5NextToolUtils(GLM4MOEToolUtils):
return "".join(calls)
class Xing4ToolUtils(ToolUtils):
r"""Xing4 tool using template."""
@override
@staticmethod
def tool_formatter(tools: list[dict[str, Any]]) -> str:
tool_text = "".join("\n" + json.dumps(tool, ensure_ascii=False) for tool in tools)
return XING4_TOOL_PROMPT.format(tool_text=tool_text)
@override
@staticmethod
def function_formatter(functions: list["FunctionCall"]) -> str:
calls = []
for name, arguments in functions:
if not isinstance(name, str) or not re.fullmatch(r"[^\s<>]+", name):
raise ValueError("Invalid xing4 function name.")
arguments = json.loads(arguments)
if not isinstance(arguments, dict):
raise ValueError("Xing4 function arguments must be a JSON object.")
call = f"<tool_call>{name}"
for key, value in arguments.items():
if not re.fullmatch(r"[^<>]+", key):
raise ValueError("Invalid xing4 parameter name.")
value = value if isinstance(value, str) else json.dumps(value, ensure_ascii=False)
call += f"<param_key>{key}</param_key><param_value>{value}</param_value>"
calls.append(call + "</tool_call>")
return "".join(calls)
@override
@staticmethod
def tool_extractor(content: str) -> Union[str, list["FunctionCall"]]:
call_pattern = re.compile(r"<tool_call>(.*?)</tool_call>", re.DOTALL)
call_matches = list(re.finditer(call_pattern, content))
if not call_matches:
return content
if content.count("<tool_call>") != len(call_matches) or content.count("</tool_call>") != len(call_matches):
return content
results = []
param_pattern = re.compile(r"<param_key>(.*?)</param_key><param_value>(.*?)</param_value>", re.DOTALL)
for call_match in call_matches:
call_content = call_match.group(1)
name_match = re.match(r"\s*([^\s<>]+)", call_content)
if name_match is None:
return content
arguments = {}
params_content = call_content[name_match.end() :]
position = 0
for param_match in re.finditer(param_pattern, params_content):
if params_content[position : param_match.start()].strip():
return content
key, raw_value = param_match.groups()
key = key.strip()
if not key:
return content
try:
value = json.loads(raw_value)
except json.JSONDecodeError:
value = raw_value
arguments[key] = value
position = param_match.end()
if params_content[position:].strip():
return content
results.append(
FunctionCall(name_match.group(1), json.dumps(arguments, ensure_ascii=False))
)
return results
class SeedToolUtils(ToolUtils):
r"""Seed tool using template."""
@@ -1031,6 +1122,7 @@ TOOLS = {
"qwen": QwenToolUtils(),
"qwen3_5": Qwen35ToolUtils(),
"qwen3_8": Qwen38ToolUtils(),
"xing4": Xing4ToolUtils(),
"glm4_moe": GLM4MOEToolUtils(),
"glm5_next": GLM5NextToolUtils(),
"seed_oss": SeedToolUtils(),

11
src/llamafactory/extras/constants.py
View File

@@ -3422,6 +3422,17 @@ register_model_group(
)
register_model_group(
models={
"Xing4.0-29B-A4B": {
DownloadSource.DEFAULT: "XingChen-AGI/Xing4.0-29B-A4B",
DownloadSource.MODELSCOPE: "XingChen-AGI/Xing4.0-29B-A4B",
},
},
template="xing4_0",
)
register_model_group(
models={
"VibeThinker-1.5B": {

1
src/llamafactory/model/loader.py
View File

@@ -60,6 +60,7 @@ def _get_init_kwargs(model_args: "ModelArguments") -> dict[str, Any]:
"""
skip_check_imports()
model_args.model_name_or_path = try_download_model_from_other_hub(model_args)
return {
"trust_remote_code": model_args.trust_remote_code,
"cache_dir": model_args.cache_dir,

4
src/llamafactory/model/model_utils/moe.py
View File

@@ -156,6 +156,10 @@ def add_z3_leaf_module(model: "PreTrainedModel") -> None:
_set_z3_leaf_modules(model, [Qwen3_5MoeSparseMoeBlock])
if model_type == "xing4_0":
# Xing4.0 uses custom code
_set_z3_leaf_modules(model, ["Xing4_0MoE"])
def configure_moe(config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool) -> None:
if not is_trainable or not model_args.moe_aux_loss_coef:

3
src/llamafactory/model/patcher.py
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@@ -369,6 +369,9 @@ def patch_config(
init_kwargs: dict[str, Any],
is_trainable: bool,
) -> None:
if getattr(config, "model_type", None) == "xing4_0":
check_version("transformers>=5.4.0", mandatory=True)
if model_args.compute_dtype is None: # priority: bf16 > fp16 > fp32
if model_args.infer_dtype != "auto" and not is_trainable:
model_args.compute_dtype = getattr(torch, model_args.infer_dtype)

16
src/llamafactory/v1/core/model_engine.py
View File

@@ -31,13 +31,14 @@ Init workflow:
import torch
from accelerate import init_empty_weights
from transformers import AutoConfig, AutoProcessor
from transformers import AutoConfig, AutoProcessor, AutoTokenizer
from ..accelerator.helper import DeviceType
from ..accelerator.interface import DistributedInterface
from ..config.model_args import ModelArguments, ModelClass
from ..utils import logging
from ..utils.helper import get_tokenizer, is_tokenizer
from ..utils.packages import check_version
from ..utils.types import HFConfig, HFModel, Processor
from .rendering import Renderer
@@ -97,10 +98,17 @@ class ModelEngine:
NOTE: Transformers v5 always use fast tokenizer.
https://github.com/huggingface/transformers/blob/v5.0.0rc1/src/transformers/models/auto/tokenization_auto.py#L642
"""
try:
return AutoProcessor.from_pretrained(
self.args.model,
trust_remote_code=self.args.trust_remote_code,
)
except Exception:
logger.info_rank0("Failed to load processor, falling back to tokenizer.")
return AutoTokenizer.from_pretrained(
self.args.model,
trust_remote_code=self.args.trust_remote_code,
)
def _sync_chat_template(self) -> None:
"""Sync chat_template and inject custom_chat_template."""
@@ -116,10 +124,14 @@ class ModelEngine:
def _init_model_config(self) -> HFConfig:
"""Init model config."""
return AutoConfig.from_pretrained(
config = AutoConfig.from_pretrained(
self.args.model,
trust_remote_code=self.args.trust_remote_code,
)
if getattr(config, "model_type", None) == "xing4_0":
check_version("transformers>=5.4.0", mandatory=True)
return config
def _init_model(self) -> HFModel:
"""Init model.

59
tests/data/test_formatter.py
View File

@@ -302,6 +302,65 @@ def test_qwen_multi_tool_extractor():
]
@pytest.mark.runs_on(["cpu", "mps", "xpu"])
def test_xing4_function_formatter():
formatter = FunctionFormatter(slots=["{{content}}", "<_end>\n"], tool_format="xing4")
tool_calls = json.dumps(
{
"name": "tool_name",
"arguments": {"foo": "bar", "size": 10, "enabled": True, "items": [1, 2]},
}
)
assert formatter.apply(content=tool_calls) == [
"<tool_call>tool_name<param_key>foo</param_key><param_value>bar</param_value>"
"<param_key>size</param_key><param_value>10</param_value>"
"<param_key>enabled</param_key><param_value>true</param_value>"
"<param_key>items</param_key><param_value>[1, 2]</param_value></tool_call>",
"<_end>\n",
]
@pytest.mark.runs_on(["cpu", "mps", "xpu"])
def test_xing4_multi_function_formatter():
formatter = FunctionFormatter(slots=["{{content}}"], tool_format="xing4")
tool_calls = json.dumps([FUNCTION, {"name": "another_tool", "arguments": {"city": "北京"}}])
assert formatter.apply(content=tool_calls) == [
"<tool_call>tool_name<param_key>foo</param_key><param_value>bar</param_value>"
"<param_key>size</param_key><param_value>10</param_value></tool_call>"
"<tool_call>another_tool<param_key>city</param_key><param_value>北京</param_value></tool_call>"
]
@pytest.mark.runs_on(["cpu", "mps", "xpu"])
def test_xing4_tool_formatter():
formatter = ToolFormatter(tool_format="xing4")
assert formatter.apply(content=json.dumps(TOOLS)) == [
"\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\n"
"You are provided with function signatures within <tools></tools> XML tags:\n<tools>"
f"\n{json.dumps(TOOLS[0], ensure_ascii=False)}"
"\n</tools>\n\nFor each function call, output the function name and arguments within the following "
"XML format:\n<tool_call>{function-name}<param_key>{param-key-1}</param_key>"
"<param_value>{param-value-1}</param_value><param_key>{param-key-2}</param_key>"
"<param_value>{param-value-2}</param_value>...</tool_call>"
]
@pytest.mark.runs_on(["cpu", "mps", "xpu"])
def test_xing4_multi_tool_extractor():
formatter = ToolFormatter(tool_format="xing4")
result = (
"<think>check tools</think>"
"<tool_call>test_tool<param_key>foo</param_key><param_value>bar</param_value>"
"<param_key>size</param_key><param_value>10</param_value></tool_call>"
"<tool_call>another_tool<param_key>enabled</param_key><param_value>true</param_value>"
"<param_key>items</param_key><param_value>[1, 2]</param_value></tool_call>"
)
assert formatter.extract(result) == [
("test_tool", """{"foo": "bar", "size": 10}"""),
("another_tool", """{"enabled": true, "items": [1, 2]}"""),
]
@pytest.mark.runs_on(["cpu", "mps", "xpu"])
def test_lfm2_function_formatter():
formatter = FunctionFormatter(slots=["{{content}}<|im_end|>\n"], tool_format="lfm2")