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[model] Adapt Qwen3.5 (#10213)

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Co-authored-by: frozenleaves <frozen@Mac.local>
Co-authored-by: Yaowei Zheng <hiyouga@buaa.edu.cn>
This commit is contained in:
浮梦
2026-02-26 20:45:02 +08:00
committed by GitHub
parent aab9b400bb
commit 2b8b871475
7 changed files with 122 additions and 9 deletions
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2
pyproject.toml
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@@ -40,7 +40,7 @@ dependencies = [
"torch>=2.4.0",
"torchvision>=0.19.0",
"torchaudio>=2.4.0",
"transformers>=4.51.0,<=5.0.0,!=4.52.0,!=4.57.0",
"transformers>=4.51.0,<=5.2.0,!=4.52.0,!=4.57.0",
"datasets>=2.16.0,<=4.0.0",
"accelerate>=1.3.0,<=1.11.0",
"peft>=0.18.0,<=0.18.1",

12
src/llamafactory/data/collator.py
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@@ -15,6 +15,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
from dataclasses import dataclass
from typing import TYPE_CHECKING, Any, Literal, Optional
@@ -189,6 +190,16 @@ class MultiModalDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
"video_grid_thw": mm_inputs.get("video_grid_thw"),
"attention_mask": (features["attention_mask"] >= 1).float(),
}
if "mm_token_type_ids" in inspect.signature(self.get_rope_func).parameters:
image_token_id = getattr(self.model.config, "image_token_id", None)
video_token_id = getattr(self.model.config, "video_token_id", None)
if image_token_id is not None or video_token_id is not None:
mm_token_type_ids = torch.zeros_like(features["input_ids"])
if image_token_id is not None:
mm_token_type_ids[features["input_ids"] == image_token_id] = 1
if video_token_id is not None:
mm_token_type_ids[features["input_ids"] == video_token_id] = 2
rope_index_kwargs["mm_token_type_ids"] = mm_token_type_ids
if "second_per_grid_ts" in mm_inputs: # for qwen2vl
rope_index_kwargs["second_per_grid_ts"] = mm_inputs.get("second_per_grid_ts")
elif "video_second_per_grid" in mm_inputs: # for qwen2.5 omni
@@ -219,6 +230,7 @@ class MultiModalDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
"qwen2_5_vl",
"qwen2_5_omni_thinker",
"qwen3_omni_moe_thinker",
"qwen3_5",
"qwen3_vl",
"qwen3_vl_moe",
]

33
src/llamafactory/data/template.py
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@@ -2029,6 +2029,39 @@ register_template(
)
register_template(
name="qwen3_5",
format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
format_function=FunctionFormatter(slots=["{{content}}<|im_end|>\n"], tool_format="qwen3_5"),
format_observation=StringFormatter(
slots=["<|im_start|>user\n<tool_response>\n{{content}}\n</tool_response><|im_end|>\n<|im_start|>assistant\n"]
),
format_tools=ToolFormatter(tool_format="qwen3_5"),
stop_words=["<|im_end|>"],
replace_eos=True,
mm_plugin=get_mm_plugin(name="qwen3_vl", image_token="<|image_pad|>", video_token="<|video_pad|>"),
template_class=ReasoningTemplate,
)
register_template(
name="qwen3_5_nothink",
format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
format_function=FunctionFormatter(slots=["{{content}}<|im_end|>\n"], tool_format="qwen3_5"),
format_observation=StringFormatter(
slots=["<|im_start|>user\n<tool_response>\n{{content}}\n</tool_response><|im_end|>\n<|im_start|>assistant\n"]
),
format_tools=ToolFormatter(tool_format="qwen3_5"),
stop_words=["<|im_end|>"],
replace_eos=True,
mm_plugin=get_mm_plugin(name="qwen3_vl", image_token="<|image_pad|>", video_token="<|video_pad|>"),
)
register_template(
name="sailor",
format_user=StringFormatter(slots=["<|im_start|>question\n{{content}}<|im_end|>\n<|im_start|>answer\n"]),

67
src/llamafactory/data/tool_utils.py
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@@ -85,6 +85,21 @@ QWEN_TOOL_PROMPT = (
""""arguments": <args-json-object>}}\n</tool_call>"""
)
QWEN35_TOOL_PROMPT = (
"\n\n# Tools\n\nYou have access to the following functions:\n\n<tools>{tool_text}"
"\n</tools>\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n"
"<tool_call>\n<function=example_function_name>\n<parameter=example_parameter_1>\nvalue_1\n</parameter>\n"
"<parameter=example_parameter_2>\nThis is the value for the second parameter\nthat can span\nmultiple lines\n"
"</parameter>\n</function>\n</tool_call>\n\n<IMPORTANT>\nReminder:\n"
"- Function calls MUST follow the specified format: "
"an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\n"
"- Required parameters MUST be specified\n"
"- You may provide optional reasoning for your function call in natural language "
"BEFORE the function call, but NOT after\n"
"- If there is no function call available, answer the question like normal with your current knowledge "
"and do not tell the user about function calls\n</IMPORTANT>"
)
SEED_TOOL_PROMPT = (
"system\nYou are Doubao, a helpful AI assistant. You may call one or more functions to assist with the user query."
"Tool List:\nYou are authorized to use the following tools (described in JSON Schema format). Before performing "
@@ -453,6 +468,57 @@ class QwenToolUtils(ToolUtils):
return results
class Qwen35ToolUtils(ToolUtils):
r"""Qwen 3.5 tool using template."""
@override
@staticmethod
def tool_formatter(tools: list[dict[str, Any]]) -> str:
tool_text = ""
for tool in tools:
tool = tool.get("function", tool) if tool.get("type") == "function" else tool
tool_text += "\n" + json.dumps(tool, ensure_ascii=False)
return QWEN35_TOOL_PROMPT.format(tool_text=tool_text)
@override
@staticmethod
def function_formatter(functions: list["FunctionCall"]) -> str:
function_texts = []
for func in functions:
name, arguments = func.name, json.loads(func.arguments)
prompt = f"<tool_call>\n<function={name}>"
for key, value in arguments.items():
prompt += f"\n<parameter={key}>"
if not isinstance(value, str):
value = json.dumps(value, ensure_ascii=False)
prompt += f"\n{value}\n</parameter>"
prompt += "\n</function>\n</tool_call>"
function_texts.append(prompt)
return "\n".join(function_texts)
@override
@staticmethod
def tool_extractor(content: str) -> Union[str, list["FunctionCall"]]:
results = []
regex = re.compile(r"<tool_call>\s*<function=\s*([^\s<>]+)\s*(.*?)\s*</function>\s*</tool_call>", re.DOTALL)
for func_name, params_block in re.findall(regex, content):
args_dict = {}
param_pattern = re.compile(r"<parameter=(.*?)>(.*?)</parameter>", re.DOTALL)
for key, raw_value in re.findall(param_pattern, params_block.strip()):
value = raw_value.strip()
try:
parsed_value = json.loads(value)
except json.JSONDecodeError:
parsed_value = raw_value.strip()
args_dict[key] = parsed_value
results.append(FunctionCall(func_name.strip(), json.dumps(args_dict, ensure_ascii=False)))
return results if results else content
class GLM4MOEToolUtils(QwenToolUtils):
r"""GLM-4-MOE tool using template."""
@@ -662,6 +728,7 @@ TOOLS = {
"minimax2": MiniMaxM2ToolUtils(),
"mistral": MistralToolUtils(),
"qwen": QwenToolUtils(),
"qwen3_5": Qwen35ToolUtils(),
"glm4_moe": GLM4MOEToolUtils(),
"seed_oss": SeedToolUtils(),
"ling": LingToolUtils(),

2
src/llamafactory/extras/misc.py
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@@ -94,7 +94,7 @@ def check_version(requirement: str, mandatory: bool = False) -> None:
def check_dependencies() -> None:
r"""Check the version of the required packages."""
check_version("transformers>=4.51.0,<=5.0.0")
check_version("transformers>=4.51.0,<=5.2.0")
check_version("datasets>=2.16.0,<=4.0.0")
check_version("accelerate>=1.3.0,<=1.11.0")
check_version("peft>=0.18.0,<=0.18.1")

13
tests/data/test_collator.py
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@@ -22,6 +22,7 @@ from transformers import AutoConfig, AutoModelForImageTextToText
from llamafactory.data import get_template_and_fix_tokenizer
from llamafactory.data.collator import MultiModalDataCollatorForSeq2Seq, prepare_4d_attention_mask
from llamafactory.extras.constants import IGNORE_INDEX
from llamafactory.extras.packages import is_transformers_version_greater_than
from llamafactory.hparams import get_infer_args
from llamafactory.model import load_tokenizer
@@ -116,14 +117,14 @@ def test_multimodal_collator():
"labels": [
[0, 1, 2, 3, q, q, q, q, q, q, q, q],
],
"position_ids": [
[[0, 1, 2, 3, 1, 1, 1, 1, 1, 1, 1, 1]],
[[0, 1, 2, 3, 1, 1, 1, 1, 1, 1, 1, 1]],
[[0, 1, 2, 3, 1, 1, 1, 1, 1, 1, 1, 1]],
],
"rope_deltas": [[-8]],
"position_ids": [[[0, 1, 2, 3, 0, 0, 0, 0, 0, 0, 0, 0]]] * 3,
"rope_deltas": [[0]],
**tokenizer_module["processor"].image_processor(fake_image),
}
if not is_transformers_version_greater_than("5.0.0"):
expected_input["position_ids"] = [[[0, 1, 2, 3, 1, 1, 1, 1, 1, 1, 1, 1]]] * 3
expected_input["rope_deltas"] = [[-8]]
assert batch_input.keys() == expected_input.keys()
for k in batch_input.keys():
assert batch_input[k].eq(torch.tensor(expected_input[k])).all()

2
tests/version.txt
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@@ -1,2 +1,2 @@
# change if test fails or cache is outdated
0.9.5.106
0.9.5.107