File size: 8,928 Bytes
db704cb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import re
from ...utils.constants import IGNORE_INDEX
from ...utils.helper import get_tokenizer
from ...utils.plugin import BasePlugin
from ...utils.types import Message, ModelInput, Processor, ToolCall
class RenderingPlugin(BasePlugin):
def render_messages(
self,
processor: Processor,
messages: list[Message],
tools: str | None = None,
is_generate: bool = False,
) -> ModelInput:
"""Render messages in the template format."""
return self["render_messages"](processor, messages, tools, is_generate)
def parse_messages(self, generated_text: str) -> Message:
"""Parse messages in the template format."""
return self["parse_messages"](generated_text)
def _update_model_input(
processor: Processor,
input_ids: list[int],
labels: list[int],
loss_weights: list[int],
temp_str: str,
temp_weight: float,
) -> str:
"""Update model input with temporary string."""
if not temp_str:
return ""
tokenizer = get_tokenizer(processor)
temp_ids = tokenizer.encode(temp_str, add_special_tokens=False)
input_ids.extend(temp_ids)
loss_weights.extend([temp_weight] * len(temp_ids))
if temp_weight > 1e-6:
labels.extend(temp_ids)
else:
labels.extend([IGNORE_INDEX] * len(temp_ids))
return ""
@RenderingPlugin("qwen3_nothink").register("render_messages")
def render_qwen3_nothink_messages(
processor: Processor,
messages: list[Message],
tools: str | None = None,
is_generate: bool = False,
) -> ModelInput:
"""Render messages in the Qwen3 nothink template format.
See https://huggingface.co/spaces/huggingfacejs/chat-template-playground?modelId=Qwen/Qwen3-4B-Instruct-2507
"""
input_ids, labels, loss_weights = [], [], []
temp_str, temp_weight = "", 0.0
if tools:
temp_str += "<|im_start|>system\n"
if messages[0]["role"] == "system":
for content in messages[0]["content"]:
if content["type"] == "text":
temp_str += content["value"]
else:
raise ValueError(f"Unsupported content type: {content['type']}")
temp_str += "\n\n"
temp_weight = messages[0].get("loss_weight", 0.0)
temp_str += (
"# 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>"
)
try:
tools = json.loads(tools)
except json.JSONDecodeError:
raise ValueError(f"Invalid tools format: {str(tools)}.")
if not isinstance(tools, list):
tools = [tools]
for tool in tools:
temp_str += "\n" + json.dumps(tool, ensure_ascii=False)
temp_str += (
"\n</tools>\n\nFor each function call, return a json object with function name "
'and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{"name": '
'<function-name>, "arguments": <args-json-object>}\n</tool_call><|im_end|>\n'
)
elif messages[0]["role"] == "system":
temp_str += "<|im_start|>system\n"
for content in messages[0]["content"]:
if content["type"] == "text":
temp_str += content["value"]
else:
raise ValueError(f"Unsupported content type: {content['type']}")
temp_str += "<|im_end|>\n"
temp_weight = messages[0].get("loss_weight", 0.0)
temp_str = _update_model_input(processor, input_ids, labels, loss_weights, temp_str, temp_weight)
for turn_idx, message in enumerate(messages):
if message["role"] == "user" or (message["role"] == "system" and turn_idx != 0):
temp_str += "<|im_start|>" + message["role"] + "\n"
for content in message["content"]:
if content["type"] == "text":
temp_str += content["value"]
else:
raise ValueError(f"Unsupported content type: {content['type']}")
temp_str += "<|im_end|>\n"
temp_weight = message.get("loss_weight", 0.0)
elif message["role"] == "assistant":
temp_str += "<|im_start|>" + message["role"] + "\n"
for val_idx, content in enumerate(message["content"]):
if content["type"] == "text":
temp_str += content["value"]
elif content["type"] == "reasoning":
temp_str += "<thinking>\n" + content["value"] + "\n</thinking>\n\n" # avoid using special tokens
elif content["type"] == "tool_call":
if val_idx != 0 and message["content"][val_idx - 1]["type"] in ["text", "tool_call"]:
temp_str += "\n"
try:
tool_call: ToolCall = json.loads(content["value"])
except json.JSONDecodeError:
raise ValueError(f"Invalid tool call format: {content['value']}.")
temp_str += (
'<tool_call>\n{"name": "'
+ tool_call["name"]
+ '", "arguments": '
+ json.dumps(tool_call["arguments"], ensure_ascii=False)
+ "}\n</tool_call>"
)
else:
raise ValueError(f"Unsupported content type: {content['type']}")
temp_str += "<|im_end|>\n"
temp_weight = message.get("loss_weight", 1.0)
elif message["role"] == "tool":
if turn_idx == 0 or messages[turn_idx - 1]["role"] != "tool":
temp_str += "<|im_start|>user"
temp_str += "\n<tool_response>\n"
for content in message["content"]:
if content["type"] == "text":
temp_str += content["value"]
else:
raise ValueError(f"Unsupported content type: {content['type']}")
temp_str += "\n</tool_response>"
if turn_idx == len(messages) - 1 or messages[turn_idx + 1]["role"] != "tool":
temp_str += "<|im_end|>\n"
temp_weight = message.get("loss_weight", 0.0)
temp_str = _update_model_input(processor, input_ids, labels, loss_weights, temp_str, temp_weight)
if is_generate:
temp_str += "<|im_start|>assistant\n"
temp_weight = 0.0
temp_str = _update_model_input(processor, input_ids, labels, loss_weights, temp_str, temp_weight)
attention_mask = [1] * len(input_ids)
return ModelInput(
input_ids=input_ids,
attention_mask=attention_mask,
labels=labels,
loss_weights=loss_weights,
)
@RenderingPlugin("qwen3_nothink").register("parse_message")
def parse_qwen3_nothink_message(generated_text: str) -> Message:
"""Parse a message in the Qwen3 nothink template format. Supports interleaved reasoning and tool calls.
Args:
generated_text (str): The generated text in the Qwen3 nothink template format.
Returns:
Message: The parsed message.
"""
pattern = re.compile(r"<(thinking|tool_call)>\s*(.*?)\s*</\1>\s*", re.DOTALL)
content = []
last_end = 0
for match in pattern.finditer(generated_text):
start, end = match.span()
if start > last_end:
text = generated_text[last_end:start].strip()
if text:
content.append({"type": "text", "value": text})
tag_type = match.group(1)
tag_value = match.group(2).strip()
if tag_type == "thinking":
content.append({"type": "reasoning", "value": tag_value.strip()})
elif tag_type == "tool_call":
try:
json.loads(tag_value.strip())
except json.JSONDecodeError:
raise ValueError(f"Invalid tool call format: {tag_value.strip()}.")
content.append({"type": "tool_call", "value": tag_value.strip()})
last_end = end
if last_end < len(generated_text):
text = generated_text[last_end:].strip()
if text:
content.append({"type": "text", "value": text})
return Message(role="assistant", content=content)
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