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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 | # 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
from typing import Any, Literal, NotRequired, TypedDict
from ...utils import logging
from ...utils.plugin import BasePlugin
from ...utils.types import DPOSample, Sample, SFTSample, ToolCall
logger = logging.get_logger(__name__)
class AlpacaSample(TypedDict, total=False):
system: NotRequired[str]
instruction: str
input: NotRequired[str]
output: str
SharegptMessage = TypedDict(
"SharegptMessage",
{"from": Literal["human", "gpt", "system", "function_call", "observation"], "value": str},
)
class SharegptSample(TypedDict, total=False):
conversations: list[SharegptMessage]
tools: NotRequired[str]
class OpenaiMessage(TypedDict, total=False):
role: Literal["user", "assistant", "tool"]
content: str
class OpenaiSample(TypedDict, total=False):
messages: list[OpenaiMessage]
class PairSample(TypedDict, total=False):
chosen: list[OpenaiMessage]
rejected: list[OpenaiMessage]
class DataConverterPlugin(BasePlugin):
"""Plugin for data converters."""
def __call__(self, raw_sample: dict[str, Any]) -> Sample:
return super().__call__(raw_sample)
@DataConverterPlugin("alpaca").register()
def alpaca_converter(raw_sample: AlpacaSample) -> SFTSample:
"""Convert Alpaca sample to SFT sample.
See raw example at: https://huggingface.co/datasets/llamafactory/alpaca_gpt4_en
Args:
raw_sample (AlpacaSample): Alpaca sample.
Returns:
SFTSample: SFT sample.
"""
messages = []
if "system" in raw_sample:
messages.append(
{"role": "system", "content": [{"type": "text", "value": raw_sample["system"]}], "loss_weight": 0.0}
)
if "instruction" in raw_sample or "input" in raw_sample:
messages.append(
{
"role": "user",
"content": [
{"type": "text", "value": raw_sample.get("instruction", "") + raw_sample.get("input", "")}
],
"loss_weight": 0.0,
}
)
if "output" in raw_sample:
messages.append(
{"role": "assistant", "content": [{"type": "text", "value": raw_sample["output"]}], "loss_weight": 1.0}
)
return {"messages": messages}
@DataConverterPlugin("sharegpt").register()
def sharegpt_converter(raw_sample: SharegptSample) -> SFTSample:
"""Convert ShareGPT sample to SFT sample.
See raw example at: https://huggingface.co/datasets/llamafactory/glaive_toolcall_en
Args:
raw_sample (SharegptSample): ShareGPT sample.
Returns:
SFTSample: SFT sample.
"""
tag_mapping = {
"system": "system",
"human": "user",
"gpt": "assistant",
"observation": "tool",
"function_call": "assistant",
}
sample = {}
messages = []
for message in raw_sample.get("conversations", []):
tag = message["from"]
if tag not in tag_mapping:
logger.warning_rank0(f"Unsupported role tag {tag} in message: {message}")
elif tag == "function_call":
try:
tool_calls: ToolCall | list[ToolCall] = json.loads(message["value"])
except json.JSONDecodeError:
logger.warning_rank0(f"Invalid tool call format: {str(message['value'])}")
continue
if not isinstance(tool_calls, list):
tool_calls = [tool_calls]
messages.append(
{
"role": "assistant",
"content": [{"type": "tool_call", "value": json.dumps(tool_call)} for tool_call in tool_calls],
"loss_weight": 1.0,
}
)
else:
messages.append(
{
"role": tag_mapping[tag],
"content": [{"type": "text", "value": message["value"]}],
"loss_weight": 1.0 if tag == "gpt" else 0.0,
}
)
sample["messages"] = messages
tools = raw_sample.get("tools")
if tools:
try:
tools: list[dict[str, Any]] = json.loads(tools)
sample["tools"] = json.dumps(tools)
except json.JSONDecodeError:
logger.warning_rank0(f"Invalid tools format: {str(tools)}")
return sample
@DataConverterPlugin("pair").register()
def pair_converter(raw_sample: PairSample) -> DPOSample:
"""Convert Pair sample to DPO sample.
See raw example at: https://huggingface.co/datasets/HuggingFaceH4/orca_dpo_pairs
Args:
raw_sample (PairSample): pair sample with chosen, rejected fields.
Returns:
DPOSample: DPO sample with chosen_messages and rejected_messages.
"""
def process_message(raw_messages: list[OpenaiMessage]):
messages = []
for message in raw_messages:
if message["role"] == "tool":
try:
tool_calls: ToolCall | list[ToolCall] = json.loads(message["content"])
except json.JSONDecodeError:
logger.warning_rank0(f"Invalid tool call format: {str(message['content'])}")
continue
if not isinstance(tool_calls, list):
tool_calls = [tool_calls]
messages.append(
{
"role": message["role"],
"content": [{"type": "tool_call", "value": json.dumps(tool_call)} for tool_call in tool_calls],
"loss_weight": 1.0 if message["role"] == "assistant" else 0.0,
}
)
else:
messages.append(
{
"role": message["role"],
"content": [{"type": "text", "value": message["content"]}],
"loss_weight": 1.0 if message["role"] == "assistant" else 0.0,
}
)
return messages
sample = {}
sample["chosen_messages"] = process_message(raw_sample.get("chosen", []))
sample["rejected_messages"] = process_message(raw_sample.get("rejected", []))
tools = raw_sample.get("tools")
if tools:
try:
tools: list[dict[str, Any]] = json.loads(tools)
sample["tools"] = json.dumps(tools)
except json.JSONDecodeError:
logger.warning_rank0(f"Invalid tools format: {str(tools)}")
return sample
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