mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-08-01 11:12:50 +08:00
239 lines
8.9 KiB
Python
239 lines
8.9 KiB
Python
# Copyright 2024 the LlamaFactory team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import base64
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import io
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import json
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import os
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import re
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import uuid
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from typing import TYPE_CHECKING, AsyncGenerator, Dict, List, Optional, Tuple
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from ..data import Role as DataRole
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from ..extras.logging import get_logger
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from ..extras.packages import is_fastapi_available, is_pillow_available, is_requests_available
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from .common import dictify, jsonify
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from .protocol import (
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ChatCompletionMessage,
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ChatCompletionResponse,
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ChatCompletionResponseChoice,
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ChatCompletionResponseUsage,
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ChatCompletionStreamResponse,
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ChatCompletionStreamResponseChoice,
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Finish,
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Function,
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FunctionCall,
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Role,
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ScoreEvaluationResponse,
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)
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if is_fastapi_available():
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from fastapi import HTTPException, status
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if is_pillow_available():
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from PIL import Image
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if is_requests_available():
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import requests
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if TYPE_CHECKING:
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from ..chat import ChatModel
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from ..data.mm_plugin import ImageInput
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from .protocol import ChatCompletionRequest, ScoreEvaluationRequest
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logger = get_logger(__name__)
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ROLE_MAPPING = {
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Role.USER: DataRole.USER.value,
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Role.ASSISTANT: DataRole.ASSISTANT.value,
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Role.SYSTEM: DataRole.SYSTEM.value,
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Role.FUNCTION: DataRole.FUNCTION.value,
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Role.TOOL: DataRole.OBSERVATION.value,
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}
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def _process_request(
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request: "ChatCompletionRequest",
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) -> Tuple[List[Dict[str, str]], Optional[str], Optional[str], Optional[List["ImageInput"]]]:
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logger.info(f"==== request ====\n{json.dumps(dictify(request), indent=2, ensure_ascii=False)}")
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if len(request.messages) == 0:
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raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid length")
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if request.messages[0].role == Role.SYSTEM:
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system = request.messages.pop(0).content
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else:
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system = None
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if len(request.messages) % 2 == 0:
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raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Only supports u/a/u/a/u...")
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input_messages = []
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images = []
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for i, message in enumerate(request.messages):
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if i % 2 == 0 and message.role not in [Role.USER, Role.TOOL]:
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raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid role")
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elif i % 2 == 1 and message.role not in [Role.ASSISTANT, Role.FUNCTION]:
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raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid role")
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if message.role == Role.ASSISTANT and isinstance(message.tool_calls, list) and len(message.tool_calls):
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tool_calls = [
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{"name": tool_call.function.name, "arguments": tool_call.function.arguments}
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for tool_call in message.tool_calls
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]
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content = json.dumps(tool_calls, ensure_ascii=False)
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input_messages.append({"role": ROLE_MAPPING[Role.FUNCTION], "content": content})
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elif isinstance(message.content, list):
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for input_item in message.content:
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if input_item.type == "text":
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input_messages.append({"role": ROLE_MAPPING[message.role], "content": input_item.text})
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else:
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image_url = input_item.image_url.url
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if re.match(r"^data:image\/(png|jpg|jpeg|gif|bmp);base64,(.+)$", image_url): # base64 image
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image_stream = io.BytesIO(base64.b64decode(image_url.split(",", maxsplit=1)[1]))
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elif os.path.isfile(image_url): # local file
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image_stream = open(image_url, "rb")
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else: # web uri
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image_stream = requests.get(image_url, stream=True).raw
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images.append(Image.open(image_stream).convert("RGB"))
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else:
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input_messages.append({"role": ROLE_MAPPING[message.role], "content": message.content})
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images = None if len(images) == 0 else images
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tool_list = request.tools
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if isinstance(tool_list, list) and len(tool_list):
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try:
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tools = json.dumps([dictify(tool.function) for tool in tool_list], ensure_ascii=False)
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except json.JSONDecodeError:
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raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid tools")
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else:
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tools = None
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return input_messages, system, tools, images
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def _create_stream_chat_completion_chunk(
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completion_id: str,
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model: str,
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delta: "ChatCompletionMessage",
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index: Optional[int] = 0,
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finish_reason: Optional["Finish"] = None,
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) -> str:
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choice_data = ChatCompletionStreamResponseChoice(index=index, delta=delta, finish_reason=finish_reason)
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chunk = ChatCompletionStreamResponse(id=completion_id, model=model, choices=[choice_data])
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return jsonify(chunk)
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async def create_chat_completion_response(
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request: "ChatCompletionRequest", chat_model: "ChatModel"
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) -> "ChatCompletionResponse":
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completion_id = f"chatcmpl-{uuid.uuid4().hex}"
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input_messages, system, tools, images = _process_request(request)
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responses = await chat_model.achat(
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input_messages,
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system,
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tools,
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images,
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do_sample=request.do_sample,
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temperature=request.temperature,
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top_p=request.top_p,
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max_new_tokens=request.max_tokens,
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num_return_sequences=request.n,
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stop=request.stop,
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)
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prompt_length, response_length = 0, 0
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choices = []
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for i, response in enumerate(responses):
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if tools:
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result = chat_model.engine.template.extract_tool(response.response_text)
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else:
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result = response.response_text
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if isinstance(result, list):
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tool_calls = []
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for tool in result:
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function = Function(name=tool[0], arguments=tool[1])
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tool_calls.append(FunctionCall(id=f"call_{uuid.uuid4().hex}", function=function))
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response_message = ChatCompletionMessage(role=Role.ASSISTANT, tool_calls=tool_calls)
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finish_reason = Finish.TOOL
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else:
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response_message = ChatCompletionMessage(role=Role.ASSISTANT, content=result)
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finish_reason = Finish.STOP if response.finish_reason == "stop" else Finish.LENGTH
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choices.append(ChatCompletionResponseChoice(index=i, message=response_message, finish_reason=finish_reason))
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prompt_length = response.prompt_length
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response_length += response.response_length
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usage = ChatCompletionResponseUsage(
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prompt_tokens=prompt_length,
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completion_tokens=response_length,
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total_tokens=prompt_length + response_length,
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)
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return ChatCompletionResponse(id=completion_id, model=request.model, choices=choices, usage=usage)
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async def create_stream_chat_completion_response(
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request: "ChatCompletionRequest", chat_model: "ChatModel"
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) -> AsyncGenerator[str, None]:
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completion_id = f"chatcmpl-{uuid.uuid4().hex}"
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input_messages, system, tools, images = _process_request(request)
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if tools:
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raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Cannot stream function calls.")
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if request.n > 1:
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raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Cannot stream multiple responses.")
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yield _create_stream_chat_completion_chunk(
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completion_id=completion_id, model=request.model, delta=ChatCompletionMessage(role=Role.ASSISTANT, content="")
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)
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async for new_token in chat_model.astream_chat(
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input_messages,
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system,
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tools,
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images,
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do_sample=request.do_sample,
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temperature=request.temperature,
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top_p=request.top_p,
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max_new_tokens=request.max_tokens,
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stop=request.stop,
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):
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if len(new_token) != 0:
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yield _create_stream_chat_completion_chunk(
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completion_id=completion_id, model=request.model, delta=ChatCompletionMessage(content=new_token)
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)
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yield _create_stream_chat_completion_chunk(
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completion_id=completion_id, model=request.model, delta=ChatCompletionMessage(), finish_reason=Finish.STOP
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)
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yield "[DONE]"
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async def create_score_evaluation_response(
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request: "ScoreEvaluationRequest", chat_model: "ChatModel"
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) -> "ScoreEvaluationResponse":
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score_id = f"scoreval-{uuid.uuid4().hex}"
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if len(request.messages) == 0:
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raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid request")
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scores = await chat_model.aget_scores(request.messages, max_length=request.max_length)
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return ScoreEvaluationResponse(id=score_id, model=request.model, scores=scores)
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