File size: 3,005 Bytes
d1ceb73 |
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 |
#!/usr/bin/env python
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# 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.
from copy import deepcopy
from enum import Enum
from typing import Dict, List
from huggingface_hub import InferenceClient
class MessageRole(str, Enum):
USER = "user"
ASSISTANT = "assistant"
SYSTEM = "system"
TOOL_CALL = "tool-call"
TOOL_RESPONSE = "tool-response"
@classmethod
def roles(cls):
return [r.value for r in cls]
def get_clean_message_list(message_list: List[Dict[str, str]], role_conversions: Dict[str, str] = {}):
"""
Subsequent messages with the same role will be concatenated to a single message.
Args:
message_list (`List[Dict[str, str]]`): List of chat messages.
"""
final_message_list = []
message_list = deepcopy(message_list) # Avoid modifying the original list
for message in message_list:
if not set(message.keys()) == {"role", "content"}:
raise ValueError("Message should contain only 'role' and 'content' keys!")
role = message["role"]
if role not in MessageRole.roles():
raise ValueError(f"Incorrect role {role}, only {MessageRole.roles()} are supported for now.")
if role in role_conversions:
message["role"] = role_conversions[role]
if len(final_message_list) > 0 and message["role"] == final_message_list[-1]["role"]:
final_message_list[-1]["content"] += "\n=======\n" + message["content"]
else:
final_message_list.append(message)
return final_message_list
llama_role_conversions = {
MessageRole.TOOL_RESPONSE: MessageRole.USER,
}
class HfEngine:
def __init__(self, model: str = "meta-llama/Meta-Llama-3-8B-Instruct"):
self.model = model
self.client = InferenceClient(model=self.model, timeout=120)
def __call__(self, messages: List[Dict[str, str]], stop_sequences=[]) -> str:
# Get clean message list
messages = get_clean_message_list(messages, role_conversions=llama_role_conversions)
# Get LLM output
response = self.client.chat_completion(messages, stop=stop_sequences, max_tokens=1500)
response = response.choices[0].message.content
# Remove stop sequences from LLM output
for stop_seq in stop_sequences:
if response[-len(stop_seq) :] == stop_seq:
response = response[: -len(stop_seq)]
return response
|