Spaces:
Sleeping
Sleeping
File size: 4,701 Bytes
9f341cc e916990 6aa8b86 9f341cc 6aa8b86 e820e51 9f341cc d2b20f2 9f341cc 6aa8b86 9f341cc e820e51 9f341cc 395ee29 e820e51 9f341cc 3dfcb72 9f341cc d2b20f2 9f341cc b96cef7 9f341cc d2b20f2 9f341cc d2b20f2 9f341cc d2b20f2 047008b d2b20f2 9f341cc 395ee29 d2b20f2 9f341cc 047008b 9f341cc d2b20f2 |
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 |
import json
import re
import requests
from messagers.message_outputer import OpenaiStreamOutputer
from utils.logger import logger
from utils.enver import enver
class MessageStreamer:
MODEL_MAP = {
"mixtral-8x7b": "mistralai/Mixtral-8x7B-Instruct-v0.1", # 72.62, fast [Recommended]
"mistral-7b": "mistralai/Mistral-7B-Instruct-v0.2", # 65.71, fast
"openchat-3.5": "openchat/openchat_3.5", # 61.24, fast
# "zephyr-7b-alpha": "HuggingFaceH4/zephyr-7b-alpha", # 59.5, fast
# "zephyr-7b-beta": "HuggingFaceH4/zephyr-7b-beta", # 61.95, slow
"default": "mistralai/Mixtral-8x7B-Instruct-v0.1",
}
def __init__(self, model: str):
if model in self.MODEL_MAP.keys():
self.model = model
else:
self.model = "default"
self.model_fullname = self.MODEL_MAP[self.model]
self.message_outputer = OpenaiStreamOutputer()
def parse_line(self, line):
line = line.decode("utf-8")
line = re.sub(r"data:\s*", "", line)
data = json.loads(line)
content = data["token"]["text"]
return content
def chat_response(
self,
prompt: str = None,
temperature: float = 0.01,
max_new_tokens: int = 8192,
):
# https://huggingface.co/docs/api-inference/detailed_parameters?code=curl
# curl --proxy http://<server>:<port> https://api-inference.huggingface.co/models/<org>/<model_name> -X POST -d '{"inputs":"who are you?","parameters":{"max_new_token":64}}' -H 'Content-Type: application/json' -H 'Authorization: Bearer <HF_TOKEN>'
self.request_url = (
f"https://api-inference.huggingface.co/models/{self.model_fullname}"
)
self.request_headers = {
"Content-Type": "application/json",
}
# References:
# huggingface_hub/inference/_client.py:
# class InferenceClient > def text_generation()
# huggingface_hub/inference/_text_generation.py:
# class TextGenerationRequest > param `stream`
# https://huggingface.co/docs/text-generation-inference/conceptual/streaming#streaming-with-curl
self.request_body = {
"inputs": prompt,
"parameters": {
"temperature": max(temperature, 0.01), # must be positive
"max_new_tokens": max_new_tokens,
"return_full_text": False,
},
"stream": True,
}
logger.back(self.request_url)
enver.set_envs(proxies=True)
stream_response = requests.post(
self.request_url,
headers=self.request_headers,
json=self.request_body,
proxies=enver.requests_proxies,
stream=True,
)
status_code = stream_response.status_code
if status_code == 200:
logger.success(status_code)
else:
logger.err(status_code)
return stream_response
def chat_return_dict(self, stream_response):
# https://platform.openai.com/docs/guides/text-generation/chat-completions-response-format
final_output = self.message_outputer.default_data.copy()
final_output["choices"] = [
{
"index": 0,
"finish_reason": "stop",
"message": {
"role": "assistant",
"content": "",
},
}
]
logger.back(final_output)
for line in stream_response.iter_lines():
if not line:
continue
content = self.parse_line(line)
if content.strip() == "</s>":
logger.success("\n[Finished]")
break
else:
logger.back(content, end="")
final_output["choices"][0]["message"]["content"] += content
return final_output
def chat_return_generator(self, stream_response):
is_finished = False
for line in stream_response.iter_lines():
if not line:
continue
content = self.parse_line(line)
if content.strip() == "</s>":
content_type = "Finished"
logger.success("\n[Finished]")
is_finished = True
else:
content_type = "Completions"
logger.back(content, end="")
output = self.message_outputer.output(
content=content, content_type=content_type
)
yield output
if not is_finished:
yield self.message_outputer.output(content="", content_type="Finished")
|