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import json
import re
import requests
from tiktoken import get_encoding as tiktoken_get_encoding
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
"nous-mixtral-8x7b": "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO",
"zephyr-7b-beta": "HuggingFaceH4/zephyr-7b-beta", # ❌ Too Slow
"starchat2-15b-v0.1": "HuggingFaceH4/starchat2-15b-v0.1", # ❌ Too Slow
# "llama-70b": "meta-llama/Llama-2-70b-chat-hf", # ❌ Require Pro User
# "codellama-34b": "codellama/CodeLlama-34b-Instruct-hf", # ❌ Low Score
# "falcon-180b": "tiiuae/falcon-180B-chat", # ❌ Require Pro User
"default": "mistralai/Mixtral-8x7B-Instruct-v0.1",
}
STOP_SEQUENCES_MAP = {
"mixtral-8x7b": "</s>",
"mistral-7b": "</s>",
"nous-mixtral-8x7b": "<|im_end|>",
"zephyr-7b-beta": "</s>",
"starchat2-15b-v0.1": "<|im_end|>",
}
TOKEN_LIMIT_MAP = {
"mixtral-8x7b": 32768,
"mistral-7b": 32768,
"nous-mixtral-8x7b": 32768,
"zephyr-7b-beta": 4096,
"starchat2-15b-v0.1": 8192,
}
TOKEN_RESERVED = 100
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()
self.tokenizer = tiktoken_get_encoding("cl100k_base")
def parse_line(self, line):
line = line.decode("utf-8")
line = re.sub(r"data:\s*", "", line)
data = json.loads(line)
try:
content = data["token"]["text"]
except:
logger.err(data)
return content
def count_tokens(self, text):
tokens = self.tokenizer.encode(text)
token_count = len(tokens)
logger.note(f"Prompt Token Count: {token_count}")
return token_count
def chat_response(
self,
prompt: str = None,
temperature: float = 0.5,
top_p: float = 0.95,
max_new_tokens: int = None,
api_key: str = None,
use_cache: bool = False,
):
# 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",
}
if api_key:
logger.note(
f"Using API Key: {api_key[:3]}{(len(api_key)-7)*'*'}{api_key[-4:]}"
)
self.request_headers["Authorization"] = f"Bearer {api_key}"
if temperature is None or temperature < 0:
temperature = 0.0
# temperature must 0 < and < 1 for HF LLM models
temperature = max(temperature, 0.01)
temperature = min(temperature, 0.99)
top_p = max(top_p, 0.01)
top_p = min(top_p, 0.99)
token_limit = int(
self.TOKEN_LIMIT_MAP[self.model]
- self.TOKEN_RESERVED
- self.count_tokens(prompt) * 1.35
)
if token_limit <= 0:
raise ValueError("Prompt exceeded token limit!")
if max_new_tokens is None or max_new_tokens <= 0:
max_new_tokens = token_limit
else:
max_new_tokens = min(max_new_tokens, token_limit)
# 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
# https://huggingface.co/docs/api-inference/detailed_parameters#text-generation-task
self.request_body = {
"inputs": prompt,
"parameters": {
"temperature": temperature,
"top_p": top_p,
"max_new_tokens": max_new_tokens,
"return_full_text": False,
},
"options": {
"use_cache": use_cache,
},
"stream": True,
}
if self.model in self.STOP_SEQUENCES_MAP.keys():
self.stop_sequences = self.STOP_SEQUENCES_MAP[self.model]
# self.request_body["parameters"]["stop_sequences"] = [
# self.STOP_SEQUENCES[self.model]
# ]
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)
final_content = ""
for line in stream_response.iter_lines():
if not line:
continue
content = self.parse_line(line)
if content.strip() == self.stop_sequences:
logger.success("\n[Finished]")
break
else:
logger.back(content, end="")
final_content += content
if self.model in self.STOP_SEQUENCES_MAP.keys():
final_content = final_content.replace(self.stop_sequences, "")
final_content = final_content.strip()
final_output["choices"][0]["message"]["content"] = final_content
return final_output
def chat_return_generator(self, stream_response):
is_finished = False
line_count = 0
for line in stream_response.iter_lines():
if line:
line_count += 1
else:
continue
content = self.parse_line(line)
if content.strip() == self.stop_sequences:
content_type = "Finished"
logger.success("\n[Finished]")
is_finished = True
else:
content_type = "Completions"
if line_count == 1:
content = content.lstrip()
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")
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